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Asilomar Conference on signals, Systems & Computers

作者： M. Hossein Attar Ramez Askar Slawomir Stanczak Network Information Theory Group Technische Universität Berlin Berlin Germany Wireless Communications and Networks Fraunhofer Institute for Telecommunications Berlin Germany

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

The purpose of the paper is to present an experimental investigation of digital self-interference cancellation (DSIC) in the presence of hardware impairments of a wireless transceiver operating in in-band full-duplex mode. The experiment will present a passive polarization-based antenna suppression followed by DSIC utilizing kernel-based methods such as the adaptive projected subgradient method (APSM) and support vector regression (SVR) as well as a combination of those. They employ various positive-definite kernels to estimate the linear and nonlinear parts of the effective self-interference (SI) channel collectively. These data-driven algorithms provide us with sparse representation of the SI signal, which not only cancels out the interference but also may be utilized to extract information from dynamic wireless channels. Besides, the adoption of these methods for classification tasks is straightforward.

关键词： Wireless communication Support vector machines Interference cancellation Dictionaries Sparse approximation Heuristic algorithms signal processing algorithms Full-duplex system Transceivers Kernel

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Research on ECG signal processing under complex noise based on machine learning algorithm

Research on ECG signal processing under complex noise based ...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Miao Zhang Songjian Huang SenPeng Chen Tianjun Ma School of Software Quanzhou University of Information Engineering Quanzhou China Songjian Huang School of Advanced Manufacturing Fuzhou University Quanzhou China School of Electronic and Communication Engineering Quanzhou University of Information Engineering Quanzhou China

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

Accurate acquisition of electrocardiogram (ECG) signals is crucial for the diagnosis of cardiovascular diseases. However, in complex environments and nonexercise states, ECG signals are often interfered by a variety of noises, which poses challenges to the stability and accuracy of the signals. This study aims to explore the application of machine learning algorithms in improving the quality of ECG signal acquisition, especially the ability to acquire valid ECG signals in complex noise *** paper classifies and analyses the types of noise that may be encountered during ECG signal acquisition, including myoelectric interference, baseline drift, power line interference, etc., and explores the impact of these noises on the quality of ECG signals, investigates the application of machine learning algorithms in ECG signal denoising and feature extraction, and proposes a framework for ECG signal processing based on the Deep Residual Shrinkage Network combined with the Fully Convolutional Self-Coder and Decoder for ECG signals in a complex noise environment. signal processing framework in a complex noise environment. Using DResNet to remove strong noise and retain ECG signal details, and using FCN-DAE to reconstruct ECG signals, this denoising algorithm demonstrates good denoising ability and reconstruction and restoration of ECG signals on the MIB-HIM dataset. Especially on wearable ECG acquisition devices, recognisable ECG data can be obtained in complex noise environments.

关键词： Machine learning algorithms Accuracy Noise Noise reduction signal processing algorithms Interference Electrocardiography Stability analysis signal denoising Internet of Things

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Multi-Channel Sampling on Graphs and Its Relationship to Graph Filter Banks

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2023年 4卷 148-156页

作者： Hara, Junya Tanaka, Yuichi Tokyo Univ Agr & Technolog Dept Elect Engn & Comp Sci Tokyo 1848588 Japan Osaka Univ Grad Sch Engn Osaka 5650871 Japan Japan Sci & Technol Agcy PRESTO Saitama 3320012 Japan

In this article, we consider multi-channel sampling (MCS) for graph signals. We generally encounter full-band graph signals beyond the bandlimited ones in many applications, such as piecewise constant/smooth graph signals and union of bandlimited graph signals. Full-band graph signals can be represented by a mixture of multiple signals conforming to different generation models. This requires the analysis of graph signals via multiple sampling systems, i.e., MCS, while existing approaches only consider single-channel sampling. We develop a MCS framework based on generalized sampling. We also present a sampling set selection (SSS) method for the proposed MCS so that the graph signal is best recovered. Furthermore, we reveal that existing graph filter banks can be viewed as a special case of the proposed MCS. In signal recovery experiments, the proposed method exhibits the effectiveness of recovery for full-band graph signals.

关键词： Transforms Generators Computational modeling Time-domain analysis signal sampling signal processing algorithms Manganese Multi-channel sampling full-band graph signals sampling set selection

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In-Depth ECG signal processing with Hardware Modules and Python Programming for Enhanced Engineering Education

In-Depth ECG Signal Processing with Hardware Modules and Pyt...

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National Conference with International Participation (TELECOM)

作者： Lyubomir Laskov Kalin Dimitrov Department of Radiocommunications and Videotechnologies Faculty of Telecommunications Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350355000

ISBN: (纸本)9798350355017

This paper present a new exercise for students about basic processing of ECG signals. In the first part of exercise, the students will assembly a hardware module to measure ECG signals from human body and transmit them to computer. In the second part they will write a code in python to receive the ECG signal from hardware module, to perform its initial signal processing, and visualize the original and processed ECG signals.

关键词： Training Visualization Noise signal processing algorithms Interference Electrocardiography Hardware Telecommunications Programming profession Python

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Variance-Reduced Shuffling Gradient Descent With Momentum for Finite-Sum Minimization

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 1700-1705页

作者： Jiang, Xia Zeng, Xianlin Xi, Lihua Sun, Jian Beijing Inst Technol Sch Automat Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 10081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Finite-sum minimization is a fundamental optimization problem in signal processing and machine learning. This letter proposes a variance-reduced shuffling gradient descent with Nesterov's momentum for smooth convex finite-sum optimization. We integrate an explicit variance reduction into the shuffling gradient descent to deal with the variance introduced by shuffling gradients. The proposed algorithm with a unified shuffling scheme converges at a rate of O((1)(T)), where T is the number of epochs. The convergence rate independent of gradient variance is better than most existing shuffling gradient algorithms for convex optimization. Finally, numerical simulations demonstrate the convergence performance of the proposed algorithm.

关键词： signal processing algorithms Convergence Radio frequency Minimization Convex functions Optimization signal processing Variance reduction shuffling gradient descent Nesterov's momentum finite-sum minimization

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Machine Learning for signal processing in Communication Systems

Machine Learning for Signal Processing in Communication Syst...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： PoliLokeshwara Reddy Chanda Shreyas Pamulapati. Bhargavi Raja G V D. Leela Rani S. Maheswari Department of Electronics and Communication Engineering Anurag University Hyderabad Telangana India Department of Computer Science Engineering KKR & KSR Institute of Technology and Sciences Guntur Andhra Pradesh Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru Karnataka Department of Electronics and Communication Engineering Mohan Babu University Tirupati Andhra Pradesh India Department of Electrical and Electronic Engineering Kongu Engineering College Perundurai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The traditional styles of signal processing are passing constraints in their capability to handle the different and dynamic character of ultramodern data transfers. This is because communication systems are getting increasingly complicated. The operation of machine literacy presents a potentially useful answer since it enables data-driven, adaptive approaches that have the eventuality to ameliorate signal processing performance. This composition provides a comprehensive examination of a variety of machine literacy ways and their operations in important disciplines such as modulation recognition, noise reduction, channel estimation, and signal discovery. The study demonstrates how machine literacy ways may vastly increase the effectiveness, delicacy, and robustness of signal-processing tasks in communication systems. This is fulfilled through detailed analysis and comparison. With the help of machine literacy, unborn communication systems will be suitable to attain advanced situations of robotization and intelligence, which will pave the way for further developments in areas similar to wireless dispatches, the Internet of Effects(IoT), and other areas. In light of these findings, it's clear that machine literacy can fully transfigure assiduity by furnishing new shoes and approaches to the optimization of signal processing in the unborn generation of communication networks.

关键词： Wireless communication Communication systems Computational modeling Heuristic algorithms Noise reduction Modulation Channel estimation signal processing algorithms Machine learning Robustness

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Quantum signal processing Based Grover's Adaptative Search Oracle for High Order Unconstrained Binary Optimization Problems

Quantum Signal Processing Based Grover's Adaptative Search O...

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Robin OLLIVE Stephane LOUISE Université Paris-Saclay CEA List Palaiseau France

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Grover's Adaptative Search (GAS) algorithm combined with the High Order Binary Optimization (HOBO) oracle proposal is currently the Fault Tolerant Quantum Computer (FTQC)'s main algorithm to solve binary optimization problems. In this paper, we propose a new way to obtain the Grover's oracle needed which is costless in terms of gates and ancilla qubits. This oracle proposal also has zero additional costs linked to decimal numbers of the problem weights and a very small overcost due to high order problems with respect to the other proposals. Nevertheless, it cannot be easily adapted to constrained problems. This new oracle is based on a completely different approach, which uses Quantum signal processing (QSP) techniques and amplitude encoding. This method can also be described in the Quantum Singular Value Transformation (QSVT) framework as a variant of eigenstate search algorithms. Our oracle combines the benefits of both the QSVT amplitude encoding of the problem and GAS strategies to seek eigenvalues.

关键词： Filtering Qubit signal processing algorithms signal processing Logic gates Search problems Encoding Proposals Optimization Genetic algorithms

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IoT Medical Device with Digital signal processing for Photoplethysmography Derived Respiratory Rate

IoT Medical Device with Digital Signal Processing for Photop...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Maria Therese B. Bonifacio Jae Malonie B. De Austria Ramon G. Garcia Electrical Electronics and Computer Engineering Mapua University Manila Philippines School of EECE Mapua University Manila Philippines

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Self-monitoring respiratory and cardiac health is challenging without proper equipment and medical knowledge. While Masimo offers ideal remote monitoring through a pulse oximeter, device accessibility is still a significant issue. Thus, we designed a novel device designed for remote monitoring of vital signs using the Internet of Things (IoT) technology and sensor technology in healthcare. Leveraging the capabilities of the modest MAX30102 sensor and ESP32 microcontroller, the device integrates seamlessly with IoT networks, enabling real-time transmission of heart and respiratory rate (RR) data with general assessment feedback to a convenient cellphone application. A sophisticated digital signal processing (DSP) algorithm was implemented in the device, which accurately derives RR from photoplethysmography (PPG) heart rate signals. This DSP-based device ensures precision in RR measurement and facilitates self-monitoring. Mean Squared Error (MSE) and Bland-Altman analysis results demonstrated the device's accuracy and reliability in capturing vital sign data. By bridging IoT capabilities with advanced DSP techniques, the device is a promising solution for remote patient monitoring to improve healthcare.

关键词： Pulse oximeter Patient monitoring signal processing algorithms Digital signal processing Medical services Photoplethysmography Real-time systems

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Research on Sidelobe Suppression Algorithm of Pulse Compression in Radar signal processing

Research on Sidelobe Suppression Algorithm of Pulse Compress...

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Chinese Automation Congress (CAC)

作者： Xu Chen Li Wang Xiaoxue Li Qiaoyu Li Yatao Liu Yong Luo University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Linear frequency modulation (LFM) signal is often used for pulse compression in radar system, but there are some problems such as high sidelobe and main lobe broadening after compression, which has certain interference to the accurate detection of actual target signal. This paper discusses the bidirectional weighted ultra-low sidelobe pulse compression algorithm, and gives an improved algorithm according to its shortcomings. Based on this, the window function linear combination method is introduced, which can obtain better sidelobe suppression ability when the main lobe width is basically unchanged.

关键词： Chirp modulation Simulation signal processing algorithms Radar detection Radar Interference Radar signal processing Faces Compression algorithms Weighted sum model

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A GPGPU-Based Algorithm Acceleration System for ECG signal processing

A GPGPU-Based Algorithm Acceleration System for ECG Signal P...

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algorithms, Computing and Artificial Intelligence (ACAI), International Conference on

作者： Tianyu Huang Zhijiong Wang Guannan Hu Sio Hang Pun Hung Chun Li Mang I Vai Department of Electrical and Computer Engineering Joint Laboratory of Zhuhai UM Science and Technology Research Institute - Lingyange Semiconductor Incorporated University of Macau Macao China Department of Electrical and Computer Engineering Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Science and Technology Research Institute - Lingyange Semiconductor Incorporated University of Macau Macao China Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Science and Technology Research Institute - Lingyange Semiconductor Incorporated Zhuhai China Department of Electrical and Computer Engineering Joint Laboratory of Zhuhai UM Institute of Microelectronics Science and Technology Research Institute - Lingyange Semiconductor Incorporated University of Macau Macao China

ISBN: (数字)9798331529314

ISBN: (纸本)9798331529321

Electrocardiogram (ECG) is an important non-invasive technique for diagnosing cardiovascular diseases (CVD). After acquiring the patients' raw ECG signal data, signal processing is essential for the diagnosis. Conventional ECG signal processing is usually performed serially, which takes a long time to process signals with large amounts of data. In this study, we have proposed a GPGPU-based algorithm acceleration system to improve the efficiency of ECG signal processing. This system utilizes OpenCL for parallel algorithm development on GPGPU. GPGPU executes certain steps in the QRS complex detection algorithm via parallel computing, thereby accelerating the algorithmic process. Experimental results showed that the system achieved a speedup of 1.279 compared to serial computing when processing 39 ECG signal sequences in parallel. In addition, our system has been equipped with a progressive transmission mechanism and a function that intelligently determines whether an algorithm can be accelerated. These features make this system practical not only in biomedical signal processing but also in engineering and research fields with large amounts of data.

关键词： signal processing algorithms signal processing Electrocardiography Cardiovascular diseases Biomedical communication Parallel algorithms Detection algorithms Artificial intelligence

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