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IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)

作者： Yeldos A. Altay Artem S. Kremlev Faculty Control Systems and Robotics ITMO University St. Petersburg Russia

ISBN: (数字)9781665404761

ISBN: (纸本)9781665446426

This article presents the results of analysis of measuring ECG signals processing using two new filtering algorithms for low-frequency and high-frequency interference. The presence of such interferences leads to a decrease in the accuracy of the selection of informative components of the measuring signals. To eliminate low-frequency and high-frequency interference, the results of evaluating the effectiveness of two new algorithms for ECG signals are presented. The processing algorithms are based on a polynomial approximation of the high-pass filter and the wide-band notch filter cascade using Newton polynomials. On the basis of these processing algorithms, its performance was analyzed on a priori models of ECG signals and interference, and on the basis of quantitative indicators, its effectiveness was compared with respect to known algorithms. It was found that processing algorithms based on Newton polynomials significantly improve the quality of filtering of measuring ECG signals and indicators of signal-to-noise ratio and mean square error.

关键词： signal processing algorithms Interference Filtering algorithms Electrocardiography signal processing Approximation algorithms signal to noise ratio

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Research on the Application of Machine Learning Big Data Mining algorithms in Digital signal processing

Research on the Application of Machine Learning Big Data Min...

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Image processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Simin Niu Taiyuan University of Technology Taiyuan China

ISBN: (纸本)9781728190198

Traditional digital signal processing technology based on DSP and FPGA is more suitable for real-time signal processing, and is limited by data scale and frequency resolution, making it unsuitable for offline data processing, analysis and mining under large-scale data Application. At present, the industrial big data analysis platform can use Spark as a calculation engine for real-time signal processing and offline signal processing acceleration, but the analysis platform lacks mathematical calculation solutions such as digital signal processing suitable for distributed parallel calculation engines. This article is based on time the parity decomposition is selected, and the fast Fourier transform is realized by MATLAB software. Based on an example of the application of the compiled FFT source program, this article analyses the frequency spectrum of discrete-time and continuous-time signals of limited length.

关键词： Machine learning algorithms signal processing algorithms Digital signal processing Machine learning Libraries Real-time systems Sparks

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MCSSA: A Stream-Based Multiconcurrency Systolic Sorting Array Combining Merge Tree

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第4期44卷 1340-1353页

作者： Huang, Lan Gao, Teng Feng, Yang Wang, Kangping Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

The exploration of utilizing reconfigurable circuits with parallel computing capabilities has been conducted to enhance sorting performance and reduce power consumption. However, most sorting algorithms using dedicated processors are based on parallelization designs of serial algorithms without considering the design method of large-scale integrated circuits. This results in various issues, including the overuse of I/O interface resources, on-chip storage resources, and complex layout wiring. In this article, we extend the 2-tuple relation in the uniform recurrence equation (URE) structure used to define the systolic array to n-tuples, and the extended structure is flexible in defining I/O bandwidth and concurrency. Then we define the multiconcurrency systolic sorter array (MCSSA) algorithm based on the extended URE structure, which has a flexible 4N/n time complexity based on the n-tuple relation. Moreover, this systolic array can simultaneously sort two independent sequences, increasing the reuse of resources. Afterwards, we encapsulate each n-tuple into a processing element (PE) cell. The entire MCSSA consists of these interconnected PE cells, each of which can be customized in terms of data bit width and type. Last but not least, we have improved the merge tree structure called MC- merge tree. The concurrency of this algorithm can also be flexibly defined, we use this algorithm combined with MCSSA to cope with large-scale sorting scenarios. In our experiments, we have demonstrated the speed-up ratio of MCSSA relative to other state of the art (SOTA) sorting algorithms. Inheriting the unity and simplicity from the Systolic Array architecture, MCSSA achieves a maximum 73.17x acceleration ratio on the U200. In addition, the MC-merge tree expands the MCSSA sorting scale with a maximum of 450.56 times while maintaining the advantage of the acceleration ratio. The results of our study demonstrate that MCSSA and MC-merge tree have better acceleration, throughput and sc

关键词： Sorting Systolic arrays signal processing algorithms Concurrent computing Hardware Time complexity Integrated circuits Field programmable gate arrays Complexity theory Design automation FPGA parallel sorting systolic array uniform recurrence equations (UREs)

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A Co-Evolutionary Dual Niching Differential Evolution Algorithm for Nonlinear Equation Systems Optimization

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 109-118页

作者： Li, Shuijia Wang, Rui Gong, Wenyin Liao, Zuowen Wang, Ling Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Xiangjiang Lab Changsha 410205 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Beibu Gulf Univ Beibu Gulf Ocean Dev Res Ctr Qinzhou 535000 Peoples R China Tsinghua Univ Dept Automat BNRIST Beijing 100084 Peoples R China

A nonlinear equation system often has multiple roots, while finding all roots simultaneously in one run remains a challenging work in numerical optimization. Although many methods have been proposed to solve the problem, few have utilised two algorithms with different characteristics to improve the root rate. To locate as many roots as possible of nonlinear equation systems, in this paper, a co-evolutionary dual niching differential evolution with information sharing and migration is developed. To be specific, firstly it utilizes a dual niching algorithm namely neighborhood-based crowding/speciation differential evolution co-evolutionary to search concurrently;secondly, a parameter adaptation strategy is employed to ameliorate the capability of the dual algorithm;finally, the dual niching differential evolution adaptively performs information sharing and migration according to the evolutionary experience, thereby balancing the population diversity and convergence. To investigate the performance of the proposed approach, thirty nonlinear equation systems with diverse characteristics and a more complex test set are used as the test suite. A comprehensive comparison shows that the proposed method performs well in terms of root rate and success rate when compared with other advanced algorithms.

关键词： Optimization Information sharing signal processing algorithms Convergence Genetic algorithms Search problems Nonlinear equations Co-evolutionary differential evolution inform-ation migration niching nonlinear equation system

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An Enhanced Empirical Mode Decomposition Technique for Rotor Fault Detection in Induction Motors

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Arifin, Md. Shamsul Wang, Wilson Uddin, Mohammad Nasir Lakehead Univ Elect & Comp Engn Barrie Campus Barrie ON L4M 3X9 Canada Lakehead Univ Mech & Mechatron Engn Thunder Bay Campus Thunder Bay ON P7B 5E1 Canada

Induction motors or machines (IMs) are the driving force in various industries such as manufacturing, transportation, and wind power generation. Hence it is essential to detect faults in IMs reliably so as to enhance the production quality and avoid operational degradation. However, it is still challenging to detect faults in IMs reliably as fault feature properties could change under variable IM operating conditions. The objective of this article is to propose an enhanced empirical mode decomposition (EEMD) technique to detect the IM broken rotor bar (BRB) fault based on motor current signature analysis (MCSA). In the proposed EEMD technique, first, a phase-insensitive similarity function is suggested to determine the representative intrinsic mode function (IMF). Second, an optimized adaptive multiband filter (OAMF) is proposed to recognize the fault characteristic features from the spectrum. Third, a modified whale optimization (MWO) algorithm is suggested to optimize the parameters in the adaptive multiband filter. A reference function is also proposed to enhance feature properties and IM fault detection. The effectiveness of the proposed EEMD technique is verified experimentally under different IM conditions.

关键词： Fault detection Feature extraction Rotors Spectral analysis Bars signal processing algorithms Empirical mode decomposition Benchmark testing Adaptive filters Accuracy Adaptive multiband filter broken rotor bars (BRBs) empirical mode decomposition (EMD) fault detection of induction motors/machines (IMs) motor current signature analysis (MCSA)

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and algorithms

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 113-129页

作者： Borsoi, Ricardo A. Usevich, Konstantin Brie, David Adali, Tulay Univ Lorraine French Natl Ctr Sci Res CNRS CRAN Lab F-54000 Vandoeuvre Les Nancy France Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

Coupled tensor decompositions (CTDs) perform data fusion by linking factors from different datasets. Although many CTDs have been already proposed, current works do not address important challenges of data fusion, where: 1) the datasets are often heterogeneous, constituting different "views" of a given phenomena (multimodality);and 2) each dataset can contain personalized or dataset-specific information, constituting distinct factors that are not coupled with other datasets. In this work, we introduce a personalized CTD framework tackling these challenges. A flexible model is proposed where each dataset is represented as the sum of two components, one related to a common tensor through a multilinear measurement model, and another specific to each dataset. Both the common and distinct components are assumed to admit a polyadic decomposition. This generalizes several existing CTD models. We provide conditions for specific and generic uniqueness of the decomposition that are easy to interpret. These conditions employ uni-mode uniqueness of different individual datasets and properties of the measurement model. Two algorithms are proposed to compute the common and distinct components: a semi-algebraic one and a coordinate-descent optimization method. Experimental results illustrate the advantage of the proposed framework compared with the state of the art approaches.

关键词： Tensors Matrix decomposition Functional magnetic resonance imaging Data integration Data models Brain modeling Couplings Vectors signal processing algorithms Image fusion Coupled tensor decomposition personalized learning shared and distinct components uniqueness

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A Universal Methodology of Complex Number Computation for Low-Complexity and High-Speed Implementation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年第3期72卷 1308-1320页

作者： Wang, Yu Zhang, Jin Wu, Youlong Lyu, Fei Luo, Yuanyong Nanjing Xiaozhuang Univ Sch Elect Engn Nanjing 211171 Peoples R China Jinling Inst Technol Sch Elect & Informat Engn Nanjing 211169 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Integrated Circuits Nanjing 211106 Peoples R China Nanjing Univ Sch Phys Nanjing 210093 Peoples R China Huawei Corp Dept Turing Architecture Design Linx Lab HiSilicon Shenzhen 518129 Peoples R China

In complex-valued neural network (CVNN) applications, complex number calculations require high performance rather than high precision. However, most previous studies focused on high-precision approaches, which have low speed and high hardware costs. This paper proposes a universal methodology of complex number computation for low-complexity and high-speed implementation. The proposed methodology is based on the piecewise linear (PWL) method and can be used for different types of complex number computations. Considering that multiplication operations consume considerable resources, multiplication, fused square-add (FSA) and fused multiply-add (FMA) operations are the focus of optimization. The partial products of the square operation are reduced by folding and merging techniques because of their symmetry in the FSA operation. The partial products of the multiplication and FMA operations are reduced via Booth encoding. In addition, the partial products are further reduced by the proposed step-by-step truncation method. The proposed segmenter, which simulates the hardware implementation, automatically divides the nonlinear functions in the complex number computations into the smallest number of segments according to the required precision. The results show that the proposed approach improves performance and reduces hardware costs compared with the state-of-the-art methods for complex number calculations involving square roots, reciprocals and logarithms.

关键词： Hardware Image segmentation Software Neural networks Costs signal processing algorithms Power demand Optimization Merging Encoding Piecewise linear (PWL) approximation method computation for complex numbers step-by-step truncation square root calculation of complex numbers reciprocal calculation of complex numbers logarithm calculation of complex numbers

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Fundamental Picking Analysis System for Electric Guitar Using Wrist-Worn Inertial Motion Sensors

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IEEE SENSORS JOURNAL 2025年第5期25卷 8849-8856页

作者： Matsushita, Soichiro Takamoto, Ayaka Tokyo Univ Technol Sch Comp Sci Entertainment Lab Hachioji Tokyo 1920982 Japan

A wrist-worn inertial motion-sensing device with a high sampling frequency was applied to evaluate fundamental electric guitar picking. A combinatorial analysis using wrist-twisting angular jerk and time-differential vertical jerk signals detected on the player's dominant wrist achieved accurate picking timing estimation. The developed signal-processing algorithm eliminated motion artifacts such as vibration from the guitar strings, especially in the case of the palm mute technique. The timing differences between the motion-based method using a sampling frequency of 1024 Hz and a sound-based onset timing analysis method as ground truth were less than 10 ms. In addition, it was found that the amplitude of sound and the time duration of picking for each note can be determined in the form of motion parameters without using sound signals. A population test in a 14-week-long guitar lesson class with 12 beginners showed convincing results that reflected the difficulties of the chord strumming and the single-note picking techniques.

关键词： Timing Sensors Hands Rhythm Accuracy Wrist Vibrations signal processing algorithms Gyroscopes Bars Angular jerk electric guitar inertial motion sensor jerk picking wrist-worn

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Design of Denoising Method for Underwater Communication Based on Adaptive Chunking Algorithm and Quantum Mechanics Theory

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IEEE INTERNET OF THINGS JOURNAL 2025年第3期12卷 2498-2513页

作者： Liu, Xin Meng, Xiangxu Xu, Yinan Wu, Yujing Lee, Hojun Liu, Bingqi Xu, Yihu Yanbian Univ Coll Engn Yanji 133002 Peoples R China Hoseo Univ Asan Dept Informat & Commun Engn Asan 31499 South Korea

With the deepening exploitation of marine resources, the application of underwater communication is becoming increasingly widespread, and the noise in the underwater channel will severely impact the reliability of underwater communication. Therefore, researchers have focused on improving the accuracy and reliability of underwater communication through denoising. Currently, denoising methods are widely used based on algorithms, such as Fourier transform, wavelet transform, convolutional neural network, and time-space domain filtering. Still, they all have some areas for improvement in the application of underwater communication. In this regard, inspired by quantum mechanical tools and adaptive algorithms, this article proposes a quantum adaptive chunk denoising algorithm applied in underwater communication. First, this article proposes an adaptive chunking algorithm to dynamically adjust the chunk size according to the local signal characteristics to capture the signal details better. Second, the chunked signal and image are taken as potentials in the discrete Schr & ouml;dinger equation and solved for the eigenvalues and eigenvectors of the discrete Hamiltonian matrix constructed by the adaptive chunking algorithm. Finally, the solved values are subjected to total variational denoising and reconstruction after denoising. The algorithm proposed in this article is compared with wavelet denoising based on hard and soft thresholding, Denoising Algorithm Based on an ideal low-pass adaptive filter, sparsity-assisted signal smoothing (SASS) algorithm and Quantum Mechanics Adaptive Basis Denoising Algorithm. The experimental results show that the quantum adaptive chunking denoising algorithm proposed in this article can effectively remove underwater noise from signals and images under the condition of low-signal-to-noise ratio, which improves the reliability and signal quality of underwater communication and meets the denoising standard of underwater communication.

关键词： Noise Noise reduction Quantum mechanics Underwater communication signal processing algorithms Thermal noise Mathematical models Wavelet transforms Classification algorithms Time-frequency analysis

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Interacting Large Language Model Agents Bayesian Social Learning Based Interpretable Models

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IEEE ACCESS 2025年 13卷 25465-25504页

作者： Jain, Adit Krishnamurthy, Vikram Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

This paper discusses the theory and algorithms for interacting large language model agents (LLMAs) using methods from statistical signal processing and microeconomics. While both fields are mature, their application to decision-making involving interacting LLMAs remains unexplored. Motivated by Bayesian sentiment analysis on online platforms, we construct interpretable models and stochastic control algorithms that enable LLMAs to interact and perform Bayesian inference. Because interacting LLMAs learn from both prior decisions and external inputs, they can exhibit bias and herding behavior. Thus, developing interpretable models and stochastic control algorithms is essential to understand and mitigate these behaviors. This paper has three main results. First, we show using Bayesian revealed preferences from microeconomics that an individual LLMA satisfies the necessary and sufficient conditions for rationally inattentive (bounded rationality) Bayesian utility maximization and, given an observation, the LLMA chooses an action that maximizes a regularized utility. Second, we utilize Bayesian social learning to construct interpretable models for LLMAs that interact sequentially with each other and the environment while performing Bayesian inference. Our proposed models capture the herding behavior exhibited by interacting LLMAs. Third, we propose a stochastic control framework to delay herding and improve state estimation accuracy under two settings: 1) centrally controlled LLMAs and 2) autonomous LLMAs with incentives. Throughout the paper, we numerically demonstrate the effectiveness of our methods on real datasets for hate speech classification and product quality assessment, using open-source models like LLaMA and Mistral and closed-source models like ChatGPT. The main takeaway of this paper, based on substantial empirical analysis and mathematical formalism, is that LLMAs act as rationally bounded Bayesian agents that exhibit social learning when interacting. Tradi

关键词： Bayes methods Stochastic processes signal processing algorithms signal processing Microeconomics Inference algorithms Engines Data models Numerical models Bayesian social learning large language models Bayesian revealed preferences structural results optimal stopping POMDPs self-attention rational inattention model collapse model collapse

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