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Sensors, Volume 12, Issue 10 (October 2012) – 70 articles , Pages 12870-14231

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560 KiB  
Article
Handmade Task Tracking Applied to Cognitive Rehabilitation
by José M. Cogollor, Charmayne Hughes, Manuel Ferre, Javier Rojo, Joachim Hermsdörfer, Alan Wing and Sandra Campo
Sensors 2012, 12(10), 14214-14231; https://doi.org/10.3390/s121014214 - 22 Oct 2012
Cited by 19 | Viewed by 9286
Abstract
This article presents research focused on tracking manual tasks that are applied in cognitive rehabilitation so as to analyze the movements of patients who suffer from Apraxia and Action Disorganization Syndrome (AADS). This kind of patients find executing Activities of Daily Living (ADL) [...] Read more.
This article presents research focused on tracking manual tasks that are applied in cognitive rehabilitation so as to analyze the movements of patients who suffer from Apraxia and Action Disorganization Syndrome (AADS). This kind of patients find executing Activities of Daily Living (ADL) too difficult due to the loss of memory and capacity to carry out sequential tasks or the impossibility of associating different objects with their functions. This contribution is developed from the work of Universidad Politécnica de Madrid and Technical University of Munich in collaboration with The University of Birmingham. The KinectTM for Windows© device is used for this purpose. The data collected is compared to an ultrasonic motion capture system. The results indicate a moderate to strong correlation between signals. They also verify that KinectTM is very suitable and inexpensive. Moreover, it turns out to be a motion-capture system quite easy to implement for kinematics analysis in ADL. Full article
(This article belongs to the Section Physical Sensors)
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<p>(<b>a</b>) A photo showing all the different parts of the sensor, external view of the 3D depth sensors, RGB camera, multi-array microphone, and a motorized tilt taken at Microsoft's E3 2010. (<b>b</b>) Internal view of the device from [<a href="#b40-sensors-12-14214" class="html-bibr">40</a>].</p>
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<p>The figure shows the modular methodology used with the Kinect™ for data analysis. First, the data is acquired so as to launch the camera sensor using Kinect SDK<sup>©</sup> functions and the interface developed, second, the data is filtered and synchronized, then the segmentation in epochs is carried out to represent and analyze the data. Finally, the information is stored.</p>
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<p>Kinect™ user interface.</p>
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<p>Task tree for tea preparation.</p>
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<p>This figure shows the object layout for the experiment. The objects involved: a cup of tea, a kettle, a tea bag; the sensor Kinect™ and the Zebris system. The general Cartesian reference system is also represented.</p>
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<p>Result of filtering Kinect™ data with a Butterworth low pass filter.</p>
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<p>Comparison of position data for the Kinect (blue lines) and the Zebris system (green lines) from a single tea making task trial for the control (<b>a</b>) and the apraxic (<b>b</b>) participant.</p>
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<p>Correlation map between axes for the control participant (left panel) and the apraxic participant (right panel).</p>
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706 KiB  
Article
Mobile Mapping of Sporting Event Spectators Using Bluetooth Sensors: Tour of Flanders 2011
by Mathias Versichele, Tijs Neutens, Stephanie Goudeseune, Frederik Van Bossche and Nico Van de Weghe
Sensors 2012, 12(10), 14196-14213; https://doi.org/10.3390/s121014196 - 22 Oct 2012
Cited by 21 | Viewed by 7071
Abstract
Accurate spatiotemporal information on crowds is a necessity for a better management in general and for the mitigation of potential security risks. The large numbers of individuals involved and their mobility, however, make generation of this information non-trivial. This paper proposes a novel [...] Read more.
Accurate spatiotemporal information on crowds is a necessity for a better management in general and for the mitigation of potential security risks. The large numbers of individuals involved and their mobility, however, make generation of this information non-trivial. This paper proposes a novel methodology to estimate and map crowd sizes using mobile Bluetooth sensors and examines to what extent this methodology represents a valuable alternative to existing traditional crowd density estimation methods. The proposed methodology is applied in a unique case study that uses Bluetooth technology for the mobile mapping of spectators of the Tour of Flanders 2011 road cycling race. The locations of nearly 16,000 cell phones of spectators along the race course were registered and detailed views of the spatiotemporal distribution of the crowd were generated. Comparison with visual head counts from camera footage delivered a detection ratio of 13.0 ± 2.3%, making it possible to estimate the crowd size. To our knowledge, this is the first study that uses mobile Bluetooth sensors to count and map a crowd over space and time. Full article
(This article belongs to the Section Remote Sensors)
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<p>Mobile sensor deployment during the race. Top: schematic overview of the methodology, bottom: actual deployment (1: Bluetooth sensor, 2: video camera, 3: GPS unit).</p>
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<p>Tour of Flanders, 2011 edition. Top: spatial view of the official track and the trajectory of the mobile platform. Bottom: elevation profile of the mobile platform trajectory (NASA Shuttle Radar Topography Mission elevation data). In both cases, the speed of the mobile platform is shown in a color scale ranging from red (temporary stops) to blue (more than 80 km/h). The 18 official climbs are shown as triangles in the spatial view and solid vertical lines in the elevation profile (non covered climbs excluded). The dashed vertical lines represent the cobblestoned segments, the dotted vertical line the first provision point, and the dashed-dotted line the village of the tour.</p>
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<p>Mobile Bluetooth detection process investigation: (<b>a</b>) experimental setup showing both mobile phones (A, B) and the two passing scenarios (1: sensor facing phones, 2: sensor not facing phones); (<b>b</b>) effect of sensor type, speed, and phone distance (both the mean and standard deviations, as well as the individual values of all combinations are shown; both error bars and individual data points are offset from their real x-value for visual clarity); (<b>c</b>) effect of sensor placement on mobile platform at a speed of 60 km/h.</p>
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<p>Mobile Bluetooth detection process investigation: (<b>a</b>) experimental setup showing both mobile phones (A, B) and the two passing scenarios (1: sensor facing phones, 2: sensor not facing phones); (<b>b</b>) effect of sensor type, speed, and phone distance (both the mean and standard deviations, as well as the individual values of all combinations are shown; both error bars and individual data points are offset from their real x-value for visual clarity); (<b>c</b>) effect of sensor placement on mobile platform at a speed of 60 km/h.</p>
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<p>Number of detected phones along 1 km long segments of the trajectory followed by the mobile platform as an indicator of crowdedness. Top: spatial view using a yellow-to-red color scale to depict the number of phones, class breaks according to the Jenks natural breaks optimization [<a href="#b32-sensors-12-14196" class="html-bibr">32</a>]. The background shows the population density of statistical sectors in the area, colored in a grey-scale, once again according to Jenks natural breaks. Bottom: one-dimensional view.</p>
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<p>Real-life influence of mobile platform speed on four trajectory segment characteristics: (<b>a</b>) number of detected phones (<span class="html-italic">log scale</span>), (<b>b</b>) number of detections/number of detected phones (<span class="html-italic">log scale</span>), (<b>c</b>) sensor overlap, (<b>d</b>) mean RSSI.</p>
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<p>Scatter plot of statistical population density <span class="html-italic">versus</span> number of detected phones over each 1 km long segment of the trajectory.</p>
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1047 KiB  
Article
Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch
by Norasyikin Fadilah, Junita Mohamad-Saleh, Zaini Abdul Halim, Haidi Ibrahim and Syed Salim Syed Ali
Sensors 2012, 12(10), 14179-14195; https://doi.org/10.3390/s121014179 - 22 Oct 2012
Cited by 74 | Viewed by 12834
Abstract
Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images [...] Read more.
Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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<p>Oil palm FFB grading system.</p>
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<p>Oil palm FFB images for four ripeness categories: (<b>a</b>) Unripe; (<b>b</b>) Underripe; (<b>c</b>) Ripe; (<b>d</b>) Overripe.</p>
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<p>Segmented images of oil palm FFB's (<b>a</b>) fruits and (<b>b</b>) spikes.</p>
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<p>Structure of MLP neural network.</p>
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<p>Two experimented methods. (<b>a</b>) Method MA; (<b>b</b>) Method MB.</p>
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<p>MLP performance based on number of features.</p>
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<p>MLP performance based on number of features (5–15 PCs).</p>
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876 KiB  
Article
Developing a Reading Concentration Monitoring System by Applying an Artificial Bee Colony Algorithm to E-Books in an Intelligent Classroom
by Chia-Cheng Hsu, Hsin-Chin Chen, Yen-Ning Su, Kuo-Kuang Huang and Yueh-Min Huang
Sensors 2012, 12(10), 14158-14178; https://doi.org/10.3390/s121014158 - 22 Oct 2012
Cited by 41 | Viewed by 8599
Abstract
A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students’ reading concentration rates and thus better increase learning effectiveness? The aim of the [...] Read more.
A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students’ reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students’ reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students’ reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students. Full article
(This article belongs to the Section Physical Sensors)
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<p>Flow chart of the ABC algorithm.</p>
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<p>Framework of the reading concentration monitoring system.</p>
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<p>Procedure of the reading concentration monitoring system.</p>
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<p>Detection function of the reading concentration monitoring system.</p>
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<p>A student using the proposed system.</p>
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<p>Results of the reading concentration monitoring system.</p>
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<p>Reading status of an individual student.</p>
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<p>Average best fitness values with 2,000 iterations and different numbers of bees.</p>
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<p>Average execution time values with 2,000 iterations and different numbers of bees.</p>
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1344 KiB  
Article
Numerical Study of Opto-Fluidic Ring Resonators for Biosensor Applications
by Han Keun Cho and Jinwoo Han
Sensors 2012, 12(10), 14144-14157; https://doi.org/10.3390/s121014144 - 22 Oct 2012
Cited by 10 | Viewed by 6334
Abstract
The opto-fluidic ring resonator (OFRR) biosensor is numerically characterized in whispering gallery mode (WGM). The ring resonator includes a ring, a waveguide and a gap separating the ring and the waveguide. Dependence of the resonance characteristics on the resonator size parameters such as [...] Read more.
The opto-fluidic ring resonator (OFRR) biosensor is numerically characterized in whispering gallery mode (WGM). The ring resonator includes a ring, a waveguide and a gap separating the ring and the waveguide. Dependence of the resonance characteristics on the resonator size parameters such as the ring diameter, the ring thickness, the waveguide width, and the gap width between the ring and the waveguide are investigated. For this purpose, we use the finite element method with COMSOL Multiphysics software to solve the Maxwell’s equations. The resonance frequencies, the free spectral ranges (FSR), the full width at half-maximum (FWHM), finesse (F), and quality factor of the resonances (Q) are examined. The resonant frequencies are dominantly affected by the resonator diameter while the gap width, the ring thickness and the waveguide width have negligible effects on the resonant frequencies. FWHM, the quality factor Q and the finesse F are most strongly affected by the gap width and moderately influenced by the ring diameter, the waveguide width and the ring thickness. In addition, our simulation demonstrates that there is an optimum range of the waveguide width for a given ring resonator and this value is between ~2.25 μm and ~2.75 μm in our case. Full article
(This article belongs to the Section Biosensors)
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<p>The schematic of (<b>a</b>) the multi-channel OFRR and (<b>b</b>) a section view.</p>
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<p>A photograph of a fabricated OFRR.</p>
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<p>Electric fields distributions for the ring-waveguide coupling part (d = 100 μm, t = 3 μm, w = 2 μm, g = 0 μm); (<b>a</b>) the regular plots and (<b>b</b>) the magnified plots. (Off-resonance (<b>a-1</b>,<b>b-1</b>); first-order resonance (<b>a-2</b>,<b>b-2</b>); second-order resonance (<b>a-3</b>,<b>b-3</b>)).</p>
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<p>Resonance spectra of the ring for the OFRR (d = 100 μm, t = 3 μm, w = 2 μm, g = 0 nm). The large peaks are for the first-order resonances while the small peaks are for the second-order resonances.</p>
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<p>Effects of the ring diameter on the resonance characteristics (t = 3 μm, w = 2 μm, g = 0 μm). Displayed four data sets are obtained from adjacent resonance peaks, respectively.</p>
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<p>Effects of the ring thickness on the resonance characteristics (d = 100 μm, w = 2 μm, g = 0 μm). Displayed four data sets are obtained from adjacent resonance peaks, respectively.</p>
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<p>Effects of the waveguide width on the resonance characteristics (d = 100 μm, t = 3 μm, g = 0 μm). Displayed four data sets are obtained from adjacent resonance peaks, respectively.</p>
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<p>Effect of the waveguide width on FWHM (d = 100 μm, g = 0 μm).</p>
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<p>Effects of the gap width on the resonance characteristics (d = 100 μm, t = 3 μm, w = 2 μm). Displayed four data sets are obtained from adjacent resonance peaks, respectively.</p>
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1585 KiB  
Article
An Embedded Real-Time Red Peach Detection System Based on an OV7670 Camera, ARM Cortex-M4 Processor and 3D Look-Up Tables
by Mercè Teixidó, Davinia Font, Tomàs Pallejà, Marcel Tresanchez, Miquel Nogués and Jordi Palacín
Sensors 2012, 12(10), 14129-14143; https://doi.org/10.3390/s121014129 - 22 Oct 2012
Cited by 14 | Viewed by 11596
Abstract
This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system [...] Read more.
This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system will be to control a robotized arm to automatically select and pick some fruit directly from the tree. The complete embedded system has been designed to be placed directly in the gripper tool of the future robotized harvesting arm. The embedded system will be able to perform real-time fruit detection and tracking by using a three-dimensional look-up-table (LUT) defined in the RGB color space and optimized for fruit picking. Additionally, two different methodologies for creating optimized 3D LUTs based on existing linear color models and fruit histograms were implemented in this work and compared for the case of red peaches. The resulting system is able to acquire general and zoomed orchard images and to update the relative tracking information of a red peach in the tree ten times per second. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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<p>The camera module, the board processor and the auxiliary color LCD.</p>
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<p>The camera module with the FIFO memory at the back.</p>
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<p>The processor used in the embedded system.</p>
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<p>Red peach segmentation LUT obtained from linear color models.</p>
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<p>Red peach segmentation LUT obtained from a three-dimensional histogram.</p>
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<p>Orchard image (320 × 240 pixels, RGB565) obtained in normal (<b>a</b>) and zoom mode (<b>b</b>).</p>
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1141 KiB  
Article
Automatic Suppression of Intense Monochromatic Light in Electro-Optical Sensors
by Gunnar Ritt and Bernd Eberle
Sensors 2012, 12(10), 14113-14128; https://doi.org/10.3390/s121014113 - 19 Oct 2012
Cited by 24 | Viewed by 8050
Abstract
Electro-optical imaging sensors are widely distributed and used for many different tasks. Due to technical improvements, their pixel size has been steadily decreasing, resulting in a reduced saturation capacity. As a consequence, this progress makes them susceptible to intense point light sources. Developments [...] Read more.
Electro-optical imaging sensors are widely distributed and used for many different tasks. Due to technical improvements, their pixel size has been steadily decreasing, resulting in a reduced saturation capacity. As a consequence, this progress makes them susceptible to intense point light sources. Developments in laser technology have led to very compact and powerful laser sources of any wavelength in the visible and near infrared spectral region, offered as laser pointers. The manifold of wavelengths makes it difficult to encounter sensor saturation over the complete operating waveband by conventional measures like absorption or interference filters. We present a concept for electro-optical sensors to suppress overexposure in the visible spectral region. The key element of the concept is a spatial light modulator in combination with wavelength multiplexing. This approach allows spectral filtering within a localized area in the field of view of the sensor. The system offers the possibility of automatic reduction of overexposure by monochromatic laser radiation. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Germany 2012)
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<p>Comparison of <a href="#FD7" class="html-disp-formula">Equations (7)</a>, <a href="#FD8" class="html-disp-formula">(8)</a> and <a href="#FD10" class="html-disp-formula">(10)</a> for the peak irradiance and mean irradiance in the focal plane of an optical system, respectively.</p>
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<p>Sensor protection concept using wavelength multiplexing by means of two direct vision prisms (Pr1/Pr2: direct vision prisms; L1/L2: lenses; SLM: spatial light modulator; P/A: polarizers). Due to the spectral dispersion, the activation of the single SLM elements attenuates only a narrow spectral band (blue dashed rays) of the incident light beam, whereas all remaining wavelengths from the incident beam can pass through the optical arrangement unaffected (red and green rays). This setup allows for combined spatial and spectral filtering of monochromatic light sources whatever the wavelength.</p>
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<p>Sensor protection concept using a DMD. (<b>a</b>) Operation mode for regular imaging: all the micromirrors are tilted towards the sensor. (<b>b</b>) Operation mode with high attenuation for dazzling light: the micromirrors which are exposed with dazzling laser light (here: the green rays) are tilted away from the sensor. Thus, the dazzling light will be strongly attenuated in the regular imaging path.</p>
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<p>Optical layout of the sensor protection concept. A digital micromirror device (DMD) is located in the intermediate focal plane of a Keplerian telescope (Achromatic lens 1 and 2). Wavelength multiplexing is implemented by the use of two diffraction gratings (Grating 1 and 2). Grating 1 is aligned so that the spectral separation takes place in a plane perpendicular to the plane of reflection at the DMD.</p>
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<p>View of an urban scene imaged with an optical system as presented in <a href="#f4-sensors-12-14113" class="html-fig">Figure 4</a>. (<b>a</b>) View of the undisturbed scene. (<b>b</b>) Disturbed scene: Laser radiation originating from the steeple dazzles the camera. (<b>c</b>) View of the scene while the filtering is switched on. As a side effect a vertically arranged colour distortion slightly is recognisable, but all geometrical details are visible.</p>
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<p>Colour response curves of the camera used in the optical setup (VRmagicVRmC-12 Pro). The dots in the plot show measured chromaticity values for monochromatic light. The solid curves are fit curves to the measurement data.</p>
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<p>(<b>a</b>) Experimental setup used to investigate the accuracy of the algorithm to estimate laser wavelength with the help of a colour camera. AOTF: Acousto-optical tunable filter. (<b>b</b>) Some examples of images taken with the camera under illumination with a specific wavelength and the corresponding chromaticities <span class="html-italic">r</span>, <span class="html-italic">g</span> and <span class="html-italic">b</span>.</p>
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<p>Accuracy of estimated laser wavelengths evaluated by the RGB values of a colour camera.</p>
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<p>Flowchart of the image analysis procedure for the estimation of the dazzling wavelength. First, the overexposed pixels (see Image 2) of a dazzled frame (see Image 1) are determined. The colour of the contour line (see Image 3) is ready to be analysed after the subtraction of the background (see Image 5). The frame preceding a dazzled frame (see Image 4) is used to determine the background light.</p>
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480 KiB  
Article
Detection of Interference Phase by Digital Computation of Quadrature Signals in Homodyne Laser Interferometry
by Simon Rerucha, Zdenek Buchta, Martin Sarbort, Josef Lazar and Ondrej Cip
Sensors 2012, 12(10), 14095-14112; https://doi.org/10.3390/s121014095 - 19 Oct 2012
Cited by 40 | Viewed by 9250
Abstract
We have proposed an approach to the interference phase extraction in the homodyne laser interferometry. The method employs a series of computational steps to reconstruct the signals for quadrature detection from an interference signal from a non-polarising interferometer sampled by a simple photodetector. [...] Read more.
We have proposed an approach to the interference phase extraction in the homodyne laser interferometry. The method employs a series of computational steps to reconstruct the signals for quadrature detection from an interference signal from a non-polarising interferometer sampled by a simple photodetector. The complexity trade-off is the use of laser beam with frequency modulation capability. It is analytically derived and its validity and performance is experimentally verified. The method has proven to be a feasible alternative for the traditional homodyne detection since it performs with comparable accuracy, especially where the optical setup complexity is principal issue and the modulation of laser beam is not a heavy burden (e.g., in multi-axis sensor or laser diode based systems). Full article
(This article belongs to the Special Issue Laser Sensing and Imaging)
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<p>Polarising laser interferometer with quadrature detection unit for homodyne detection: the interferometer employs the polarising divider, the reference corner cube reflector CC<span class="html-italic"><sub>ref</sub></span> and measurement reflector CC<span class="html-italic"><sub>meas</sub></span>. The output beam is compound from two beams with a different polarisation. The detection unit splits the beam into two detection branches on the non-polarising divider NP In the first branch, the polarising beam splitter PBS1 adjusts the polarisation to a single plane so that the interference occurs and is observed by photodetectors D1, D1′ (mutually in an opposite phase). In the second branch, one of the wave components is retarded by the retarder plate RP The photodetectors D2, D2′ observe the interference that occurs due to rotation of the polarisation plane on the polarising beam splitter PBS2. The subtracter SI produces the signal <span class="html-italic">I<sub>x</sub></span> as a difference between signals D1 and D1′, the subtracter S2 produces <span class="html-italic">I<sub>y</sub></span> from D2, D2′ in the same way.</p>
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<p>Detection of the unwanted phase shift <span class="html-italic">δ<sub>a</sub></span>: the known instantaneous values <span class="html-italic">I<sub>x</sub>, I<sub>y</sub></span> are projected to phasors <span class="html-italic">K⃗<sub>x</sub>, K⃗<sub>y</sub></span> that form the angles <span class="html-italic">φ</span> (equal to the immediate interference phase) and <span class="html-italic">φ</span>′ = <span class="html-italic">φ</span> − <span class="html-italic">π</span>/2 + <span class="html-italic">δ<sub>a</sub></span> with axis <span class="html-italic">x</span> (<b>a</b>); a combination of <span class="html-italic">K⃗<sub>x</sub></span> and <span class="html-italic">K⃗<sub>y</sub></span> forms phasor <span class="html-italic">K⃗<sub>xy</sub></span> (<b>b</b>). Since there exists such <span class="html-italic">φ</span> such that <span class="html-italic">K⃗<sub>xy</sub></span> is parallel to axis <span class="html-italic">x</span>, the real angle <span class="html-italic">α</span> = <span class="html-italic">π</span>/2 + <span class="html-italic">δ<sub>a</sub></span> can be calculated using law of cosines.</p>
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<p>Experimental setup scheme: the interferometer employs the polarising divider, the reference corner cube CC<span class="html-italic"><sub>ref</sub></span> and measurement corner cube CC<span class="html-italic"><sub>meas</sub></span>. The laser input is attenuated by a neutral density absorption niters ND, collimated by an optical expander EXP and its polarisation plane is rotated by the half-wave plate <span class="html-italic">λ</span>/2. The interferometer output is split into two detection branches by the non-polarising divider NP In the first branch, the interference occurs on the polariser POL and the intensity is observed by a photodetector PD. In the second branch, the signal from the interferometer is processed by the reference quadrature detection unit QDU. The acquisition and control hardware drives the modulation of the laser source optical frequency and evaluates the signals from both the photodetector and the quadrature detection unit.</p>
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<p>Evaluation of single experimental cycle: the X- and Y-axis signals from quadrature detection unit, <span class="html-italic">i.e.</span>, the 〈<span class="html-italic">Ĩ<sub>x</sub></span>(<span class="html-italic">τ</span>, <span class="html-italic">t</span>)〉 and 〈<span class="html-italic">Ĩ<sub>y</sub></span>(<span class="html-italic">τ</span>, <span class="html-italic">t</span>)〉 (refer to <a href="#FD30" class="html-disp-formula">Equation (30)</a>), with scale linearity compensation applied (<b>a</b>); X- and Y-axis signals reconstructed by proposed method, <span class="html-italic">i.e.</span>, 〈<span class="html-italic">Ĩ<sub>x</sub></span>(<span class="html-italic">τ</span>, <span class="html-italic">t</span>)〉 and <span class="html-italic">I<sub>d</sub></span> (<a href="#FD35" class="html-disp-formula">Equation (35)</a>), reconstructed from the signal observed by the photodetector with the phase shift removal and scale linearization applied (<b>b</b>) and the phase determination error, PDE (<b>c</b>).</p>
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<p>Experimental results: the red points show the periodic error, <span class="html-italic">i.e.</span>, the dependence of the mean phase determination error on immediate interference phase; the blue error bars indicate the interval of radius of standard deviation around the mean PDE at each corresponding point <span class="html-italic">i</span> (<span class="html-italic">n<sub>i</sub></span> ≈ 1,485)</p>
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<p>Experimental results: the red points show the dependence of the mean phase determination error on the path length difference; the blue error bars indicate the interval of radius of standard deviation around the mean PDE at each corresponding point <span class="html-italic">i</span> (<span class="html-italic">n<sub>i</sub></span> ≈ 2,670)</p>
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966 KiB  
Article
Refractive Index Compensation in Over-Determined Interferometric Systems
by Josef Lazar, Miroslava Holá, Ondřej Číp, Martin Čížek, Jan Hrabina and Zdeněk Buchta
Sensors 2012, 12(10), 14084-14094; https://doi.org/10.3390/s121014084 - 19 Oct 2012
Cited by 26 | Viewed by 6881
Abstract
We present an interferometric technique based on a differential interferometry setup for measurement under atmospheric conditions. The key limiting factor in any interferometric dimensional measurement are fluctuations of the refractive index of air representing a dominating source of uncertainty when evaluated indirectly from [...] Read more.
We present an interferometric technique based on a differential interferometry setup for measurement under atmospheric conditions. The key limiting factor in any interferometric dimensional measurement are fluctuations of the refractive index of air representing a dominating source of uncertainty when evaluated indirectly from the physical parameters of the atmosphere. Our proposal is based on the concept of an over-determined interferometric setup where a reference length is derived from a mechanical frame made from a material with a very low thermal coefficient. The technique allows one to track the variations of the refractive index of air on-line directly in the line of the measuring beam and to compensate for the fluctuations. The optical setup consists of three interferometers sharing the same beam path where two measure differentially the displacement while the third evaluates the changes in the measuring range, acting as a tracking refractometer. The principle is demonstrated in an experimental setup. Full article
(This article belongs to the Section Physical Sensors)
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<p>Principal schematics of the inteferometric system with two countermeasuring interferometers and an interferometer monitoring overall length L<sub>C</sub>. L<sub>A</sub>, L<sub>B</sub>: particular lengths determining the position of the moving carriage.</p>
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<p>Configuration with corner-cube reflectors measuring directly the overall length and two particular displacements. CC: corner-cube reflector, PBS: polarizing beamsplitter, NP: non-polarizing plane, λ/2: half-wave plate, F: fiber-optic light delivery, O<sub>A</sub>, O<sub>B</sub>, O<sub>C</sub>: outputs.</p>
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<p>Recording of the variations of the interferometers A (red line), B (yellow line), and overall length measuring C (blue line) together with the sum of A and B (green line) over time in a closed thermal box.</p>
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<p>Recording of the variations of the interferometers A (red line), B (yellow line), and overall length measuring C (blue line) together with the sum of A and B (green line) over time in under laboratory environment.</p>
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<p>Recording of a slow refractive index drift evaluated from measurement of air temperature, pressure, humidity and CO<sub>2</sub> content (blue line) and polynomial approximation (red line).</p>
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<p>Recording of the output from the interferometer C, responding to variations of the overall optical length.</p>
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636 KiB  
Article
FPGA-Based Multiprocessor System for Injection Molding Control
by Benigno Muñoz-Barron, Luis Morales-Velazquez, Rene J. Romero-Troncoso, Carlos Rodriguez-Donate, Miguel Trejo-Hernandez, Juan P. Benitez-Rangel and Roque A. Osornio-Rios
Sensors 2012, 12(10), 14068-14083; https://doi.org/10.3390/s121014068 - 18 Oct 2012
Cited by 4 | Viewed by 12225
Abstract
The plastic industry is a very important manufacturing sector and injection molding is a widely used forming method in that industry. The contribution of this work is the development of a strategy to retrofit control of an injection molding machine based on an [...] Read more.
The plastic industry is a very important manufacturing sector and injection molding is a widely used forming method in that industry. The contribution of this work is the development of a strategy to retrofit control of an injection molding machine based on an embedded system microprocessors sensor network on a field programmable gate array (FPGA) device. Six types of embedded processors are included in the system: a smart-sensor processor, a micro fuzzy logic controller, a programmable logic controller, a system manager, an IO processor and a communication processor. Temperature, pressure and position are controlled by the proposed system and experimentation results show its feasibility and robustness. As validation of the present work, a particular sample was successfully injected. Full article
(This article belongs to the Section Sensor Networks)
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<p>Typical control in a conventional plastic injection molding machine.</p>
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<p>Embedded smart-sensor and controlling network architecture to control a plastic injection molding machine with six different types of embedded processors.</p>
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<p>The smart-sensor processor is structured by the processing unit (uIS core) and the on board hardware elements (uIS hardware).</p>
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<p>Overall micro fuzzy logic controller architecture, it shows the fuzzy logic elements and the error calculation along with its derivative, which are used in the control algorithm.</p>
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<p>(<b>a</b>) PLC memory map. (<b>b</b>) PLC architecture.</p>
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<p>Experimental setup.</p>
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<p>Temperature control test for different zones, the tests shows each zone with a different set point to evidence the decoupling.</p>
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<p>Pressure control test showing the reference and sensor measurement.</p>
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<p>Screw position control test showing (<b>a</b>) the reference and the sensor measurement; (<b>b</b>) the control signal.</p>
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677 KiB  
Article
Cooperative Suction by Vertical Capillary Array Pump for Controlling Flow Profiles of Microfluidic Sensor Chips
by Tsutomu Horiuchi, Katsuyoshi Hayashi, Michiko Seyama, Suzuyo Inoue and Emi Tamechika
Sensors 2012, 12(10), 14053-14067; https://doi.org/10.3390/s121014053 - 18 Oct 2012
Cited by 11 | Viewed by 8680
Abstract
A passive pump consisting of integrated vertical capillaries has been developed for a microfluidic chip as an useful component with an excellent flow volume and flow rate. A fluidic chip built into a passive pump was used by connecting the bottoms of all [...] Read more.
A passive pump consisting of integrated vertical capillaries has been developed for a microfluidic chip as an useful component with an excellent flow volume and flow rate. A fluidic chip built into a passive pump was used by connecting the bottoms of all the capillaries to a top surface consisting of a thin layer channel in the microfluidic chip where the thin layer channel depth was smaller than the capillary radius. As a result the vertical capillaries drew fluid cooperatively rather than independently, thus exerting the maximum suction efficiency at every instance. This meant that a flow rate was realized that exhibited little variation and without any external power or operation. A microfluidic chip built into this passive pump had the ability to achieve a quasi-steady rather than a rapidly decreasing flow rate, which is a universal flow characteristic in an ordinary capillary. Full article
(This article belongs to the Special Issue Microfluidic Devices)
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<p>Schematic illustrations of a fluidic chip including a passive pump composed of an integrated vertical capillary array and its suction profile. (<b>A</b>) Definitions of position and the notations are summarized in the top and side view illustrations. (<b>B</b>) Fluid can propagate in the area of the main flow channel and the junction zone except at the bottoms of the capillaries when <span class="html-italic">d</span> &lt; <span class="html-italic">r<sub>c</sub></span>. (<b>C</b>) A quasi-steady flow is realized after the B stage. (<b>D</b>) No quasi-steady flow is realized if <span class="html-italic">d</span> &lt; <span class="html-italic">r<sub>c</sub></span> is not satisfied. (<b>E</b>) Comparison with a surface tension pump. (<b>F</b>) Time-series imagery of the suction profile of 4-mm-long capillaries in a 3 × 5 arrangement in a square lattice and 2 mm apart; <span class="html-italic">d</span>, <span class="html-italic">r<sub>c</sub></span>, and <span class="html-italic">r</span><sub>i</sub> were 0.05, 0.2, and 1.5 mm, respectively. (<b>G</b>) A fluidic chip with a highly integrated vertical capillary array provided a large volume sample for actual sensor application. The main flow channel (not shown) was surrounded by capillaries and an inlet to realize a large flow volume on a small footprint. Sample fluid injected into the inlet propagates in the main channel and branches to the right and left junction zones. The capillaries were arranged in a hexagonal lattice and were 0.35 mm apart. There were 816 capillaries, and the capillary length, <span class="html-italic">d</span>, <span class="html-italic">r<sub>c</sub></span>, and <span class="html-italic">r<sub>i</sub></span> were 4, 0.05, 0.14 and 1.5 mm, respectively. Image obtained after several refill operations.</p>
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<p>Structure of fluidic chip (A–D) and setup for flow rate measurement (E). An inlet (3 mm diameter) and a vertical capillary (0.4 mm diameter) array were opened in the thick (4 mm) acrylic resin plate (<b>A</b>). 15 capillaries were arranged in a square lattice and 2 mm apart. A flow channel was opened in a thin (0.05 mm-thick) two-sided adhesive film (<b>B</b>). The bottom of the device was a 1 mm-thick acrylic resin plate (<b>C</b>). The assembled flow device is shown in (<b>D</b>). Liquid fronts in the inlet and capillaries were observed and recorded from the side of the fluidic chip using a video camera. All the capillaries can be observed without any overlaps from an oblique direction. Individual capillaries are identified by the numbers shown in (<b>E</b>). These numbers are common throughout this paper.</p>
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<p>Screen shots of a computational fluidic dynamics simulation. (<b>A</b>) Channel depth d is smaller than the capillary radius r (<span class="html-italic">d</span> &lt; <span class="html-italic">r</span>). Time is 0.17 s. All capillaries waited to draw fluid up until the junction zone was full. (<b>B</b>) The channel depth and capillary radius are the same (<span class="html-italic">d</span> = <span class="html-italic">r</span>). Time is 0.1 s. The fluid front propagated in both capillaries and the junction zone concurrently.</p>
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<p>(<b>A</b>) Height of fluid front in each capillary obtained by experiment in <a href="#f1-sensors-12-14053" class="html-fig">Figure 1(F)</a> as a function of time after fluid injection. The numbers in the figure are capillary identification labels (See <a href="#f2-sensors-12-14053" class="html-fig">Figure 2(E)</a>). (<b>B</b>) Sum of fluid front heights of all capillaries in A as a function of time after fluid injection. The irregular disorganized suction pattern of A was subject to the fact that the increase rate of the sum is constant as shown in B.</p>
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<p>Height of fluid front in each capillary (<b>A</b>,<b>C</b>) and sum of fluid fronts of all capillaries (<b>B</b>,<b>D</b>) obtained by CFD simulations as a function of time. The numbers in the figures are capillary identification labels. There are 4-mm-long capillaries in a 3 × 5 arrangement in a square lattice and 0.6 mm apart. <span class="html-italic">r<sub>c</sub></span> and <span class="html-italic">r<sub>i</sub></span> were 0.1 and 1.5 mm, respectively. <span class="html-italic">d</span> was 0.075 mm in A and B and 0.1 mm in C and D. The two different slopes in D means that there was no clear separation in the flow profile transition.</p>
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<p>Linear velocity profiles at a height of <span class="html-italic">d</span>/2 from the bottom in the region of the detection area obtained by simulations. The flow rate fluctuation was confined to the second stage (before 0.17 s) and after that a quasi-steady flow was observed with the fluidic chip where <span class="html-italic">d</span> &lt; <span class="html-italic">r<sub>c</sub></span>, (d = 0.075 mm, black). Simulation conditions were the same except that the gravitational acceleration constant was zero (red). The flow rate fluctuation remained for almost the entire period and the flow rate decreased slightly (d = 0.1 mm).</p>
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<p>Color depth change of a capillary in the presence of the pullback phenomenon. The color depth corresponds to the liquid front height. A certain capillary exhibited its maximum color depth at 10 s. This value decreased immediately and returned to the value.</p>
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<p>Velocity of fluid front in each capillary calculated using the data in <a href="#f5-sensors-12-14053" class="html-fig">Figure 5(A)</a>. A negative velocity indicates the pullback phenomenon. Capillaries #4, 11, 7 and 14 exhibit their maximum negative velocity at 0.22, 0.33, 0.34, and 0.41 s, respectively.</p>
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397 KiB  
Article
Thermoresponsive Magnetic Nano-Biosensors for Rapid Measurements of Inorganic Arsenic and Cadmium
by Mohammad Shohel Rana Siddiki, Shun Shimoaoki, Shunsaku Ueda and Isamu Maeda
Sensors 2012, 12(10), 14041-14052; https://doi.org/10.3390/s121014041 - 18 Oct 2012
Cited by 14 | Viewed by 8431
Abstract
Green fluorescent protein-tagged sensor proteins, ArsR-GFP and CadC-GFP, have been produced as biosensors for simple and low-cost quantification of As(III) or Cd(II). In this study, the sensor protein-promoter DNA complexes were reconstructed on the surfaces of magnetic particles of different sizes. After the [...] Read more.
Green fluorescent protein-tagged sensor proteins, ArsR-GFP and CadC-GFP, have been produced as biosensors for simple and low-cost quantification of As(III) or Cd(II). In this study, the sensor protein-promoter DNA complexes were reconstructed on the surfaces of magnetic particles of different sizes. After the surface modification all the particles could be attracted by magnets, and released different amounts of GFP-tagged protein, according to the metal concentrations within 5 min, which caused significant increases in fluorescence. A detection limit of 1 µg/L for As(III) and Cd(II) in purified water was obtained only with the nanoparticles exhibiting enough magnetization after heat treatment for 1 min. Therefore, thermoresponsive magnetic nano-biosensors offer great advantages of rapidity and sensitivity for the measurement of the toxic metals in drinking water. Full article
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<p>Schematic presentation of principle involved in the solid phase biosensors constructed on magnetic particles. In the case of thermoresponsive nanoparticles, flocculation was needed for magnetic separation.</p>
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<p>Fluorescence changes after 5-min incubation with sample on the solid phase biosensors constructed on the surface of microplate wells in response to As(III) (left bars) and Cd(II) (right bars). White bars shows intensities obtained without addition of the toxic metals, whereas gray and black bars show those obtained at concentrations of 10 μg/L and 100 μg/L, respectively. Fluorescence was measured by fluorometer.</p>
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<p>Effects of the surface modification of Mag Sepharose (<b>A</b>) and Dynabeads (<b>C</b>) plain particles (grey line) with DNA (black line) or DNA and protein (bold black line) on magnetic driving. Arrow shows the start of measurement. Fluorescence intensities of the Mag Sepharose (<b>B</b>) and Dynabeads (<b>D</b>) particles modified with DNA and protein.</p>
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<p>Effects of the surface modification of Therma-max plain particles (grey line) with DNA (black line) or DNA and protein (bold black line) on thermoresponsive flocculation (<b>A</b>). Fluorescence intensity of the particles modified with DNA and protein (<b>B</b>).</p>
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<p>Fluorescence responses of the magnetic biosensors constructed on different types of particles to As(III) after a 5-min incubation. Fluorescence of ArsR-GFP released from <span class="html-italic">O</span><sub>ars</sub>−30-down was measured by fluororeader (<b>A</b>) and fluorometer (<b>B</b>). White bars show intensities obtained without addition of As(III), whereas gray and black bars show those obtained at concentrations of 10 μg/L and 100 μg/L As(III), respectively. Incubation for 1 min at 42 °C was included in case of Therma-max.</p>
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<p>A dose-response of ArsR-GFP on the thermoresponsive magnetic nano-biosensor to As(III) concentration after a 5-min incubation including a 1-min treatment at 42 °C. The solid line and two broken lines show mean ± SD. Statistical significance (<span class="html-italic">P</span> &lt; 0.001) against data obtained in water without addition of As(III) was shown with asterisk.</p>
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<p>Fluorescence responses of the magnetic biosensors constructed on different types of particles to Cd(II) after a 1-min incubation. Fluorescence of CadC-GFP released from <span class="html-italic">P</span><sub>cad</sub>−<span class="html-italic">O</span><sub>cad</sub> was measured by fluororeader (<b>A</b>) and fluorometer (<b>B</b>). White bars shows intensities obtained without addition of Cd(II), whereas gray and black bars show those obtained at concentrations of 10 μg/L and 100 μg/L Cd(II), respectively. Incubation for 2 min at 42 °C was added in case of Therma-max.</p>
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<p>A dose-response of CadC-GFP on the thermoresponsive magnetic nano-biosensor to Cd(II) concentration after 2-min incubation including 1-min treatment at 42 °C. The solid line and two broken lines show mean ± SD. Statistical significance (<span class="html-italic">P</span> &lt; 0.001) against data obtained in water without addition of Cd(II) was shown with asterisk.</p>
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614 KiB  
Article
A Hybrid Sensing Approach for Pure and Adulterated Honey Classification
by Norazian Subari, Junita Mohamad Saleh, Ali Yeon Md Shakaff and Ammar Zakaria
Sensors 2012, 12(10), 14022-14040; https://doi.org/10.3390/s121014022 - 17 Oct 2012
Cited by 78 | Viewed by 10065
Abstract
This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were [...] Read more.
This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data. Full article
(This article belongs to the Section Chemical Sensors)
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<p>E-nose setup for headspace evaluation of honey, sugar and adulteration sample.</p>
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<p>(<b>a</b>) All Pure Tualang honey and sugar solutions for full spectra range (650 cm<sup>−1</sup> to 4,000 cm<sup>−1</sup>). (<b>b</b>) All AG honey sample for full spectra range (650 cm<sup>−1</sup> to 4,000 cm<sup>−1</sup>). (<b>c</b>) AG samples for spectra range from 750 cm<sup>−1</sup> to 1,500 cm<sup>−1</sup>.</p>
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<p>(<b>a</b>) All Pure Tualang honey and sugar solutions for full spectra range (650 cm<sup>−1</sup> to 4,000 cm<sup>−1</sup>). (<b>b</b>) All AG honey sample for full spectra range (650 cm<sup>−1</sup> to 4,000 cm<sup>−1</sup>). (<b>c</b>) AG samples for spectra range from 750 cm<sup>−1</sup> to 1,500 cm<sup>−1</sup>.</p>
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<p>All Agromas honey brand sample with the corrected peak height.</p>
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<p>Low-level fusion scheme.</p>
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<p>Intermediate-level or feature-level fusion scheme.</p>
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<p>PCA plot of normalized E-nose data.</p>
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<p>LDA plot of normalized E-nose data.</p>
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<p>PCA plot of normalized FTIR data.</p>
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<p>LDA plot of normalized FTIR data.</p>
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1148 KiB  
Article
Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks
by Víctor Martínez-Martínez, Carlos Baladrón, Jaime Gomez-Gil, Gonzalo Ruiz-Ruiz, Luis M. Navas-Gracia, Javier M. Aguiar and Belén Carro
Sensors 2012, 12(10), 14004-14021; https://doi.org/10.3390/s121014004 - 17 Oct 2012
Cited by 38 | Viewed by 9440
Abstract
This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless [...] Read more.
This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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<p>(<b>a</b>) Cross section of the tobacco dryer, where an air fan, a water valve, and two air hatchways are represented in their real locations. (<b>b</b>) Spatial distribution of the measurement motes inside the drying chamber: next to the supervision window (green), in the second container in the tobacco mass (red), and next to the dryer roof (purple).</p>
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<p>Target temperature evolution in the analysed drying processes.</p>
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<p>Diagram of the validation trials.</p>
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<p><span class="html-italic">Data Estimation</span> results (% error ANN/% error interpolation) for Temperature (T) in Sensor 3 (S3), combining Temperature (T) and Humidity (H) in Sensors 1 and 2 (S1 and S2) as inputs.</p>
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<p>Data Estimation results (% error ANN/% error interpolation) for Humidity (H) in Sensor 3 (S3), combining Temperature (T) and Humidity (H) in Sensors 1 and 2 (S1 and S2) as input.</p>
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<p>Example of ANN estimation of Temperature (T) at Sensor 3 (S3) for one drying process using Sensors 1 and 2 (S1 and S2) as input. (<b>a</b>) Input is only Temperature (T). (<b>b</b>) Input combines Temperature (T) and Humidity (H).</p>
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<p>Example of ANN estimation of Humidity (H) at Sensor 3 (S3) for one drying process using Sensors 1 and 2 (S1 and S2) as input. (<b>a</b>) Input is only Humidity (H). (<b>b</b>) Input combines Temperature (T) and Humidity (H).</p>
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<p><span class="html-italic">Data prediction</span> results (% error ANN/% error interpolation) for temperature in Sensor 1, using temperature in Sensor 1 as an input.</p>
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<p><span class="html-italic">Data prediction</span> results (% error ANN/% error interpolation) for temperature in Sensor 3, using temperature in Sensor 1 as an input.</p>
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1287 KiB  
Article
Physically-Based Reduced Order Modelling of a Uni-Axial Polysilicon MEMS Accelerometer
by Aldo Ghisi, Stefano Mariani, Alberto Corigliano and Sarah Zerbini
Sensors 2012, 12(10), 13985-14003; https://doi.org/10.3390/s121013985 - 17 Oct 2012
Cited by 10 | Viewed by 7371
Abstract
In this paper, the mechanical response of a commercial off-the-shelf, uni-axial polysilicon MEMS accelerometer subject to drops is numerically investigated. To speed up the calculations, a simplified physically-based (beams and plate), two degrees of freedom model of the movable parts of the sensor [...] Read more.
In this paper, the mechanical response of a commercial off-the-shelf, uni-axial polysilicon MEMS accelerometer subject to drops is numerically investigated. To speed up the calculations, a simplified physically-based (beams and plate), two degrees of freedom model of the movable parts of the sensor is adopted. The capability and the accuracy of the model are assessed against three-dimensional finite element simulations, and against outcomes of experiments on instrumented samples. It is shown that the reduced order model provides accurate outcomes as for the system dynamics. To also get rather accurate results in terms of stress fields within regions that are prone to fail upon high-g shocks, a correction factor is proposed by accounting for the local stress amplification induced by re-entrant corners. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Italy 2012)
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<p>Geometry of the uni-axial MEMS accelerometer, and notation.</p>
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<p>First five vibration modes of the uni-axial accelerometer, and relevant resonance frequencies.</p>
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<p>Shock tests. Top row: acceleration histories felt by the sensor during the (<b>a</b>) low-<span class="html-italic">g</span> and (<b>b</b>) high-<span class="html-italic">g</span> experiments. Bottom row (<b>c</b>,<b>d</b>): energy spectral density (ESD) obtained through a Fourier transform of the relevant input accelerations.</p>
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<p>Low-<span class="html-italic">g</span> test: time evolution of the relative displacement Δ<span class="html-italic">u<sub>A</sub></span> at the plate corner A (see <a href="#f1-sensors-12-13985" class="html-fig">Figure 1</a>). Comparison between FE and ROM results.</p>
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<p>Low-<span class="html-italic">g</span> test: time evolution of the maximum principal stress in the plate-spring connection region. Comparison between FE and ROM results.</p>
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<p>Low-<span class="html-italic">g</span> test: time evolution of MEMS output. Comparison between experimental data and numerical results.</p>
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<p>High-<span class="html-italic">g</span> test: time evolution of the relative displacement Δ<span class="html-italic">u<sub>A</sub></span> at the plate corner A (see <a href="#f1-sensors-12-13985" class="html-fig">Figure 1</a>). Comparison between FE and ROM results.</p>
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<p>High-<span class="html-italic">g</span> test: time evolution of the maximum principal stress in the plate-spring connection region. Comparison between FE and ROM results.</p>
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<p>High-<span class="html-italic">g</span> test: time evolution of MEMS output. Comparison between experimental data and numerical results.</p>
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1201 KiB  
Article
Floating Chip Mounting System Driven by Repulsive Force of Permanent Magnets for Multiple On-Site SPR Immunoassay Measurements
by Tsutomu Horiuchi, Tatsuya Tobita, Toru Miura, Yuzuru Iwasaki, Michiko Seyama, Suzuyo Inoue, Jun-ichi Takahashi, Tsuneyuki Haga and Emi Tamechika
Sensors 2012, 12(10), 13964-13984; https://doi.org/10.3390/s121013964 - 17 Oct 2012
Cited by 7 | Viewed by 6662
Abstract
We have developed a measurement chip installation/removal mechanism for a surface plasmon resonance (SPR) immunoassay analysis instrument designed for frequent testing, which requires a rapid and easy technique for changing chips. The key components of the mechanism are refractive index matching gel coated [...] Read more.
We have developed a measurement chip installation/removal mechanism for a surface plasmon resonance (SPR) immunoassay analysis instrument designed for frequent testing, which requires a rapid and easy technique for changing chips. The key components of the mechanism are refractive index matching gel coated on the rear of the SPR chip and a float that presses the chip down. The refractive index matching gel made it possible to optically couple the chip and the prism of the SPR instrument easily via elastic deformation with no air bubbles. The float has an autonomous attitude control function that keeps the chip parallel in relation to the SPR instrument by employing the repulsive force of permanent magnets between the float and a float guide located in the SPR instrument. This function is realized by balancing the upward elastic force of the gel and the downward force of the float, which experiences a leveling force from the float guide. This system makes it possible to start an SPR measurement immediately after chip installation and to remove the chip immediately after the measurement with a simple and easy method that does not require any fine adjustment. Our sensor chip, which we installed using this mounting system, successfully performed an immunoassay measurement on a model antigen (spiked human-IgG) in a model real sample (non-homogenized milk) that included many kinds of interfering foreign substances without any sample pre-treatment. The ease of the chip installation/removal operation and simple measurement procedure are suitable for frequent on-site agricultural, environmental and medical testing. Full article
(This article belongs to the Section Biosensors)
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<p>Portable SPR instrument (<b>a</b>) and floating chip mounting system (<b>b</b>). Images of main measurement chip components, top view (<b>c</b>, left) and rear view (<b>c</b>, right). Cross sectional view of chip (<b>d</b>) and its components (<b>e</b>–<b>k</b>). Hatched areas in (d) correspond to the legends for components (e-k).</p>
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<p>Schematic diagram of the optical configuration of an SPR instrument (<b>a</b>). Optical configuration at the interface of the prism and the sensor chip (<b>b</b>). The CCD cannot detect wedge shaped reflected light when the sensor chip is mounted incorrectly (<b>c</b>). The CCD can detect all wedge shaped reflected light when the sensor chip is mounted correctly (<b>d</b>). SPR curves are obtained if the sample has a resonance angle within wedge shaped light and condition (d) is satisfied (<b>e</b>). The total internal reflection point is displaced by x and the illuminated position on the CCD is displaced by y as a result of applying refractive index matching gel of thickness d (<b>f</b>).</p>
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<p>Close-up view of floating chip mounting system in (<b>a</b>) open state and (<b>b</b>) closed state. A black sensor chip in a black enclosure is located in the center of (a). The attitude of the sensor chip to the SPR instrument is adjusted by the leveling force of the float shown in (b).</p>
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<p>Operation of floating chip mounting system, chip is attached to prism (<b>a</b>), float is attached and pressed down (<b>b</b>), test solution is introduced and SPR measurement is carried out (<b>c</b>), float guide is pressed (<b>d</b>) and float returns to initial position (<b>e</b>). Top view of floating chip mounting system and chip (<b>f</b>). Autonomous attitude control mechanism (<b>g</b>).</p>
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<p>Thickness of gel along long axes and short axes as a function of distance from center. Several measurement results are superimposed.</p>
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<p>Frequency distribution of center thickness of gel.</p>
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<p>Total intensity of CCD in SPR instrument as a function of mounting time, repulsive magnetic force driven (open circles) and attractive magnetic force driven (filled circles).</p>
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<p>CCD output of SPR instrument at typical points indicated in <a href="#f7-sensors-12-13964" class="html-fig">Figure 7</a> (<b>a</b>–<b>d</b>). The horizontal axis is the angle of the reflected light. The vertical axis is the sensing position along a focal line (See <a href="#f2-sensors-12-13964" class="html-fig">Figure 2(b)</a>).</p>
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<p>Stability experiments of repulsive force driven mounting system. Total intensity of CCD in SPR instrument as a function of mounting time, with no water introduced into chip (filled circles) and with water introduced (open circles).</p>
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2091 KiB  
Article
Implementation of Obstacle-Avoidance Control for an Autonomous Omni-Directional Mobile Robot Based on Extension Theory
by Neng-Sheng Pai, Hung-Hui Hsieh and Yi-Chung Lai
Sensors 2012, 12(10), 13947-13963; https://doi.org/10.3390/s121013947 - 16 Oct 2012
Cited by 8 | Viewed by 8909
Abstract
The paper demonstrates a following robot with omni-directional wheels, which is able to take action to avoid obstacles. The robot design is based on both fuzzy and extension theory. Fuzzy theory was applied to tune the PMW signal of the motor revolution, and [...] Read more.
The paper demonstrates a following robot with omni-directional wheels, which is able to take action to avoid obstacles. The robot design is based on both fuzzy and extension theory. Fuzzy theory was applied to tune the PMW signal of the motor revolution, and correct path deviation issues encountered when the robot is moving. Extension theory was used to build a robot obstacle-avoidance model. Various mobile models were developed to handle different types of obstacles. The ultrasonic distance sensors mounted on the robot were used to estimate the distance to obstacles. If an obstacle is encountered, the correlation function is evaluated and the robot avoids the obstacle autonomously using the most appropriate mode. The effectiveness of the proposed approach was verified through several tracking experiments, which demonstrates the feasibility of a fuzzy path tracker as well as the extensible collision avoidance system. Full article
(This article belongs to the Section Physical Sensors)
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<p>Omni-directional mobile robot.</p>
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<p>The robot system hardware link.</p>
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<p>The IR source carried by the user.</p>
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<p>Proposed system flowchart.</p>
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<p>Configuration of the omni-directional mobile robot.</p>
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<p>The experimental setup of the omni-directional mobile robot.</p>
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<p>The control system block diagram.</p>
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<p>Input and output membership functions.</p>
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<p>The fuzzy input and output relationship graph.</p>
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1376 KiB  
Review
A Review on Architectures and Communications Technologies for Wearable Health-Monitoring Systems
by Víctor Custodio, Francisco J. Herrera, Gregorio López and José Ignacio Moreno
Sensors 2012, 12(10), 13907-13946; https://doi.org/10.3390/s121013907 - 16 Oct 2012
Cited by 122 | Viewed by 14772
Abstract
Nowadays society is demanding more and more smart healthcare services that allow monitoring patient status in a non-invasive way, anywhere and anytime. Thus, healthcare applications are currently facing important challenges guided by the u-health (ubiquitous health) and p-health (pervasive health) paradigms. New emerging [...] Read more.
Nowadays society is demanding more and more smart healthcare services that allow monitoring patient status in a non-invasive way, anywhere and anytime. Thus, healthcare applications are currently facing important challenges guided by the u-health (ubiquitous health) and p-health (pervasive health) paradigms. New emerging technologies can be combined with other widely deployed ones to develop such next-generation healthcare systems. The main objective of this paper is to review and provide more details on the work presented in “LOBIN: E-Textile and Wireless-Sensor-Network-Based Platform for Healthcare Monitoring in Future Hospital Environments”, published in the IEEE Transactions on Information Technology in Biomedicine, as well as to extend and update the comparison with other similar systems. As a result, the paper discusses the main advantages and disadvantages of using different architectures and communications technologies to develop wearable systems for pervasive healthcare applications. Full article
(This article belongs to the Special Issue Body Sensor Networks for Healthcare and Pervasive Applications)
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<p>General Communications Architecture for WHMS.</p>
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<p>Communications Technologies and Communications Segments.</p>
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<p>System Architecture.</p>
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<p>Healthcare Monitoring Subsystem Block Diagram.</p>
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<p>Physiological Sensors.</p>
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<p>(<b>a</b>) Wearable Data Acquisition Device (WDAD) (<b>b</b>) Healthcare Monitoring Wireless Transmission Board (WTB).</p>
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<p>Overall Location Subsystem architecture.</p>
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<p>Overall WCI Subsystem architecture.</p>
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<p>Management Subsystem client-server architecture.</p>
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97 KiB  
Article
Organic Electroluminescent Sensor for Pressure Measurement
by Yu Matsuda, Kaori Ueno, Hiroki Yamaguchi, Yasuhiro Egami and Tomohide Niimi
Sensors 2012, 12(10), 13899-13906; https://doi.org/10.3390/s121013899 - 16 Oct 2012
Cited by 14 | Viewed by 7525
Abstract
We have proposed a novel concept of a pressure sensor called electroluminescent pressure sensor (ELPS) based on oxygen quenching of electroluminescence. The sensor was fabricated as an organic light-emitting device (OLED) with phosphorescent dyes whose phosphorescence can be quenched by oxygenmolecules, and with [...] Read more.
We have proposed a novel concept of a pressure sensor called electroluminescent pressure sensor (ELPS) based on oxygen quenching of electroluminescence. The sensor was fabricated as an organic light-emitting device (OLED) with phosphorescent dyes whose phosphorescence can be quenched by oxygenmolecules, and with a polymer electrode which permeates oxygen molecules. The sensor was a single-layer OLED with Platinum (II) octaethylporphine (PtOEP) doped into poly(vinylcarbazole) (PVK) as an oxygen sensitive emissive layer and poly(3,4-ethylenedioxythiophene) mixed with poly(styrenesulfonate) (PEDOT:PSS) as an oxygen permeating polymer anode. The pressure sensitivity of the fabricated ELPS sample was equivalent to that of the sensor excited by an illumination light source. Moreover, the pressure sensitivity of the sensor is equivalent to that of conventional pressure-sensitive paint (PSP), which is an optical pressure sensor based on photoluminescence. Full article
(This article belongs to the Section Chemical Sensors)
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<p>Configuration of ELPS.</p>
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<p>Schematic image of experimental setup. The illumination light source inside the dashed box is only used for the photoluminescence experiment.</p>
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<p>Luminescent intensity against current density for the ELPS sample.</p>
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<p>Stern–Volmer plots of electroluminescence (EL) and photoluminescence (PL), where the reference pressure <span class="html-italic">p</span><sub>ref</sub> = 101 kPa. The data shown in open circle were obtained first with decreasing pressure, and the data shown in closed circle were obtained with increasing pressure.</p>
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1530 KiB  
Article
Frequency Identification of Vibration Signals Using Video Camera Image Data
by Yih-Nen Jeng and Chia-Hung Wu
Sensors 2012, 12(10), 13871-13898; https://doi.org/10.3390/s121013871 - 16 Oct 2012
Cited by 8 | Viewed by 8705
Abstract
This study showed that an image data acquisition system connecting a high-speed camera or webcam to a notebook or personal computer (PC) can precisely capture most dominant modes of vibration signal, but may involve the non-physical modes induced by the insufficient frame rates. [...] Read more.
This study showed that an image data acquisition system connecting a high-speed camera or webcam to a notebook or personal computer (PC) can precisely capture most dominant modes of vibration signal, but may involve the non-physical modes induced by the insufficient frame rates. Using a simple model, frequencies of these modes are properly predicted and excluded. Two experimental designs, which involve using an LED light source and a vibration exciter, are proposed to demonstrate the performance. First, the original gray-level resolution of a video camera from, for instance, 0 to 256 levels, was enhanced by summing gray-level data of all pixels in a small region around the point of interest. The image signal was further enhanced by attaching a white paper sheet marked with a black line on the surface of the vibration system in operation to increase the gray-level resolution. Experimental results showed that the Prosilica CV640C CMOS high-speed camera has the critical frequency of inducing the false mode at 60 Hz, whereas that of the webcam is 7.8 Hz. Several factors were proven to have the effect of partially suppressing the non-physical modes, but they cannot eliminate them completely. Two examples, the prominent vibration modes of which are less than the associated critical frequencies, are examined to demonstrate the performances of the proposed systems. In general, the experimental data show that the non-contact type image data acquisition systems are potential tools for collecting the low-frequency vibration signal of a system. Full article
(This article belongs to the Section Physical Sensors)
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<p>The test case of LED system: (<b>a</b>) the LED setup. (<b>b</b>) the luminescent state of LED system.</p>
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<p>The experimental setup: (<b>a</b>) vibration exciter, accelerometer, and microphone. (<b>b</b>) overall setup.</p>
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<p>The LED image data of the high speed camera with 10.2 Hz input: solid line is the raw data and dashed line is the smooth part.</p>
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<p>A strip type image of the high speed camera covers the accelerometer (involving two squares) and white paper sheet (involving another two squares).</p>
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<p>The gray level summation data of the four squares of <a href="#f2-sensors-12-13871" class="html-fig">Figure 2</a>: (<b>a</b>) data of the leftmost square in a region of the accelerometer. (<b>b</b>) data of the second left square in another region of the acceleration. (<b>c</b>) data of the square in the left part of the white paper. (<b>d</b>) data of the rightmost square in the central part of the white paper and covering a black strip.</p>
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<p>Comparison between experimental and modeled data: (<b>a</b>) data of the experiment; (<b>b</b>) spectrum of experiment; (<b>c</b>) data of simulation; and (<b>d</b>) spectrum of simulation.</p>
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<p>Spectra distribution of LED signal collected by the high speed camera: (<b>a</b>) 0.5 Hz input signal. (<b>b</b>) 1 Hz input signal.</p>
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<p>Spectra distribution of LED signal collected by the webcam: (<b>a</b>) 0.5 Hz input signal. (<b>b</b>) 1.0 Hz input signal.</p>
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<p>Comparison of the frequency response of the vibration exciter's signal, input frequency is about 30 Hz: (<b>a</b>) spectra of accelerometer. (<b>b</b>) spectra of microphone. (<b>c</b>) spectra of high speed camera.</p>
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1975 KiB  
Article
Thermography and Sonic Anemometry to Analyze Air Heaters in Mediterranean Greenhouses
by Alejandro López, Diego L. Valera, Francisco Molina-Aiz and Araceli Peña
Sensors 2012, 12(10), 13852-13870; https://doi.org/10.3390/s121013852 - 16 Oct 2012
Cited by 4 | Viewed by 7375
Abstract
The present work has developed a methodology based on thermography and sonic anemometry for studying the microclimate in Mediterranean greenhouses equipped with air heaters and polyethylene distribution ducts to distribute the warm air. Sonic anemometry allows us to identify the airflow pattern generated [...] Read more.
The present work has developed a methodology based on thermography and sonic anemometry for studying the microclimate in Mediterranean greenhouses equipped with air heaters and polyethylene distribution ducts to distribute the warm air. Sonic anemometry allows us to identify the airflow pattern generated by the heaters and to analyze the temperature distribution inside the greenhouse, while thermography provides accurate crop temperature data. Air distribution by means of perforated polyethylene ducts at ground level, widely used in Mediterranean-type greenhouses, can generate heterogeneous temperature distributions inside the greenhouse when the system is not correctly designed. The system analyzed in this work used a polyethylene duct with a row of hot air outlet holes (all of equal diameter) that expel warm air toward the ground to avoid plant damage. We have observed that this design (the most widely used in Almería’s greenhouses) produces stagnation of hot air in the highest part of the structure, reducing the heating of the crop zone. Using 88 kW heating power (146.7 W∙m−2) the temperature inside the greenhouse is maintained 7.2 to 11.2 °C above the outside temperature. The crop temperature (17.6 to 19.9 °C) was maintained above the minimum recommended value of 10 °C. Full article
(This article belongs to the Section Physical Sensors)
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<p>Location of the experimental greenhouse at the farm.</p>
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<p>(<b>a</b>) Heater installed at the northeast corner of greenhouse 1. (<b>b</b>) Heater parts diagram.</p>
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<p>Measurement points with ultrasonic anemometers inside greenhouse 1.</p>
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<p>Details of the experimental setup using three 2D ultrasonic anemometers (<b>a</b>) and (<b>b</b>) one 3D and two 2D ultrasonic anemometers.</p>
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<p>Two-dimensional resultants of air velocity in the <span class="html-italic">XY</span> plane (<span class="html-italic">l</span>) and polar plots of airflow direction in measurement test 1.</p>
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<p>Two-dimensional resultants of air velocity in the <span class="html-italic">XY</span> (<span class="html-italic">l</span>) and polar plots of airflow direction in measurement test 2.</p>
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<p>(<b>a</b>) Two-dimensional resultants of air velocity in the <span class="html-italic">XZ</span> plane (<span class="html-italic">v</span>) and polar plots of airflow direction in measurement test 2 (measurement line 2). (<b>b</b>) Approximate representation of the inside airflow.</p>
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<p>(<b>a</b>) Air velocity; (<b>b</b>) temperature distributions along the duct for the three tests carried out the 10/02/2011 (Img), the 16/02/2011 (- - -) and the 18/02/2011 (–––, black).</p>
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<p>Difference in corrected air temperature (<span class="html-italic">ΔT<sub>io</sub><sup>c</sup></span>) in greenhouse 1 (heated) at 0.8 m height. Tests 1 (<b>a</b>), 2 (<b>b</b>) and 3 (<b>c</b>).</p>
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795 KiB  
Article
Synthesis of ZnO Nanostructures for Low Temperature CO and UV Sensing
by Muhammad Amin, Umair Manzoor, Mohammad Islam, Arshad Saleem Bhatti and Nazar Abbas Shah
Sensors 2012, 12(10), 13842-13851; https://doi.org/10.3390/s121013842 - 16 Oct 2012
Cited by 37 | Viewed by 7708
Abstract
In this paper, synthesis and results of the low temperature sensing of carbon monoxide (CO) gas and room temperature UV sensors using one dimensional (1-D) ZnO nanostructures are presented. Comb-like structures, belts and rods, and needle-shaped nanobelts were synthesized by varying synthesis temperature [...] Read more.
In this paper, synthesis and results of the low temperature sensing of carbon monoxide (CO) gas and room temperature UV sensors using one dimensional (1-D) ZnO nanostructures are presented. Comb-like structures, belts and rods, and needle-shaped nanobelts were synthesized by varying synthesis temperature using a vapor transport method. Needle-like ZnO nanobelts are unique as, according to our knowledge, there is no evidence of such morphology in previous literature. The structural, morphological and optical characterization was carried out using X-ray diffraction, scanning electron microscopy and diffused reflectance spectroscopy techniques. It was observed that the sensing response of comb-like structures for UV light was greater as compared to the other grown structures. Comb-like structure based gas sensors successfully detect CO at 75 °C while other structures did not show any response. Full article
(This article belongs to the Section Chemical Sensors)
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<p>SEM images of different morphologies of ZnO Nanostructures at different synthesis temperatures. (<b>a</b>) Comb-like structures at 950 °C. The inset shows the schematic diagram of these combs; (<b>b</b>) Nanobelts with needles at 925 °C; (<b>c</b>) Mixture of rod and belts at 900 °C.</p>
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<p>XRD results of as-synthesized ZnO nanostructures. Continuous shift in the peak towards higher angle and narrower FWHM suggests better crystalinity.</p>
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<p>Reflectance Spectroscopy results after Kubelka-Munk treatment of as-synthesized ZnO nanostructures clearly shows direct band emission and a defect related smaller peak for all the samples. Comb-like structures show steeper slope as compared to other samples.</p>
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<p>Sensing results for 2% CO gas at 75 °C suggest that only comb-like structures show cyclic changed in resistance with on and off cycles of CO gas.</p>
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<p>UV sensing at room temperature with (<b>a</b>) Comb-like structures; (<b>b</b>) Nanobelts with needles and (<b>c</b>) Mixture of rod and belts. 18 W UV lamp was used for this purpose.</p>
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778 KiB  
Article
Non-Contact Translation-Rotation Sensor Using Combined Effects of Magnetostriction and Piezoelectricity
by Bintang Yang, Qingwei Liu, Ting Zhang, Yudong Cao, Zhiqiang Feng and Guang Meng
Sensors 2012, 12(10), 13829-13841; https://doi.org/10.3390/s121013829 - 15 Oct 2012
Cited by 7 | Viewed by 8342
Abstract
Precise displacement sensors are an important topic in precision engineering. At present, this type of sensors typically have a single feature of either translation or rotation measurement. They are also inconvenient to integrate with the host devices. In this report we propose a [...] Read more.
Precise displacement sensors are an important topic in precision engineering. At present, this type of sensors typically have a single feature of either translation or rotation measurement. They are also inconvenient to integrate with the host devices. In this report we propose a new kind of sensor that enables both translation and rotation measurement by using the combined effect of magnetostriction and piezoelectricity. As a proof of concept, we experimentally realized a prototype of non-contact translation-rotation precise sensor. In the current research stage, through both theoretical and experimental study, the non-contact displacement sensor is shown to be feasible for measuring both translation and rotation either in coarse or fine measurement. Moreover, owing to its compact, rigid structure and fewer components, it can be easily embedded in host equipment. Full article
(This article belongs to the Special Issue Piezoelectric Sensors and Actuators)
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<p>The schematic diagram and the prototype photograph of the non-contact displacement sensor.</p>
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<p>The schematic diagram of the sensing mechanism.</p>
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<p>The schematic diagram and the photograph of the experimental setup for displacment measurement.</p>
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<p>The structure diagram of angle sensor.</p>
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<p>Schematic diagram and photograph of the experimental setup for angle measurement.</p>
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<p>Comparison between the experimental and theoretical results on wide-range measurement.</p>
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<p>The slope of the curve in <a href="#f6-sensors-12-13829" class="html-fig">Figure 6</a>.</p>
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<p>The sensing effect under applied current <span class="html-italic">I</span>(<span class="html-italic">t</span>) = sin(2π<span class="html-italic">ft</span>) A (<span class="html-italic">f</span> = 10 Hz).</p>
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<p>The sensing effect under applied current <span class="html-italic">I</span>(<span class="html-italic">t</span>) = 0.5sin(2π<span class="html-italic">ft</span>) A (<span class="html-italic">f</span> = 10 Hz).</p>
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916 KiB  
Article
Two Proximal Skin Electrodes — A Respiration Rate Body Sensor
by Roman Trobec, Aleksandra Rashkovska and Viktor Avbelj
Sensors 2012, 12(10), 13813-13828; https://doi.org/10.3390/s121013813 - 15 Oct 2012
Cited by 43 | Viewed by 10227
Abstract
We propose a new body sensor for extracting the respiration rate based on the amplitude changes in the body surface potential differences between two proximal body electrodes. The sensor could be designed as a plaster-like reusable unit that can be easily fixed onto [...] Read more.
We propose a new body sensor for extracting the respiration rate based on the amplitude changes in the body surface potential differences between two proximal body electrodes. The sensor could be designed as a plaster-like reusable unit that can be easily fixed onto the surface of the body. It could be equipped either with a sufficiently large memory for storing the measured data or with a low-power radio system that can transmit the measured data to a gateway for further processing. We explore the influence of the sensor’s position on the quality of the extracted results using multi-channel ECG measurements and considering all the pairs of two neighboring electrodes as potential respiration-rate sensors. The analysis of the clinical measurements, which also include reference thermistor-based respiration signals, shows that the proposed approach is a viable option for monitoring the respiration frequency and for a rough classification of breathing types. The obtained results were evaluated on a wireless prototype of a respiration body sensor. We indicate the best positions for the respiration body sensor and prove that a single sensor for body surface potential difference on proximal skin electrodes can be used for combined measurements of respiratory and cardiac activities. Full article
(This article belongs to the Special Issue Body Sensor Networks for Healthcare and Pervasive Applications)
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<p>Schematic locations and numbering of 35-channel MECG electrodes on the chest. Potential differences between neighboring electrodes, e.g., (13,18), (21,25), (18,15), (14,19), (22,23), (31,35), <span class="html-italic">etc.</span>, are denoted as differential leads (DLs).</p>
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<p>Block diagram of the implemented EDR algorithm.</p>
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<p>(<b>a</b>) Prototype of wireless respiration sensor with two, self-adhesive, disposable electrodes, a lithium coin battery, and a ceramic chip antenna. (<b>b</b>) Raw signal from the respiration sensor with ECG R-peaks modulated by breathing.</p>
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<p>First measurement from the first subject case. (<b>a</b>) thermistor signal (black) with 78 RCs and (21,18) signal (red) with 385 R-peaks (blue dots) and 79 identified RCs (blue circles). (<b>b</b>) Zoomed section of the initial 90 seconds interval in (21,18). (<b>c</b>) (21,25) with 385 R-peaks and 79 identified RCs. (<b>d</b>) Zoomed section of the initial 90 seconds interval in (21,25).</p>
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<p>First measurement from the first subject case. (<b>a</b>) thermistor signal (black) with 78 RCs and (21,18) signal (red) with 385 R-peaks (blue dots) and 79 identified RCs (blue circles). (<b>b</b>) Zoomed section of the initial 90 seconds interval in (21,18). (<b>c</b>) (21,25) with 385 R-peaks and 79 identified RCs. (<b>d</b>) Zoomed section of the initial 90 seconds interval in (21,25).</p>
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<p>Bar plots of the number of detected RCs from the first measurement of the first subject case from (<b>a</b>) respiration signal in positive amplitudes for all DL and (<b>b</b>) respiration signal in negative amplitudes for all DLs.</p>
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<p>Bar plots of the lead quality (<b>a</b>) Q0, (<b>b</b>) Q1, (<b>c</b>) Q2 and (<b>d</b>) Q3 for all leads from a 12-lead ECG.</p>
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<p>Bar plots of the lead quality (<b>a</b>) Q0, (<b>b</b>) Q1, (<b>c</b>) Q2 and (<b>d</b>) Q3 for all DLs.</p>
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<p>Schematic locations of the best DLs that correctly identify all RCs in 12 out of 15 measurements (blue), DLs with the relative error of 2.5% in RCs identification in 14 measurements (green), and DLs with the relative error of 5% in RCs identification in all 15 measurements (red).</p>
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876 KiB  
Article
Source Localization with Acoustic Sensor Arrays Using Generative Model Based Fitting with Sparse Constraints
by Jose Velasco, Daniel Pizarro and Javier Macias-Guarasa
Sensors 2012, 12(10), 13781-13812; https://doi.org/10.3390/s121013781 - 15 Oct 2012
Cited by 33 | Viewed by 7815
Abstract
This paper presents a novel approach for indoor acoustic source localization using sensor arrays. The proposed solution starts by defining a generative model, designed to explain the acoustic power maps obtained by Steered Response Power (SRP) strategies. An optimization approach is then proposed [...] Read more.
This paper presents a novel approach for indoor acoustic source localization using sensor arrays. The proposed solution starts by defining a generative model, designed to explain the acoustic power maps obtained by Steered Response Power (SRP) strategies. An optimization approach is then proposed to fit the model to real input SRP data and estimate the position of the acoustic source. Adequately fitting the model to real SRP data, where noise and other unmodelled effects distort the ideal signal, is the core contribution of the paper. Two basic strategies in the optimization are proposed. First, sparse constraints in the parameters of the model are included, enforcing the number of simultaneous active sources to be limited. Second, subspace analysis is used to filter out portions of the input signal that cannot be explained by the model. Experimental results on a realistic speech database show statistically significant localization error reductions of up to 30% when compared with the SRP-PHAT strategies. Full article
(This article belongs to the Section Physical Sensors)
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<p>Geometric places with equal <span class="html-italic">srp</span>(<b>q</b><span class="html-italic"><sub>h</sub></span>) generated for a microphone pair and a single acoustic source (a) 3D hyperboloid; (b) 3D hyperboloid cut by a plane; (c) Resulting 2D hyperbola (cutting hyperboloid by a plane).</p>
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<p>Geometric places generated for two microphone pairs and a single acoustic source (a) 3D hyperboloids; (b) 3D hyperboloids cut by a plane; (c) Resulting 2D hyperbolas (cutting hyperboloids by a plane).</p>
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<p>Real <span class="html-italic">SRP-PHAT</span> power map generated for a single speaker located in the red circle with two microphone pairs (blue and green dots), (a) Plain power map; (b) Superimposing ideal hyperbolas that should be generated by the single speaker.</p>
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<p>Model content defined for a single active speaker located in the position of the red circle.</p>
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<p>Explicit matrix layout for the model proposal given by <a href="#FD14" class="html-disp-formula">Equation (13)</a>.</p>
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<p>Comparison between real <span class="html-italic">SRP-PHAT</span> power map and its denoised version, (a) Denoised acoustic power map described by <b>ŷ</b>′; (b) Real <span class="html-italic">SRP-PHAT</span> acoustic power map described by <b>y</b>.</p>
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<p>Idiap Smart Meeting Room for AV16.3 recordings (a) Room layout showing the microphone positions in two circular arrays (MA1 and MA2), three cameras (C1, C2 and C3), and the L-shaped area for speaker locations in the recordings, (b) Sample of recorded video frame.</p>
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<p>Microphone pairs setups used in the experiments (microphones with the same color belong to the same pair).</p>
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<p>Optimization results for <span class="html-italic">MOTP</span> and relative improvements as a function of <span class="html-italic">λ</span><sub>norm</sub> and <span class="html-italic">e<sub>ψ</sub></span>, for microphone setups A and B on sequence <span class="html-italic">seq01</span>. The black trace is the baseline <span class="html-italic">SRP-PHAT</span> system.</p>
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850 KiB  
Review
Italian Contributions to the Development of Continuous Glucose Monitoring Sensors for Diabetes Management
by Giovanni Sparacino, Mattia Zanon, Andrea Facchinetti, Chiara Zecchin, Alberto Maran and Claudio Cobelli
Sensors 2012, 12(10), 13753-13780; https://doi.org/10.3390/s121013753 - 12 Oct 2012
Cited by 30 | Viewed by 12249
Abstract
Monitoring glucose concentration in the blood is essential in the therapy of diabetes, a pathology which affects about 350 million people around the World (three million in Italy), causes more than four million deaths per year and consumes a significant portion of the [...] Read more.
Monitoring glucose concentration in the blood is essential in the therapy of diabetes, a pathology which affects about 350 million people around the World (three million in Italy), causes more than four million deaths per year and consumes a significant portion of the budget of national health systems (10% in Italy). In the last 15 years, several sensors with different degree of invasiveness have been proposed to monitor glycemia in a quasi-continuous way (up to 1 sample/min rate) for relatively long intervals (up to 7 consecutive days). These continuous glucose monitoring (CGM) sensors have opened new scenarios to assess, off-line, the effectiveness of individual patient therapeutic plans from the retrospective analysis of glucose time-series, but have also stimulated the development of innovative on-line applications, such as hypo/hyper-glycemia alert systems and artificial pancreas closed-loop control algorithms. In this review, we illustrate some significant Italian contributions, both from industry and academia, to the growth of the CGM sensors research area. In particular, technological, algorithmic and clinical developments performed in Italy will be discussed and put in relation with the advances obtained in the field in the wider international research community. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Italy 2012)
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<p>An example of the “smart” CGM sensor architecture: cascade of a commercial CGM sensor (black box) and three software modules working in real time for denoising, enhancement and prediction. The denoising module receives in input raw CGM data and returns a smoothed CGM time-series. The enhancement module processes the smoothed CGM data and produces a more accurate CGM profile. The prediction module forecasts the future glucose values, from which “preventive” alerts can be generated before critical hypoglycemic/hyperglycemic events occur.</p>
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<p>Denoising. Example of real-time application of the algorithm in [<a href="#b59-sensors-12-13753" class="html-bibr">59</a>] to a representative Glucoday<sup>®</sup> time-series measured in a diabetic volunteer. Original (blue) and denoised (red) CGM profiles are shown. The black horizontal line represents the hypoglycaemic threshold.</p>
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<p>Enhancement. Example of real-time application of the algorithm in [<a href="#b66-sensors-12-13753" class="html-bibr">66</a>] to a representative DexCom<sup>®</sup> SevenPlus<sup>®</sup> time-series measured in a diabetic volunteer. The gray rectangle highlights the two SMBG values (magenta triangles) and the portion of CGM data (blue line) used by the algorithm to estimate the internal regressor parameters. Original CGM data (blue line) and enhanced glucose profile (green line) are then shown against BG references (red stars), collected in parallel using a laboratory instrument.</p>
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<p>Prediction for alert generation (simulation context). (<b>A</b>). Hypoglycemic alert generated on the basis of CGM data. CGM data before (black line) and after (blue dashed line) the carbohydrates ingestion (blue arrow) in response to alert (blue bell) generated at hypoglycemic threshold crossing. (<b>B</b>). Hypoglycemic alert generated on the basis of predicted CGM data. CGM data before (black line) and after (red dashed line) the carbohydrates ingestion (red arrow) in response to alert (red bell) generated when predicted CGM (red line) exceeds hypoglycemic threshold.</p>
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<p>(<b>A</b>) Identification of the model allowing to estimate glucose concentration from the 150 signals returned by the Solianis Monitoring AG Multisensor system. The inlaid graphs show, for a real subject, the time-course for the 16 representative channels numbered as 105–120 (left) and the reference BG glucose concentration levels measured in parallel (open bullets, right). (<b>B</b>) Example of estimated glucose profiles during model test for a representative experimental session with the PLS (blue line) and with the LASSO (green line) models against BG samples [<a href="#b129-sensors-12-13753" class="html-bibr">129</a>].</p>
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1112 KiB  
Article
On-Site Sensor Recalibration of a Spinning Multi-Beam LiDAR System Using Automatically-Detected Planar Targets
by Chia-Yen Chen and Hsiang-Jen Chien
Sensors 2012, 12(10), 13736-13752; https://doi.org/10.3390/s121013736 - 12 Oct 2012
Cited by 30 | Viewed by 8227
Abstract
This paper presents a fully-automated method to establish a calibration dataset from on-site scans and recalibrate the intrinsic parameters of a spinning multi-beam 3-D scanner. The proposed method has been tested on a Velodyne HDL-64E S2 LiDAR system, which contains 64 rotating laser [...] Read more.
This paper presents a fully-automated method to establish a calibration dataset from on-site scans and recalibrate the intrinsic parameters of a spinning multi-beam 3-D scanner. The proposed method has been tested on a Velodyne HDL-64E S2 LiDAR system, which contains 64 rotating laser rangefinders. By time series analysis, we found that the collected range data have random measurement errors of around ±25 mm. In addition, the layered misalignment of scans among the rangefinders, which is identified as a systematic error, also increases the difficulty to accurately locate planar surfaces. We propose a temporal-spatial range data fusion algorithm, along with a robust RANSAC-based plane detection algorithm to address these issues. Furthermore, we formulate an alternative geometric interpretation of sensory data using linear parameters, which is advantageous for the calibration procedure. The linear representation allows the proposed method to be generalized to any LiDAR system that follows the rotating beam model. We also confirmed in this paper, that given effective calibration datasets, the pre-calibrated factory parameters can be further tuned to achieve significantly improved performance. After the optimization, the systematic error is noticeable lowered, and evaluation shows that the recalibrated parameters outperform the factory parameters with the RMS planar errors reduced by up to 49%. Full article
(This article belongs to the Special Issue Laser Sensing and Imaging)
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<p>(<b>a</b>) point cloud of corridor; colour-coded to differentiate laser sources; (<b>b</b>) A close look at the marked wall; (<b>c</b>) range data of laser #40 returned in six subsequent spins.</p>
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<p>Process of the proposed automatic on-site recalibration method.</p>
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<p>(<b>a</b>) coordinate system of the Velodyne LiDAR system; (<b>b</b>) geometric relation of laser intrinsic parameters in linear representation, a calibration plane, and the residual.</p>
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<p>Kernel-based Range Data Fusion with Various σ.</p>
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<p>(<b>a</b>) initial estimate; (<b>b</b>) refined estimate using ICP-based algorithm; and (<b>c</b>) final result of the detection of floor surface; (<b>d</b>–<b>f</b>) the same process applied to find another surface.</p>
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<p>Established calibration data containing seven planar surfaces.</p>
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<p>The corridor selected to evaluate the proposed method.</p>
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<p>Point residuals colour-coded to show calibration planes (<b>a</b>) factory parameters; (<b>b</b>) recalibrated parameters.</p>
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<p>Distribution of point residuals (<b>a</b>) factory parameters; (<b>b</b>) recalibrated parameters.</p>
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641 KiB  
Article
Computational Design of a Carbon Nanotube Fluorofullerene Biosensor
by Tamsyn A. Hilder, Ron J. Pace and Shin-Ho Chung
Sensors 2012, 12(10), 13720-13735; https://doi.org/10.3390/s121013720 - 12 Oct 2012
Cited by 7 | Viewed by 6870
Abstract
Carbon nanotubes offer exciting opportunities for devising highly-sensitive detectors of specific molecules in biology and the environment. Detection limits as low as 10−11 M have already been achieved using nanotube-based sensors. We propose the design of a biosensor comprised of functionalized carbon [...] Read more.
Carbon nanotubes offer exciting opportunities for devising highly-sensitive detectors of specific molecules in biology and the environment. Detection limits as low as 10−11 M have already been achieved using nanotube-based sensors. We propose the design of a biosensor comprised of functionalized carbon nanotube pores embedded in a silicon-nitride or other membrane, fluorofullerene-Fragment antigen-binding (Fab fragment) conjugates, and polymer beads with complementary Fab fragments. We show by using molecular and stochastic dynamics that conduction through the (9, 9) exohydrogenated carbon nanotubes is 20 times larger than through the Ion Channel Switch ICSTM biosensor, and fluorofullerenes block the nanotube entrance with a dissociation constant as low as 37 pM. Under normal operating conditions and in the absence of analyte, fluorofullerenes block the nanotube pores and the polymer beads float around in the reservoir. When analyte is injected into the reservoir the Fab fragments attached to the fluorofullerene and polymer bead crosslink to the analyte. The drag of the much larger polymer bead then acts to pull the fluorofullerene from the nanotube entrance, thereby allowing the flow of monovalent cations across the membrane. Assuming a tight seal is formed between the two reservoirs, such a biosensor would be able to detect one channel opening and thus one molecule of analyte making it a highly sensitive detection design. Full article
(This article belongs to the Section Biosensors)
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<p>(<b>A</b>) Schematic of the (9, 9) exohydrogenated carbon nanotube with hydrogen atoms shown in light grey; (<b>B</b>) Free energy profile along the axial coordinate for potassium (K<sup>+</sup>) and chloride (Cl<sup>−</sup>) ions; (<b>C</b>) Dwell histogram of potassium ions in the absence of an applied electric potential.</p>
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<p>(<b>A</b>) Current-voltage profile for potassium ions at an ionic concentration of 500 mM, and (<b>B</b>) Current-concentration profile for potassium ions at an applied potential of 200 mV. Data points represent the average of five sets of simulations, each simulation lasting 0.8 μs. Error bars represent one standard error of the mean and error bars smaller than the data points are not shown.</p>
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<p>Fluorofullerene PMF. PMF profile for the unbinding of the C<sub>60</sub>F<sub>36</sub> and C<sub>60</sub>F<sub>60</sub> fluorofullerenes from the (9, 9) exohydrogenated carbon nanotube.</p>
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<p>Illustration of the bound configuration of C<sub>60</sub>F<sub>60</sub> to the carbon nanotube. (<b>A</b>) top view and (<b>B</b>) side view.</p>
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<p>Schematic representation of the carbon nanotube-fluorofullerene biosensor. (<b>A</b>) In the absence of analyte (orange) all nanotube pores are blocked by fluorofullerene (red)—Fab fragment (purple) conjugates. (<b>B</b>) Analyte is injected into the sample, and binds to the Fab fragments on the fluorofullerene and polymer bead (blue) floating in solution. The flow from the injected analyte solution is sufficient to pull a fluorofullerene from its docked position and potassium current is observed.</p>
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1051 KiB  
Article
Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals
by Zhanguo Xia, Shixiong Xia, Ling Wan and Shiyu Cai
Sensors 2012, 12(10), 13694-13719; https://doi.org/10.3390/s121013694 - 12 Oct 2012
Cited by 76 | Viewed by 11015
Abstract
Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence [...] Read more.
Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. Full article
(This article belongs to the Section Physical Sensors)
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<p>An example of three-level wavelet packet decomposition.</p>
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<p>The flow chart of the proposed scheme.</p>
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<p>The test-rig.</p>
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<p>The vibration signal waveforms from the different fault types: (<b>a</b>) Health bearing. (<b>b</b>) Inner race fault. (<b>c</b>) Outer race fault. (<b>d</b>) Ball fault.</p>
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<p>The vibration signal waveforms from the different fault types: (<b>a</b>) Health bearing. (<b>b</b>) Inner race fault. (<b>c</b>) Outer race fault. (<b>d</b>) Ball fault.</p>
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<p>The normalized time domain features in the D_MIX dataset: (<b>a</b>) RMS. (<b>b</b>) kurtosis value. (<b>c</b>) peak-peak value. (<b>d</b>) impulse factor. (Note: Sample data No. 1–100, 101–200, 201–300,301–400 represent Normal, IRF14, ORF14 and BF14, respectively).</p>
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<p>The normalized time domain features in the D_IRF dataset: (<b>a</b>) RMS. (<b>b</b>) kurtosis value. (<b>c</b>) peak-peak value. (<b>d</b>) impulse factor. (Note: Sample data No. 1–100, 101–200, 201–300, 301–400, 401–500 represent Normal, IRF07, IRF14, IRF21 and IRF28 respectively.)</p>
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<p>The normalized time domain features in the D_IRF dataset: (<b>a</b>) RMS. (<b>b</b>) kurtosis value. (<b>c</b>) peak-peak value. (<b>d</b>) impulse factor. (Note: Sample data No. 1–100, 101–200, 201–300, 301–400, 401–500 represent Normal, IRF07, IRF14, IRF21 and IRF28 respectively.)</p>
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<p>The single-sided amplitude spectrum based on FFT in the D_MIX dataset: (<b>a</b>) the normal bearing. (<b>b</b>) IRF14 bearing. (<b>c</b>) ORF14 bearing. (<b>d</b>) BF14 bearing.</p>
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<p>The single-sided amplitude spectrum based on FFT in the D_ORF dataset: (<b>a</b>) the normal bearing. (<b>b</b>) ORF07 bearing. (<b>c</b>) ORF14 bearing. (<b>d</b>) ORF21 bearing.</p>
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<p>The signals of the decomposed by WPT from: (<b>a</b>) the normal bearing. (<b>b</b>) the ball fault bearing.</p>
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5025 KiB  
Article
In Tube Integrated Electronic Nose System on a Flexible Polymer Substrate
by Thomas Kinkeldei, Christoph Zysset, Niko Münzenrieder, Luisa Petti and Gerhard Tröster
Sensors 2012, 12(10), 13681-13693; https://doi.org/10.3390/s121013681 - 12 Oct 2012
Cited by 7 | Viewed by 7418
Abstract
The fabrication of electronic devices, such as gas sensors on flexible polymer substrates, enables the use of electronics in applications where conventional devices on stiff substrates could not be used. We demonstrate the development of a new intra-tube electronic-nose (e-nose) gas sensor device [...] Read more.
The fabrication of electronic devices, such as gas sensors on flexible polymer substrates, enables the use of electronics in applications where conventional devices on stiff substrates could not be used. We demonstrate the development of a new intra-tube electronic-nose (e-nose) gas sensor device with multiple sensors fabricated and integrated on a flexible substrate. For this purpose, we developed a new method of fabricating a sensor array of four gas sensors on a flexible polymer substrate. The method allowed the use of lithography techniques to pattern different polymers with a broad range of solubility parameters. Conductive polymer composites were used as a gas sensitive layer due to the high stretchability of the material. Each of the 30 e-nose devices on one substrate was designed to fit on a polymer strip with a width of 2 mm. A single e-nose strip was successfully integrated into the inlet tube of a gas-measurement apparatus with an inner-tube diameter of 3 mm. Using the e-nose, we were able to differentiate between four different volatile solvent vapors. The tube-integrated e-nose outperformed a chamber-integrated e-nose of the same type in terms of response time and flow-rate influences. The sensor array inside the tube showed a faster response time and detected short pulses of analyte exposure compared to the same sensor array outside of the tube. We measured gas flow rates from 1,000 to 30 sccm without significant changes in sensor performance using this intra-tube e-nose prototype. The tube could be bent to radii < 15 mm with a sensor performance similar to an unbent sensor. Full article
(This article belongs to the Section Chemical Sensors)
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<p>Fabrication process for the electronic nose using orthogonal resist: (<b>a</b>) cleaning of the substrate; (<b>b</b>) e-beam evaporation of gold; (<b>c</b>) patterned sensor structure; (<b>d</b>) gas sensitive layer spin-coating; (<b>e</b>) dry etched gas sensitive layer; (<b>f</b>) patterned fluorinated resist; (<b>g</b>) gas sensitive layer spin-coating; (<b>h</b>) lift-off patterned sensitive layer.</p>
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<p>Photograph of the fabricated e-nose on a flexible substrate inserted into a polymer tube.</p>
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<p>Normalized Resistance <span class="html-italic">vs</span>. time and exposure to solvents for the four different polymer gas sensors (PIS, PS, PVP, PVB). The volatile solvent vapors are isopropanol, acetone, toluene and methanol.</p>
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<p>Principal component analysis of the sensor exposure to four different analytes: isopropanol, acetone, toluene and methanol. Each point in the plot represents a single measurement fragment of 1 s. The transition between a stable concentration of solvent or air is excluded from the plot.</p>
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<p>Normalized resistance <span class="html-italic">versus</span> temperature for the four different polymer gas sensors (PIS, PS, PVP, PVB).</p>
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<p>Normalized resistance over time and exposure to 5000 ppm acetone for a PVB gas sensors, showing the difference in reaction time. One PVB sensor is mounted inside the chamber and another inside the inlet tube of the chamber.</p>
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<p>Normalized resistance over time and exposure to 5,000 ppm acetone with increasing flow rate. Two different sensor polymer materials are shown, <span class="html-italic">i.e.</span>, PIS and PVB. The flow rate was increased from 30 sccm to 1,000 sccm.</p>
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<p>Normalized resistance over time and exposure to 2,500 ppm acetone. Shown are the same PIS and PVB sensors in a flat tube and in a tube bent to about 15 mm. The inset shows the bent tube with an integrated sensor wrapped around a pen.</p>
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<p>Micrograph of a structured gas sensor layer onto metal electrodes on a PI substrate. Structuring was achieved with dry etching. The inset shows a patterned polymer layer on a PI substrate with feature sizes below 50 μm.</p>
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