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Recent Trends in Electronics and Communication (ICRTEC), International Conference on

作者： M. Prathiksha Priyanka R. Badiger Varshini Thangaraj D. A. Varshini Sahana Srikanth Sanjeev Gurugopinath Department of Electronics and Communication Engineering PES University Bengaluru India

In recent years, there has been a significant growth in the development of machine learning algorithms towards better experience in patient care. In this paper, a contemporary survey on the deep learning and machine learning techniques used in multimodal signal processing for biomedical applications is presented. Specifically, an overview of the preprocessing approaches and the algorithms proposed for five major biomedical applications are presented, namely detection of cardiovascular diseases, retinal disease detection, stress detection, cancer detection and COVID-19 detection. In each case, processing on each multimodal data type, such as an image or a text is discussed in detail. A list of various publicly available datasets for each of these applications is also presented.

关键词： COVID-19 Machine learning algorithms signal processing algorithms signal processing Retina Market research Skin

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Synthesis of the UWB signal processing algorithm on an artificial neural network

Synthesis of the UWB signal processing algorithm on an artif...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Sergey A. Serov Alexander A. Chugunov Roman S. Kulikov Alexander P. Malyshev Oleg V. Glukhov Tatiana A. Brovko Radio Technical Systems Department National Research University "MPEI" Moscow Russian Federation

In this work, the navigation device is simulated on the basis of receiving and processing a UWB radio signal. The task is not only to simulate the navigation device, but also to build an information processing mechanism based on the neural network. The aim of the work is to develop a method for local navigation in a room by receiving and processing an ultra-wideband signal reflected from obstacles and boundaries of the room. This realized by step-by-step evaluation of a map of the depths of space using an neural network that processes the in-phase and quadrature components of the UWB radio signal entering the input. In this paper, the possibility of using an artificial neural network to create an autonomous radio navigation system for local navigation is analyzed. In the course of the work, the possibility of creating such a system was proved, as well as the problems arising during its creation were analyzed.

关键词： Power engineering Radio navigation signal processing algorithms Artificial neural networks Information processing Reflection Computational efficiency

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Digital signal processing on 3-Axis Accelerometer

Digital Signal Processing on 3-Axis Accelerometer

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Intelligent Systems for Communication, IoT and Security (ICISCoIS), International Conference on

作者： Sreehari Krishnakumar Vignesh Karthikeyan R Manoharan V K Saritakumar N Department of ECE PSG College of Technology Coimbatore India PSG Space Centre PSG College of Technology Coimbatore India

This paper presents a method for processing 3-axis accelerometer signals using a Cortex M4F microcontroller. The accelerometer is a device that measures acceleration, which is the rate of change of velocity. It is often used in applications such as motion sensing and gesture recognition. The process begins by acquiring the accelerometer signals and transforming them using the Fast Fourier Transform (FFT). The FFT is an algorithm that decomposes a signal into its frequency components, which can be useful for identifying patterns and trends in the data. The transformed signals are then passed through a digital filter, which is a mathematical tool that can be used to remove or enhance specific frequencies from a signal. In this case, the filter is used to eliminate or reduce certain frequencies that are not of interest. The purpose of this process is not specified, but it is likely that the accelerometer and microcontroller are being used to perform tasks such as analyzing device movements or identifying specific gestures made by a user. Overall, this paper provides an insight into the use of a 3-axis accelerometer and Cortex M4F microcontroller for signal processing, with the application of the FFT and digital filter allowing for the extraction of useful information from the accelerometer signals that can potentially be used in a variety of applications.

关键词： Accelerometers Vibrations Microcontrollers Fast Fourier transforms signal processing algorithms Market research Information filters

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Distributed signal processing for Out-of-System Interference Suppression in Cell-Free Massive MIMO

Distributed Signal Processing for Out-of-System Interference...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zakir Hussain Shaik Erik G. Larsson Department of Electrical Engineering (ISY) Linköping University Sweden

Cell-free massive multiple-input-multiple-output (CF-mMIMO) is a next-generation wireless access technology that offers superior coverage and spectral efficiency compared to conventional MIMO. With many future applications in unlicensed spectrum bands, networks will likely experience and may even be limited by out-of-system (OoS) interference. The OoS interference differs from the in-system interference from other serving users in that for OoS interference, the associated pilot signals are unknown or non-existent, which makes estimation of the OoS interferer channel *** this paper, we propose a novel sequential algorithm for the suppression of OoS interference for uplink CF-mMIMO with a stripe (daisy-chain) topology. The proposed method has comparable performance to that of a fully centralized interference rejection combining algorithm but has substantially less fronthaul load requirements.

关键词： Wireless communication Spectral efficiency signal processing algorithms Interference signal processing Numerical simulation Topology

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Simulation of Wireless Video Multipath signal processing Model Based on Adaptive Filtering Algorithm

Simulation of Wireless Video Multipath Signal Processing Mod...

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Telecommunications, Electronics and Informatics (ICTEI), International Conference on

作者： Junyi Zheng The College of IOT Engineering Hohai University Changzhou Jiangsu China

ISBN: (数字)9798350342963

ISBN: (纸本)9798350342970

The research of adaptive filtering algorithm has always been a hot spot in the field of signal and information processing, and has been widely used in Speech processing, echo cancellation, channel equalization, system identification, spectral line enhancement, adaptive array processing, biomedical signal processing and many other fields. The amplitude control effect of multipath wireless video transmission is unstable, resulting in low bit rates for multipath single view transmission. In order to denoise and filter wireless video multipath signals, this paper proposes a variable step size LMS adaptive filtering algorithm for wireless video multipath signal processing to address the problem of wireless video signals being covered by noise. The algorithm is based on the feedback control function and combines the arctangent function and Exponential function together. The filtering performance of the algorithm is analyzed through MATLAB, and is applied to wireless video multi-path signal processing. The simulation results show that the algorithm has improved in Rate of convergence and steady-state error, and the error suppression ability has also improved in the filtering processing effect of wireless video multi-path signals, with good convergence performance and steady-state characteristics. The filtering effect has achieved satisfactory results.

关键词： Wireless communication Adaptive systems Filtering Noise signal processing algorithms signal processing Filtering algorithms

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Continuous Time Digital signal processing and signal Reconstruction

Continuous Time Digital Signal Processing and Signal Reconst...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Patrick Jungwirth W. Michael Crowe Advanced Computing Branch DEVCOM Army Research Lab Aberdeen MD Embedded Systems DEVCOM Aviation and Missile Center Redstone Arsenal AL

Continuous Time (CT) Systems offer several advantages over traditional digital signal processing (DSP); however, CT is not linear time invariant like DSP. Since discrete time and DSP are linear time invariant, signal reconstruction is applied after all signal processing steps have been completed. A DSP approach cannot be applied to continuous time systems. For CT systems, level crossing sampling signals are nonlinear. For linearity to hold in CT systems, signal reconstruction must be applied before any signal processing steps. In 2003, Tsividis showed an asynchronous zero order hold is the simplest signal reconstruction algorithm for CT. In 2008, Schell and Tsividis demonstrated an offline exact reconstruction algorithm with greater than 100 dB signal-to-noise and distortion ratio (SINAD). This paper focuses on CT level crossing signal reconstruction. For practical CT signal reconstruction, a low complexity approximation to offline signal reconstruction is preferred with higher signal-to-noise ratio than simple asynchronous zero order hold reconstruction. A Gaussian-sinc( ) reconstruction kernel is investigated as an approximation to offline reconstruction.

关键词： Nonlinear distortion signal processing algorithms Linearity Digital signal processing Reconstruction algorithms signal reconstruction Complexity theory

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Auto-Determining Mainlobe Width for Beampattern Synthesis via Relaxation Optimization

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IEEE signal processing LETTERS 2022年 29卷 314-318页

作者： Tang, Kangyu Zhang, Xuejing Univ Elect Sci & Technol China Chengdu 611731 Peoples R China

Thispaper presents a novelarray pattern synthesis algorithm based on relaxation optimization. We consider the problem of how to auto-determine the width of mainlobe region in beampattern synthesis. By introducing a slack region and a slack vector, the beampattern synthesis problem can be transformed as a relaxation optimization problem. The relaxation optimization problem is non-convex with an objective of sparsifying the slack vector. The sequential convex optimization procedure is then applied to solve the relaxation optimization problem and find its sparse solution. Representative simulations are provided to demonstrate the effectiveness of the proposed method in beampattern synthesis.

关键词： Optimization Convex functions signal processing algorithms Shape Radar antennas Genetic algorithms Convergence Beampattern synthesis relaxation optimization mainlobe width sparse optimization

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An Innovative Transient Analysis of Adaptive Filter With Maximum Correntropy Criterion

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IEEE signal processing LETTERS 2022年 29卷 1689-1693页

作者： Hou, Xinyan Zhao, Haiquan Long, Xiaoqiang Minist Educ Key Lab Magnet Suspens Technol & Maglev Vehicle Beijing 100816 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

The adaptive filtering algorithm based on the maximum correntropy criterion (MCC) is very effective in suppressing non-Gaussian noises and therefore attracts widespread attentions. At present, some works have been done for study the convergence and steady-state performance analysis of the MCC algorithm, but its transient performance analysis is still an open problem. To provide a comprehensive theoretical foundation for the MCC algorithm, we propose a method for transient performance analysis based on moment generating function (MGF). Since this method can efficiently calculate the expected value of the exponential term in the iterative update equation, it can avoid the discrepancies caused by introducing some approximation methods such as Taylor expansions in the analysis process. To date, there is no precedent for using this method to analyze the transient performance of the MCC algorithm. In addition, the steady-state performance and stability conditions of the MCC algorithm are discussed based on this method. Finally, the proposed analytical method is applied to the system identification problem, and the results show that the theoretical analysis results are agree well with the Monte Carlo simulation results.

关键词： signal processing algorithms Transient analysis Noise measurement Behavioral sciences Steady-state Kernel Prediction algorithms Adaptive filtering maximum correntropy criterion moment generating function performance analysis

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Polynomial-Decomposition-Based LPC for Formant Estimation

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IEEE signal processing LETTERS 2022年 29卷 1392-1396页

作者： Fu, Meijun Wang, Xiaomin Wang, Jun Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Sch Informat Sci & Technol Chengdu 611756 Peoples R China Yunnan Minzu Univ Sch Math & Comp Sci Kunming 650500 Yunnan Peoples R China

This letter proposes a robust polynomial-decompo- sition-based linear prediction coding algorithm, PDLPC, for formant estimation, which can effectively eliminate merger peaks. PDLPC first combines LPC with statistical analysis to obtain the peak and suspicious merger peak screening criteria;then uses Cauchy integral formula to calculate the number of poles located in the fan-shaped area near the suspicious merger peak to determine whether the merger peak occurs;finally, utilizes division algorithm for polynomial to separate those merger peaks. The evaluations on Primi and VTR show that PDLPC is effective in separating merger peaks, and has certain competitiveness compared with some existing formant estimation algorithms.

关键词： Corporate acquisitions Estimation signal processing algorithms Prediction algorithms Statistical analysis Frequency estimation Analytical models PDLPC formant merger peak Cauchy integral formula division algorithm for polynomial

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Fast Diffusion Minimum Generalized Rank Norm Based on QR Decomposition

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第3期69卷 1942-1946页

作者： Modalavalasa, Sowjanya Sahoo, Suraj Prakash Sahoo, Upendra Kumar Sahoo, Ajit Kumar Natl Inst Technol Rourkela EC Dept Rourkela 769008 India Chaitanya Bharathi Inst Technol EC Dept Hyderabad 500075 India

The outliers or impulsive noise is prominent in practical wireless sensor networks due to saturation effects, non-linearities, malfunction of sensors, environmental abnormalities, etc. The classical diffusion algorithms based on mean square error cost function are sensitive to outliers and their performance degrades in the presence of outliers in either desired data or the input data. A fast diffusion minimum generalized rank norm based on QR decomposition (FDGR-QR) is proposed, which is robust against outliers in both desired and input data and has faster convergence than the state of the art algorithms. Different outlier percentages distribution across the network is considered for the simulations based experiments. The proposed algorithm is validated for both stationary and non-stationary parameter estimation scenarios.

关键词： signal processing algorithms Convergence Matrix decomposition Estimation Cost function Sensors Robustness Diffusion strategy generalized rank norm impulsive noise QR decomposition

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