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

作者： Wenqi Ding Yuanchao Liu Zhongjie Wang Zheng Chu Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Learning (FL) enables collaborative training while protecting the privacy of participant data. However, typical centralized FL structures are vulnerable to malicious client attacks. To mitigate such vulnerabilities, blockchain technology has been used to develop a decentralized FL framework. Yet, this approach leads to significant transmission and computation overhead. In order to improve robustness as well as efficiency, we propose Multi-Population Federated Learning (MPFL), which optimizes blockchain-based federated learning by incorporating operators of multi-population genetic algorithm (MPGA), such as selection, crossover, mutation and migration. Moreover, we introduce a committee consensus mechanism tailored to MPFL, where honest clients are elected to govern each population, thereby facilitating intraspecific and interspecific competition. Our experiments demonstrate that MPFL outperforms various aggregation algorithms in achieving superior robustness, and it reduces the costs of transmission and computation compared to the blockchain-based FL framework.

关键词： Training Federated learning Voting Pipelines signal processing algorithms signal processing Genetics Robustness Speech processing Genetic algorithms

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Highly-Efficient Sidelink SSB Format and Synchronization Algorithm for Common-Mode signaling in 5G NR Sidelink-Based mmWave WPAN Systems

Highly-Efficient Sidelink SSB Format and Synchronization Alg...

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IEEE Conference on Wireless Communications and Networking

作者： Satoshi Uemori Ryogo Okura Yusuke Koda Hiroshi Harada Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Japan

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Sidelink communication has evolved as a distributed device-to-device (D2D) communication system within the third-generation partnership project (3GPP) for the advancements in next-generation distributed autonomous systems. However, the current sidelink communication system basically being considered for operation at microwave band is not suitable for distributed wireless networking with a Gbit/s-level data rate. In this context, we have conceptualized a millimeter wave (mmWave) sidelink-based wireless personal area network (WPAN) to enable high-speed distributed wireless networking among indoor proximate devices. Particularly, inspired by the notion of “common-mode signaling (CMS)” in the IEEE 802.15.3c/11ad mmWave WPAN standards, we proposed an innovative physical sidelink broadcast channel (PSBCH) coined CMS- PSBCH to reach a robust inter-node control signaling to maintain a connection under harsh mmWave channel characteristics. As a sequential study, this paper proposes a robust signal detection and timing synchronization scheme tailored for CMS-PSBCH to lead a successful detection and demodulation, thereby providing a complete set of robust synchronization signal block (SSB) for the sidelink-based mmWave WPAN. Our link-level evaluation shows that the proposed scheme effectively detects synchronization signals and symbol timing even under the signal-to-noise ratio (SNR) of −18 dB. Moreover, the proposed scheme leads to successful demodulation of CMS- PSBCH payload for various multipath channels for a wideband indoor short-range communication scenario at the 60 GHz band.

关键词： Wireless communication Wireless personal area networks Symbols signal processing algorithms Sidelink Synchronization Millimeter wave communication Standards signal detection signal to noise ratio

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Research on Condition Monitoring and Health Assessment Technology of 10kv Overhead Lines Based on Electromagnetic and Ultrasonic Collaborative Induction

Research on Condition Monitoring and Health Assessment Techn...

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Digital Analysis and processing, Intelligent Computation (DAPIC), International Conference on

作者： Weibin Xu Weitang Zhang Zhuojian Li Jinxiang Lu Jiangmen Power Supply Bureau of Guangdong Power Grid Co. Ltd Jiangmen Guangdong China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Aiming at the aging, insulation damage and overheating of 10kV overhead lines exposed to the natural environment for a long time, this study proposes a new monitoring technology, which uses electromagnetic ultrasonic technology to generate and spread ultrasonic signals, and improves the detection accuracy and anti-interference ability of signals through collaborative induction technology to realize comprehensive, real-time and efficient monitoring of overhead lines. The system architecture includes perception layer, transport layer, processing layer and application layer, in which the perception layer is responsible for collecting state information, the transport layer ensures the safe and reliable transmission of data, the processing layer contains data preprocessing and health assessment modules, and the application layer provides user interface and decision support. The experimental results show that the system can effectively identify and locate the cracks and corrosion areas in the line, accurately detect the new defects of artificial simulation, and give the overall health index of the line. Through the time series analysis algorithm, the system can also predict the future health trend of the line and send out early warning signals before potential faults occur. The research results verify the effectiveness and reliability of the monitoring system, which provides strong support for the safety and reliability of power system.

关键词： Condition monitoring Accuracy Collaboration signal processing algorithms Power system stability Acoustics Real-time systems Power system reliability Electromagnetics Monitoring

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Identical-Delay Based 2-D DOA and Frequency Joint Estimation With Sub-Nyquist Sampling for URA

Identical-Delay Based 2-D DOA and Frequency Joint Estimation...

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

作者： Liang Liu Xinyun Zhang Xinyi Zhou Lu Gan Jiancheng An Hongbin Li University of Electronic Science and Technology of China Chengdu China Yibin Institute of UESTC Yibin China Nanyang Technological University Singapore Stevens Institute of Technology Hoboken USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As spectrum congestion intensifies in wireless communication, efficient spectrum utilization through advanced sensing techniques has become increasingly important. This paper proposes a joint carrier frequency and two-dimensional (2-D) Direction of Arrival (DOA) estimation algorithm with signal recovery, utilizing identical-delay channels and sub-Nyquist sampling rates with a Uniform Rectangular Array (URA). Compared to multi-coset structures, the proposed method places identical-delay channels only along the edges of the URA, eliminating the need for additional ADCs and reducing hardware cost. Moreover, by leveraging tensor techniques, the spatial structure of the array is preserved, and the parameter pairing problem is avoided, leading to higher precision in estimation. Simulation results demonstrate the superior performance of the proposed method.

关键词： Wireless communication Direction-of-arrival estimation Tensors Two-dimensional displays signal processing algorithms Estimation Frequency estimation Hardware Sensors Speech processing

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Radon-Fourier Transform for Timing Correction in Uncoupled Digital Radar Networks

Radon-Fourier Transform for Timing Correction in Uncoupled D...

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German Microwave Conference

作者： Julian Aguilar Lukas Paulus David Werbunat Alexander Grathwohl Christian Waldschmidt Institut of Microwave Engineering Ulm University Germany

ISBN: (数字)9783982039749

ISBN: (纸本)9798331521790

Uncoupled radar networks offer many desired features such as large virtual apertures and flexibility in sensor placement without costly radio frequency (RF) cables. However, they also introduce novel challenges, especially the recovery of coherency between individual radar sensors. The correction of timing errors due to sampling frequency offsets (SFO) between a transmitter’s digital-to-analog converter (DAC) and receiver’s analog-to-digital converter (ADC) is essential for the coherent processing of uncoupled digital radar network measurements. Systems with different error estimation and correction approaches have been proposed recently, which utilize resampling-based algorithms. However, they are costly to implement, i.e. on an FPGA, and require additional signal processing steps. The herein presented Radon-Fourier Transform (RFT)-based algorithm is capable of directly calculating the corrected radar image without any resampling, negating the downside of additional signal processing steps required after resampling. It is shown that the RFT approach can almost completely restore erroneous radar data, and even outperforms the resampling-based algorithm. In this work, an experimental orthogonal frequency-division multiplexing (OFDM) demonstrator is utilized to verify the concepts, which are applicable to other modulation types typically used for digital radar.

关键词： OFDM Computational modeling signal processing algorithms Radar Transforms Radar imaging Data models Image restoration Frequency measurement Frequency division multiplexing

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A PD measuring and evaluation system based on digital signal processing

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2000年第1期7卷 21-29页

作者： Borsi, H Univ Hannover Schering Inst High Voltage Technique & Engn D-30167 Hannover Germany

Many current partial discharge (PD) fault diagnosing systems are based on conventional PD measurements with limited bandwidth. In this contribution, a modular structured system is introduced, which analyzes the characteristics of measured broadband pn pulses and their distortion on the way to the measuring system by using digital signal processing algorithms. Future requirements of PD monitoring systems are discussed.

关键词： Partial discharges Distortion measurement Partial discharge measurement Pulse measurements Current measurement Bandwidth signal analysis Algorithm design and analysis Digital signal processing signal processing algorithms

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ADAPTIVE RECURSIVE-LEAST-SQUARES LATTICE AND TRANSVERSAL FILTERS FOR CONTINUOUS-TIME signal-processing

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL signal processing 1992年第2期39卷 81-89页

作者： LEVARI, H KAILATH, T CIOFFI, JM STANFORD UNIV INFORMAT SYST LABSTANFORDCA 94305

The computational rate required by most digital adaptive filters limits their real-time applications to low-bandwidth signals. This difficulty may be overcome by using electronically controlled analog filters with continuously varying gains. In this paper we present lattice and transversal filters for adaptive recursive-least-squares (RLS) processing of continuous-time signals. They have the same structure as the discrete-time RLS adaptive lattice and transversal configurations, namely, a lattice cascade and a tapped-delay-line, respectively, but with continuously adjustable parameters that are updated by propagating a set of coupled differential equations. While the discrete-time scheme involves a fundamental unit of time (viz., the sampling period of the signal) that determines both the duration of the delay and the rate of gain-updating, the new continuous-time scheme involves a delay of arbitrary duration and continuously varying gains. It turns out that the conceptual and computational complexity of the continuous-time filters is less than for their discrete-time counterparts. The rate of parameter updating in our continuous-time configurations is essentially independent of the bandwidth and center frequency of the processed signal. Consequently, the gain-update module of the proposed algorithm can be implemented with slow devices (or with digital hardware), so that only the signal-path-filter must match the frequency characteristics of the processed signal.

关键词： Lattices Transversal filters signal processing Resonance light scattering Hardware Adaptive filters signal processing algorithms Frequency Digital filters RF signals

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signal processing for Finance, Economics, and Marketing

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IEEE signal processing MAGAZINE 2017年第3期34卷 14-35页

作者： Zhang, Xiao-Ping (Steven) Wang, Fang Ryerson Univ Elect & Comp Engn Toronto ON Canada Ryerson Univ Finance Dept Ted Rogers Sch Management Toronto ON Canada EidoSearch Toronto ON Canada Wilfrid Laurier Univ Lazaridis Sch Business & Econ Mkt Waterloo ON Canada

Economic data and financial markets are intriguing to researchers working on data and quantitative models. With rapid growth of and increasing access to data in digital form, finance, economics, and marketing data are poised to become one of the most important and tangible big data applications, owing not only to the relatively clean organization and structure of the data but also to clear application objectives and market demands. However, data-related economic studies often have different viewpoints from signal processing (SP). Also, many fundamental economics and business problems have been well formulated and studied in both theory and practice. The knowledge of foundational finance and economic theories will help SP and data researchers avoid reinventing the wheel and develop meaningful and useful research in these areas.

关键词： Economics Finance Biological system modeling Big Data applications Utility theory Analytical models signal processing algorithms

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PulmoScan: A Practical Pulmonary Disease Pre-Screening System

PulmoScan: A Practical Pulmonary Disease Pre-Screening Syste...

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

作者： Baixu Yan Shijia Ge Meizi Lu Weixiang Zhang Shuzhao Xie Zhi Wang Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Automation of pulmonary disease identification has been a long-standing area of research and gained increased attention after the COVID-19 pandemic. However, existing respiratory sound classification algorithms exhibit significant limitations, including suboptimal performance, insufficient input robustness, and inadequate alignment with clinical evaluation metrics, thereby hindering their practical implementation. To address these limitations, we introduce PulmoScan, a practical pulmonary disease pre-screening system. PulmoScan comprises three fundamental modules: a Respiratory Sound Quality Validator that ensures the robustness of input data, a Runtime Decision Booster that improves performance and adapting to variating evaluation metrics, and a Symptom Enhancement Diagnoser that augments respiratory sound classification with comprehensive disease pre-screening capabilities. Beyond its primary function, PulmoScan exemplifies a methodological framework for translating theoretically limited algorithms into viable clinical applications, demonstrating essential considerations and procedural adaptations for real-world implementation.

关键词： Measurement Translation Runtime Pulmonary diseases signal processing algorithms signal processing Robustness Data models Classification algorithms Speech processing

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Predicting Stock Market Movements in Response to Global Events

Predicting Stock Market Movements in Response to Global Even...

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International Conference on signal processing and Communication (ICSC)

作者： N H Shrinandh Sanidhya Sharma Shivram Mohan Minakshi M Unnithan S Lalitha Department of Electronics and Communication Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Stock market has been using prediction models that have often focused on historical data, giving less attention to how external factors like elections, inflation and conflicts affect the stock prices. This proposal examines the influence possessed by these external factors on stock market behaviour. Machine learning models, including Random Forest, Linear Regression, Gradient Boosting and Long Short-Term Memory (LSTM), is applied as well as investigated on the live dataset of Yahoo Finance, Geopolitical Data, Covid-19 Data and Inflation in GDP, to see which algorithm performs best in predicting the changes in stock value caused by the external factors. The best-case accuracy was 99.9%, showing the importance of consideration of external events for more accurate stock market predictions in the real world. This will help investors make better decisions and improve the risk management strategies required in business.

关键词： Accuracy Voting Linear regression signal processing algorithms Predictive models Boosting Data models Stock markets Long short term memory Random forests

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