Search Results (145)

Online maps are of great importance in modern life, especially in commuting, traveling and urban planning. The accessibility of remote sensing (RS) images has contributed to the widespread practice of generating online maps based on RS images. The previous works leverage an idea of domain mapping to achieve end-to-end remote sensing image-to-map translation (RSMT). Although existing methods are effective and efficient for online map generation, generated online maps still suffer from ground features distortion and boundary inaccuracy to a certain extent. Recently, the emergence of diffusion models has signaled a significant advance in high-fidelity image synthesis. Based on rigorous mathematical theories, denoising diffusion models can offer controllable generation in sampling process, which are very suitable for end-to-end RSMT. Therefore, we design a novel end-to-end diffusion model to generate online maps directly from remote sensing images, called MapGen-Diff. We leverage a strategy inspired by Brownian motion to make a trade-off between the diversity and the accuracy of generation process. Meanwhile, an image compression module is proposed to map the raw images into the latent space for capturing more perception features. In order to enhance the geometric accuracy of ground features, a consistency regularization is designed, which allows the model to generate maps with clearer boundaries and colorization. Compared to several state-of-the-art methods, the proposed MapGen-Diff achieves outstanding performance, especially a $5\%$ RMSE and $7\%$ SSIM improvement on Los Angeles and Toronto datasets. The visualization results also demonstrate more accurate local details and higher quality. Full article

Intelligent Reflecting Surfaces (IRS) and Multiple-Input Single-Output (MISO) technologies are essential in the fifth generation (5G) networks and beyond. IRS optimizes the signal propagation and the coverage and is a viable approach to address the issues caused by fading channels that limits the spectral efficiency, while MIMO enhances data rates, reliability, and spectral efficiency by using multiple antennas at both transmitter and receiver ends. This paper proposes an IRS-assisted MISO system using the Orthogonal Space-Time Block Code (OSTBC) scheme to enhance the channel reliability and reduce the Bit Error Rate (BER) in wireless communication systems. The proposed system exploits the benefits from the transmit diversity gain of the OSTBC scheme as well as from the bit energy to noise power spectral density (E_b/N_o) improvement of the IRS technology. The presented work explores these combined technologies across different modulation schemes. The obtained results outperform the similar previously published works by considering higher-order modulation schemes as well as the deployment of rate ¾ OSTBC-assisted IRS. Moreover, the obtained results demonstrate that the integration of OSTBC with IRS can yield significant performance improvements in terms of E_b/N_o by 7 dB and 13 dB when using 16 reflecting elements and 64 reflecting elements, respectively. Full article

To effectively mitigate the effects of atmospheric turbulence in free space optical (FSO) communication, we propose a parallel carrier frequency offset estimation (FOE) scheme based on time-tagged QPSK partitioning (TTQP). This scheme can be applied to spatial diversity polarization multiplexing (PM) coherent FSO communication systems. Specifically, the TTQP scheme performs QPSK partitioning by time-tagging signal points, accurately recording the time intervals between signals, and significantly reducing implementation complexity through a modified Mth power algorithm. The simulation results for the PM 16-quadrature amplitude modulation (QAM) validate the effectiveness of the proposed scheme. Compared to traditional QPSK partitioning algorithms, the TTQP algorithm achieves high accuracy, low complexity, and multi-format versatility in high-speed coherent FSO communication. Full article

For a multiuser multiple-input–multiple-output (MIMO) overlay cognitive radio (CR) network, an opportunistic interference alignment (IA) technique has been proposed that allows spectrum sharing between primary users (PUs) and secondary users (SUs) while ensuring zero interference to the PU. The CR system consists of one PU and K SUs where the PU uses space-time water-filling (ST-WF) algorithm to optimize its transmission and in the process, frees up unused eigenmodes that can be exploited by the SU. The SUs make use of an optimal power allocation algorithm to align their transmitted signals in such a way their interference impairs only the PUs unused eigenmodes. Since the SUs optimal power allocation algorithm turns out to be an optimal beamformer with multiple eigen-beams, this work initially proposes combining the diversity gain property of space-time block codes, the zero-forcing function of IA and beamforming to optimize the SUs transmission rates. This proposed solution requires availability of channel state information (CSI), and to eliminate the need for CSI, this work then combines Differential Space-Time Block Coding (DSTBC) scheme with optimal IA precoders (consisting of beamforming and zero-forcing) to maximize the SUs data rates. Simulation results confirm the accuracy of the proposed solution. Full article

This article presents a spatiotemporal super-resolution (SR) reconstruction model for two common flame types, a swirling and then a jet flame, using double generative adversarial network (GAN) architectures. The approach develops two sets of generator and discriminator networks to learn topographic and temporal features and infer high spatiotemporal resolution turbulent flame structure from supplied low-resolution counterparts at two time points. In this work, numerically simulated 3D turbulent swirling and jet flame structures were used as training data to update the model parameters of the GAN networks. The effectiveness of our model was then thoroughly evaluated in comparison to other traditional interpolation methods. An upscaling factor of 2 in space, which corresponded to an 8-fold increase in the total voxel number and a double time frame acceleration, was used to verify the model’s ability on a swirling flame. The results demonstrate that the assessment metrics, peak signal-to-noise ratio (PSNR), overall error (E_R), and structural similarity index (SSIM), with average values of 35.27 dB, 1.7%, and 0.985, respectively, in the spatiotemporal SR results, can reach acceptable accuracy. As a second verification to highlight the present model’s potential universal applicability to flame data of diverse types and shapes, we applied the model to a turbulent jet flame and had equal success. This work provides a different method for acquiring high-resolution 3D structure and further boosting repeat rate, demonstrating the potential of deep learning technology for combustion diagnosis. Full article

Bistatic radar exhibits spatial isomerism and diverse configurations, leading to unique clutter characteristics distinct from those of monostatic radar. The clutter rank serves as a pivotal indicator of clutter characteristics, enabling the quantification of clutter severity. Space-time adaptive processing (STAP) is a critical technique to detect moving targets, and clutter rank determines the number of independent and identically distributed (IID) training samples and the degree of freedom (DOF) for effective suppression of clutter that STAP requires. Therefore, the accurate estimation of clutter rank for bistatic radar can provide a crucial indicator for designing and constructing STAP processors, thereby facilitating fast and efficient clutter suppression in bistatic radar systems. This study is based on the idea that clutter rank is the number of prolate spheroidal wave function (PSWF) orthogonal bases utilized for approximating the clutter signal. Firstly, the challenge of utilizing PSWF orthogonal bases for approximating the clutter signal in bistatic radar is elucidated. This pertains to the fact that, unlike monostatic radar clutter, bistatic radar clutter is not capable of being expressed as a single-frequency signal. The clutter rank estimation for bistatic radar is thus derived as the frequency bandwidth estimation. Secondly, to achieve this estimation, the frequency distribution of each individual scattering unit is investigated, thereby determining their extending frequency broadening (EFB) as compared to that of single-frequency. Subsequently, the integral average of EFB across the entire range bin is computed, ultimately enabling the acquisition of bistatic radar’s frequency bandwidth. Finally, the estimation method is extended to non-side-looking mode and limited observation areas with pattern modulation. Simulation experiments confirm that our proposed method provides accurate clutter rank estimations, surpassing 99% proportions of large eigenvalues across various bistatic configurations, observation modes, and areas. Full article

Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology and response to environmental conditions. This analysis leverages both spectroscopic and phenological information to characterize vegetation communities in the Sundarban riverine mangrove forest of the Ganges–Brahmaputra delta. Parallel analyses of surface reflectance spectra from NASA’s EMIT imaging spectrometer and MODIS vegetation abundance time series (2000–2022) reveal the spectroscopic and phenological diversity of the Sundarban mangrove communities. A comparison of spectral and temporal feature spaces rendered with low-order principal components and 3D embeddings from Uniform Manifold Approximation and Projection (UMAP) reveals similar structures with multiple spectral and temporal endmembers and multiple internal amplitude continua for both EMIT reflectance and MODIS Enhanced Vegetation Index (EVI) phenology. The spectral and temporal feature spaces of the Sundarban represent independent observations sharing a common structure that is driven by the physical processes controlling tree canopy spectral properties and their temporal evolution. Spectral and phenological endmembers reside at the peripheries of the mangrove forest with multiple outward gradients in amplitude of reflectance and phenology within the forest. Longitudinal gradients of both phenology and reflectance amplitude coincide with LiDAR-derived gradients in tree canopy height and sub-canopy ground elevation, suggesting the influence of surface hydrology and sediment deposition. RGB composite maps of both linear (PC) and nonlinear (UMAP) 3D feature spaces reveal a strong contrast between the phenological and spectroscopic diversity of the eastern Sundarban and the less diverse western Sundarban. Full article

Electroencephalography (EEG)-based applications in brain–computer interfaces (BCIs), neurological disease diagnosis, rehabilitation, etc., rely on supervised approaches such as classification that requires given labels. However, with the ever-increasing amount of EEG data, incomplete or incorrectly labeled or unlabeled EEG data are increasing. It likely degrades the performance of supervised approaches. In this work, we put forward a novel unsupervised exploratory EEG analysis solution by clustering based on low-dimensional prototypes in latent space that are associated with the respective clusters. Having the prototype as a baseline of each cluster, a compositive similarity is defined to act as the critic function in clustering, which incorporates similarities on three levels. The approach is implemented with a Generative Adversarial Network (GAN), termed W-SLOGAN, by extending the Stein Latent Optimization for GANs (SLOGAN). The Gaussian Mixture Model (GMM) is utilized as the latent distribution to adapt to the diversity of EEG signal patterns. The W-SLOGAN ensures that images generated from each Gaussian component belong to the associated cluster. The adaptively learned Gaussian mixing coefficients make the model remain effective in dealing with an imbalanced dataset. By applying the proposed approach to two public EEG or intracranial EEG (iEEG) epilepsy datasets, our experiments demonstrate that the clustering results are close to the classification of the data. Moreover, we present several findings that were discovered by intra-class clustering and cross-analysis of clustering and classification. They show that the approach is attractive in practice in the diagnosis of the epileptic subtype, multiple labelling of EEG data, etc. Full article

This work presents a methodology for extracting vehicle trajectories from six partially-overlapping roadside radars through a signalized corridor. The methodology incorporates radar calibration, transformation to the Frenet space, Kalman filtering, short-term prediction, lane-classification, trajectory association, and a covariance intersection-based approach to track fusion. The resulting dataset contains 79,000 fused radar trajectories over a 26-h period, capturing diverse driving scenarios including signalized intersections, merging behavior, and a wide range of speeds. Compared to popular trajectory datasets such as NGSIM and highD, this dataset offers extended temporal coverage, a large number of vehicles, and varied driving conditions. The filtered leader–follower pairs from the dataset provide a substantial number of trajectories suitable for car-following model calibration. The framework and dataset presented in this work has the potential to be leveraged broadly in the study of advanced traffic management systems, autonomous vehicle decision-making, and traffic research. Full article

As urbanization accelerates, urban greenery, particularly street greenery, emerges as a vital strategy for enhancing residents’ quality of life, demanding attention for its alignment with pedestrian flows to foster sustainable urban development and ensure urban dwellers’ wellbeing. The advent of diverse urban data has significantly advanced this area of study. Focusing on Chengdu’s central urban district, this research assesses street greening metrics against pedestrian flow indicators, employing spatial autocorrelation techniques to investigate the interplay between street greenery and pedestrian flow over time and space. Our findings reveal a prevalent negative spatial autocorrelation between street greenery and pedestrian flow within the area, underscored by temporal disparities in greenery demands across various urban functions during weekdays versus weekends. This study innovatively incorporates mobile phone signal-based population heat maps into the mismatch analysis of street greenery for the first time, moving beyond the conventional static approach of space syntax topology in assessing pedestrian flow. By leveraging dynamic pedestrian flow data, it enriches our understanding of the disconnect between street greening plans and pedestrian circulation, highlighting the concept of urban flow and delving into the intricate nexus among time, space, and human activity. Moreover, this study meticulously examines multiple street usage scenarios, reflecting diverse behavior patterns, with the objective of providing nuanced and actionable strategies for urban renewal initiatives aimed at creating more inviting and sustainable urban habitats. Full article

Background: Community design features, such as sidewalks and street crossings, present significant challenges for individuals with disabilities, hindering their physical performance and social integration. However, limited research has been conducted on the application of Universal Design (UD) to address these challenges, particularly concerning specific demographic groups and population cohorts. Understanding the influence of environmental features on physical performance is crucial for developing inclusive solutions like UD, which can enhance usability and social integration across diverse populations. Objective: This study aims to bridge this gap by investigating the complex relationships between environmental barriers, health conditions, and routine activity performance. An index was developed to evaluate users’ UD performance based on functional capacity, providing scientifically rigorous and objectively measured evidence of UD effectiveness in creating inclusive built environments. Method: Using data from the Problematic Activities Survey (PAS) conducted in the U.S., Canada, and Australia and targeting individuals with and without functional limitations, multinomial logit models were employed to estimate the probabilities of encountering performance problems. This analysis led to the development of the Problematic Performance Value (PPV) score. Results: The results demonstrated significant disparities in PPVs across various health conditions, particularly concerning curb ramps. Individuals facing mobility issues in their legs/feet, arms/hands, or back/neck encounter more pronounced challenges, especially when curb ramps lack proper design elements. Similarly, individuals with vision impairments face heightened difficulties with traffic signals, particularly due to issues with audible signal systems. These findings underscore the importance of addressing micro-level environmental challenges to accommodate individuals with varying functional capacities effectively. Conclusions: By providing insights into the most problematic daily activities encountered by diverse populations, the PPV score serves as a valuable indicator for guiding environmental design improvements and promoting equitable space usage. This can be used to guide improved UD solutions and decide areas of concentration by providing generalized information on specific environmental features that contribute to user performance. Full article

For the past 20 years, Global Navigation Satellite System reflectometry (GNSS-R) technology has successfully shown its potential for remote sensing of the Earth’s surface, including ocean and land surfaces. It is a multistatic radar that uses the GNSS signals reflected from the Earth’s surface to extract land and ocean characteristics. Because of its numerous advantages such as low cost, multiple signal sources, and all-day/weather and high-spatiotemporal-resolution observations, this new technology has attracted the attention of many researchers. One of its most promising applications is GNSS-R ocean altimetry, which can complement existing techniques such as tide gauging and radar satellite altimetry. Since this technology for ocean altimetry was first proposed in 1993, increasing progress has been made including diverse methods for processing reflected signals (such as GNSS interferometric reflectometry, conventional GNSS-R, and interferometric GNSS-R), different instruments (such as an RHCP antenna with one geodetic receiver, a linearly polarized antenna, and a system of simultaneously used RHCP and LHCP antennas with a dedicated receiver), and different platform applications (such as ground-based, air-borne, or space-borne). The development of multi-mode and multi-frequency GNSS, especially for constructing the Chinese BeiDou Global Navigation Satellite System (BDS-3), has enabled more free signals to be used to further promote GNSS-R applications. The GNSS has evolved from its initial use of GPS L1 and L2 signals to include other GNSS bands and multi-GNSS signals. Using more advanced, multi-frequency, and multi-mode signals will bring new opportunities to develop GNSS-R technology. In this paper, studies of GNSS-R altimetry are reviewed from four perspectives: (1) classifications according to different data processing methods, (2) different platforms, (3) development of different receivers, and (4) our work. We overview the current status of GNSS-R altimetry and describe its fundamental principles, experiments, recent applications to ocean altimetry, and future directions. Full article

The equatorial post-sunset ionospheric irregularities induce rapid fluctuations in the phase and amplitude of global navigation satellite system (GNSS) signals which may lead to the loss of lock and can potentially degrade the position accuracy. This study presents a new analysis of L-band scintillation from a low latitude station at Guntur (Geographic 16.44°N, 80.62°E, dip 22.18°), India, for the period of 18 months from August 2021 to January 2023. The observations are categorized either in the medium Earth-orbiting (MEO) or geosynchronous orbiting (GSO) satellites (GSO is considered as a set of the geostationary and inclined geosynchronous satellites) for L1, L2, and L5 signals. The results show a higher occurrence of moderate (0.5 < S4 ≤ 0.8) and strong (S4 > 0.8) scintillations on different signals from the MEO compared to the GSO satellites. Statistically, the average of peak S4 values provides a higher confidence in the severity of scintillations on a given night, which is found to be in-line with the scintillation occurrences. The percentage occurrence of scintillation-affected satellites is found to be higher on L1 compared to other signals, wherein a contrasting higher percentage of affected satellites over GSO than MEO is observed. While a clear demarcation between the L2/L5 signals and L1 is found over the MEO, in the case of GSO, the CCDF over L5 is found to match mostly with the L1 signal. This could possibly originate from the space diversity gain effect known to impact the closely spaced geostationary satellite links. Another major difference of higher slopes and less scatter of S4 values corresponding to L1 versus L2/L5 from the GSO satellite is found compared to mostly non-linear highly scattered relations from the MEO. The distribution of the percentage of scintillation-affected satellites on L1 shows a close match between MEO and GSO in a total number of minutes up to ~60%. However, such a number of minutes corresponding to higher than 60% is found to be larger for GSO. Thus, the results indicate the possibility of homogeneous spatial patterns in a scintillation distribution over a low latitude site, which could originate from the closely spaced GSO links and highlight the role of the number of available satellites with the geometry of the links, being the deciding factors. This helps the ionospheric community to develop inter-GNSS (MEO and GSO) operability models for achieving highly accurate positioning solutions during adverse ionospheric weather conditions. Full article

It is necessary to develop a health monitoring system (HMS) for complex systems to improve safety and reliability and prevent potential failures. Time-series signals are collected from multiple sensors installed on the equipment that can reflect the health condition of them. In this study, a novel interpretable recurrent variational state-space model (IRVSSM) is proposed for time-series modeling and anomaly detection. To be specific, the deterministic hidden state of a recursive neural network is used to capture the latent structure of sensor data, while the stochastic latent variables of a nonlinear deep state-space model capture the diversity of sensor data. Temporal dependencies are modeled through a nonlinear transition matrix; an automatic relevance determination network is introduced to selectively emphasize important sensor data. Experimental results demonstrate that the proposed algorithm effectively captures vital information within the sensor data and provides accurate and reliable fault diagnosis during the steady-state phase of liquid rocket engine operation. Full article

Precision beam pointing plays a critical role in free-space optical communications terminals in uplink, downlink and inter-satellite link scenarios. Among the various methods of beam steering, the use of fast steering mirrors (FSM) is widely adopted, with many commercial solutions employing diverse technologies, particularly focusing on small, high-bandwidth mirrors. This paper introduces a method using lightweight, commercial off-the-shelf components to construct a custom closed-loop steering mirror platform, suitable for mirror apertures exceeding 100 mm. The approach involves integrating optical encoders into two off-the-shelf open-loop actuators. These encoders read the signal reflected on purposefully diamond-machined knurled screw knobs, providing maximum contrast between light and dark lines. The resulting steering mirror has the potential to complement or replace FSM in applications requiring a larger stroke, at the expense of motion speed. In the presented setup, the mirror tilt resolution achieved based on the encoder closed-loop signal feedback is 45 $\mathsf{\mu }$rad, with a mean slew rate of 1.5 mrad/s. Importantly, the steering assembly is self-locking, requiring no power to maintain a steady pointing angle. Using the mirror to actively correct for a constantly moving incoming beam, a 5-fold increase in concentration of the beam spot on the center of the detector was obtained compared to a fixed position mirror, demonstrating the mirrors ability to correct for satellite platform jitter and drift. Full article