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Entropy, Volume 19, Issue 9 (September 2017) – 73 articles

Cover Story (view full-size image): In order to understand the logical architecture of living systems, von Neumann introduced the idea of a universal constructor—a physical device capable of self-reproduction. In order for such a device to exist, the laws of physics must permit physical universality. This has already been demonstrated in cellular automata models, yet they have not displayed a key feature of life—open-ended evolution. We show that both should be possible in irreversible dynamical systems with state-dependent laws. We demonstrate how the accessibility of state space can yield open-ended trajectories and discuss implications for developing a physics for life. View Paper here
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149 KiB  
Editorial
Nonequilibrium Phenomena in Confined Systems
by Giancarlo Franzese, Ivan Latella and J. Miguel Rubi
Entropy 2017, 19(9), 507; https://doi.org/10.3390/e19090507 - 20 Sep 2017
Cited by 6 | Viewed by 3252
Abstract
Confined systems exhibit a large variety of nonequilibrium phenomena. In this special issue, we have collected a limited number of papers that were presented during the XXV Sitges Conference on Statistical Mechanics, devoted to “Nonequilibrium phenomena in confined systems”.[...] Full article
(This article belongs to the Special Issue Nonequilibrium Phenomena in Confined Systems)
579 KiB  
Article
An Efficient Advantage Distillation Scheme for Bidirectional Secret-Key Agreement
by Yan Feng, Xue-Qin Jiang, Jia Hou, Hui-Ming Wang and Yi Yang
Entropy 2017, 19(9), 505; https://doi.org/10.3390/e19090505 - 18 Sep 2017
Cited by 4 | Viewed by 4446
Abstract
The classical secret-key agreement (SKA) scheme includes three phases: (a) advantage distillation (AD), (b) reconciliation, and (c) privacy amplification. Define the transmission rate as the ratio between the number of raw key bits obtained by the AD phase and the number of transmitted [...] Read more.
The classical secret-key agreement (SKA) scheme includes three phases: (a) advantage distillation (AD), (b) reconciliation, and (c) privacy amplification. Define the transmission rate as the ratio between the number of raw key bits obtained by the AD phase and the number of transmitted bits in the AD. The unidirectional SKA, whose transmission rate is 0 . 5, can be realized by using the original two-way wiretap channel as the AD phase. In this paper, we establish an efficient bidirectional SKA whose transmission rate is nearly 1 by modifying the two-way wiretap channel and using the modified two-way wiretap channel as the AD phase. The bidirectional SKA can be extended to multiple rounds of SKA with the same performance and transmission rate. For multiple rounds of bidirectional SKA, we have provided the bit error rate performance of the main channel and eavesdropper’s channel and the secret-key capacity. It is shown that the bit error rate (BER) of the main channel was lower than the eavesdropper’s channel and we prove that the transmission rate was nearly 1 when the number of rounds was large. Moreover, the secret-key capacity C s was from 0 . 04 to 0 . 1 as the error probability of channel was from 0 . 01 to 0 . 15 in binary symmetric channel (BSC). The secret-key capacity was close to 0 . 3 as the signal-to-noise ratio increased in the additive white Gaussian noise (AWGN) channel. Full article
(This article belongs to the Special Issue Information-Theoretic Security)
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<p>Block diagram of the secret key agreement (SKA) scheme.</p>
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<p>Advantage Distillation Scheme based on the original two-way wiretap channel.</p>
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<p>Original two-way wiretap channel.</p>
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<p>First round of bidirectional secret-key agreement.</p>
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<p>The <span class="html-italic">i</span>-th round of bidirectional secret-key agreement.</p>
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<p>Proposed bidirectional advantage distillation scheme.</p>
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<p>The <span class="html-italic">i</span>-th round of bidirectional secret-key agreement over AWGN channel.</p>
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<p>The BER performance of the main channel and eavesdropper’s channel over binary symmetric channel (BSC).</p>
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<p>The secret-key capacity and capacities of the main channel and eavesdropper’s channel over BSC channel.</p>
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<p>The secret-key capacity and capacities over additive white Gaussian noise (AWGN) channel.</p>
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1758 KiB  
Article
Second Law Analysis for Couple Stress Fluid Flow through a Porous Medium with Constant Heat Flux
by Samuel Olumide Adesanya and Michael Bamidele Fakoya
Entropy 2017, 19(9), 498; https://doi.org/10.3390/e19090498 - 18 Sep 2017
Cited by 17 | Viewed by 4476
Abstract
In the present work, entropy generation in the flow and heat transfer of couple stress fluid through an infinite inclined channel embedded in a saturated porous medium is presented. Due to the channel geometry, the asymmetrical slip conditions are imposed on the channel [...] Read more.
In the present work, entropy generation in the flow and heat transfer of couple stress fluid through an infinite inclined channel embedded in a saturated porous medium is presented. Due to the channel geometry, the asymmetrical slip conditions are imposed on the channel walls. The upper wall of the channel is subjected to a constant heat flux while the lower wall is insulated. The equations governing the fluid flow are formulated, non-dimensionalized and solved by using the Adomian decomposition method. The Adomian series solutions for the velocity and temperature fields are then used to compute the entropy generation rate and inherent heat irreversibility in the flow domain. The effects of various fluid parameters are presented graphically and discussed extensively. Full article
(This article belongs to the Special Issue Entropy in Computational Fluid Dynamics)
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<p>Flow geometry.</p>
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<p>Velocity profile: (<b>a</b>) effect of lower slip parameter; (<b>b</b>) effect of upper slip parameter; (<b>c</b>) effect of porous permeability parameter; (<b>d</b>) effect of the couple stress inverse parameter.</p>
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<p>Velocity profile: (<b>a</b>) effect of lower slip parameter; (<b>b</b>) effect of upper slip parameter; (<b>c</b>) effect of porous permeability parameter; (<b>d</b>) effect of the couple stress inverse parameter.</p>
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<p>Temperature profile (<b>a</b>) effect of lower slip parameter; (<b>b</b>) effect of upper slip parameter; (<b>c</b>) effect of couple stress inverse parameter; (<b>d</b>) effect of porous permeability parameter.</p>
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<p>Entropy generation (<b>a</b>) effect of lower slip parameter; (<b>b</b>) effect of upper slip parameter; (<b>c</b>) effect of porous permeability parameter; (<b>d</b>) effect of couple stress inverse parameter.</p>
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<p>Bejan number (<b>a</b>) effect of lower slip parameter; (<b>b</b>) effect of upper slip parameter; (<b>c</b>) effect of couple stress inverse parameter; (<b>d</b>) effect of porous permeability parameter.</p>
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5347 KiB  
Article
Traction Inverter Open Switch Fault Diagnosis Based on Choi–Williams Distribution Spectral Kurtosis and Wavelet-Packet Energy Shannon Entropy
by Shuangshuang Lin, Zhigang Liu and Keting Hu
Entropy 2017, 19(9), 504; https://doi.org/10.3390/e19090504 - 16 Sep 2017
Cited by 9 | Viewed by 5326
Abstract
In this paper, a new approach for fault detection and location of open switch faults in the closed-loop inverter fed vector controlled drives of Electric Multiple Units is proposed. Spectral kurtosis (SK) based on Choi–Williams distribution (CWD) as a statistical tool can effectively [...] Read more.
In this paper, a new approach for fault detection and location of open switch faults in the closed-loop inverter fed vector controlled drives of Electric Multiple Units is proposed. Spectral kurtosis (SK) based on Choi–Williams distribution (CWD) as a statistical tool can effectively indicate the presence of transients and locations in the frequency domain. Wavelet-packet energy Shannon entropy (WPESE) is appropriate for the transient changes detection of complex non-linear and non-stationary signals. Based on the analyses of currents in normal and fault conditions, SK based on CWD and WPESE are combined with the DC component method. SK based on CWD and WPESE are used for the fault detection, and the DC component method is used for the fault localization. This approach can diagnose the specific locations of faulty Insulated Gate Bipolar Transistors (IGBTs) with high accuracy, and it requires no additional devices. Experiments on the RT-LAB platform are carried out and the experimental results verify the feasibility and effectiveness of the diagnosis method. Full article
(This article belongs to the Special Issue Entropy in Signal Analysis)
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<p>Schematic diagram of vector controlled traction system.</p>
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<p>Diagnosis plan of open switch faults in the inverter.</p>
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<p>(<b>a</b>) Spectra kurtosis (SK) results in fault-free case (sampling frequency is 50 kHz); (<b>b</b>) SK results with <span class="html-italic">S</span>1 open (sampling frequency is 50 kHz); (<b>c</b>) SK results with <span class="html-italic">S</span>1, <span class="html-italic">S</span>4 open (sampling frequency is 50 kHz); (<b>d</b>) SK results with <span class="html-italic">S</span>1, <span class="html-italic">S</span>6 open (sampling frequency is 50 kHz).</p>
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<p>(<b>a</b>) Wavelet-packet energy Shannon entropy (WPESE) results in fault-free case; (<b>b</b>) WPESE results with <span class="html-italic">S</span>1, <span class="html-italic">S</span>6 open with “db2”; (<b>c</b>) WPESE results with <span class="html-italic">S</span>1, <span class="html-italic">S</span>6 open with “db3”; (<b>d</b>) WPESE results with <span class="html-italic">S</span>1, <span class="html-italic">S</span>6 open with “db4”.</p>
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<p>(<b>a</b>) Current flow of upper switch fault (USF); Current wave when USF occurs in(<b>b</b>) negative current flow; (<b>c</b>) Positive current flow; (<b>d</b>) Current flow of lower switch fault (LSF); Current wave when LSF occurs in (<b>e</b>) positive current flow; (<b>f</b>) Negative current flow.</p>
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<p>Fault cases diagnosis flow. (<b>a</b>) Fault detection part; (<b>b</b>) Fault location part.</p>
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<p>(<b>a</b>) SK result with Hanning window (sampling frequency is 50 kHz); (<b>b</b>) SK result with rectangular window (sampling frequency is 50 kHz); (<b>c</b>) The result of curvature with Hanning window; (<b>d</b>) The result of curvature with rectangular window.</p>
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<p>(<b>a</b>) SK results with different noise in normal case (sampling frequency is 50 kHz); (<b>b</b>) SK results with different noise in Fault A case (sampling frequency is 50 kHz); (<b>c</b>) SK results with different noise in Fault B case (sampling frequency is 50 kHz).</p>
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<p>(<b>a</b>) Empirical mode decomposition (EMD) results of A-phase fault current with <span class="html-italic">S</span>1 open; (<b>b</b>) empirical mode decomposition Shannon entropy (EMDESE) results of A-phase fault current with <span class="html-italic">S</span>1 open.</p>
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<p>Real-time online simulation system. (<b>a</b>) Real products of the system; (<b>b</b>) Structure diagram of the system.</p>
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<p>(<b>a</b>) Three-phase currents in fault-free case; (<b>b</b>) SK results in fault-free condition.</p>
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<p>(<b>a</b>) Three-phase currents with <span class="html-italic">S</span>1 open; (<b>b</b>) Results of SK with <span class="html-italic">S</span>1 open; (<b>c</b>) DC component of A-phase with <span class="html-italic">S</span>1 open.</p>
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<p>(<b>a</b>) Three-phase currents with <span class="html-italic">S</span>1, <span class="html-italic">S</span>6 open; (<b>b</b>) SK results with <span class="html-italic">S</span>1, <span class="html-italic">S</span>6 open; (<b>c</b>) DC components of three-phase currents with <span class="html-italic">S</span>1, <span class="html-italic">S</span>6 open; (<b>d</b>) WPESE results of three-phase currents.</p>
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<p>(<b>a</b>) Three-phase currents with <span class="html-italic">S</span>1, <span class="html-italic">S</span>4 open; (<b>b</b>) SK results with <span class="html-italic">S</span>1, <span class="html-italic">S</span>4 open.8.</p>
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<p>(<b>a</b>) Three-phase currents with <span class="html-italic">S</span>1, <span class="html-italic">S</span>3 open; (<b>b</b>) Results of SK with <span class="html-italic">S</span>1, <span class="html-italic">S</span>3 open; (<b>c</b>) Results of WPESE in three-phase currents with <span class="html-italic">S</span>1, <span class="html-italic">S</span>3 open; (<b>d</b>) DC components of three-phase currents with <span class="html-italic">S</span>1, <span class="html-italic">S</span>3 open.</p>
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435 KiB  
Article
Attribute Value Weighted Average of One-Dependence Estimators
by Liangjun Yu, Liangxiao Jiang, Dianhong Wang and Lungan Zhang
Entropy 2017, 19(9), 501; https://doi.org/10.3390/e19090501 - 16 Sep 2017
Cited by 25 | Viewed by 4468
Abstract
Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, semi-naive Bayesian classifiers which utilize one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies; meanwhile, the probability estimation in ODEs is [...] Read more.
Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, semi-naive Bayesian classifiers which utilize one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies; meanwhile, the probability estimation in ODEs is effective, thus leading to excellent performance. In previous studies, ODEs were exploited directly in a simple way. For example, averaged one-dependence estimators (AODE) weaken the attribute independence assumption by directly averaging all of a constrained class of classifiers. However, all one-dependence estimators in AODE have the same weights and are treated equally. In this study, we propose a new paradigm based on a simple, efficient, and effective attribute value weighting approach, called attribute value weighted average of one-dependence estimators (AVWAODE). AVWAODE assigns discriminative weights to different ODEs by computing the correlation between the different root attribute value and the class. Our approach uses two different attribute value weighting measures: the Kullback–Leibler (KL) measure and the information gain (IG) measure, and thus two different versions are created, which are simply denoted by AVWAODE-KL and AVWAODE-IG, respectively. We experimentally tested them using a collection of 36 University of California at Irvine (UCI) datasets and found that they both achieved better performance than some other state-of-the-art Bayesian classifiers used for comparison. Full article
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<p>An example of naive Bayes (NB).</p>
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<p>An example of tree-augmented naive Bayesian (TAN).</p>
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<p>A structure of hidden naive Bayes (HNB).</p>
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<p>An example of aggregating one-dependence estimators (AODE).</p>
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<p>An example of weighted average of one-dependence estimators (WAODE).</p>
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<p>An example of attribute value weighted average of one-dependence estimators (AVWAODE)-KL.</p>
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<p>An example of AVWAODE-IG.</p>
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4562 KiB  
Article
Modeling NDVI Using Joint Entropy Method Considering Hydro-Meteorological Driving Factors in the Middle Reaches of Hei River Basin
by Gengxi Zhang, Xiaoling Su, Vijay P. Singh and Olusola O. Ayantobo
Entropy 2017, 19(9), 502; https://doi.org/10.3390/e19090502 - 15 Sep 2017
Cited by 11 | Viewed by 4906
Abstract
Terrestrial vegetation dynamics are closely influenced by both hydrological process and climate change. This study investigated the relationships between vegetation pattern and hydro-meteorological elements. The joint entropy method was employed to evaluate the dependence between the normalized difference vegetation index (NDVI) and coupled [...] Read more.
Terrestrial vegetation dynamics are closely influenced by both hydrological process and climate change. This study investigated the relationships between vegetation pattern and hydro-meteorological elements. The joint entropy method was employed to evaluate the dependence between the normalized difference vegetation index (NDVI) and coupled variables in the middle reaches of the Hei River basin. Based on the spatial distribution of mutual information, the whole study area was divided into five sub-regions. In each sub-region, nested statistical models were applied to model the NDVI on the grid and regional scales, respectively. Results showed that the annual average NDVI increased at a rate of 0.005/a over the past 11 years. In the desert regions, the NDVI increased significantly with an increase in precipitation and temperature, and a high accuracy of retrieving NDVI model was obtained by coupling precipitation and temperature, especially in sub-region I. In the oasis regions, groundwater was also an important factor driving vegetation growth, and the rise of the groundwater level contributed to the growth of vegetation. However, the relationship was weaker in artificial oasis regions (sub-region III and sub-region V) due to the influence of human activities such as irrigation. The overall correlation coefficient between the observed NDVI and modeled NDVI was observed to be 0.97. The outcomes of this study are suitable for ecosystem monitoring, especially in the realm of climate change. Further studies are necessary and should consider more factors, such as runoff and irrigation. Full article
(This article belongs to the Special Issue Entropy Applications in Environmental and Water Engineering)
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<p>Map of the middle reaches of Hei River basin and the monitoring wells.</p>
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<p>Annual discharge of Hei River at Yinluoxia and Zhengyixia stations.</p>
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<p>The temporal variation of average temperature of growing season (<b>a</b>); cumulative precipitation (<b>b</b>) and average ground depths (<b>c</b>).</p>
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<p>The spatial distribution of (<b>a</b>) average temperature of growing season; (<b>b</b>) cumulative precipitation.</p>
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<p>Inter-annual variability of annual NDVI (<b>a</b>) and the trends of NDVI (<b>b</b>).</p>
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<p>The distribution of mutual information of NDVI and three coupling hydro-meteorological variables (<b>a</b>–<b>c</b>) and the divided sub-regions (<b>d</b>).</p>
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<p>The distribution of correlation coefficients of estimated NDVI by coupling hydro-meteorological variables.</p>
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<p>Three-dimensional scatter plots of NDVI (multiplied by 10<sup>4</sup>) in five sub-regions (<b>a</b>–<b>e</b>) and the relationship between observed NDVI modeled NDVI in the whole region (<b>f</b>).</p>
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<p>The variation of NDVI, temperature and precipitation on region I (<b>a</b>) and region IV (<b>b</b>). Note: The black, red and blue lines express the trend of variation in NDVI, temperature and precipitation respectively.</p>
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1152 KiB  
Article
Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization
by Jonathan Schrock, Alex J. McCaskey, Kathleen E. Hamilton, Travis S. Humble and Neena Imam
Entropy 2017, 19(9), 500; https://doi.org/10.3390/e19090500 - 15 Sep 2017
Cited by 6 | Viewed by 4154
Abstract
A content-addressable memory (CAM) stores key-value associations such that the key is recalled by providing its associated value. While CAM recall is traditionally performed using recurrent neural network models, we show how to solve this problem using adiabatic quantum optimization. Our approach maps [...] Read more.
A content-addressable memory (CAM) stores key-value associations such that the key is recalled by providing its associated value. While CAM recall is traditionally performed using recurrent neural network models, we show how to solve this problem using adiabatic quantum optimization. Our approach maps the recurrent neural network to a commercially available quantum processing unit by taking advantage of the common underlying Ising spin model. We then assess the accuracy of the quantum processor to store key-value associations by quantifying recall performance against an ensemble of problem sets. We observe that different learning rules from the neural network community influence recall accuracy but performance appears to be limited by potential noise in the processor. The strong connection established between quantum processors and neural network problems supports the growing intersection of these two ideas. Full article
(This article belongs to the Special Issue Foundations of Quantum Mechanics)
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<p>A photograph of the latest member of the DW processor family: the DW2X processor is composed from 1152 physical qubits, whose connectivity graph is shown in <a href="#entropy-19-00500-f002" class="html-fig">Figure 2</a>. Credit: D-Wave Systems, Inc. (Burnaby, BC, Canada).</p>
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<p>The hardware connectivity graph describing the D-Wave processor family, in which nodes represent physical qubits and edges signify tunable interactions. This diagram presents 512 qubits, or an 8-by-8 lattice of unit cells. The most recent processor supports a 12-by-12 lattice and 1152 qubits.</p>
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<p>Content-addressable memory (CAM) recall accuracy for problems with orthogonal memories. The number of memories stored is <math display="inline"> <semantics> <mrow> <mo>[</mo> <mi>n</mi> <mo>*</mo> <mi>m</mi> <mo>]</mo> </mrow> </semantics> </math>. For (<b>a</b>) the capacity is <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>2</mn> </mrow> </semantics> </math> for (<b>b</b>), and <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>3</mn> </mrow> </semantics> </math> for (<b>c</b>).</p>
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<p>CAM recall accuracy for problems with random memories. The number of memories stored is <math display="inline"> <semantics> <mrow> <mo>[</mo> <mi>n</mi> <mo>*</mo> <mi>m</mi> <mo>]</mo> </mrow> </semantics> </math>. For (<b>a</b>) the capacity is <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>2</mn> </mrow> </semantics> </math> for (<b>b</b>), and <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>3</mn> </mrow> </semantics> </math> for (<b>c</b>).</p>
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<p>Error for problems with random memories. The number of memories stored is <math display="inline"> <semantics> <mrow> <mo>[</mo> <mi>n</mi> <mo>*</mo> <mi>m</mi> <mo>]</mo> </mrow> </semantics> </math>. For (<b>a</b>) the capacity is <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>3</mn> </mrow> </semantics> </math> for (<b>b</b>), and <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>5</mn> </mrow> </semantics> </math> for (<b>c</b>).</p>
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<p>Error for problems with random memories. The number of memories stored is <math display="inline"> <semantics> <mrow> <mo>[</mo> <mi>n</mi> <mo>*</mo> <mi>m</mi> <mo>]</mo> </mrow> </semantics> </math>. For (<b>a</b>) the capacity is <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>3</mn> </mrow> </semantics> </math> for (<b>b</b>), and <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>5</mn> </mrow> </semantics> </math> for (<b>c</b>).</p>
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<p>Histogram of recall accuracies for problems on 28 spins with random memories. The capacity is <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>3</mn> </mrow> </semantics> </math>.</p>
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<p>Cumulative distribution of recall accuracies for problems on 28 spins with random memories. The capacity is <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>3</mn> </mrow> </semantics> </math>.</p>
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568 KiB  
Article
Thermodynamics of Small Magnetic Particles
by Eugenio E. Vogel, Patricio Vargas, Gonzalo Saravia, Julio Valdes, Antonio Jose Ramirez-Pastor and Paulo M. Centres
Entropy 2017, 19(9), 499; https://doi.org/10.3390/e19090499 - 15 Sep 2017
Cited by 3 | Viewed by 4735
Abstract
In the present paper, we discuss the interpretation of some of the results of the thermodynamics in the case of very small systems. Most of the usual statistical physics is done for systems with a huge number of elements in what is called [...] Read more.
In the present paper, we discuss the interpretation of some of the results of the thermodynamics in the case of very small systems. Most of the usual statistical physics is done for systems with a huge number of elements in what is called the thermodynamic limit, but not all of the approximations done for those conditions can be extended to all properties in the case of objects with less than a thousand elements. The starting point is the Ising model in two dimensions (2D) where an analytic solution exits, which allows validating the numerical techniques used in the present article. From there on, we introduce several variations bearing in mind the small systems such as the nanoscopic or even subnanoscopic particles, which are nowadays produced for several applications. Magnetization is the main property investigated aimed for two singular possible devices. The size of the systems (number of magnetic sites) is decreased so as to appreciate the departure from the results valid in the thermodynamic limit; periodic boundary conditions are eliminated to approach the reality of small particles; 1D, 2D and 3D systems are examined to appreciate the differences established by dimensionality is this small world; upon diluting the lattices, the effect of coordination number (bonding) is also explored; since the 2D Ising model is equivalent to the clock model with q = 2 degrees of freedom, we combine previous results with the supplementary degrees of freedom coming from the variation of q up to q = 20 . Most of the previous results are numeric; however, for the case of a very small system, we obtain the exact partition function to compare with the conclusions coming from our numerical results. Conclusions can be summarized in the following way: the laws of thermodynamics remain the same, but the interpretation of the results, averages and numerical treatments need special care for systems with less than about a thousand constituents, and this might need to be adapted for different properties or devices. Full article
(This article belongs to the Special Issue Thermodynamics and Statistical Mechanics of Small Systems)
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<p>Magnetic switches. (<b>a</b>) Top: Under <math display="inline"> <semantics> <msup> <mi>T</mi> <mo>*</mo> </msup> </semantics> </math> the magnetic nanoparticle (nm) sticks to the ferromagnetic material (F) even against a restoring elastic force represented by a zig-zag line. Bottom: As temperature <span class="html-italic">T</span> goes over <math display="inline"> <semantics> <msup> <mi>T</mi> <mo>*</mo> </msup> </semantics> </math>, nm loses enough magnetization, so the elastic force dominates switching off the contact; (<b>b</b>) Top: Under <math display="inline"> <semantics> <msup> <mi>T</mi> <mrow> <mo>*</mo> <mo>*</mo> </mrow> </msup> </semantics> </math>, a weak magnet labeled NS, with high enough <math display="inline"> <semantics> <msub> <mi>T</mi> <mi>C</mi> </msub> </semantics> </math>, attracts the north pole (painted black) of a rod-like nanomagnet made of a material with lower <math display="inline"> <semantics> <msub> <mi>T</mi> <mi>C</mi> </msub> </semantics> </math>; the black part has a conducting element that closes a circuit. Bottom: Over <math display="inline"> <semantics> <msup> <mi>T</mi> <mrow> <mo>*</mo> <mo>*</mo> </mrow> </msup> </semantics> </math>, an internal magnetization reversal occurs in the nanomagnet; the north pole is now in the sector painted white; the rod rotates with respect to an axis going through the center of the rod and perpendicular to the figure; in this new position, the circuit is switched off.</p>
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<p>Magnetization for a lattice <math display="inline"> <semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math> obtained using the analytic expressions (<a href="#FD16-entropy-19-00499" class="html-disp-formula">16</a>) (up blue triangles) and (<a href="#FD17-entropy-19-00499" class="html-disp-formula">17</a>) (down yellow triangles) to compare with the results obtained from numeric simulations using PBC (empty magenta diamonds) and FBC (empty black squares); the Onsager solution is also plotted to mark the expected <math display="inline"> <semantics> <msub> <mi>T</mi> <mi>C</mi> </msub> </semantics> </math> in the thermodynamic limit (TL) (solid red circles).</p>
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<p>Solid (black) squares: last instantaneous magnetization <math display="inline"> <semantics> <mrow> <mi>μ</mi> <mo stretchy="false">(</mo> <mi>T</mi> <mo>,</mo> <mn>2</mn> <mi>τ</mi> </mrow> </semantics> </math>) measured after <math display="inline"> <semantics> <mrow> <mn>2</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>6</mn> </msup> </mrow> </semantics> </math> Monte Carlo steps (MCSs) for each temperature; empty circles (red) represent the average magnetization over <math display="inline"> <semantics> <mrow> <mi>ν</mi> <mo>=</mo> </mrow> </semantics> </math> 50,000 instantaneous measurements of magnetization at intervals of 20 MCSs after <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>6</mn> </msup> </semantics> </math> MCSs of equilibration.</p>
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<p>Simulations on <math display="inline"> <semantics> <mrow> <mn>16</mn> <mo>×</mo> <mn>16</mn> </mrow> </semantics> </math> lattices for different effective coordination numbers <math display="inline"> <semantics> <mi>κ</mi> </semantics> </math> whose values are given in the inset: 2.000 corresponds to PBC; 1.875 to FBC; 1.75 to the dilution of the lattice according to the decoration proposed in Figure 5 where four pieces <math display="inline"> <semantics> <mrow> <mi>ℓ</mi> <mo>×</mo> <mi>ℓ</mi> </mrow> </semantics> </math> with <math display="inline"> <semantics> <mrow> <mi>ℓ</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math> have been withdrawn; 1.500 corresponds to a similar decoration with <math display="inline"> <semantics> <mrow> <mi>ℓ</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics> </math>; 1.00 corresponds to a similar decoration with <math display="inline"> <semantics> <mrow> <mi>ℓ</mi> <mo>=</mo> <mn>7</mn> </mrow> </semantics> </math>. <span class="html-italic">B</span> means <span class="html-italic">P</span> when <math display="inline"> <semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>2</mn> <mo>.</mo> <mn>000</mn> </mrow> </semantics> </math>, and it represents <span class="html-italic">F</span> for all of the other cases. For clarity, not all curves are shown with error bars.</p>
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<p>Diluted <math display="inline"> <semantics> <mrow> <mn>16</mn> <mo>×</mo> <mn>16</mn> </mrow> </semantics> </math> lattice upon decoration: four sectors <math display="inline"> <semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics> </math> are removed to leave <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>192</mn> </mrow> </semantics> </math> spins with a total of <math display="inline"> <semantics> <mrow> <mi>B</mi> <mo>=</mo> <mn>336</mn> </mrow> </semantics> </math> bonds; FBC are imposed to get <math display="inline"> <semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>1</mn> <mo>.</mo> <mn>750</mn> </mrow> </semantics> </math>. Other similar decorations are defined in the text and in the previous figure.</p>
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<p>Average absolute magnetization for lattices of different sizes showing the tails for the artificial remnant magnetization induced by forced ergodicity breaking. The inset shows the decrease with the size of the number of magnetization reversal within 50,000 measurements after equilibration. For clarity, error bars are given for <math display="inline"> <semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>16</mn> </mrow> </semantics> </math> only.</p>
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<p>Instantaneous magnetization after <math display="inline"> <semantics> <mrow> <mi>τ</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>6</mn> </msup> </mrow> </semantics> </math> MCSs of equilibration for the lattice <math display="inline"> <semantics> <mrow> <mn>16</mn> <mo>×</mo> <mn>16</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>1</mn> <mo>.</mo> <mn>0</mn> </mrow> </semantics> </math>, with FBC at <math display="inline"> <semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>6</mn> </mrow> </semantics> </math>. Measurements are separated by 20 MCSs.</p>
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<p>Magnetization for different systems based on <math display="inline"> <semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics> </math> layers where the effective coordination number <math display="inline"> <semantics> <mi>κ</mi> </semantics> </math> varies as given in the inset: 1.50 (one layer ; B = F); 2.0 (two layers <math display="inline"> <semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics> </math>; B = F); 2.167 (three layers <math display="inline"> <semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics> </math>; B = F); 2.25 (four layers <math display="inline"> <semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics> </math>; B = F); 2.50 (four layers <math display="inline"> <semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics> </math>; B = 2F + 1P); 2.75 (four layers <math display="inline"> <semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics> </math>; B = 1F + 2P); 3.0 (four layers <math display="inline"> <semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics> </math>; B = P) (the first curve is truly 2D, and part of it was already included in <a href="#entropy-19-00499-f002" class="html-fig">Figure 2</a>; it is given here for completeness and comparison purposes).</p>
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<p>Sensitive temperature <math display="inline"> <semantics> <msup> <mi>T</mi> <mo>*</mo> </msup> </semantics> </math> from a magnetic ordered to a disordered phase for the <span class="html-italic">q</span>-states clock model as a function of <math display="inline"> <semantics> <mrow> <mn>1</mn> <mo>/</mo> <mi>q</mi> </mrow> </semantics> </math>. A square lattice <math display="inline"> <semantics> <mrow> <mn>8</mn> <mo>×</mo> <mn>8</mn> </mrow> </semantics> </math> with FBC is used. Measurements are obtained after <math display="inline"> <semantics> <mrow> <mn>5</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>6</mn> </msup> </mrow> </semantics> </math> MCSs for averaging.</p>
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1699 KiB  
Article
Sum Capacity for Single-Cell Multi-User Systems with M-Ary Inputs
by Pei Yang, Yue Wu, Liqiang Jin and Hongwen Yang
Entropy 2017, 19(9), 497; https://doi.org/10.3390/e19090497 - 15 Sep 2017
Cited by 5 | Viewed by 4527
Abstract
This paper investigates the sum capacity of a single-cell multi-user system under the constraint that the transmitted signal is adopted from M-ary two-dimensional constellation with equal probability for both uplink, i.e., multiple access channel (MAC), and downlink, i.e., broadcast channel (BC) scenarios. [...] Read more.
This paper investigates the sum capacity of a single-cell multi-user system under the constraint that the transmitted signal is adopted from M-ary two-dimensional constellation with equal probability for both uplink, i.e., multiple access channel (MAC), and downlink, i.e., broadcast channel (BC) scenarios. Based on the successive interference cancellation (SIC) and the entropy power Gaussian approximation, it is shown that both the multi-user MAC and BC can be approximated to a bank of parallel channels with the channel gains being modified by an extra attenuate factor that equals to the negative exponential of the capacity of interfering users. With this result, the capacity of MAC and BC with arbitrary number of users and arbitrary constellations can be easily calculated which in sharp contrast with using traditional Monte Carlo simulation that the calculating amount increases exponentially with the increase of the number of users. Further, the sum capacity of multi-user under different power allocation strategies including equal power allocation, equal capacity power allocation and maximum capacity power allocation is also investigated. For the equal capacity power allocation, a recursive relation for the solution of power allocation is derived. For the maximum capacity power allocation, the necessary condition for optimal power allocation is obtained and an optimal algorithm for the power allocation optimization problem is proposed based on the necessary condition. Full article
(This article belongs to the Special Issue Multiuser Information Theory)
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<p>System model for a multi-user MAC system.</p>
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<p>System model for a multi-user BC system.</p>
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<p>Parallelization for MAC.</p>
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<p>Parallelization for BC.</p>
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<p>Approximations for the capacity with QPSK, 8PSK, 16QAM modulations, respectively.</p>
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<p>Approximations for the sum capacity with <span class="html-italic">K</span> = 2,4,10, respectively.</p>
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<p>Sum capacity for 2 users MAC with 16QAM.</p>
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<p>Sum capacity for 4 users MAC with 16QAM.</p>
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<p>Sum capacity with total power <math display="inline"> <semantics> <mrow> <mi>P</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics> </math> dB and 30 dB.</p>
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<p>Sum capacity with total power <math display="inline"> <semantics> <mrow> <mi>P</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> dB.</p>
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<p>Power allocation with total power <math display="inline"> <semantics> <mrow> <mi>P</mi> <mo>=</mo> <mo>−</mo> <mn>3</mn> </mrow> </semantics> </math> dB.</p>
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<p>Power allocation with total power <math display="inline"> <semantics> <mrow> <mi>P</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics> </math> dB.</p>
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<p>Power allocation with total power <math display="inline"> <semantics> <mrow> <mi>P</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics> </math> dB.</p>
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453 KiB  
Article
Entropic Data Envelopment Analysis: A Diversification Approach for Portfolio Optimization
by Paulo Rotela Junior, Luiz Célio Souza Rocha, Giancarlo Aquila, Pedro Paulo Balestrassi, Rogério Santana Peruchi and Liviam Soares Lacerda
Entropy 2017, 19(9), 352; https://doi.org/10.3390/e19090352 - 15 Sep 2017
Cited by 5 | Viewed by 4692
Abstract
Recently, different methods have been proposed for portfolio optimization and decision making on investment issues. This article aims to present a new method for portfolio formation based on Data Envelopment Analysis (DEA) and Entropy function. This new portfolio optimization method applies DEA in [...] Read more.
Recently, different methods have been proposed for portfolio optimization and decision making on investment issues. This article aims to present a new method for portfolio formation based on Data Envelopment Analysis (DEA) and Entropy function. This new portfolio optimization method applies DEA in association with a model resulting from the insertion of the Entropy function directly into the optimization procedure. First, the DEA model was applied to perform a pre-selection of the assets. Then, assets given as efficient were submitted to the proposed model, resulting from the insertion of the Entropy function into the simplified Sharpe’s portfolio optimization model. As a result, an improved asset participation was provided in the portfolio. In the DEA model, several variables were evaluated and a low value of beta was achieved, guaranteeing greater robustness to the portfolio. Entropy function has provided not only greater diversity but also more feasible asset allocation. Additionally, the proposed method has obtained a better portfolio performance, measured by the Sharpe Ratio, in relation to the comparative methods. Full article
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<p>Entropy in the case of two possibilities.</p>
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1194 KiB  
Article
Log Likelihood Spectral Distance, Entropy Rate Power, and Mutual Information with Applications to Speech Coding
by Jerry D. Gibson and Preethi Mahadevan
Entropy 2017, 19(9), 496; https://doi.org/10.3390/e19090496 - 14 Sep 2017
Cited by 2 | Viewed by 5319
Abstract
We provide a new derivation of the log likelihood spectral distance measure for signal processing using the logarithm of the ratio of entropy rate powers. Using this interpretation, we show that the log likelihood ratio is equivalent to the difference of two differential [...] Read more.
We provide a new derivation of the log likelihood spectral distance measure for signal processing using the logarithm of the ratio of entropy rate powers. Using this interpretation, we show that the log likelihood ratio is equivalent to the difference of two differential entropies, and further that it can be written as the difference of two mutual informations. These latter two expressions allow the analysis of signals via the log likelihood ratio to be extended beyond spectral matching to the study of their statistical quantities of differential entropy and mutual information. Examples from speech coding are presented to illustrate the utility of these new results. These new expressions allow the log likelihood ratio to be of interest in applications beyond those of just spectral matching for speech. Full article
(This article belongs to the Special Issue Entropy in Signal Analysis)
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<p><span class="html-italic">D</span> values for G.726 at various bit rates for “We were away a year ago”. ____: Statistically significant threshold. _ _ _: Perceptually significant threshold.</p>
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<p>Comparison of linear prediction (LP) spectra of original and 24 Kbps G.726 “We were away a year ago” where <span class="html-italic">D</span> = 11.</p>
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<p>Comparison of LP spectra of original and 24 Kbps G.726 “We were away a year ago” where <span class="html-italic">D</span> = 36.5.</p>
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<p>Comparison of LP spectra of original and 24 Kbps G.726 “We were away a year ago” where <span class="html-italic">D</span> = 104.5.</p>
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<p>General Block Diagram of a code-excited linear prediction (CELP) Decoder.</p>
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632 KiB  
Article
On the Capacity and the Optimal Sum-Rate of a Class of Dual-Band Interference Channels
by Subhajit Majhi and Patrick Mitran
Entropy 2017, 19(9), 495; https://doi.org/10.3390/e19090495 - 14 Sep 2017
Cited by 2 | Viewed by 3519
Abstract
We study a class of two-transmitter two-receiver dual-band Gaussian interference channels (GIC) which operates over the conventional microwave and the unconventional millimeter-wave (mm-wave) bands. This study is motivated by future 5G networks where additional spectrum in the mm-wave band complements transmission in the [...] Read more.
We study a class of two-transmitter two-receiver dual-band Gaussian interference channels (GIC) which operates over the conventional microwave and the unconventional millimeter-wave (mm-wave) bands. This study is motivated by future 5G networks where additional spectrum in the mm-wave band complements transmission in the incumbent microwave band. The mm-wave band has a key modeling feature: due to severe path loss and relatively small wavelength, a transmitter must employ highly directional antenna arrays to reach its desired receiver. This feature causes the mm-wave channels to become highly directional, and thus can be used by a transmitter to transmit to its designated receiver or the other receiver. We consider two classes of such channels, where the underlying GIC in the microwave band has weak and strong interference, and obtain sufficient channel conditions under which the capacity is characterized. Moreover, we assess the impact of the additional mm-wave band spectrum on the performance, by characterizing the transmit power allocation for the direct and cross channels that maximizes the sum-rate of this dual-band channel. The solution reveals conditions under which different power allocations, such as allocating the power budget only to direct or only to cross channels, or sharing it among them, becomes optimal. Full article
(This article belongs to the Special Issue Multiuser Information Theory)
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Graphical abstract

Graphical abstract
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<p>System model of the Gaussian DCLIC, which consists of an underlying GIC in the microwave band and the set of direct channels and cross channels in the mm-wave band.</p>
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<p>In (<bold>a</bold>,<bold>b</bold>), we plot <inline-formula> <mml:math id="mm1238" display="block"> <mml:semantics> <mml:msubsup> <mml:mi>c</mml:mi> <mml:mrow> <mml:mi mathvariant="sans-serif">min</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msubsup> </mml:semantics> </mml:math> </inline-formula> and <inline-formula> <mml:math id="mm1239" display="block"> <mml:semantics> <mml:msub> <mml:mi>α</mml:mi> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mi mathvariant="sans-serif">min</mml:mi> </mml:mrow> </mml:msub> </mml:semantics> </mml:math> </inline-formula>, respectively. In (<bold>c</bold>), the channel gains of a symmetric CLIC is partitioned based on whether its capacity has been characterized in each set.</p>
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<p>Due to its WF-like property, <inline-formula> <mml:math id="mm1240" display="block"> <mml:semantics> <mml:msub> <mml:mi mathvariant="bold">OA</mml:mi> <mml:mn mathvariant="bold">1</mml:mn> </mml:msub> </mml:semantics> </mml:math> </inline-formula> follows one of the three sequences depending on <inline-formula> <mml:math id="mm1241" display="block"> <mml:semantics> <mml:msubsup> <mml:mi>P</mml:mi> <mml:mi>k</mml:mi> <mml:mo>*</mml:mo> </mml:msubsup> </mml:semantics> </mml:math> </inline-formula>. The saturation levels in the cross channels are due to its max-min property.</p>
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<p>Optimal power allocation that follows the sequence [S1] <inline-formula> <mml:math id="mm1242" display="block"> <mml:semantics> <mml:mrow> <mml:msub> <mml:mi mathvariant="script">S</mml:mi> <mml:mi>D</mml:mi> </mml:msub> <mml:mo>→</mml:mo> <mml:msub> <mml:mi mathvariant="script">S</mml:mi> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>D</mml:mi> </mml:mrow> </mml:msub> <mml:mo>→</mml:mo> <mml:msub> <mml:mi mathvariant="script">S</mml:mi> <mml:mi mathvariant="sans-serif">sat</mml:mi> </mml:msub> <mml:mo>.</mml:mo> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (<bold>a</bold>) optimal power allocation <inline-formula> <mml:math id="mm1243" display="block"> <mml:semantics> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mi>p</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>q</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>p</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>q</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:semantics> </mml:math> </inline-formula>; (<bold>b</bold>) the resulting sum-rate constraints.</p>
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<p>Optimal power allocation that follows the sequence [S2] <inline-formula> <mml:math id="mm1244" display="block"> <mml:semantics> <mml:mrow> <mml:msub> <mml:mi mathvariant="script">S</mml:mi> <mml:mi>C</mml:mi> </mml:msub> <mml:mo>→</mml:mo> <mml:msub> <mml:mi mathvariant="script">S</mml:mi> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>D</mml:mi> </mml:mrow> </mml:msub> <mml:mo>→</mml:mo> <mml:msub> <mml:mi mathvariant="script">S</mml:mi> <mml:mi mathvariant="sans-serif">sat</mml:mi> </mml:msub> <mml:mo>.</mml:mo> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (<bold>a</bold>) optimal power allocation <inline-formula> <mml:math id="mm1245" display="block"> <mml:semantics> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mi>p</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>q</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>p</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>q</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:semantics> </mml:math> </inline-formula>; (<bold>b</bold>) the resulting sum-rate constraints.</p>
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<p>The set of all <inline-formula> <mml:math id="mm1246" display="block"> <mml:semantics> <mml:msup> <mml:mi>c</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:semantics> </mml:math> </inline-formula> and <inline-formula> <mml:math id="mm1247" display="block"> <mml:semantics> <mml:msup> <mml:mi>d</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:semantics> </mml:math> </inline-formula> is partitioned depending on whether the cross or the direct channels are “stronger”.</p>
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Article
Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition
by Michael Wibral, Conor Finn, Patricia Wollstadt, Joseph T. Lizier and Viola Priesemann
Entropy 2017, 19(9), 494; https://doi.org/10.3390/e19090494 - 14 Sep 2017
Cited by 44 | Viewed by 8356
Abstract
Information processing performed by any system can be conceptually decomposed into the transfer, storage and modification of information—an idea dating all the way back to the work of Alan Turing. However, formal information theoretic definitions until very recently were only available for information [...] Read more.
Information processing performed by any system can be conceptually decomposed into the transfer, storage and modification of information—an idea dating all the way back to the work of Alan Turing. However, formal information theoretic definitions until very recently were only available for information transfer and storage, not for modification. This has changed with the extension of Shannon information theory via the decomposition of the mutual information between inputs to and the output of a process into unique, shared and synergistic contributions from the inputs, called a partial information decomposition (PID). The synergistic contribution in particular has been identified as the basis for a definition of information modification. We here review the requirements for a functional definition of information modification in neuroscience, and apply a recently proposed measure of information modification to investigate the developmental trajectory of information modification in a culture of neurons vitro, using partial information decomposition. We found that modification rose with maturation, but ultimately collapsed when redundant information among neurons took over. This indicates that this particular developing neural system initially developed intricate processing capabilities, but ultimately displayed information processing that was highly similar across neurons, possibly due to a lack of external inputs. We close by pointing out the enormous promise PID and the analysis of information modification hold for the understanding of neural systems. Full article
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<p>Decomposition of the joint mutual information between two input variables <math display="inline"> <semantics> <msub> <mi>X</mi> <mn>1</mn> </msub> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>X</mi> <mn>2</mn> </msub> </semantics> </math> and the output variable <span class="html-italic">Y</span>. Modified from [<a href="#B17-entropy-19-00494" class="html-bibr">17</a>], CC-BY license.</p>
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<p>Mapping between the decomposition into storage and transfer (<b>A</b>) and individual or joint mutual information terms, and PID components (<b>B</b>). Numbers in (<b>B</b>) refer to the enumeration of components given in <a href="#sec2dot2-entropy-19-00494" class="html-sec">Section 2.2</a>. Number “4” is the modified information.</p>
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<p><b>Left:</b> development of the joint mutual information with network maturation. Grey symbols and lines—joint mutual information (MI) from individual pairs of spike time series, red symbols—median over all pairs. Horizontal black lines connect significantly different pairs of median values (<math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </mrow> </semantics> </math>, permutation test, Bonferroni corrected for multiple comparisons); <b>Right:</b> magnification of the joint mutual information estimates in the first two recording weeks. Note that the three large outliers from week 2 have been omitted from the plot. These tiny, but non-zero, values form the basis for the <span class="html-italic">normalized</span> non-zero PID terms presented in <a href="#entropy-19-00494-f004" class="html-fig">Figure 4</a>—also leading to considerable variance there.</p>
Full article ">Figure 4
<p>Development of <span class="html-italic">normalized</span> PID contributions (i.e., PID terms normalized by the joint mutual information) with network maturation. Grey symbols and lines—PID values from individual pairs of spike time series, red symbols—median over all pairs. Horizontal black lines connect significantly different pairs of median values (<math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </mrow> </semantics> </math>, permutation test, Bonferroni corrected for multiple comparisons). On the lower right, note the sudden increase in normalized shared mutual information from week 2 to 3 that coincides with the onset of system spanning neural avalanches (see text).</p>
Full article ">Figure 5
<p>Development of <span class="html-italic">raw</span> PID contributions with network maturation. Grey symbols and lines—PID values from individual pairs of spike time series, blue symbols—median over all pairs. Note that we do not provide statistical tests here as the visible differences are heavily influenced by the corresponding differences in the joint mutual information (see <a href="#entropy-19-00494-f003" class="html-fig">Figure 3</a>).</p>
Full article ">Figure 6
<p>PID diagram for three input variables—two of them external inputs (<math display="inline"> <semantics> <msub> <mi>X</mi> <mn>1</mn> </msub> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>X</mi> <mn>2</mn> </msub> </semantics> </math>), and one representing the past state of the receiving system (<math display="inline"> <semantics> <mrow> <mi>M</mi> <mo>=</mo> <msup> <mi mathvariant="bold">Y</mi> <mo>−</mo> </msup> </mrow> </semantics> </math>). The parts of the diagram highlighted in green would be considered information modification. These parts represent the information in the receiver that can only be explained by two or more input variables considered jointly.</p>
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489 KiB  
Article
On Generalized Stam Inequalities and Fisher–Rényi Complexity Measures
by Steeve Zozor, David Puertas-Centeno and Jesús S. Dehesa
Entropy 2017, 19(9), 493; https://doi.org/10.3390/e19090493 - 14 Sep 2017
Cited by 9 | Viewed by 3784
Abstract
Information-theoretic inequalities play a fundamental role in numerous scientific and technological areas (e.g., estimation and communication theories, signal and information processing, quantum physics, …) as they generally express the impossibility to have a complete description of a system via a finite number of [...] Read more.
Information-theoretic inequalities play a fundamental role in numerous scientific and technological areas (e.g., estimation and communication theories, signal and information processing, quantum physics, …) as they generally express the impossibility to have a complete description of a system via a finite number of information measures. In particular, they gave rise to the design of various quantifiers (statistical complexity measures) of the internal complexity of a (quantum) system. In this paper, we introduce a three-parametric Fisher–Rényi complexity, named ( p , β , λ ) -Fisher–Rényi complexity, based on both a two-parametic extension of the Fisher information and the Rényi entropies of a probability density function ρ characteristic of the system. This complexity measure quantifies the combined balance of the spreading and the gradient contents of ρ , and has the three main properties of a statistical complexity: the invariance under translation and scaling transformations, and a universal bounding from below. The latter is proved by generalizing the Stam inequality, which lowerbounds the product of the Shannon entropy power and the Fisher information of a probability density function. An extension of this inequality was already proposed by Bercher and Lutwak, a particular case of the general one, where the three parameters are linked, allowing to determine the sharp lower bound and the associated probability density with minimal complexity. Using the notion of differential-escort deformation, we are able to determine the sharp bound of the complexity measure even when the three parameters are decoupled (in a certain range). We determine as well the distribution that saturates the inequality: the ( p , β , λ ) -Gaussian distribution, which involves an inverse incomplete beta function. Finally, the complexity measure is calculated for various quantum-mechanical states of the harmonic and hydrogenic systems, which are the two main prototypes of physical systems subject to a central potential. Full article
(This article belongs to the Special Issue Foundations of Quantum Mechanics)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) the domain <math display="inline"> <semantics> <msub> <mi mathvariant="script">D</mi> <mi>p</mi> </msub> </semantics> </math> for a given <span class="html-italic">p</span> is represented by the gray area (here <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>&gt;</mo> <mn>2</mn> </mrow> </semantics> </math>). The thick line belongs to <math display="inline"> <semantics> <msub> <mi mathvariant="script">D</mi> <mi>p</mi> </msub> </semantics> </math>. The dashed line represents <math display="inline"> <semantics> <msub> <mi mathvariant="script">L</mi> <mi>p</mi> </msub> </semantics> </math>, corresponding to the Lutwak situation of <a href="#sec2dot3dot1-entropy-19-00493" class="html-sec">Section 2.3.1</a>, where the relation holds and the minimizers are explicitly known (stretched deformed Gaussian distributions), whereas <math display="inline"> <semantics> <msub> <mover> <mi mathvariant="script">L</mi> <mo stretchy="false">¯</mo> </mover> <mi>p</mi> </msub> </semantics> </math> corresponds to <a href="#sec2dot3dot2-entropy-19-00493" class="html-sec">Section 2.3.2</a> (<math display="inline"> <semantics> <msub> <mi mathvariant="script">B</mi> <mi>p</mi> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mover> <mi mathvariant="script">B</mi> <mo stretchy="false">¯</mo> </mover> <mi>p</mi> </msub> </semantics> </math> obtained by the Gagliardo–Nirenberg inequality are their restrictions to <math display="inline"> <semantics> <msub> <mi mathvariant="script">D</mi> <mi>p</mi> </msub> </semantics> </math>); (<b>b</b>) same situation for <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>, with the domains <math display="inline"> <semantics> <msub> <mi mathvariant="script">A</mi> <mn>2</mn> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mover> <mi mathvariant="script">A</mi> <mo stretchy="false">¯</mo> </mover> <mn>2</mn> </msub> </semantics> </math> (dashed lines) that correspond to the situations of <a href="#sec2dot3dot3-entropy-19-00493" class="html-sec">Section 2.3.3</a> and <a href="#sec2dot3dot4-entropy-19-00493" class="html-sec">Section 2.3.4</a>, respectively, (<math display="inline"> <semantics> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mover> <mi mathvariant="script">L</mi> <mo stretchy="false">¯</mo> </mover> <mn>2</mn> </msub> </semantics> </math> are not represented for the clarity of the figure).</p>
Full article ">Figure 2
<p>Given a <span class="html-italic">p</span>, the domain in gray represents <math display="inline"> <semantics> <msub> <mover accent="true"> <mi mathvariant="script">D</mi> <mo stretchy="false">˜</mo> </mover> <mi>p</mi> </msub> </semantics> </math>, where we know that the <math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> <mo>)</mo> </mrow> </semantics> </math>-Fisher–Rényi complexity is optimally lower bounded and where the minimizers can be deduced from proposition 2. (<b>a</b>) the domain in dark gray represents <math display="inline"> <semantics> <msub> <mi mathvariant="script">D</mi> <mi>p</mi> </msub> </semantics> </math>, which is obviously included in <math display="inline"> <semantics> <msub> <mover accent="true"> <mi mathvariant="script">D</mi> <mo stretchy="false">˜</mo> </mover> <mi>p</mi> </msub> </semantics> </math>; the dot is a particular point <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> <mo>)</mo> </mrow> <mo>∈</mo> <msub> <mi mathvariant="script">D</mi> <mi>p</mi> </msub> </mrow> </semantics> </math> and the dotted line represents its transform by <math display="inline"> <semantics> <mi mathvariant="fraktur">A</mi> </semantics> </math>; (<b>b</b>) the domain in dark gray represents <math display="inline"> <semantics> <mrow> <mi mathvariant="fraktur">A</mi> <mrow> <mo>(</mo> <msub> <mi mathvariant="script">L</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>⊂</mo> <msub> <mover accent="true"> <mi mathvariant="script">D</mi> <mo stretchy="false">˜</mo> </mover> <mi>p</mi> </msub> </mrow> </semantics> </math>, which obviously contains <math display="inline"> <semantics> <msub> <mi mathvariant="script">L</mi> <mi>p</mi> </msub> </semantics> </math> represented by the dashed line; (<b>c</b>) same as (<b>b</b>) with <math display="inline"> <semantics> <msub> <mover> <mi mathvariant="script">L</mi> <mo stretchy="false">¯</mo> </mover> <mi>p</mi> </msub> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi mathvariant="fraktur">A</mi> <mrow> <mo>(</mo> <msub> <mover> <mi mathvariant="script">L</mi> <mo stretchy="false">¯</mo> </mover> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>⊂</mo> <msub> <mover accent="true"> <mi mathvariant="script">D</mi> <mo stretchy="false">˜</mo> </mover> <mi>p</mi> </msub> </mrow> </semantics> </math>. This illustrates that <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi mathvariant="script">D</mi> <mo stretchy="false">˜</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <mi mathvariant="fraktur">A</mi> <mrow> <mo>(</mo> <msub> <mi mathvariant="script">L</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>∪</mo> <mi mathvariant="fraktur">A</mi> <mrow> <mo>(</mo> <msub> <mover> <mi mathvariant="script">L</mi> <mo stretchy="false">¯</mo> </mover> <mi>p</mi> </msub> <mo>)</mo> </mrow> </mrow> </semantics> </math>.</p>
Full article ">Figure 3
<p>Fisher information <math display="inline"> <semantics> <msub> <mi>F</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>β</mi> </mrow> </msub> </semantics> </math> (left graph), Rényi entropy power <math display="inline"> <semantics> <msub> <mi>N</mi> <mi>λ</mi> </msub> </semantics> </math> (center graph), and <math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> <mo>)</mo> </mrow> </semantics> </math>-Fisher–Rényi complexity <math display="inline"> <semantics> <msub> <mi>C</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </msub> </semantics> </math> (right graph) of the radial hydrogenic distribution in position space with dimensions <math display="inline"> <semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>3</mn> <mo>(</mo> <mo>∘</mo> <mo>)</mo> <mo>,</mo> <mspace width="0.222222em"/> <mn>12</mn> <mo>(</mo> <mo>*</mo> <mo>)</mo> </mrow> </semantics> </math> versus the quantum numbers <span class="html-italic">n</span> and <span class="html-italic">l</span>. The complexity parameters are <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mspace width="0.166667em"/> <mi>β</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mspace width="0.166667em"/> <mi>λ</mi> <mo>=</mo> <mn>7</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 4
<p><math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> <mo>)</mo> </mrow> </semantics> </math>-Fisher–Rényi complexity (normalized to its lower bound), <math display="inline"> <semantics> <msub> <mi>C</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </msub> </semantics> </math>, with <math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>λ</mi> <mo>,</mo> <mi>β</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>2</mn> <mo>,</mo> <mn>0.8</mn> <mo>,</mo> <mn>7</mn> <mo>)</mo> <mo>,</mo> <mo>(</mo> <mn>2</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> <mo>,</mo> <mo>(</mo> <mn>5</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>7</mn> <mo>)</mo> </mrow> </semantics> </math> for the radial hydrogenic distribution in the position space with dimensions <math display="inline"> <semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>3</mn> <mo>(</mo> <mo>∘</mo> <mo>)</mo> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mn>12</mn> <mo>(</mo> <mo>*</mo> <mo>)</mo> </mrow> </semantics> </math>.</p>
Full article ">Figure 5
<p>Fisher information <math display="inline"> <semantics> <msub> <mi>F</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>β</mi> </mrow> </msub> </semantics> </math> (left graph), Rényi entropy power <math display="inline"> <semantics> <msub> <mi>N</mi> <mi>λ</mi> </msub> </semantics> </math> (center graph), and <math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> <mo>)</mo> </mrow> </semantics> </math>-Fisher–Rényi complexity <math display="inline"> <semantics> <msub> <mi>C</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </msub> </semantics> </math> (right graph) of the radial hydrogenic distribution in momentum space with dimensions <math display="inline"> <semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>3</mn> <mo>(</mo> <mo>∘</mo> <mo>)</mo> <mo>,</mo> <mspace width="0.222222em"/> <mn>12</mn> <mo>(</mo> <mo>*</mo> <mo>)</mo> </mrow> </semantics> </math> versus the quantum numbers <span class="html-italic">n</span> and <span class="html-italic">l</span>. The complexity parameters are <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mspace width="0.166667em"/> <mi>β</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mspace width="0.166667em"/> <mi>λ</mi> <mo>=</mo> <mn>7</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 6
<p><math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> <mo>)</mo> </mrow> </semantics> </math>-Fisher–Rényi complexity (normalized to its lower bound), <math display="inline"> <semantics> <msub> <mi>C</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </msub> </semantics> </math>, with <math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>λ</mi> <mo>,</mo> <mi>β</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>2</mn> <mo>,</mo> <mn>0.8</mn> <mo>,</mo> <mn>7</mn> <mo>)</mo> <mo>,</mo> <mspace width="0.222222em"/> <mo>(</mo> <mn>2</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> <mo>,</mo> <mspace width="0.222222em"/> <mo>(</mo> <mn>5</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>7</mn> <mo>)</mo> </mrow> </semantics> </math> for the radial hydrogenic distribution in the momentum space with dimensions <math display="inline"> <semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>3</mn> <mo>(</mo> <mo>∘</mo> <mo>)</mo> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mn>12</mn> <mo>(</mo> <mo>*</mo> <mo>)</mo> </mrow> </semantics> </math>.</p>
Full article ">Figure 7
<p>Fisher information <math display="inline"> <semantics> <msub> <mi>F</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>β</mi> </mrow> </msub> </semantics> </math> (left graph), Rényi entropy power <math display="inline"> <semantics> <msub> <mi>N</mi> <mi>λ</mi> </msub> </semantics> </math> (center graph), and <math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> <mo>)</mo> </mrow> </semantics> </math>-Fisher–Rényi complexity <math display="inline"> <semantics> <msub> <mi>C</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </msub> </semantics> </math> (right graph) versus <span class="html-italic">n</span> and <span class="html-italic">l</span> for the radial harmonic system in position space with dimensions <math display="inline"> <semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>3</mn> <mo>(</mo> <mo>∘</mo> <mo>)</mo> <mo>,</mo> <mspace width="4pt"/> <mn>12</mn> <mo>(</mo> <mo>*</mo> <mo>)</mo> </mrow> </semantics> </math>. The informational parameters are <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mspace width="0.166667em"/> <mi>β</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mspace width="0.166667em"/> <mi>λ</mi> <mo>=</mo> <mn>7</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 8
<p><math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> <mo>)</mo> </mrow> </semantics> </math>-Fisher–Rényi complexity (normalized to its lower bound) <math display="inline"> <semantics> <msub> <mi>C</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </msub> </semantics> </math> with <math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>λ</mi> <mo>,</mo> <mi>β</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>2</mn> <mo>,</mo> <mn>0.8</mn> <mo>,</mo> <mn>7</mn> <mo>)</mo> <mo>,</mo> <mspace width="0.166667em"/> <mo>(</mo> <mn>2</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> <mo>,</mo> <mspace width="0.166667em"/> <mo>(</mo> <mn>5</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>7</mn> <mo>)</mo> </mrow> </semantics> </math> for the oscillator system in the position space with dimensions <math display="inline"> <semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>3</mn> <mo>(</mo> <mo>∘</mo> <mo>)</mo> <mo>,</mo> <mn>12</mn> <mo>(</mo> <mo>*</mo> <mo>)</mo> </mrow> </semantics> </math>.</p>
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1712 KiB  
Article
Eigentimes and Very Slow Processes
by Bjarne Andresen and Christopher Essex
Entropy 2017, 19(9), 492; https://doi.org/10.3390/e19090492 - 14 Sep 2017
Cited by 4 | Viewed by 4877
Abstract
We investigate the importance of the time and length scales at play in our descriptions of Nature. What can we observe at the atomic scale, at the laboratory (human) scale, and at the galactic scale? Which variables make sense? For every scale we [...] Read more.
We investigate the importance of the time and length scales at play in our descriptions of Nature. What can we observe at the atomic scale, at the laboratory (human) scale, and at the galactic scale? Which variables make sense? For every scale we wish to understand we need a set of variables which are linked through closed equations, i.e., everything can meaningfully be described in terms of those variables without the need to investigate other scales. Examples from physics, chemistry, and evolution are presented. Full article
(This article belongs to the Special Issue Entropy, Time and Evolution)
Show Figures

Figure 1

Figure 1
<p>Two images of the same Niagara Falls downstream flow. The image (<b>a</b>) is an exposure of 0.4 s, while the image (<b>b</b>) is exposed for 50 s. Note the flow features visible in (b) (stream lines, bow waves, standing waves, vortices, etc.) that are not clearly visible or simply invisible in (a).</p>
Full article ">Figure 2
<p>The velocity distribution <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>(</mo> <mi>v</mi> <mo>;</mo> <mi>w</mi> <mo>,</mo> <mi>ψ</mi> <mo>)</mo> </mrow> </semantics> </math> of Equation (<a href="#FD4-entropy-19-00492" class="html-disp-formula">4</a>) for <math display="inline"> <semantics> <mrow> <mi>w</mi> <mo>=</mo> <mn>2</mn> <mo>.</mo> <mn>5</mn> </mrow> </semantics> </math> and center value <math display="inline"> <semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> (red). A pure Gaussian thermal distribution is shown in green for comparison. The frame (<b>a</b>) is a normal linear plot and the frame (<b>b</b>) is a semilog plot where the agreement between the slow time distribution (red) and a thermal distribution (green) for small velocities but large discrepancy at large velocities is even more evident.</p>
Full article ">Figure 3
<p>The free energy landscape of the four species <math display="inline"> <semantics> <mrow> <mi>A</mi> <mo>,</mo> <mi>B</mi> <mo>,</mo> <mi>C</mi> <mo>,</mo> <mi>E</mi> </mrow> </semantics> </math> and the transition states between them for collision rates <span class="html-italic">Z</span> in the range <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>12</mn> </msup> </semantics> </math> to <math display="inline"> <semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics> </math> <math display="inline"> <semantics> <msup> <mi>s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics> </math>. This shows that for timescales larger than <math display="inline"> <semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mi>Z</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> s (red curve) states <span class="html-italic">B</span> and <span class="html-italic">C</span> are equilibrated and should rather be thought of as a single state. The portions representing negative <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msup> <mi>G</mi> <mo>‡</mo> </msup> </mrow> </semantics> </math> have been dotted; on these timescales the two species should be combined to give one effective species.</p>
Full article ">Figure 4
<p>Decay curves for 3 parallel processes of distinctly different eigentimes, in plot (<b>a</b>) on a linear scale, in plot (<b>b</b>) on a semilog scale. The 3 exponential decays are most clearly separatred on a semilog plot (b), where the green lines indicate each of the exponential decays separately.</p>
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560 KiB  
Article
Semantic Security with Practical Transmission Schemes over Fading Wiretap Channels
by Linda Senigagliesi, Marco Baldi and Franco Chiaraluce
Entropy 2017, 19(9), 491; https://doi.org/10.3390/e19090491 - 13 Sep 2017
Cited by 1 | Viewed by 4672
Abstract
We propose and assess an on–off protocol for communication over wireless wiretap channels with security at the physical layer. By taking advantage of suitable cryptographic primitives, the protocol we propose allows two legitimate parties to exchange confidential messages with some chosen level of [...] Read more.
We propose and assess an on–off protocol for communication over wireless wiretap channels with security at the physical layer. By taking advantage of suitable cryptographic primitives, the protocol we propose allows two legitimate parties to exchange confidential messages with some chosen level of semantic security against passive eavesdroppers, and without needing either pre-shared secret keys or public keys. The proposed method leverages the noisy and fading nature of the channel and exploits coding and all-or-nothing transforms to achieve the desired level of semantic security. We show that the use of fake packets in place of skipped transmissions during low channel quality periods yields significant advantages in terms of time needed to complete transmission of a secret message. Numerical examples are provided considering coding and modulation schemes included in the WiMax standard, thus showing that the proposed approach is feasible even with existing practical devices. Full article
(This article belongs to the Special Issue Information-Theoretic Security)
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<p>Block diagram of: (<b>a</b>) AONT encryption and (<b>b</b>) AONT decryption of a secret message <span class="html-italic">M</span>.</p>
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<p>Block diagram of: (<b>a</b>) the procedure for transforming a secret message <span class="html-italic">M</span> into a set of <span class="html-italic">n</span>-bit codewords <math display="inline"> <semantics> <msub> <mi>c</mi> <mi>i</mi> </msub> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mi>N</mi> </mrow> </semantics> </math>, to be transmitted and (<b>b</b>) its inverse.</p>
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<p>Flow chart of the transmission of a packet according to the OOT-FP protocol.</p>
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<p>Exact and approximate input-constrained capacity for BPSK, 4-QAM and 16-QAM, as a function of the SNR <math display="inline"> <semantics> <mi>γ</mi> </semantics> </math>.</p>
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<p>Number of single packet sessions needed to achieve 128-bit SS versus SNR gap with WiMax LDPC codes having length <math display="inline"> <semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>2304</mn> </mrow> </semantics> </math>, rate <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </semantics> </math> and BPSK, for the case of <math display="inline"> <semantics> <mrow> <msup> <mi>k</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>9</mn> <mi>k</mi> </mrow> </semantics> </math>.</p>
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<p>Number of single packet sessions needed to achieve 128-bit SS versus SNR gap with WiMax LDPC codes having length <math display="inline"> <semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>2304</mn> </mrow> </semantics> </math>, rate <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>5</mn> <mo>/</mo> <mn>6</mn> </mrow> </semantics> </math> and 16-QAM, for the case of <math display="inline"> <semantics> <mrow> <msup> <mi>k</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>9</mn> <mi>k</mi> </mrow> </semantics> </math>.</p>
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<p>Number of single packet sessions needed to achieve 128-bit SS in terms of SNR gap with WiMax LDPC codes having length <math display="inline"> <semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>2304</mn> </mrow> </semantics> </math>, BPSK and different rates, for the cases of <math display="inline"> <semantics> <mrow> <msup> <mi>k</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>8</mn> <mi>k</mi> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msup> <mi>k</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>9</mn> <mi>k</mi> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msup> <mi>k</mi> <mo>′</mo> </msup> <mo>=</mo> <mi>k</mi> </mrow> </semantics> </math>, and shape factor <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>, using: (<b>a</b>) the OOT-FP protocol and (<b>b</b>) the OOT protocol.</p>
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<p>Number of single packet sessions needed to achieve 128-bit SS in terms of SNR gap with WiMax LDPC codes having length <math display="inline"> <semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>2304</mn> </mrow> </semantics> </math>, rate <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </semantics> </math> and three different modulations, for the cases of <math display="inline"> <semantics> <mrow> <msup> <mi>k</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>8</mn> <mi>k</mi> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msup> <mi>k</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>9</mn> <mi>k</mi> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msup> <mi>k</mi> <mo>′</mo> </msup> <mo>=</mo> <mi>k</mi> </mrow> </semantics> </math>, and shape factor <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>, using: (<b>a</b>) the OOT-FP protocol and (<b>b</b>) the OOT protocol.</p>
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2347 KiB  
Article
Use of Mutual Information and Transfer Entropy to Assess Interaction between Parasympathetic and Sympathetic Activities of Nervous System from HRV
by Lianrong Zheng, Weifeng Pan, Yifan Li, Daiyi Luo, Qian Wang and Guanzheng Liu
Entropy 2017, 19(9), 489; https://doi.org/10.3390/e19090489 - 13 Sep 2017
Cited by 31 | Viewed by 5137
Abstract
Obstructive sleep apnea (OSA) is a common sleep disorder that often associates with reduced heart rate variability (HRV) indicating autonomic dysfunction. HRV is mainly composed of high frequency components attributed to parasympathetic activity and low frequency components attributed to sympathetic activity. Although, time [...] Read more.
Obstructive sleep apnea (OSA) is a common sleep disorder that often associates with reduced heart rate variability (HRV) indicating autonomic dysfunction. HRV is mainly composed of high frequency components attributed to parasympathetic activity and low frequency components attributed to sympathetic activity. Although, time domain and frequency domain features of HRV have been used to sleep studies, the complex interaction between nonlinear independent frequency components with OSA is less known. This study included 30 electrocardiogram recordings (20 OSA patient recording and 10 healthy subjects) with apnea or normal label in 1-min segment. All segments were divided into three groups: N-N group (normal segments of normal subjects), P-N group (normal segments of OSA subjects) and P-OSA group (apnea segments of OSA subjects). Frequency domain indices and interaction indices were extracted from segmented RR intervals. Frequency domain indices included nuLF, nuHF, and LF/HF ratio; interaction indices included mutual information (MI) and transfer entropy (TE (H→L) and TE (L→H)). Our results demonstrated that LF/HF ratio was significant higher in P-OSA group than N-N group and P-N group. MI was significantly larger in P-OSA group than P-N group. TE (H→L) and TE (L→H) showed a significant decrease in P-OSA group, compared to P-N group and N-N group. TE (H→L) were significantly negative correlation with LF/HF ratio in P-N group (r = −0.789, p = 0.000) and P-OSA group (r = −0.661, p = 0.002). Our results indicated that MI and TE is powerful tools to evaluate sympathovagal modulation in OSA. Moreover, sympathovagal modulation is more imbalance in OSA patients while suffering from apnea event compared to free event. Full article
(This article belongs to the Special Issue Transfer Entropy II)
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<p>TE matrix representation indicated the causality between X (driver system) and Y (target system). The color indicates the value of the TE averaged over 100 realizations of simulation.</p>
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<p>A plot of mean transfer entropy over 100 realizations of simulation with different lag time. # represents statistically significant difference <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Mean and standard error of nuLF, nuHF and LF/HF ratio in N-N, P-N and P-OSA group. (<b>a</b>) nuLF of three groups. (<b>b</b>) nuHF of three groups. (<b>c</b>) The LF/HF ratio of three groups. nuLF: LF power in normalized units, nuHF: HF power in normalized units, LF/HF ratio: the power in LF⁄HF. *, ** and *** represent statistically significant difference <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01 and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>The influence of lag time on mean transfer entropy of N-N group. # represents statistically significant difference <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The results of interaction feature in N-N, P-N and P-OSA group. (<b>a</b>) The mean value of MI in three groups. (<b>b</b>) The mean value of TE (L→H) in three groups. (<b>c</b>) The mean value of TE (H→L) in three groups. (<b>d</b>) The differences between TE (L→H) and TE (H→L) in three groups. MI: mutual information; TE: transfer entropy; TE (L→H): transfer entropy from low frequency to high frequency; TE (H→L): transfer entropy high from frequency to low frequency. ** and *** represents statistically significant difference <span class="html-italic">p</span> &lt; 0.01 and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>The relationship between LF/HF ratio and interaction features with regard to the P-N group and P-OSA group. (<b>a</b>) Scatterplot of TE (L→H) with LF/HF ratio in P-OSA group; (<b>b</b>) Scatterplot of TE (H→L) with LF/HF ratio in P-N group; (<b>c</b>) Scatterplot of TE (H→L) with LF/HF ratio in P-OSA group.</p>
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834 KiB  
Article
Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework
by Mohit Kumar, Ram Bilas Pachori and U. Rajendra Acharya
Entropy 2017, 19(9), 488; https://doi.org/10.3390/e19090488 - 13 Sep 2017
Cited by 121 | Viewed by 9968
Abstract
Myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. It expands rapidly and, if not treated timely, continues to damage the heart muscles. An electrocardiogram (ECG) is generally used by the clinicians to diagnose the MI patients. Manual identification [...] Read more.
Myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. It expands rapidly and, if not treated timely, continues to damage the heart muscles. An electrocardiogram (ECG) is generally used by the clinicians to diagnose the MI patients. Manual identification of the changes introduced by MI is a time-consuming and tedious task, and there is also a possibility of misinterpretation of the changes in the ECG. Therefore, a method for automatic diagnosis of MI using ECG beat with flexible analytic wavelet transform (FAWT) method is proposed in this work. First, the segmentation of ECG signals into beats is performed. Then, FAWT is applied to each ECG beat, which decomposes them into subband signals. Sample entropy (SEnt) is computed from these subband signals and fed to the random forest (RF), J48 decision tree, back propagation neural network (BPNN), and least-squares support vector machine (LS-SVM) classifiers to choose the highest performing one. We have achieved highest classification accuracy of 99.31% using LS-SVM classifier. We have also incorporated Wilcoxon and Bhattacharya ranking methods and observed no improvement in the performance. The proposed automated method can be installed in the intensive care units (ICUs) of hospitals to aid the clinicians in confirming their diagnosis. Full article
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<p>The proposed method to diagnose the myocardial infarction (MI) patients.</p>
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<p>Plot of decomposed subband signals: (<b>a</b>) normal electrocardiogram (ECG) beat, (<b>b</b>) MI ECG beat.</p>
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<p>Plot of accuracy (%) versus <span class="html-italic">r</span> of SEnt with RF classifier.</p>
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<p>Plot of accuracy (%) versus level of decomposition with RF classifier.</p>
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<p>Plot of accuracies versus <math display="inline"> <semantics> <mi>σ</mi> </semantics> </math> of RBF kernel.</p>
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<p>Plot of accuracies versus <span class="html-italic">q</span> of Morlet wavelet kernel.</p>
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<p>Plot of accuracy (%) versus number of features using LS-SVM with RBF kernel.</p>
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<p>Plot of accuracy (%) versus number of features using LS-SVM with Morlet wavelet kernel.</p>
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911 KiB  
Article
Simultaneous Wireless Information and Power Transfer for MIMO Interference Channel Networks Based on Interference Alignment
by Anming Dong, Haixia Zhang, Minglei Shu and Dongfeng Yuan
Entropy 2017, 19(9), 484; https://doi.org/10.3390/e19090484 - 13 Sep 2017
Cited by 6 | Viewed by 5230
Abstract
This paper considers power splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) for multiple-input multiple-output (MIMO) interference channel networks where multiple transceiver pairs share the same frequency spectrum. As the PS model is adopted, an individual receiver splits the received signal into [...] Read more.
This paper considers power splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) for multiple-input multiple-output (MIMO) interference channel networks where multiple transceiver pairs share the same frequency spectrum. As the PS model is adopted, an individual receiver splits the received signal into two parts for information decoding (ID) and energy harvesting (EH), respectively. Aiming to minimize the total transmit power, transmit precoders, receive filters and PS ratios are jointly designed under a predefined signal-to-interference-plus-noise ratio (SINR) and EH constraints. The formulated joint transceiver design and power splitting problem is non-convex and thus difficult to solve directly. In order to effectively obtain its solution, the feasibility conditions of the formulated non-convex problem are first analyzed. Based on the analysis, an iterative algorithm is proposed by alternatively optimizing the transmitters together with the power splitting factors and the receivers based on semidefinite programming (SDP) relaxation. Moreover, considering the prohibitive computational cost of the SDP for practical applications, a low-complexity suboptimal scheme is proposed by separately designing interference-suppressing transceivers based on interference alignment (IA) and optimizing the transmit power allocation together with splitting factors. The transmit power allocation and receive power splitting problem is then recast as a convex optimization problem and solved efficiently. To further reduce the computational complexity, a low-complexity scheme is proposed by calculating the transmit power allocation and receive PS ratios in closed-form. Simulation results show the effectiveness of the proposed schemes in achieving SWIPT for MIMO interference channel (IC) networks. Full article
(This article belongs to the Special Issue Network Information Theory)
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<p>MIMO interference channel SWIPT system.</p>
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<p>Convergence property of the semidefinite programming (SDP)-joint transceiver design and power splitting (JTDPS) scheme for the <math display="inline"> <semantics> <msup> <mrow> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> <mn>3</mn> </msup> </semantics> </math> network.</p>
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<p>Convergence property of the SDP-JTDPS scheme for the <math display="inline"> <semantics> <msup> <mrow> <mo>(</mo> <mn>6</mn> <mo>,</mo> <mn>6</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> <mn>4</mn> </msup> </semantics> </math> network.</p>
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<p>Empirical distribution of achieved SINR at ID receivers with different SINR and energy harvesting (EH) thresholds for the <math display="inline"> <semantics> <msup> <mrow> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> <mn>3</mn> </msup> </semantics> </math>.</p>
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<p>Empirical distribution of harvested energy at ID receivers with different SINR and EH thresholds for the <math display="inline"> <semantics> <msup> <mrow> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> <mn>3</mn> </msup> </semantics> </math>.</p>
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<p>Average total transmit power versus SINR thresholds for the <math display="inline"> <semantics> <msup> <mrow> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> <mn>3</mn> </msup> </semantics> </math>.</p>
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<p>Average total transmit power versus EH thresholds for the <math display="inline"> <semantics> <msup> <mrow> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> <mn>3</mn> </msup> </semantics> </math> network.</p>
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<p>Average total transmit power versus SINR thresholds for the <math display="inline"> <semantics> <msup> <mrow> <mo>(</mo> <mn>2</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> <mn>3</mn> </msup> </semantics> </math> network. The schemes proposed in [<a href="#B41-entropy-19-00484" class="html-bibr">41</a>] are compared.</p>
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<p>Total transmit power versus SINR thresholds for the <math display="inline"> <semantics> <msup> <mrow> <mo>(</mo> <mn>6</mn> <mo>,</mo> <mn>6</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> <mn>4</mn> </msup> </semantics> </math> network over one channel realization. Our schemes are compared with the DIA scheme proposed in [<a href="#B41-entropy-19-00484" class="html-bibr">41</a>].</p>
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<p>Total transmit power versus EH thresholds for the <math display="inline"> <semantics> <msup> <mrow> <mo>(</mo> <mn>6</mn> <mo>,</mo> <mn>6</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> <mn>4</mn> </msup> </semantics> </math> network over one channel realizations. Our schemes are compared with the DIAscheme proposed in [<a href="#B41-entropy-19-00484" class="html-bibr">41</a>].</p>
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<p>Average execution time versus <span class="html-italic">M</span> at <math display="inline"> <semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics> </math> dB and <math display="inline"> <semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics> </math> dBm.</p>
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920 KiB  
Review
Born-Kothari Condensation for Fermions
by Arnab Ghosh
Entropy 2017, 19(9), 479; https://doi.org/10.3390/e19090479 - 13 Sep 2017
Cited by 1 | Viewed by 4797
Abstract
In the spirit of Bose–Einstein condensation, we present a detailed account of the statistical description of the condensation phenomena for a Fermi–Dirac gas following the works of Born and Kothari. For bosons, while the condensed phase below a certain critical temperature, permits macroscopic [...] Read more.
In the spirit of Bose–Einstein condensation, we present a detailed account of the statistical description of the condensation phenomena for a Fermi–Dirac gas following the works of Born and Kothari. For bosons, while the condensed phase below a certain critical temperature, permits macroscopic occupation at the lowest energy single particle state, for fermions, due to Pauli exclusion principle, the condensed phase occurs only in the form of a single occupancy dense modes at the highest energy state. In spite of these rudimentary differences, our recent findings [Ghosh and Ray, 2017] identify the foregoing phenomenon as condensation-like coherence among fermions in an analogous way to Bose–Einstein condensate which is collectively described by a coherent matter wave. To reach the above conclusion, we employ the close relationship between the statistical methods of bosonic and fermionic fields pioneered by Cahill and Glauber. In addition to our previous results, we described in this mini-review that the highest momentum (energy) for individual fermions, prerequisite for the condensation process, can be specified in terms of the natural length and energy scales of the problem. The existence of such condensed phases, which are of obvious significance in the context of elementary particles, have also been scrutinized. Full article
(This article belongs to the Special Issue Foundations of Quantum Mechanics)
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<p>Schematic diagrams illustrating the basic differences and innate similarities between two kinds of condensations: (<b>a</b>) Bose–Einstein condensation (forms coherent matter wave) and (<b>b</b>) Born-Kothari condensation (forms condensation-like coherence).</p>
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8487 KiB  
Article
Use of the Principles of Maximum Entropy and Maximum Relative Entropy for the Determination of Uncertain Parameter Distributions in Engineering Applications
by José-Luis Muñoz-Cobo, Rafael Mendizábal, Arturo Miquel, Cesar Berna and Alberto Escrivá
Entropy 2017, 19(9), 486; https://doi.org/10.3390/e19090486 - 12 Sep 2017
Cited by 29 | Viewed by 6569
Abstract
The determination of the probability distribution function (PDF) of uncertain input and model parameters in engineering application codes is an issue of importance for uncertainty quantification methods. One of the approaches that can be used for the PDF determination of input and model [...] Read more.
The determination of the probability distribution function (PDF) of uncertain input and model parameters in engineering application codes is an issue of importance for uncertainty quantification methods. One of the approaches that can be used for the PDF determination of input and model parameters is the application of methods based on the maximum entropy principle (MEP) and the maximum relative entropy (MREP). These methods determine the PDF that maximizes the information entropy when only partial information about the parameter distribution is known, such as some moments of the distribution and its support. In addition, this paper shows the application of the MREP to update the PDF when the parameter must fulfill some technical specifications (TS) imposed by the regulations. Three computer programs have been developed: GEDIPA, which provides the parameter PDF using empirical distribution function (EDF) methods; UNTHERCO, which performs the Monte Carlo sampling on the parameter distribution; and DCP, which updates the PDF considering the TS and the MREP. Finally, the paper displays several applications and examples for the determination of the PDF applying the MEP and the MREP, and the influence of several factors on the PDF. Full article
(This article belongs to the Special Issue Maximum Entropy and Bayesian Methods)
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<p>Expected value <math display="inline"> <semantics> <mrow> <mi>μ</mi> </mrow> </semantics> </math> versus <math display="inline"> <semantics> <mrow> <msubsup> <mi>λ</mi> <mn>1</mn> <mo>′</mo> </msubsup> </mrow> </semantics> </math> for the case with <span class="html-italic">a</span> = −1 and <span class="html-italic">b</span> = 1.</p>
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<p>Plot of <math display="inline"> <semantics> <mrow> <mfrac> <mi>σ</mi> <mi>s</mi> </mfrac> </mrow> </semantics> </math> versus <math display="inline"> <semantics> <msup> <mi>λ</mi> <mo>′</mo> </msup> </semantics> </math> for the Gaussian truncated distribution with half width <span class="html-italic">s</span>.</p>
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<p>Probability density function <math display="inline"> <semantics> <mrow> <msub> <mi>f</mi> <mi>Z</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> versus z for the truncated Gaussian exponential for <math display="inline"> <semantics> <mrow> <msup> <mi>λ</mi> <mo>′</mo> </msup> <mo>=</mo> <mo>−</mo> <mn>10</mn> <mo>,</mo> <mo>−</mo> <mn>9</mn> <mo>,</mo> <mo>−</mo> <mn>8</mn> <mo>,</mo> <mo>−</mo> <mn>7</mn> <mo>,</mo> <mo>−</mo> <mn>6</mn> <mo>,</mo> <mo>−</mo> <mn>5</mn> <mo>,</mo> <mo>−</mo> <mn>4</mn> <mo>,</mo> <mo>−</mo> <mn>3</mn> <mo>,</mo> <mo>−</mo> <mn>2</mn> <mo>,</mo> <mo>−</mo> <mn>1</mn> <mo>,</mo> <mo>−</mo> <mn>0.5</mn> </mrow> </semantics> </math>. The more flat curve is <math display="inline"> <semantics> <mrow> <msup> <mi>λ</mi> <mo>′</mo> </msup> <mo>=</mo> <mo>−</mo> <mn>0.5</mn> </mrow> </semantics> </math> with blue colour.</p>
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<p>Probability distribution function for the acceptance interval [1.97, 2.03] (blue line) and the change in the PDF produced by a new imposed acceptance interval [1.98, 2.02] (green line) obtained for BEPU analysis with 95/95 coverage and confidence and <span class="html-italic">N =</span> 93.</p>
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<p>Probability distribution functions for the acceptance interval [1.97, 2.03] computed for <span class="html-italic">N =</span> 93 cases (blue line) and <span class="html-italic">N =</span> 59 cases (green line).</p>
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<p>Probability distribution function for a parameter that follows a log-normal distribution and with acceptance interval [0.985, 1.015] (blue Line) and the change in the PDF produced by a new imposed acceptance interval [0.99, 1.01] (green line) obtained for BEPU analysis with 95/95 coverage and confidence.</p>
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<p>Probability distribution function for the one side lower limit acceptance interval [2, ∞] (blue line) with <span class="html-italic">L</span> = 2 and the change in the PDF produced by the TS (green line) obtained for BEPU analysis with 95/95 coverage and confidence and <span class="html-italic">p</span> = 0.9786.</p>
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<p>Probability distribution function for the one side upper limit acceptance interval [−<math display="inline"> <semantics> <mo>∞</mo> </semantics> </math>, <math display="inline"> <semantics> <mn>2</mn> </semantics> </math> ] (blue line) with <span class="html-italic">U</span> = 2 and the change in the PDF produced by the TS (green line) obtained for BEPU analysis with 95/95 coverage and confidence and <span class="html-italic">p</span> = 0.9786.</p>
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<p>Probability distribution function for one side upper limit acceptance interval [−<math display="inline"> <semantics> <mo>∞</mo> </semantics> </math>, <math display="inline"> <semantics> <mn>2</mn> </semantics> </math> ] (blue line) with <span class="html-italic">U</span> = 2 and the change in the PDF produced imposing a value of <span class="html-italic">p</span> = 0.878 (green line) that not fulfills the TS and reduce the coverage and maintaining the confidence from 0.95/0.95 to 0.8/0.95</p>
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<p>Probability distribution obtained using the MEP for the parameter <math display="inline"> <semantics> <mrow> <msub> <mi>f</mi> <mi>K</mi> </msub> </mrow> </semantics> </math> in the interval [0.67, 1.5], blue decreasing line. The red line is the histogram from reference [<a href="#B30-entropy-19-00486" class="html-bibr">30</a>].</p>
Full article ">Figure 11
<p>Truncated Gaussian with support [0.85, 1.15] and standard deviation <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>0.04</mn> </mrow> </semantics> </math>.</p>
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<p>P(<span class="html-italic">x</span>,<math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>,</mo> <mi>β</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math> versus x for the following set of values of the parameters <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mi>α</mi> <mo>,</mo> <mi>β</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math>, data1 (1, 1), data2 (3, 2), data3 (5, 3), data4 (8, 4).</p>
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397 KiB  
Article
Optomechanical Analogy for Toy Cosmology with Quantized Scale Factor
by Joseph A. Smiga and Jacob M. Taylor
Entropy 2017, 19(9), 485; https://doi.org/10.3390/e19090485 - 12 Sep 2017
Viewed by 5163
Abstract
The simplest cosmology—the Friedmann–Robertson–Walker–Lemaître (FRW) model— describes a spatially homogeneous and isotropic universe where the scale factor is the only dynamical parameter. Here we consider how quantized electromagnetic fields become entangled with the scale factor in a toy version of the FRW model. [...] Read more.
The simplest cosmology—the Friedmann–Robertson–Walker–Lemaître (FRW) model— describes a spatially homogeneous and isotropic universe where the scale factor is the only dynamical parameter. Here we consider how quantized electromagnetic fields become entangled with the scale factor in a toy version of the FRW model. A system consisting of a photon, source, and detector is described in such a universe, and we find that the detection of a redshifted photon by the detector system constrains possible scale factor superpositions. Thus, measuring the redshift of the photon is equivalent to a weak measurement of the underlying cosmology. We also consider a potential optomechanical analogy system that would enable experimental exploration of these concepts. The analogy focuses on the effects of photon redshift measurement as a quantum back-action on metric variables, where the position of a movable mirror plays the role of the scale factor. By working in the rotating frame, an effective Hubble equation can be simulated with a simple free moving mirror. Full article
(This article belongs to the Collection Quantum Information)
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<p>The Friedmann–Robertson–Walker–Lemaître (FRW) for an exponentially expanding universe in (<b>a</b>) coordinate and (<b>b</b>) conformal time. Blue vertical lines are separated by one unit of proper distance, while horizontal lines are separated by one unit of proper time. The universe is populated by comoving particles (black disks) which exchange a photon. Color is used to emphasize which frequencies shift.</p>
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<p>The result of <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>c</mi> <mo>,</mo> <mi>a</mi> <mo>)</mo> </mrow> </semantics> </math> for the simple example with the Lorentzian wavefunctions. Here, <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mn>0</mn> </msub> <mo>=</mo> <msub> <mi>γ</mi> <mn>1</mn> </msub> <mo>=</mo> <msub> <mo>Γ</mo> <mn>0</mn> </msub> <mo>=</mo> <msub> <mo>Γ</mo> <mn>1</mn> </msub> <mo>=</mo> <mi>c</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics> </math>, and <math display="inline"> <semantics> <mrow> <msub> <mi>ω</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>.</p>
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<p>A schematic of the coupled two-cavity system. Mirrors to the left of the cavities enable photons to transfer between cavities. The right cavity mirrors share a coupling to a mechanical system (denoted as a spring in the schematic). In addition, the top mirror is driven by a source, while the bottom mirror has an attached detector.</p>
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10408 KiB  
Article
On the Fragility of Bulk Metallic Glass Forming Liquids
by Isabella Gallino
Entropy 2017, 19(9), 483; https://doi.org/10.3390/e19090483 - 10 Sep 2017
Cited by 30 | Viewed by 9485
Abstract
In contrast to pure metals and most non-glass forming alloys, metallic glass-formers are moderately strong liquids in terms of fragility. The notion of fragility of an undercooling liquid reflects the sensitivity of the viscosity of the liquid to temperature changes and describes the [...] Read more.
In contrast to pure metals and most non-glass forming alloys, metallic glass-formers are moderately strong liquids in terms of fragility. The notion of fragility of an undercooling liquid reflects the sensitivity of the viscosity of the liquid to temperature changes and describes the degree of departure of the liquid kinetics from the Arrhenius equation. In general, the fragility of metallic glass-formers increases with the complexity of the alloy with differences between the alloy families, e.g., Pd-based alloys being more fragile than Zr-based alloys, which are more fragile than Mg-based alloys. Here, experimental data are assessed for 15 bulk metallic glasses-formers including the novel and technologically important systems based on Ni-Cr-Nb-P-B, Fe-Mo-Ni-Cr-P-C-B, and Au-Ag-Pd-Cu-Si. The data for the equilibrium viscosity are analyzed using the Vogel–Fulcher–Tammann (VFT) equation, the Mauro–Yue–Ellison–Gupta–Allan (MYEGA) equation, and the Adam–Gibbs approach based on specific heat capacity data. An overall larger trend of the excess specific heat for the more fragile supercooled liquids is experimentally observed than for the stronger liquids. Moreover, the stronger the glass, the higher the free enthalpy barrier to cooperative rearrangements is, suggesting the same microscopic origin and rigorously connecting the kinetic and thermodynamic aspects of fragility. Full article
(This article belongs to the Special Issue Thermodynamics in Material Science)
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Graphical abstract

Graphical abstract
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<p>Specific heat capacity plot for the Au<sub>49</sub>Cu<sub>26.9</sub>Si<sub>16.3</sub>Ag<sub>5.5</sub>Pd<sub>2.3</sub> BMG composition. The symbols are C<sub>p</sub>(T) data measured in isothermal steps for the glass, the crystalline solid, the supercooled liquid and the stable liquid (melt). The error bar has the size of the symbols. The curves are the fits to Equations (4). T<sub>D</sub> marks the Debye temperature; T<sub>liq</sub> the calorimetric liquidus temperature; T<sub>g</sub>* and T<sub>m</sub>* are the temperature values for which the viscosity of the liquid is 10<sup>12</sup> Pa s and 1 Pa s, respectively.</p>
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<p>Experimentally determined isothermal viscosities (open circles) for the Au<sub>49</sub>Cu<sub>26.9</sub>Si<sub>16.3</sub>Ag<sub>5.5</sub>Pd<sub>2.3</sub> BMG composition at annealing temperatures below T<sub>g</sub>. The dashed lines are the KWW-fits to the data (Equation (5)) from which the equilibrium viscosity values are obtained. The equilibrium viscosity η<sub>eq</sub>. corresponds to the plateau value reached by the KWW fit for long times.</p>
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<p>Isothermal equilibrium viscosities of the Au<sub>49</sub>Cu<sub>26.9</sub>Si<sub>16.3</sub>Ag<sub>5.5</sub>Pd<sub>2.3</sub> BMG measured by isothermal three-point beam bending at temperatures below the glass transition (open circles) [<a href="#B23-entropy-19-00483" class="html-bibr">23</a>,<a href="#B42-entropy-19-00483" class="html-bibr">42</a>]. The lines are fits of the VFT, MYEGA, and Adam–Gibbs equation to the experimental data.</p>
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<p>Fragility plot of selected bulk metallic glass forming liquids in comparison to that for SiO<sub>2</sub> and o-terphenyl. T<sub>g</sub>* is the temperature at which the supercooled liquid assumes a viscosity value of 10<sup>12</sup> Pa s. The continuous lines are the fits of equilibrium viscosity data to the VFT equation. The fragility plots based on MYEGA fits and Adam–Gibbs fits give similar results. D*, C, K and H are the VFT, Adam–Gibbs, and MYEGA parameters, respectively.</p>
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<p>Plot of specific heat capacity for selected BMG forming liquids, as calculated from Equation (4), as a function T/T<sub>g</sub>*. T<sub>g</sub>* is the temperature at which the supercooled liquid assumes a viscosity value of 10<sup>12</sup> Pa s.</p>
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<p>Plot of the kinetic fragility parameter D* against free activation energy per particle to cooperative rearrangements C of the Adam–Gibbs equation for selected bulk metallic glass forming liquids. The lines are linear fits to the data.</p>
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<p>Plot of the kinetic fragility parameter D* against the Adam–Gibbs scaled C-parameter for the selected bulk metallic glass forming liquids. The error bar is smaller than the size of the symbols. The red line is the linear fits to the data with slope value of approximately 0.9. The extrapolation of the trend to C = 0 results in D* = 2.</p>
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653 KiB  
Article
A Characterization of the Domain of Beta-Divergence and Its Connection to Bregman Variational Model
by Hyenkyun Woo
Entropy 2017, 19(9), 482; https://doi.org/10.3390/e19090482 - 9 Sep 2017
Cited by 2 | Viewed by 4780
Abstract
In image and signal processing, the beta-divergence is well known as a similarity measure between two positive objects. However, it is unclear whether or not the distance-like structure of beta-divergence is preserved, if we extend the domain of the beta-divergence to the negative [...] Read more.
In image and signal processing, the beta-divergence is well known as a similarity measure between two positive objects. However, it is unclear whether or not the distance-like structure of beta-divergence is preserved, if we extend the domain of the beta-divergence to the negative region. In this article, we study the domain of the beta-divergence and its connection to the Bregman-divergence associated with the convex function of Legendre type. In fact, we show that the domain of beta-divergence (and the corresponding Bregman-divergence) include negative region under the mild condition on the beta value. Additionally, through the relation between the beta-divergence and the Bregman-divergence, we can reformulate various variational models appearing in image processing problems into a unified framework, namely the Bregman variational model. This model has a strong advantage compared to the beta-divergence-based model due to the dual structure of the Bregman-divergence. As an example, we demonstrate how we can build up a convex reformulated variational model with a negative domain for the classic nonconvex problem, which usually appears in synthetic aperture radar image processing problems. Full article
(This article belongs to the Special Issue Entropy in Signal Analysis)
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<p>The graphs of the <math display="inline"> <semantics> <mi>β</mi> </semantics> </math>-divergence <math display="inline"> <semantics> <mrow> <msub> <mi>D</mi> <mi>β</mi> </msub> <mrow> <mo>(</mo> <mi>b</mi> <mo>|</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math>, which is based on the proposed extended logarithmic function <math display="inline"> <semantics> <mrow> <msub> <mo form="prefix">ln</mo> <mi>α</mi> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> in (<a href="#FD4-entropy-19-00482" class="html-disp-formula">4</a>). (<b>a</b>) and (<b>b</b>) shows <math display="inline"> <semantics> <mrow> <msub> <mi>D</mi> <mi>β</mi> </msub> <mrow> <mo>(</mo> <mi>b</mi> <mo>|</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> for <math display="inline"> <semantics> <mrow> <mi>β</mi> <mo>≥</mo> <mn>1</mn> </mrow> </semantics> </math> with different choice of <span class="html-italic">b</span>, i.e., <math display="inline"> <semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>c</b>) and (<b>d</b>) shows <math display="inline"> <semantics> <mrow> <msub> <mi>D</mi> <mi>β</mi> </msub> <mrow> <mo>(</mo> <mi>b</mi> <mo>|</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> for <math display="inline"> <semantics> <mrow> <mi>β</mi> <mo>≤</mo> <mn>1</mn> </mrow> </semantics> </math> with different choice of <span class="html-italic">b</span> i.e., <math display="inline"> <semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics> </math>. Note that <math display="inline"> <semantics> <mrow> <msub> <mi>D</mi> <mi>β</mi> </msub> <mrow> <mo>(</mo> <mi>b</mi> <mo>|</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> with <math display="inline"> <semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> is not defined if <math display="inline"> <semantics> <mrow> <mi>u</mi> <mo>∈</mo> <msub> <mi mathvariant="double-struck">R</mi> <mrow> <mo>−</mo> <mo>−</mo> </mrow> </msub> <mo>.</mo> </mrow> </semantics> </math></p>
Full article ">Figure 2
<p>The graphs of the extended logarithmic function in Definition 1. (<b>a</b>) shows an equivalence class <math display="inline"> <semantics> <mrow> <msub> <mrow> <mo>[</mo> <msub> <mo form="prefix">ln</mo> <mi>α</mi> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> <mi>c</mi> </msub> <mo>=</mo> <mrow> <mo>{</mo> <msub> <mo form="prefix">ln</mo> <mrow> <mi>α</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mo form="prefix">ln</mo> <mrow> <mi>α</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mo form="prefix">ln</mo> <mrow> <mi>α</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>}</mo> </mrow> </mrow> </semantics> </math> with <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mfrac> <mn>2</mn> <mn>3</mn> </mfrac> </mrow> </semantics> </math>; (<b>b</b>) shows <math display="inline"> <semantics> <mrow> <msub> <mo form="prefix">ln</mo> <mrow> <mi>α</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mo>∫</mo> <mn>1</mn> <mi>u</mi> </msubsup> <mfrac> <mn>1</mn> <msup> <mi>x</mi> <mi>α</mi> </msup> </mfrac> <mi>d</mi> <mi>x</mi> </mrow> </semantics> </math> with different choice of <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mfrac> <mn>2</mn> <mn>7</mn> </mfrac> <mo>,</mo> <mfrac> <mn>4</mn> <mn>7</mn> </mfrac> <mo>,</mo> <mfrac> <mn>6</mn> <mn>7</mn> </mfrac> </mrow> </semantics> </math>; (<b>c</b>) and (<b>d</b>) show <math display="inline"> <semantics> <mrow> <msub> <mo form="prefix">ln</mo> <mi>α</mi> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> in (<a href="#FD4-entropy-19-00482" class="html-disp-formula">4</a>) for different choices of <math display="inline"> <semantics> <mi>α</mi> </semantics> </math>. Note that <math display="inline"> <semantics> <mrow> <msub> <mo form="prefix">ln</mo> <mi>α</mi> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> is an extended logarithmic function without a constant term.</p>
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5855 KiB  
Article
Entropy Analysis on Electro-Kinetically Modulated Peristaltic Propulsion of Magnetized Nanofluid Flow through a Microchannel
by Muhammad Mubashir Bhatti, Mohsen Sheikholeslami and Ahmed Zeeshan
Entropy 2017, 19(9), 481; https://doi.org/10.3390/e19090481 - 9 Sep 2017
Cited by 64 | Viewed by 5436
Abstract
A theoretical and a mathematical model is presented to determine the entropy generation on electro-kinetically modulated peristaltic propulsion on the magnetized nanofluid flow through a microchannel with joule heating. The mathematical modeling is based on the energy, momentum, continuity, and entropy equation in [...] Read more.
A theoretical and a mathematical model is presented to determine the entropy generation on electro-kinetically modulated peristaltic propulsion on the magnetized nanofluid flow through a microchannel with joule heating. The mathematical modeling is based on the energy, momentum, continuity, and entropy equation in the Cartesian coordinate system. The effects of viscous dissipation, heat absorption, magnetic field, and electrokinetic body force are also taken into account. The electric field terms are helpful to model the electrical potential terms by means of Poisson–Boltzmann equations, ionic Nernst–Planck equation, and Debye length approximation. A perturbation method has been applied to solve the coupled nonlinear partial differential equations and a series solution is obtained up to second order. The physical behavior of all the governing parameters is discussed for pressure rise, velocity profile, entropy profile, and temperature profile. Full article
(This article belongs to the Special Issue Entropy Generation in Nanofluid Flows)
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Figure 1
<p>Flow structure of the electro-osmotically modulated peristaltic flow.</p>
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<p>Comparison of velocity distribution with previously published results [<a href="#B35-entropy-19-00481" class="html-bibr">35</a>].</p>
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<p>Velocity distribution against multiple values of <math display="inline"> <semantics> <mi>M</mi> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>G</mi> <mi>r</mi> </mrow> </semantics> </math>.</p>
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<p>Velocity distribution against multiple values of <math display="inline"> <semantics> <mrow> <msub> <mi>U</mi> <mrow> <mi>H</mi> <mi>S</mi> </mrow> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mi>m</mi> </semantics> </math>.</p>
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<p>Temperature distribution against multiple values of <math display="inline"> <semantics> <mi>β</mi> </semantics> </math> and <math display="inline"> <semantics> <mi>S</mi> </semantics> </math>.</p>
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<p>Temperature distribution against multiple values of <math display="inline"> <semantics> <mi>M</mi> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>B</mi> <mi>r</mi> </mrow> </semantics> </math>.</p>
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<p>Pressure distribution against multiple values of <math display="inline"> <semantics> <mi>M</mi> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>U</mi> <mrow> <mi>H</mi> <mi>S</mi> </mrow> </msub> </mrow> </semantics> </math>.</p>
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<p>Pressure distribution against multiple values of <math display="inline"> <semantics> <mi>m</mi> </semantics> </math> and <math display="inline"> <semantics> <mi>ϕ</mi> </semantics> </math>.</p>
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<p>Entropy profile against multiple values of <math display="inline"> <semantics> <mi>M</mi> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mi>T</mi> </msub> </mrow> </semantics> </math>.</p>
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<p>Entropy profile against multiple values of <math display="inline"> <semantics> <mi>S</mi> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>B</mi> <mi>r</mi> </mrow> </semantics> </math>.</p>
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<p>Variation of Bejan number against multiple values of <math display="inline"> <semantics> <mi>M</mi> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>B</mi> <mi>r</mi> </mrow> </semantics> </math>.</p>
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<p>Variation of Bejan number against multiple values of <math display="inline"> <semantics> <mi>S</mi> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mi>T</mi> </msub> </mrow> </semantics> </math>.</p>
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327 KiB  
Article
Robust Biometric Authentication from an Information Theoretic Perspective
by Andrea Grigorescu, Holger Boche and Rafael F. Schaefer
Entropy 2017, 19(9), 480; https://doi.org/10.3390/e19090480 - 9 Sep 2017
Cited by 7 | Viewed by 4827
Abstract
Robust biometric authentication is studied from an information theoretic perspective. Compound sources are used to account for uncertainty in the knowledge of the source statistics and are further used to model certain attack classes. It is shown that authentication is robust against source [...] Read more.
Robust biometric authentication is studied from an information theoretic perspective. Compound sources are used to account for uncertainty in the knowledge of the source statistics and are further used to model certain attack classes. It is shown that authentication is robust against source uncertainty and a special class of attacks under the strong secrecy condition. A single-letter characterization of the privacy secrecy capacity region is derived for the generated and chosen secret key model. Furthermore, the question is studied whether small variations of the compound source lead to large losses of the privacy secrecy capacity region. It is shown that biometric authentication is robust in the sense that its privacy secrecy capacity region depends continuously on the compound source. Full article
(This article belongs to the Special Issue Information-Theoretic Security)
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Figure 1
<p>The biometric measurements <math display="inline"> <semantics> <msup> <mi>X</mi> <mi>n</mi> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mi>Y</mi> <mi>n</mi> </msup> </semantics> </math> are observed in the enrollment and authentication terminal, respectively. In the enrollment terminal, the key <span class="html-italic">K</span> and the helper data <math display="inline"> <semantics> <msup> <mi>M</mi> <mo>′</mo> </msup> </semantics> </math> are generated. The helper data is public, hence the eavesdropper also has access to it. In the authentication terminal, an estimation of a key <math display="inline"> <semantics> <mover accent="true"> <mi>K</mi> <mo stretchy="false">^</mo> </mover> </semantics> </math> is made based on the observed biometric measurements <math display="inline"> <semantics> <msup> <mi>Y</mi> <mi>n</mi> </msup> </semantics> </math> and the helper data <math display="inline"> <semantics> <msup> <mi>M</mi> <mo>′</mo> </msup> </semantics> </math>.</p>
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<p>The biometric sequences <math display="inline"> <semantics> <msup> <mi>X</mi> <mi>n</mi> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mi>Y</mi> <mi>n</mi> </msup> </semantics> </math> are observed at the enrollment and authentication terminal, respectively. In the enrollment terminal, the helper data <math display="inline"> <semantics> <msup> <mi>M</mi> <mo>′</mo> </msup> </semantics> </math> is generated for a given secret key <span class="html-italic">K</span>. The helper data is public, hence the eavesdropper also has access to it. In the authentication terminal, an estimation of a key <math display="inline"> <semantics> <mover accent="true"> <mi>K</mi> <mo stretchy="false">^</mo> </mover> </semantics> </math> is made based on the observed biometric authentication sequence <math display="inline"> <semantics> <msup> <mi>Y</mi> <mi>n</mi> </msup> </semantics> </math> and the helper data <math display="inline"> <semantics> <msup> <mi>M</mi> <mo>′</mo> </msup> </semantics> </math>.</p>
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<p>The attacker controls the state of the source <math display="inline"> <semantics> <mrow> <mi>s</mi> <mo>∈</mo> <mi mathvariant="script">S</mi> </mrow> </semantics> </math>. The biometric sequences <math display="inline"> <semantics> <msup> <mi>X</mi> <mi>n</mi> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mi>Y</mi> <mi>n</mi> </msup> </semantics> </math> are observed at the enrollment and authentication, terminal respectively. In the enrollment terminal, the key <span class="html-italic">K</span> and the helper data <math display="inline"> <semantics> <msup> <mi>M</mi> <mo>′</mo> </msup> </semantics> </math> are generated. The helper data is public, hence the attacker also has access to it. In the authentication terminal, an estimation of a key <math display="inline"> <semantics> <mover accent="true"> <mi>K</mi> <mo stretchy="false">^</mo> </mover> </semantics> </math> is made based on the observed authentication sequence <math display="inline"> <semantics> <msup> <mi>Y</mi> <mi>n</mi> </msup> </semantics> </math> and the helper data <math display="inline"> <semantics> <msup> <mi>M</mi> <mo>′</mo> </msup> </semantics> </math>.</p>
Full article ">Figure 4
<p>The attacker controls the state of the source <math display="inline"> <semantics> <mrow> <mi>s</mi> <mo>∈</mo> <mi mathvariant="script">S</mi> </mrow> </semantics> </math>. The biometric sequences <math display="inline"> <semantics> <msup> <mi>X</mi> <mi>n</mi> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mi>Y</mi> <mi>n</mi> </msup> </semantics> </math> are observed in the enrollment and authentication terminal, respectively. In the enrollment terminal, the key <span class="html-italic">K</span> is predefined and the helper data <math display="inline"> <semantics> <msup> <mi>M</mi> <mo>′</mo> </msup> </semantics> </math> is generated. The helper data is public, hence the attacker also has access to it. In the authentication terminal, an estimation of a key <math display="inline"> <semantics> <mover accent="true"> <mi>K</mi> <mo stretchy="false">^</mo> </mover> </semantics> </math> is made based on the observed authentication sequences <math display="inline"> <semantics> <msup> <mi>Y</mi> <mi>n</mi> </msup> </semantics> </math> and the helper data <math display="inline"> <semantics> <msup> <mi>M</mi> <mo>′</mo> </msup> </semantics> </math>.</p>
Full article ">
20915 KiB  
Article
Coupled Effects of Turing and Neimark-Sacker Bifurcations on Vegetation Pattern Self-Organization in a Discrete Vegetation-Sand Model
by Feifan Zhang, Huayong Zhang, Tousheng Huang, Tianxiang Meng and Shengnan Ma
Entropy 2017, 19(9), 478; https://doi.org/10.3390/e19090478 - 8 Sep 2017
Cited by 6 | Viewed by 4860
Abstract
Wind-induced vegetation patterns were proposed a long time ago but only recently a dynamic vegetation-sand relationship has been established. In this research, we transformed the continuous vegetation-sand model into a discrete model. Fixed points and stability analyses were then studied. Bifurcation analyses are [...] Read more.
Wind-induced vegetation patterns were proposed a long time ago but only recently a dynamic vegetation-sand relationship has been established. In this research, we transformed the continuous vegetation-sand model into a discrete model. Fixed points and stability analyses were then studied. Bifurcation analyses are done around the fixed point, including Neimark-Sacker and Turing bifurcation. Then we simulated the parameter space for both bifurcations. Based on the bifurcation conditions, simulations are carried out around the bifurcation point. Simulation results showed that Neimark-Sacker bifurcation and Turing bifurcation can induce the self-organization of complex vegetation patterns, among which labyrinth and striped patterns are the key results that can be presented by the continuous model. Under the coupled effects of the two bifurcations, simulation results show that vegetation patterns can also be self-organized, but vegetation type changed. The type of the patterns can be Turing type, Neimark-Sacker type, or some other special type. The difference may depend on the relative intensity of each bifurcation. The calculation of entropy may help understand the variance of pattern types. Full article
(This article belongs to the Special Issue Complex Systems, Non-Equilibrium Dynamics and Self-Organisation)
Show Figures

Figure 1

Figure 1
<p>Bifurcation diagram and phase portraits of flip bifurcation. (<b>a</b>) Bifurcation diagram with parameters: <span class="html-italic">k</span><sub>0</sub> = 3.095 cm·d<sup>−1</sup>; <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>; <span class="html-italic">V<sub>m</sub></span> = 100%; <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>; <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>; <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>; <span class="html-italic">V</span><sub>0</sub> = 200%; <span class="html-italic">C</span> = 10%; <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>; (<b>b</b>–<b>d</b>) Phase portraits with parameter (<b>b</b>) <span class="html-italic">τ</span> = 16.7013 days; (<b>c</b>) <span class="html-italic">τ</span> = 16.755 days; (<b>d</b>) <span class="html-italic">τ</span> = 16.8667 days. Note that the figures are obtained by the software MATLAB 7.12.0 2011a (The MathWorks, Inc., Natick, MA, USA).</p>
Full article ">Figure 1 Cont.
<p>Bifurcation diagram and phase portraits of flip bifurcation. (<b>a</b>) Bifurcation diagram with parameters: <span class="html-italic">k</span><sub>0</sub> = 3.095 cm·d<sup>−1</sup>; <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>; <span class="html-italic">V<sub>m</sub></span> = 100%; <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>; <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>; <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>; <span class="html-italic">V</span><sub>0</sub> = 200%; <span class="html-italic">C</span> = 10%; <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>; (<b>b</b>–<b>d</b>) Phase portraits with parameter (<b>b</b>) <span class="html-italic">τ</span> = 16.7013 days; (<b>c</b>) <span class="html-italic">τ</span> = 16.755 days; (<b>d</b>) <span class="html-italic">τ</span> = 16.8667 days. Note that the figures are obtained by the software MATLAB 7.12.0 2011a (The MathWorks, Inc., Natick, MA, USA).</p>
Full article ">Figure 2
<p>Variations of eigenvalues <span class="html-italic">λ<sub>m</sub></span>(<span class="html-italic">k</span>,<span class="html-italic">l</span>) with perturbation numbers <span class="html-italic">k</span> and <span class="html-italic">l</span>. Parameters: <span class="html-italic">k</span><sub>0</sub> = 3.095 cm·d<sup>−1</sup>; <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>; <span class="html-italic">V<sub>m</sub></span> = 100%; <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>; <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>; <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>; <span class="html-italic">V</span><sub>0</sub> = 200%; <span class="html-italic">C</span> = 10%; <span class="html-italic">D</span><sub>1</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>; <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>; <span class="html-italic">a</span><sub>2</sub> = 0.01 m·d<sup>−1</sup>; (<b>a</b>) <span class="html-italic">a</span><sub>1</sub> = 0.15 m·d<sup>−1</sup>; (<b>b</b>) Let <span class="html-italic">k = l</span>, three curves of eigenvalues <span class="html-italic">λ<sub>m</sub></span>(<span class="html-italic">k</span>,<span class="html-italic">l</span>) are shown with parameter <span class="html-italic">a</span><sub>1</sub> = 0.005, 0.027 and 0.15 m·d<sup>−1</sup> respectively. Note that the color scale in all the following figures are automatically selected by the MATLAB 7.12.0 (2011a) software command “pcolor” according to the minimum and the maximum values of <span class="html-italic">λ<sub>m</sub></span>(<span class="html-italic">k</span>,<span class="html-italic">l</span>).</p>
Full article ">Figure 3
<p>Variations of parameter <span class="html-italic">τ</span> and <span class="html-italic">a</span><sub>1</sub> satisfying Turing and Neimark-Sacker bifurcations around bifurcation point. Parameters: <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>; <span class="html-italic">V<sub>m</sub></span> = 100%; <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>; <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>; <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>; <span class="html-italic">V</span><sub>0</sub> = 200%; <span class="html-italic">C</span> = 10%; <span class="html-italic">D</span><sub>1</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>; <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>; <span class="html-italic">a</span><sub>2</sub> = 0.01 m·d<sup>−1</sup>; (<b>a</b>) <span class="html-italic">k</span><sub>0</sub> = 3.095 cm·d<sup>−1</sup>; <span class="html-italic">d</span> = 2 m; (<b>b</b>) <span class="html-italic">k</span><sub>0</sub> = 3.83 cm·d<sup>−1</sup>; <span class="html-italic">d</span> = 1 m. Light green area means parameter values satisfy only Turing bifurcation condition. Blue area means parameter values satisfy only Neimark-Sacker bifurcation. And Dark green area means parameter values satisfy both Turing and Neimark-Sacker bifurcation. Note that the figures are obtained by the software MATLAB 7.12.0 (2011a).</p>
Full article ">Figure 4
<p>Patterns induced by Neimark-Sacker bifurcations. Parameters: <span class="html-italic">k</span><sub>0</sub> = 3.095 cm·d<sup>−1</sup>; <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>; <span class="html-italic">V<sub>m</sub></span> = 100%; <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>; <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>; <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>; <span class="html-italic">V</span><sub>0</sub> = 200%; <span class="html-italic">C</span> = 10%; <span class="html-italic">D</span><sub>1</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>; <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>; <span class="html-italic">a</span><sub>1</sub> = 0.01 m·d<sup>−1</sup>; <span class="html-italic">a</span><sub>2</sub> = 0.01 m·d<sup>−1</sup>; <span class="html-italic">d</span> = 2 m; (<b>a</b>) <span class="html-italic">τ</span> = 18.3696 days; (<b>b</b>) <span class="html-italic">τ</span> = 20.0396 days. Simulations are carried out on 100 × 100 lattices with periodic boundary conditions. Initial conditions are set as fixed points with heterogeneous random disturbance (0.5%). After <span class="html-italic">t</span> = 2000 days, the patterns can be obtained.</p>
Full article ">Figure 5
<p>Patterns induced by Turing and Turing-Neimark-Sacker bifurcations. Parameters: <span class="html-italic">k</span><sub>0</sub> = 3.095 cm·d<sup>−1</sup>, <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>, <span class="html-italic">V<sub>m</sub></span> = 100%, <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>, <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>, <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>, <span class="html-italic">V</span><sub>0</sub> = 200%, <span class="html-italic">C</span> = 10%, <span class="html-italic">D</span><sub>1</sub> = 0.02 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">a</span><sub>1</sub> = 0.04 m·d<sup>−1</sup>, <span class="html-italic">a</span><sub>2</sub> = 0.02 m·d<sup>−1</sup>, <span class="html-italic">d</span> = 2 m. (<b>a</b>) <span class="html-italic">τ</span> = 16.2822 days; (<b>b</b>) <span class="html-italic">τ</span> = 16.3323 days; (<b>c</b>) <span class="html-italic">τ</span> = <span class="html-italic">τ</span><sub>0</sub> = 16.6997 days; (<b>d</b>) <span class="html-italic">τ</span> = 17.0337 days; (<b>e</b>) <span class="html-italic">τ</span> = 17.3677 days. Simulations are carried out on 100 × 100 lattices with periodic boundary conditions. Initial conditions are set as fixed points with heterogeneous random disturbance (0.5%). After <span class="html-italic">t</span> = 2000 days, the patterns can be obtained.</p>
Full article ">Figure 5 Cont.
<p>Patterns induced by Turing and Turing-Neimark-Sacker bifurcations. Parameters: <span class="html-italic">k</span><sub>0</sub> = 3.095 cm·d<sup>−1</sup>, <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>, <span class="html-italic">V<sub>m</sub></span> = 100%, <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>, <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>, <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>, <span class="html-italic">V</span><sub>0</sub> = 200%, <span class="html-italic">C</span> = 10%, <span class="html-italic">D</span><sub>1</sub> = 0.02 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">a</span><sub>1</sub> = 0.04 m·d<sup>−1</sup>, <span class="html-italic">a</span><sub>2</sub> = 0.02 m·d<sup>−1</sup>, <span class="html-italic">d</span> = 2 m. (<b>a</b>) <span class="html-italic">τ</span> = 16.2822 days; (<b>b</b>) <span class="html-italic">τ</span> = 16.3323 days; (<b>c</b>) <span class="html-italic">τ</span> = <span class="html-italic">τ</span><sub>0</sub> = 16.6997 days; (<b>d</b>) <span class="html-italic">τ</span> = 17.0337 days; (<b>e</b>) <span class="html-italic">τ</span> = 17.3677 days. Simulations are carried out on 100 × 100 lattices with periodic boundary conditions. Initial conditions are set as fixed points with heterogeneous random disturbance (0.5%). After <span class="html-italic">t</span> = 2000 days, the patterns can be obtained.</p>
Full article ">Figure 6
<p>Patterns induced by Turing and Turing-Neimark-Sacker bifurcations. Parameters: <span class="html-italic">k</span><sub>0</sub> = 3.83 cm·d<sup>−1</sup>, <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>, <span class="html-italic">V<sub>m</sub></span> = 100%, <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>, <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>, <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>, <span class="html-italic">V</span><sub>0</sub> = 200%, <span class="html-italic">C</span> = 10%, <span class="html-italic">D</span><sub>1</sub> = 0.014 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">a</span><sub>1</sub> = 0.0196 m·d<sup>−1</sup>, <span class="html-italic">a</span><sub>2</sub> = 0.014 m·d<sup>−1</sup>, <span class="html-italic">d</span> = 1 m. (<b>a</b>) <span class="html-italic">τ</span> = 15.4011 days; (<b>b</b>) <span class="html-italic">τ</span> = 15.7219 days; (<b>c</b>) <span class="html-italic">τ</span> = <span class="html-italic">τ</span><sub>0</sub> = 16.0428 days; (<b>d</b>) <span class="html-italic">τ</span> = 16.0909 days; (<b>e</b>) <span class="html-italic">τ</span> = 16.1230 days. Simulations are carried out on 100 × 100 lattices with periodic boundary conditions. Initial conditions are set as fixed points with heterogeneous random disturbance (0.5%). After <span class="html-italic">t</span> = 2000 days, the patterns can be obtained.</p>
Full article ">Figure 6 Cont.
<p>Patterns induced by Turing and Turing-Neimark-Sacker bifurcations. Parameters: <span class="html-italic">k</span><sub>0</sub> = 3.83 cm·d<sup>−1</sup>, <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>, <span class="html-italic">V<sub>m</sub></span> = 100%, <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>, <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>, <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>, <span class="html-italic">V</span><sub>0</sub> = 200%, <span class="html-italic">C</span> = 10%, <span class="html-italic">D</span><sub>1</sub> = 0.014 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">a</span><sub>1</sub> = 0.0196 m·d<sup>−1</sup>, <span class="html-italic">a</span><sub>2</sub> = 0.014 m·d<sup>−1</sup>, <span class="html-italic">d</span> = 1 m. (<b>a</b>) <span class="html-italic">τ</span> = 15.4011 days; (<b>b</b>) <span class="html-italic">τ</span> = 15.7219 days; (<b>c</b>) <span class="html-italic">τ</span> = <span class="html-italic">τ</span><sub>0</sub> = 16.0428 days; (<b>d</b>) <span class="html-italic">τ</span> = 16.0909 days; (<b>e</b>) <span class="html-italic">τ</span> = 16.1230 days. Simulations are carried out on 100 × 100 lattices with periodic boundary conditions. Initial conditions are set as fixed points with heterogeneous random disturbance (0.5%). After <span class="html-italic">t</span> = 2000 days, the patterns can be obtained.</p>
Full article ">Figure 7
<p>Patterns induced by Turing and Turing-Neimark-Sacker bifurcations. Parameters: <span class="html-italic">k</span><sub>0</sub> = 3.865 cm·d<sup>−1</sup>, <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>, <span class="html-italic">V<sub>m</sub></span> = 100%, <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>, <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>, <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>, <span class="html-italic">V</span><sub>0</sub> = 200%, <span class="html-italic">C</span> = 10%, <span class="html-italic">D</span><sub>1</sub> = 0.015 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">a</span><sub>1</sub> = 0 m·d<sup>−1</sup>, <span class="html-italic">a</span><sub>2</sub> = 0 m·d<sup>−1</sup>, <span class="html-italic">d</span> = 1 m. (<b>a</b>) <span class="html-italic">τ</span> = 21.6614 days; (<b>b</b>) <span class="html-italic">τ</span> = 21.8825 days; (<b>c</b>) <span class="html-italic">τ</span> = <span class="html-italic">τ</span><sub>0</sub> = 22.1035 days; (<b>d</b>) <span class="html-italic">τ</span> = 22.3245 days; (<b>e</b>) <span class="html-italic">τ</span> = 22.5456 days. Simulations are carried out on 100 × 100 lattices with periodic boundary conditions. Initial conditions are set as fixed points with heterogeneous random disturbance (0.5%). After <span class="html-italic">t</span> = 2000 days, the patterns can be obtained.</p>
Full article ">Figure 7 Cont.
<p>Patterns induced by Turing and Turing-Neimark-Sacker bifurcations. Parameters: <span class="html-italic">k</span><sub>0</sub> = 3.865 cm·d<sup>−1</sup>, <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>, <span class="html-italic">V<sub>m</sub></span> = 100%, <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>, <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>, <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>, <span class="html-italic">V</span><sub>0</sub> = 200%, <span class="html-italic">C</span> = 10%, <span class="html-italic">D</span><sub>1</sub> = 0.015 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">a</span><sub>1</sub> = 0 m·d<sup>−1</sup>, <span class="html-italic">a</span><sub>2</sub> = 0 m·d<sup>−1</sup>, <span class="html-italic">d</span> = 1 m. (<b>a</b>) <span class="html-italic">τ</span> = 21.6614 days; (<b>b</b>) <span class="html-italic">τ</span> = 21.8825 days; (<b>c</b>) <span class="html-italic">τ</span> = <span class="html-italic">τ</span><sub>0</sub> = 22.1035 days; (<b>d</b>) <span class="html-italic">τ</span> = 22.3245 days; (<b>e</b>) <span class="html-italic">τ</span> = 22.5456 days. Simulations are carried out on 100 × 100 lattices with periodic boundary conditions. Initial conditions are set as fixed points with heterogeneous random disturbance (0.5%). After <span class="html-italic">t</span> = 2000 days, the patterns can be obtained.</p>
Full article ">Figure 8
<p>Patterns induced by Turing and Turing-Neimark-Sacker bifurcations. Parameters: <span class="html-italic">k</span><sub>0</sub> = 3.83 cm·d<sup>−1</sup>, <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>, <span class="html-italic">V<sub>m</sub></span> = 100%, <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>, <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>, <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>, <span class="html-italic">V</span><sub>0</sub> = 200%, <span class="html-italic">C</span> = 10%, <span class="html-italic">D</span><sub>1</sub> = 0.015 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">a</span><sub>1</sub> = 0.225 m·d<sup>−1</sup>, <span class="html-italic">a</span><sub>2</sub> = 0.015 m·d<sup>−1</sup>, d = 1 m. (<b>a</b>) <span class="html-italic">τ</span> = 15.4011 days; (<b>b</b>) <span class="html-italic">τ</span> = 15.7219 days; (<b>c</b>) <span class="html-italic">τ</span> = <span class="html-italic">τ</span><sub>0</sub> = 16.0428 days; (<b>d</b>) <span class="html-italic">τ</span> = 16.0909 days; (<b>e</b>) <span class="html-italic">τ</span> = 16.1230 days. Simulations are carried out on 100 × 100 lattices with periodic boundary conditions. Initial conditions are set as fixed points with heterogeneous random disturbance (0.5%). After <span class="html-italic">t</span> = 2000 days, the patterns can be obtained.</p>
Full article ">Figure 8 Cont.
<p>Patterns induced by Turing and Turing-Neimark-Sacker bifurcations. Parameters: <span class="html-italic">k</span><sub>0</sub> = 3.83 cm·d<sup>−1</sup>, <span class="html-italic">h</span> = 0.2 d<sup>−1</sup>, <span class="html-italic">V<sub>m</sub></span> = 100%, <span class="html-italic">p</span> = 0.045 cm<sup>−1</sup>·d<sup>−1</sup>, <span class="html-italic">m</span> = 0.2 cm·d<sup>−1</sup>, <span class="html-italic">n</span> = 0.07 d<sup>−1</sup>, <span class="html-italic">V</span><sub>0</sub> = 200%, <span class="html-italic">C</span> = 10%, <span class="html-italic">D</span><sub>1</sub> = 0.015 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">D</span><sub>2</sub> = 0.01 m<sup>2</sup>·d<sup>−1</sup>, <span class="html-italic">a</span><sub>1</sub> = 0.225 m·d<sup>−1</sup>, <span class="html-italic">a</span><sub>2</sub> = 0.015 m·d<sup>−1</sup>, d = 1 m. (<b>a</b>) <span class="html-italic">τ</span> = 15.4011 days; (<b>b</b>) <span class="html-italic">τ</span> = 15.7219 days; (<b>c</b>) <span class="html-italic">τ</span> = <span class="html-italic">τ</span><sub>0</sub> = 16.0428 days; (<b>d</b>) <span class="html-italic">τ</span> = 16.0909 days; (<b>e</b>) <span class="html-italic">τ</span> = 16.1230 days. Simulations are carried out on 100 × 100 lattices with periodic boundary conditions. Initial conditions are set as fixed points with heterogeneous random disturbance (0.5%). After <span class="html-italic">t</span> = 2000 days, the patterns can be obtained.</p>
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490 KiB  
Article
Implications of Coupling in Quantum Thermodynamic Machines
by George Thomas, Manik Banik and Sibasish Ghosh
Entropy 2017, 19(9), 442; https://doi.org/10.3390/e19090442 - 8 Sep 2017
Cited by 19 | Viewed by 6016
Abstract
We study coupled quantum systems as the working media of thermodynamic machines. Under a suitable phase-space transformation, the coupled systems can be expressed as a composition of independent subsystems. We find that for the coupled systems, the figures of merit, that is the [...] Read more.
We study coupled quantum systems as the working media of thermodynamic machines. Under a suitable phase-space transformation, the coupled systems can be expressed as a composition of independent subsystems. We find that for the coupled systems, the figures of merit, that is the efficiency for engine and the coefficient of performance for refrigerator, are bounded (both from above and from below) by the corresponding figures of merit of the independent subsystems. We also show that the optimum work extractable from a coupled system is upper bounded by the optimum work obtained from the uncoupled system, thereby showing that the quantum correlations do not help in optimal work extraction. Further, we study two explicit examples; coupled spin- 1 / 2 systems and coupled quantum oscillators with analogous interactions. Interestingly, for particular kind of interactions, the efficiency of the coupled oscillators outperforms that of the coupled spin- 1 / 2 systems when they work as heat engines. However, for the same interaction, the coefficient of performance behaves in a reverse manner, while the systems work as the refrigerator. Thus, the same coupling can cause opposite effects in the figures of merit of heat engine and refrigerator. Full article
(This article belongs to the Special Issue Quantum Thermodynamics)
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Figure 1

Figure 1
<p>(color online) Pictorial representation of a quantum Otto cycle. The working medium of this cycle is a harmonic oscillator. Stage 1 and Stage 3 are thermalization processes, in which the system exchanges heat with the bath. Stages 2 and 4 correspond to adiabatic processes where the frequency of the oscillator changes from <math display="inline"> <semantics> <mi>ω</mi> </semantics> </math> to <math display="inline"> <semantics> <msup> <mi>ω</mi> <mo>′</mo> </msup> </semantics> </math> and back, by doing a certain amount of work.</p>
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<p>The coupling parameter <math display="inline"> <semantics> <mi>λ</mi> </semantics> </math> is changed during the adiabatic process of quantum Otto cycle.</p>
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<p>(<b>a</b>) Diagram represents a Carnot cycle undergone by one of the subsystems (say A). The cycle consists of two isothermal processes and two adiabatic processes. Here, <math display="inline"> <semantics> <msub> <mi>ω</mi> <mi>A</mi> </msub> </semantics> </math>, <math display="inline"> <semantics> <msubsup> <mi>ω</mi> <mi>A</mi> <mo>′</mo> </msubsup> </semantics> </math>, <math display="inline"> <semantics> <msubsup> <mi>ω</mi> <mi>A</mi> <mrow> <mo>′</mo> <mo>′</mo> </mrow> </msubsup> </semantics> </math> and <math display="inline"> <semantics> <msubsup> <mi>ω</mi> <mi>A</mi> <mrow> <mo>′</mo> <mo>′</mo> <mo>′</mo> </mrow> </msubsup> </semantics> </math> are the eigenmode frequencies at different stages of the cycle; (<b>b</b>) Diagram pictorially represents quantum Stirling cycle, consisting of two isochoric and two isothermal processes.</p>
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<p>(color online) The two dotted curves show the upper bound (<math display="inline"> <semantics> <msub> <mi>η</mi> <mi>B</mi> </msub> </semantics> </math>) and lower bound (<math display="inline"> <semantics> <msub> <mi>η</mi> <mi>A</mi> </msub> </semantics> </math>) . The continuous curve represents the efficiency of the coupled oscillator. The efficiency of the coupled spin system is denoted by the dashed curve. Carnot value is represented by the horizontal line. When the independent systems work in engine mode, the global efficiency of the coupled system lies inside the bounds. The plot also shows that the global efficiency of the coupled oscillators is higher than that of the coupled spins for small values of <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>J</mi> </msub> </semantics> </math> (see Equation (<a href="#FD38-entropy-19-00442" class="html-disp-formula">38</a>)). When the upper bound reaches Carnot value, <math display="inline"> <semantics> <mrow> <msub> <mi>η</mi> <mi>B</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>−</mo> <msub> <mi>T</mi> <mi>c</mi> </msub> <mo stretchy="false">/</mo> <msub> <mi>T</mi> <mi>h</mi> </msub> <mo>=</mo> <msub> <mi>η</mi> <mi>c</mi> </msub> </mrow> </semantics> </math> for <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>J</mi> </msub> <mo>=</mo> <msub> <mi>λ</mi> <mi>c</mi> </msub> </mrow> </semantics> </math> (represented by vertical dashed-dotted line), then we get <math display="inline"> <semantics> <mrow> <msup> <mi>η</mi> <mi>os</mi> </msup> <mo>=</mo> <msup> <mi>η</mi> <mi>sp</mi> </msup> <mo>=</mo> <msub> <mi>η</mi> <mi>A</mi> </msub> </mrow> </semantics> </math>. Here we take <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>h</mi> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msup> <mi>ω</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>. Note that, when <math display="inline"> <semantics> <mrow> <msub> <mi>η</mi> <mi>B</mi> </msub> <mo>&gt;</mo> <msub> <mi>η</mi> <mi>c</mi> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>η</mi> <mi>B</mi> </msub> </semantics> </math> does not represent efficiency because the subsystem <span class="html-italic">B</span> works as a refrigerator.</p>
Full article ">Figure 5
<p>The figure shows the behavior of work versus concurrence of the coupled spin systems (XX model). In (<b>a</b>), the total work is plotted versus concurrence at the end of stage 3 (<math display="inline"> <semantics> <msub> <mi>C</mi> <mi>c</mi> </msub> </semantics> </math>) and in (<b>b</b>) total work versus concurrence at the end of stage 1 (<math display="inline"> <semantics> <msub> <mi>C</mi> <mi>h</mi> </msub> </semantics> </math>) is plotted. The horizontal line represents the maximum work for an uncoupled system which is only obtained for a zero concurrence. The temperature of the hot and the cold baths are fixed at, <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>h</mi> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> respectively. The parameters <math display="inline"> <semantics> <mi>ω</mi> </semantics> </math>, <math display="inline"> <semantics> <msup> <mi>ω</mi> <mo>′</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>J</mi> </msub> <mo>=</mo> <msub> <mi>J</mi> <mi>x</mi> </msub> <mo>=</mo> <msub> <mi>J</mi> <mi>y</mi> </msub> </mrow> </semantics> </math> are randomly chosen from <math display="inline"> <semantics> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics> </math> such that the system should work as an engine. Each point in the plot refers to a given values of <math display="inline"> <semantics> <mi>ω</mi> </semantics> </math>, <math display="inline"> <semantics> <msup> <mi>ω</mi> <mo>′</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>J</mi> </msub> </semantics> </math>. There are more than <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>5</mn> </msup> </semantics> </math> points in the plot.</p>
Full article ">Figure 6
<p>(color online) The upper bound (<math display="inline"> <semantics> <msub> <mi>ζ</mi> <mi>A</mi> </msub> </semantics> </math>) and the lower bound (<math display="inline"> <semantics> <msub> <mi>ζ</mi> <mi>B</mi> </msub> </semantics> </math>) are shown with the dotted curves. The continuous curve represents the COP of the coupled oscillators while the COP of the coupled spin system is denoted by the dashed curve. The horizontal line represents the Carnot value for the refrigerator. When the independent subsystems work in refrigerator mode, the global COP of the coupled system is bounded by <math display="inline"> <semantics> <msub> <mi>ζ</mi> <mi>A</mi> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>ζ</mi> <mi>B</mi> </msub> </semantics> </math>. The plot also shows that the global COP of the coupled spins is higher than that of the coupled oscillators for small values of <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>J</mi> </msub> </semantics> </math> as seen in Equation (<a href="#FD50-entropy-19-00442" class="html-disp-formula">50</a>). When the upper bound achieves Carnot value, <math display="inline"> <semantics> <mrow> <msub> <mi>ζ</mi> <mi>A</mi> </msub> <mo>=</mo> <msub> <mi>T</mi> <mi>c</mi> </msub> <mo stretchy="false">/</mo> <mrow> <mo>(</mo> <msub> <mi>T</mi> <mi>h</mi> </msub> <mo>−</mo> <msub> <mi>T</mi> <mi>c</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>ζ</mi> <mi>c</mi> </msub> </mrow> </semantics> </math> for <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>J</mi> </msub> <mo>=</mo> <msubsup> <mi>λ</mi> <mi>c</mi> <mo>′</mo> </msubsup> </mrow> </semantics> </math> (shown by vertical dashed-dotted line), thus we get <math display="inline"> <semantics> <mrow> <msup> <mi>ζ</mi> <mi>os</mi> </msup> <mo>=</mo> <msup> <mi>ζ</mi> <mi>sp</mi> </msup> <mo>=</mo> <msub> <mi>ζ</mi> <mi>B</mi> </msub> </mrow> </semantics> </math>. The inset shows the enlarged region near <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>J</mi> </msub> <mo>=</mo> <msubsup> <mi>λ</mi> <mi>c</mi> <mo>′</mo> </msubsup> </mrow> </semantics> </math>. Here we take <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>h</mi> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msup> <mi>ω</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>. Note that, when <math display="inline"> <semantics> <mrow> <msub> <mi>ζ</mi> <mi>A</mi> </msub> <mo>&gt;</mo> <msub> <mi>ζ</mi> <mi>c</mi> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>ζ</mi> <mi>A</mi> </msub> </semantics> </math> does not represent COP, as the subsystem <span class="html-italic">A</span> works as an engine.</p>
Full article ">Figure 7
<p>(<b>a</b>) The continuous and the dashed curves represent the efficiencies of coupled oscillators and coupled spins respectively. The efficiency of the uncoupled oscillator (or spin) given in Equations (<a href="#FD5-entropy-19-00442" class="html-disp-formula">5</a>) and (<a href="#FD9-entropy-19-00442" class="html-disp-formula">9</a>) is shown by the horizontal dotted line. The parameter values are <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>h</mi> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msup> <mi>ω</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>; (<b>b</b>) The COPs of coupled oscillators and spins are shown by continuous and dashed curves respectively. The horizontal dotted line represents COP of the uncoupled oscillator (or spin) given in Equation (<a href="#FD46-entropy-19-00442" class="html-disp-formula">46</a>). Here we used <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>h</mi> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msup> <mi>ω</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 8
<p>Pictorial representation of thermalization of coupled system.</p>
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1032 KiB  
Article
A Fuzzy-Based Adaptive Streaming Algorithm for Reducing Entropy Rate of DASH Bitrate Fluctuation to Improve Mobile Quality of Service
by Linh Van Ma, Jaehyung Park, Jiseung Nam, HoYong Ryu and Jinsul Kim
Entropy 2017, 19(9), 477; https://doi.org/10.3390/e19090477 - 7 Sep 2017
Cited by 13 | Viewed by 5554
Abstract
Dynamic adaptive streaming over Hypertext Transfer Protocol (HTTP) is an advanced technology in video streaming to deal with the uncertainty of network states. However, this technology has one drawback as the network states frequently and continuously change. The quality of a video streaming [...] Read more.
Dynamic adaptive streaming over Hypertext Transfer Protocol (HTTP) is an advanced technology in video streaming to deal with the uncertainty of network states. However, this technology has one drawback as the network states frequently and continuously change. The quality of a video streaming fluctuates along with the network changes, and it might reduce the quality of service. In recent years, many researchers have proposed several adaptive streaming algorithms to reduce such changes. However, these algorithms only consider the current state of a network. Thus, these algorithms might result in inaccurate estimates of a video quality in the near term. Therefore, in this paper, we propose a method using fuzzy logic and a mathematics moving average technique, in order to reduce mobile video quality fluctuation in Dynamic Adaptive Streaming over HTTP (DASH). First, we calculate the moving average of the bandwidth and buffer values for a given period. On the basis of differences between real and average values, we propose a fuzzy logic system to deduce the value of the video quality representation for the next request. In addition, we use the entropy rate of a bandwidth measurement sequence to measure the predictable/stabilization of our method. The experiment results show that our proposed method reduces video quality fluctuation as well as improves 40% of bandwidth utilization compared to existing methods. Full article
(This article belongs to the Special Issue Information Theory and 5G Technologies)
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Figure 1

Figure 1
<p>Change of bitrates over bandwidth fluctuation.</p>
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<p>Moving average and time series data trend.</p>
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<p>Overview of the proposed fuzzy system.</p>
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<p>Base idea of estimating network change state.</p>
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<p>Input membership functions.</p>
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<p>Output membership functions.</p>
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<p>Correlation graph between input and output of the fuzzy system.</p>
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<p>Defuzzification with forty-nine rules, two inputs and one output.</p>
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<p>Variation of buffer length.</p>
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<p>Variation of video representations in adaptive streaming.</p>
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<p>Video width variations.</p>
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<p>Video height variations.</p>
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<p>Comparison of buffer variation with another fuzzy-based method.</p>
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<p>Simulation with different numbers of trace-back steps.</p>
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309 KiB  
Article
Entropies of Weighted Sums in Cyclic Groups and an Application to Polar Codes
by Emmanuel Abbe, Jiange Li and Mokshay Madiman
Entropy 2017, 19(9), 235; https://doi.org/10.3390/e19090235 - 7 Sep 2017
Cited by 8 | Viewed by 4872
Abstract
In this note, the following basic question is explored: in a cyclic group, how are the Shannon entropies of the sum and difference of i.i.d. random variables related to each other? For the integer group, we show that they can differ by any [...] Read more.
In this note, the following basic question is explored: in a cyclic group, how are the Shannon entropies of the sum and difference of i.i.d. random variables related to each other? For the integer group, we show that they can differ by any real number additively, but not too much multiplicatively; on the other hand, for Z / 3 Z , the entropy of the difference is always at least as large as that of the sum. These results are closely related to the study of more-sums-than-differences (i.e., MSTD) sets in additive combinatorics. We also investigate polar codes for q-ary input channels using non-canonical kernels to construct the generator matrix and present applications of our results to constructing polar codes with significantly improved error probability compared to the canonical construction. Full article
(This article belongs to the Special Issue Entropy and Information Inequalities)
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Figure 1

Figure 1
<p>Plot of <math display="inline"> <semantics> <mrow> <mi>I</mi> <mo>(</mo> <mi>W</mi> <mo>)</mo> </mrow> </semantics> </math> (horizontal axis) vs. <math display="inline"> <semantics> <mrow> <mi>I</mi> <mrow> <mo>(</mo> <msup> <mi>W</mi> <mo>+</mo> </msup> <mo>)</mo> </mrow> <mo>−</mo> <mi>I</mi> <mrow> <mo>(</mo> <mi>W</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> for all possible binary input channels (the tick on the horizontal axis is at one, and the tick on vertical axis is at <math display="inline"> <semantics> <mrow> <mn>1</mn> <mo>/</mo> <mn>4</mn> </mrow> </semantics> </math>).</p>
Full article ">Figure 2
<p>For an additive noise channel over <math display="inline"> <semantics> <msub> <mi mathvariant="double-struck">F</mi> <mn>3</mn> </msub> </semantics> </math> with noise distribution <math display="inline"> <semantics> <mrow> <mo>{</mo> <mn>0.7</mn> <mo>,</mo> <mn>0.3</mn> <mo>,</mo> <mn>0</mn> <mo>}</mo> </mrow> </semantics> </math>, the block error probability (in <math display="inline"> <semantics> <msub> <mo form="prefix">log</mo> <mn>10</mn> </msub> </semantics> </math> scale) of a polar code with block length of <math display="inline"> <semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1024</mn> </mrow> </semantics> </math> is plotted against the rate of the code. The red curve (lower curve) is for the polar code using the two-optimal kernel, whereas the blue curve is for the polar code using the original kernel.</p>
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<p>For an additive noise channel over <math display="inline"> <semantics> <msub> <mi mathvariant="double-struck">F</mi> <mn>5</mn> </msub> </semantics> </math> with noise distribution <math display="inline"> <semantics> <mrow> <mo>{</mo> <mn>0.7</mn> <mo>,</mo> <mn>0.3</mn> <mo>,</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>}</mo> </mrow> </semantics> </math>, the block error probability (in <math display="inline"> <semantics> <msub> <mo form="prefix">log</mo> <mn>10</mn> </msub> </semantics> </math> scale) of a polar code with block length of <math display="inline"> <semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1024</mn> </mrow> </semantics> </math> is plotted against the rate of the code. The red curve (lower curve) is for the polar code using the two-optimal kernel, whereas the blue curve is for the polar code using the original kernel.</p>
Full article ">
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