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IEEE India Conference (INDICON)

作者： Satvika Anand Vijay Kumar Chakka Priyanka Mathur Dept. of Electrical Engineering Shiv Nadar University Greater Noida India

ISBN: (纸本)9781665452724

Emotion classification plays an important role in the domain of human-computer interaction (HCI). In this paper, a novel framework for learning emotion-specific brain functional connectivity from EEG signals, with blockwise time-varying graph signal processing (GSP), is proposed. Graph corresponding to the last temporal block, which captures the spatio-temporal smoothness from all of the previous blocks is considered for extracting the Laplacian-based graph spectral features. The deviation range of the eigenvalues and their ratio corresponding to low-frequency and high-frequency components are proposed in the form of a convex sum feature. This feature is further utilized to classify cross-subject based positive and negative emotions using the KNN classifier. Simulation results on the benchmark DREAMER database validate the performance of the proposed method with metrics of accuracy, sensitivity, and specificity and are comparable with the existing state-of-the-art techniques.

关键词： Human computer interaction Measurement Simulation signal processing algorithms signal processing Sensitivity and specificity Feature extraction

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Digital signal processing simulation for plasma electron density measurement on FPGA

Digital signal processing simulation for plasma electron den...

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Microwaves Antennas and Propagation Conference (MAPCON)

作者： Kiran Patel Umesh Nagora H.C. Joshi Surya Pathak Institute for Plasma Research A CI of Homi Bhabha National Institute Gandhinagar Gujarat India

ISBN: (纸本)9781665452045

Heterodyne interferometry is an active diagnostic techniques for electron density measurement. Measured line integrated electron density is related directly to phase difference between transmitted and received microwave sig-nals. Phase variation measurement requires different phase detection techniques. Analog and digital phase measurement techniques are available for real time density estimation during plasma discharge. However, the analog circuit of the phase measurement is prone to noise handling and phase jump. This can be overcome by digital phase measurement techniques. Digital phase measurement having controller and high speed FPGA have become very popular for electron density measurement. FPGA based digital phase measurement techniques (zero cross, Fast Fourier Transform, CORDIC, ArcTAN and cross -correlation) are available for real time density measurement in tokamak. The zero cross and CORDIC plus zero cross algorithm have been developed on FPGA for phase estimation between two electromagnetic waves. The FPGA algorithm has been simulated with Vivado-15 and the density compared with density measured by 140 GHz heterodyne interferometer for functional validation.

关键词： Microwave measurement Phase measurement Density measurement signal processing algorithms Plasma measurements Microwave theory and techniques Microwave circuits

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Corrections and Comments on An Efficient Algorithm for Maneuvering Target Tracking

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IEEE signal processing MAGAZINE 2022年第4期39卷 138-139页

作者： Dulek, Berkan Hacettepe Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey

Presents corrections to the above named article.

关键词： Target tracking signal processing algorithms

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Comments on "Design of Asymmetric Shift Operators for Efficient Decentralized Subspace Projection"

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 2899-2900页

作者： Romero, Daniel Univ Agder Dept Informat & Commun Technol N-4879 Grimstad Norway

This correspondence disproves the main results in the paper "Design of Asymmetric Shift Operators for Efficient Decentralized Subspace Projection" by S. Mollaebrahim and B. Beferull-Lozano. Counterexamples and counterproofs are provided when applicable. A correction is suggested for some of the flaws. However, it does not seem possible to amend most of the flaws since the overall approach based on a Schur decomposition of the shift matrix does not appear to be helpful to solve the desired problem.

关键词： Symmetric matrices signal processing algorithms Mathematical models Matrix decomposition Optimization Eigenvalues and eigenfunctions Symbols

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Deep Learning Approach for Sound signal processing

Deep Learning Approach for Sound Signal Processing

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International Conference on Futuristic Technologies (INCOFT)

作者： M Bhavya M R Anala Department of ISE R V College of Engineering Bangalore India

In recent years, environmental sound classification has been a burgeoning subject of study. The unstructured nature of environmental sounds makes analysis challenging. However, sound signals have spectro-temporal patterns makes analysis easier using deep learning algorithms. Based on created spectrogram images and several feature extraction techniques Mel Spectrogram and Mel Frequency Cepstral Coefficients, we shall analyze sound signals using Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) in this paper. All the three models use UrbanSound8k based modified dataset and compared for the accuracy achieved on train and test datasets are: 98% and 88% for CNN, 95% and 86% for LSTM and 81% and 76% for ANN

关键词： Deep learning Stacking signal processing algorithms Feature extraction Data models Classification algorithms Convolutional neural networks

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High-Resolution Method for Primary signal processing in HFSWR

High-Resolution Method for Primary Signal Processing in HFSW...

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European signal processing Conference (EUSIPCO)

作者： Dragan Golubović Miljko Erić Nenad Vukmirović University of Belgrade School of Electrical Engineering Vlatacom Institute Belgrade Serbia Vlatacom Institute University of Belgrade School of Electrical Engineering Belgrade Serbia University of Belgrade School of Electrical Engineering Innovation Center of the School of Electrical Engineering Belgrade Serbia

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

In this paper we present a novel high-resolution algorithm for primary signal processing in High Frequency Surface Wave Radar (HFSWR). The high-resolution properties of the algorithm contribute to better ship detectability, as well as the ability to detect some ships, which are not visible at all using the currently used primary signal processing algorithms. The proposed algorithm is based on a high-resolution estimate of the range-Doppler map. We also proposed a numerically efficient Image processing method for detection on the range-Doppler map. Azimuth estimation is performed by a high-resolution MUSIC-type algorithm that is executed for all targets detected on the range-Doppler map. The experimental results showed that the percentage of successful detections was high.

关键词： Azimuth Surface waves Image processing signal processing algorithms Estimation Radar Radar signal processing

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Synthetic Aperture Radar signal processing Algorithm Implementation in Python

Synthetic Aperture Radar Signal Processing Algorithm Impleme...

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IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)

作者： Bambang Setiadi Andria Arisal Iman Firmansyah Agus Subekti Nurul Chasanah Jefri Abner Hamonangan Farohaji Kurniawan Research Center for Data and Information Sciences National Research and Innovation Agendy Bandung Indonesia Research Center for Telecommunication National Research and Innovation Agency Bandung Indonesia Research Center for Aeronautics Technology National Research and Innovation Agency Bogor Indonesia

The implementation of signal processing algorithms is crucial in the development of Synthetic Aperture Radar (SAR) systems. However, many references do not provide source code-level explanations, making it difficult for researchers and students to understand and implement these algorithms. This paper presents the implementation of the Omega-K algorithm (WKA) for SAR signal processing using Python. By using a freely accessible programming language, we aim to provide a low-cost, simple, and portable way to implement SAR signal processing algorithms compared to using commercial software packages. We describe the implementation details of two WKA variants at the source code level and demonstrate their use in processing Radarsat data. The execution results for various image sizes are tested, and the image results are compared with other reference implementations. Our results indicate that Python has considerable potential for SAR signal processing tasks.

关键词： Visualization Source coding Spaceborne radar Software algorithms signal processing algorithms signal processing Approximation algorithms

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pyNeurode: a real-time neural signal processing framework

pyNeurode: a real-time neural signal processing framework

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IEEE International Symposium on Circuits and Systems

作者： Wing-Kin Tam Matthew F. Nolan Cenre for Discovery Brin Sciences Universiy of Edinburgh Einburgh Unied KingdoCenre for Discovery Brin Sciences Universiy of Edinburgh Einburgh Unied Kingdo

ISBN: (数字)9781665484855

ISBN: (纸本)9781665484862

Accurate decoding of neural signals often requires assigning extracellular waveforms acquired on the same electrode to their originating neurons, a process known as spike sorting. While many offline sorters are available, accurate online sorting of spikes with many channels is still a challenging problem. Existing online sorters either use simple algorithms with low accuracy, can only process a handful of channels, or depend on a complex runtime environment that is difficult to set up. We have developed a state-of-the-art online spike sorting platform in Python that enables large-scale, fully automatic real-time spike sorting and decoding on hundreds of channels. Our system is cross-platform and works seamlessly with the Open Ephys suite of open-source hardware and software widely used in many neuroscience laboratories worldwide. It also comes with a user-friendly graphical user interface to monitor the cluster quality, spike waveforms and neuronal firing rate. Our platform has comparable accuracy to offline sorters and can achieve an end-to-end sorting latency of around 160 ms for 128-channel signals. It will be useful for research in fundamental neuroscience, closed-loop feedback neuromodulation and brain-computer interfaces.

关键词： Runtime environment Neuroscience signal processing algorithms signal processing Real-time systems Software Decoding

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Unobtrusive Heart Rate Monitoring using Near-Infrared Imaging During Driving 44

Unobtrusive Heart Rate Monitoring using Near-Infrared Imagin...

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44th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Selvaraju, Vinothini Spicher, Nicolai Swaminathan, Ramakrishnan Deserno, Thomas M. TU Braunschweig Peter L Reichertz Inst Med Informat Braunschweig Germany Hannover Med Sch Braunschweig Germany Indian Inst Technol Madras Dept Appl Mech Biomed Engn Chennai Tamil Nadu India

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

In-vehicle health monitoring allows for continuous vital sign measurement in everyday life. Eventually, this could lead to early detection of cardiovascular diseases. In this work, we propose non-contact heart rate (HR) monitoring utilizing near-infrared (NIR) camera technology. Ten healthy volunteers are monitored in a realistic driving simulator during resting (5 min) and driving (10 min). We synchronously acquire videos using an out-of-the-shelf, low-cost NIR camera and 3-lead electrocardiography (ECG) serves as ground truth. The MediaPipe face detector delivers the region of interest (ROI) and we determine the HR from the peak with maximum amplitude within the power spectrum of skin color changes. We compare video-based with ECG-based HR, resulting in a mean absolute error (MAE) of 7.8 bpm and 13.0 bpm in resting and driving condition, respectively. As we apply only a simple signal processing pipeline without sophisticated filtering, we conclude that NIR camera-based HR measurements enables unobtrusive and non-contact monitoring to a certain extent, but artifacts from subject movement pose a challenge. If these issues can be addressed, continuous vital sign measurement in everyday life could become reality.

关键词： Heart rate Pipelines signal processing algorithms signal processing Electrocardiography Cameras Skin

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A Review of Spectral Estimation in Underwater Acoustic signal processing

A Review of Spectral Estimation in Underwater Acoustic Signa...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Chao Peng International School of Microelectronics Dongguan University of Technology DongGuan China

ISBN: (纸本)9781665479691

This paper first introduces the direct method of classical power spectrum estimation, and then proposes two improved classical power spectrum estimation methods: Welch Method and MultiTaper (MTM) Method. Then the Maximum Entropy Spectral Estimation (based on Burg) in parametric spectral estimation is introduced. Then the power spectrum is extended in the spatial domain, and two kinds of spatial spectrum estimation methods, MUSIC Method and ESPRIT method, and their improved algorithms are introduced. All of the above methods have been simulated in Matlab environment. We have compared various spectrum estimation methods in detail, and analyzed and summarized their respective advantages and disadvantages. Finally, the paper points out the shortcomings of current power spectrum estimation and the future development direction.

关键词： Estimation signal processing algorithms Process control signal processing Entropy Multiple signal classification Information management

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