The following topics were dealt with: beamforming; direction-of-arrival estimation; tracking; multi-sensors application; multi-channel processing; sensorarrayprocessing; MIMO processing; space-time coding; synthetic...
The following topics were dealt with: beamforming; direction-of-arrival estimation; tracking; multi-sensors application; multi-channel processing; sensorarrayprocessing; MIMO processing; space-time coding; synthetic aperture space-time processing; imaging; space-time processing for radar; sensor networking; sensor management; sonar; and microphone arrayprocessing
Jun.8～11,2020 Hangzhou,China Submission deadline:Dec.15,2019 ABOUT CONFERENCE The SAM workshop is an important IEEE signalprocessing Society event dedicated to sensorarray and multichannelsignalprocessing with ab...
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Jun.8～11,2020 Hangzhou,China Submission deadline:Dec.15,2019 ABOUT CONFERENCE The SAM workshop is an important IEEE signalprocessing Society event dedicated to sensorarray and multichannelsignalprocessing with about 200 *** organizing committee invites the international community to contribute with state-of-the-art developments in the field.
In this paper, we focus on diagnosing Parkinson's patients using dynamic plantar pressure data collected via sensor devices. We employ data preprocessing methods, including clustering, dimensionality reduction, an...
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In this paper, we focus on diagnosing Parkinson's patients using dynamic plantar pressure data collected via sensor devices. We employ data preprocessing methods, including clustering, dimensionality reduction, and multichannel feature screening. Our approach proposes a comprehensive set of data processing techniques, including data cleaning, constrained clustering, and dimensionality reduction, to convert sensor data into a multichannel multivariate time series suitable for neural network input. Unlike current methods that use all features for automatic filtering by the network - adding complexity and resource burden - we introduce a data analysis method combining statistical features and Recursive Feature Elimination. This reduces the number of channels and simplifies the model. We used a simplified 1D-convnet model, achieving a 10-fold accuracy of 91.09%, segmentation accuracy of 95.54%, individual accuracy of 97.33%, weighted precision of 95.71%, weighted recall of 95.56%, and a weighted F1-score of 95.61%. Our results validate the effectiveness of our data acquisition and feature screening methods, and notably, our processing speed is nearly three times faster.
Chemical vapor sensors are essential for various fields, including medical diagnostics and environmental monitoring. Notably, the identification of components in unknown gas mixtures has great potential for noninvasiv...
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Chemical vapor sensors are essential for various fields, including medical diagnostics and environmental monitoring. Notably, the identification of components in unknown gas mixtures has great potential for noninvasive diagnosis of diseases such as lung cancer. However, current gas identification techniques, despite the development of electronic nose-based sensor platforms, still lack sufficient classification accuracy for mixed gases. In our previous study, we introduced multichannel hierarchical analysis using a time-resolved hyperspectral system to address the spectral ambiguity of conventional RGB sensor-based colorimetric e-noses. Here, we demonstrate the identification of mixed gas components through time-resolved line hyperspectral measurements with an eight-colorimetric sensorarray that uses genetically engineered M13 bacteriophages as gas-selective colorimetric sensors. The time-dependent spectral variations induced by mixed gas in the different colorimetric sensors are converted into a hyperspectral three-dimensional (3D) data cube. For efficient machine learning classification, the data cube was converted into a multichannel spectrogram by applying a novel data processing method, including dimensionality reduction and a block average filter to reduce high-dimensional complexity and improve the signal-to-noise ratio. A convolution filter was then used for hierarchical analysis of the multichannel spectrogram, effectively capturing the complex gas-induced spectral patterns and temporal dynamics. Our study demonstrates a classification accuracy of 93.9% for pure and mixed gases of acetone, ethanol, and xylene at a low concentration of 2 ppm.
This work presents an analog on-chip neural network for embedded processing of gamma rays in radiation detectors, featuring 64 inputs, two 20-neurons hidden layers, and two outputs. Leveraging in-sensorprocessing of ...
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This work presents an analog on-chip neural network for embedded processing of gamma rays in radiation detectors, featuring 64 inputs, two 20-neurons hidden layers, and two outputs. Leveraging in-sensorprocessing of analog signals coming from photodetectors permits to reduce the amount of data to transmit and digitize, as well as eliminating the need of FPGAs for signalprocessing, allowing for an easier scale up of complex multichannel systems. Fabricated in a 0.35 mu m CMOS process node, this first prototype chip demonstrates an energy efficiency of 46.8 GOP/J. Its functionality is showcased through the localization of X and Y coordinates of gamma photons interacting in a scintillator crystal readout by a planar array of silicon photomultipliers (SiPMs), in the field of medical imaging applications based on emission tomography (such as PET and SPECT). The neural network's weight reconfigurability broadens its applicability beyond gamma-ray detection, making it suitable for other edge-computing applications requiring a feedforward neural network architecture.
This article follows the ICASSP 2011 Trends in sensorarray and multichannelsignalprocessing (SAM SP) expert session and represents our views on continuing and emerging research areas in this field.
This article follows the ICASSP 2011 Trends in sensorarray and multichannelsignalprocessing (SAM SP) expert session and represents our views on continuing and emerging research areas in this field.
The ubiquity of smart devices equipped with microphones in modern environments has opened the door to performing audio processing tasks such as speech enhancement over networks of microphones instead of traditional co...
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The ubiquity of smart devices equipped with microphones in modern environments has opened the door to performing audio processing tasks such as speech enhancement over networks of microphones instead of traditional compact arrays. However, wireless acoustic sensor networks (WASNs) also introduce geometry estimation, clock synchronisation, and latency compensation as often necessary steps prior to traditional enhancement techniques. Instead, this paper investigates the use of blind polynomial eigenvalue decomposition (PEVD)-based enhancement in such networks with an emphasis on its robustness to communication latency between nodes. Simulations and experiments on real data show that the network-based PEVD enhancement consistently gives equivalent or better signal-to-noise ratio (SNR) than the best node in the network, even when experiencing communication latency. In-depth analysis of the network geometry and the PEVD filterbanks provides insights into the strengths and shortcomings of PEVD-based enhancement in networks, and shows it is robust to the issue of reference microphone selection. Finally, evaluation of PEVD as a pre-processing step to automatic speech recognition (ASR) shows an improvement of 7 points in word error rate (WER) over multichannel Wiener filtering for very noisy real conversational data.
We introduce EddyBot, a novel integration of a multi-channel FPGA-based Eddy Current Testing (ECT) scanner within a mobile robot designed for autonomous non-destructive testing (NDT). EddyBot distinguishes itself by l...
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We introduce EddyBot, a novel integration of a multi-channel FPGA-based Eddy Current Testing (ECT) scanner within a mobile robot designed for autonomous non-destructive testing (NDT). EddyBot distinguishes itself by leveraging advanced signalprocessing, real-time robotics control, and efficient data synchronization to significantly enhance inspection accuracy and efficiency. Our contributions are (i) the deployment of a Zynq7020 FPGA for fast data acquisition and processing, (ii) alongside the Robot Operating System (ROS) for precise and real-time control with Kalman filtering (KF) sensor fusion for pose estimation. (iii) We further optimize defect detection and localization through a novel data synchronization approach, correlating the robot's pose with ECT signals to a mean synchronization delay of only 11.77 ms. This allows for precise localization of defects within 0.18 cm, improving the overall reliability. The robot's performance was evaluated on aluminum and ferromagnetic specimens, highlighting the system's capability to detect defects with high accuracy.
Presents the mission and work of the SPS society sensorarray and multichannel Technical Committee. Also reports on major events, conferences, and meetings.
Presents the mission and work of the SPS society sensorarray and multichannel Technical Committee. Also reports on major events, conferences, and meetings.
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