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A novel amalgamation of pre-processing technique and CNN model for accurate classification of power quality disturbances

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ELECTRICAL ENGINEERING 2025年第4期107卷 5187-5206页

作者： Soni, Prity Mishra, Pankaj Mondal, Debasmita Birla Inst Technol Dept Elect & Elect Engn Ranchi 835215 India Thapar Inst Engn & Technol Dept Elect & Instrumentat Engn Patiala 147004 Punjab India

This work presents an innovative framework that combines the recurrence plots (RP) method with ResNet-50 (a convolutional neural network (CNN)) to autonomously extract relevant features for classifying multiple power quality disturbances for a power signal using the support vector machine. The ResNet-50 is employed to extract the most discriminated features from the two-dimensional images obtained from 1-D signals using the RP method. The work investigates synthetic power quality disturbances, including nine single disturbances, eight double disturbances, and seven triple disturbances. The validation of the proposed framework is conducted on the Standard ieee 5-bus system under diverse fault scenarios, demonstrating the method's efficiency and reliability. The statistical stability of the proposed framework is assessed using two non-parametric tests, the Sign test and the Wilcoxon test, to ensure the reliability of the results. Furthermore, the proposed work is compared with other advanced CNN models and pre-processing techniques, highlighting its superior performance and effectiveness.

关键词： Power quality disturbances signal processing Image processing Feature extraction Deep learning Classification

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Prototype Imputation Guided Incomplete Multi-View Clustering

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ieee signal processing LETTERS 2025年 32卷 1565-1569页

作者： Hong, Rui Chen, Xiao-Ping Zhou, Yan Liu, Hui Wan, Tiancai Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Avic Chengdu Aircraft Design & Res Inst Chengdu 610091 Peoples R China

In real-world data collection, certain views may become partially unavailable due to sensor malfunctions or equipment faults, giving rise to the challenge of incomplete multi-view clustering (IMVC). Although some IMVC methods that utilize prototype completion yield acceptable performance, the majority implicitly assume the correct alignment of prototypes across all views. However, the generated prototypes could have the potential misalignment of prototypes across views. To tackle this issue, we propose a new Prototype Imputation Guided Incomplete Multi-view Clustering (PIGIMC), which can reduce the impact of prototype mismatch. Specifically, RPCIC first employs a multi-view contrastive learning module to obtain consistent feature representations across various views. To mitigate the adverse impact caused by multi-view prototype mismatch, we propose a prototype matching learning to achieve prototype alignment. Lastly, we propose a prototype imputation strategy to impute the missing data, thereby achieving IMVC. Extensive experiments confirm that PIGIMC surpasses 11 state-of-the-art IMVC methods.

关键词： Prototypes Imputation Decoding Contrastive learning Training Data mining machine learning Interference Clustering algorithms Bipartite graph Incomplete multi-view clustering prototype alignment prototype matching learning

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Formant Tracking by Combining Deep Neural Network and Linear Prediction

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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ieee OPEN JOURNAL OF signal processing 2025年 6卷 222-230页

作者： Kadiri, Sudarsana Reddy Huang, Kevin Hagedorn, Christina Byrd, Dani Alku, Paavo Narayanan, Shrikanth Univ Southern Calif Los Angeles CA 90007 USA CUNY Coll Staten Isl Grad Ctr New York NY 10017 USA Aalto Univ Espoo 02150 Finland

Formant tracking is an area of speech science that has recently undergone a technology shift from classical model-driven signal processing methods to modern data-driven deep learning methods. In this study, these two domains are combined in formant tracking by refining the formants estimated by a data-driven deep neural network (DNN) with formant estimates given by a model-driven linear prediction (LP) method. In the refinement process, the three lowest formants, initially estimated by the DNN-based method, are frame-wise replaced with local spectral peaks identified by the LP method. The LP-based refinement stage can be seamlessly integrated into the DNN without any training. As an LP method, the study advocates the use of quasiclosed phase forward-backward (QCP-FB) analysis. Three spectral representations are compared as DNN inputs: mel-frequency cepstral coefficients (MFCCs), the spectrogram, and the complex spectrogram. Formant tracking performance was evaluated by comparing the proposed refined DNN tracker with seven reference trackers, which included both signal processing and deep learning based methods. As evaluation data, ground truth formants of the Vocal Tract Resonance (VTR) corpus were used. The results demonstrate that the refined DNN trackers outperformed all conventional trackers. The best results were obtained by using the MFCC input for the DNN. The proposed MFCC refinement (MFCC-DNNQCP-FB) reduced estimation errors by 0.8 Hz, 12.9 Hz, and 11.7 Hz for the first (F1), second (F2), and third (F3) formants, respectively, compared to the Deep Formants refinement (DeepF(QCP-FB)). When compared to the model-driven KARMA tracking method, the proposed refinement reduced estimation errors by 2.3 Hz, 55.5 Hz, and 143.4 Hz for F1, F2, and F3, respectively. A detailed evaluation across various phonetic categories and gender groups showed that the proposed hybrid refinement approach improves formanttracking performance across most test conditions.

关键词： Estimation Spectrogram signal processing Artificial neural networks Training Computational modeling Accuracy Data models Predictive models Overfitting Formant tracking linear prediction MFCCs spectrogram machine learning deep learning

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Toward Automated Air Leak Localization: A machine learning-Enhanced Ultrasonic and LiDAR-SLAM Framework for Industrial Environments

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ieee ACCESS 2025年 13卷 66492-66504页

作者： Schenck, Anthony Daems, Walter Steckel, Jan Univ Antwerp Fac Appl Engn Cosys Lab B-2000 Antwerp Belgium Flanders Make Strateg Res Ctr B-3920 Lommel Belgium

Pressurized air leaks are the source of a significant amount of energy loss in industrial installations. Automating the detection process of these air leaks can help prevent this loss of energy, as detecting and localizing them is the first necessary step in efficient maintenance. Typically ultrasonic microphone arrays are used to detect the leaks, since the escaping air causes turbulences around the leak opening. When the inspection is done manually, the operator of the ultrasonic sensor can evaluate whether or not an ultrasonic sound is originating from an air leak. However, during the automated process of pressurized air leak detection using ultrasonic microphone arrays, many interfering sources arise which lead to the detection of false-positive leak locations. While some of these locations can be easily filtered out using known information of the environment, such as the locations of the compressed air network, many others are more cumbersome to filter out. In order to efficiently suppress these spurious leak signals, we propose the usage of machine learning algorithms to perform classification of the signal sources. To this end, we trained a series of classifiers using real-world experiment data, and show that, by using the best performing classifiers in our leak detection algorithm, we are able to drastically reduce the amount of false-positive leaks, without any meaningful impact on the true-positive leak detection performance. We demonstrate the efficacy of the proposed approach using real-world experimental data gathered in an operating industrial setting.

关键词： Location awareness Acoustics Leak detection Microphone arrays Simultaneous localization and mapping Array signal processing Acoustic measurements Receivers Manuals Recording Air leak localization industrial acoustic sensing robot audition acoustic signal classifier

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LSHIM: Low-Power and Small-Area Inexact Multiplier for High-Speed Error-Resilient Applications

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ieee JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2025年第1期15卷 94-104页

作者： Izadi, Azin Jamshidi, Vahid Shahid Bahonar Univ Kerman Dept Comp Engn Kerman *** Iran

Numerical computations in various applications can often tolerate a small degree of error. In fields such as data mining, encoding algorithms, image processing, machine learning, and signal processing where error resilience is crucial approximate computing can effectively replace precise computing to minimize circuit delay and power consumption. In these contexts, a certain level of error is permissible. Multiplication, a fundamental arithmetic operation in computer systems, often leads to increased circuit delay, power usage, and area occupation when performed accurately by multipliers, which are key components in these applications. Thus, developing an optimal multiplier represents a significant advantage for inexact computing systems. In this paper, we introduce a novel approximate multiplier based on the Mitchell algorithm. The proposed design has been implemented using the Cadence software environment with the TSMC 45nm standard-cell library and a supply voltage of 1.1V. Simulation results demonstrate an average reduction of 31.7% in area, 46.8% in power consumption, and 36.1% in circuit delay compared to previous works. The mean relative error distance (MRED) for the proposed method is recorded at 2.6%.

关键词： Approximation algorithms signal processing algorithms Circuits Accuracy Delays Performance evaluation Power demand Arithmetic Adders Energy consumption Approximate multiplier approximate computing Mitchell's multiplication algorithm energy-consumption area delay

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Model-Based learning for Multi-Antenna Multi-Frequency Location-to-Channel Mapping

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ieee Journal on Selected Topics in signal processing 2025年第3期19卷 520-535页

作者： Chatelier, Baptiste Corlay, Vincent Crussiére, Matthieu Le Magoarou, Luc Univ Rennes INSA Rennes CNRS IETR-UMR 6164 Mitsubishi Electric R&D Centre Europe bcom Rennes35708 France Mitsubishi Electric R&D Centre Europe bcom Rennes35708 France IETR-UMR 6164 Univ Rennes INSA Rennes CNRS bcom bcom Rennes35708 France

Years of study of the propagation channel showed a close relation between a location and the associated communication channel response. The use of a neural network to learn the location-to-channel mapping can therefore be envisioned. The Implicit Neural Representation (INR) literature showed that classical neural architecture are biased towards learning low-frequency content, making the location-to-channel mapping learning a non-trivial problem. Indeed, it is well known that this mapping is a function rapidly varying with the location, on the order of the wavelength. This paper leverages the model-based machine learning paradigm to derive a problem-specific neural architecture from a propagation channel model. The resulting architecture efficiently overcomes the spectral-bias issue. It only learns low-frequency sparse correction terms activating a dictionary of high-frequency components. The proposed architecture is evaluated against classical INR architectures on realistic synthetic data, showing much better accuracy. Its mapping learning performance is explained based on the approximated channel model, highlighting the explainability of the model-based machine learning paradigm. © 2007-2012 ieee.

关键词： Mapping

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Proximity Detection and Trajectory Recognition with machine learning for UHF RFID Systems

Proximity Detection and Trajectory Recognition with Machine ...

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2025 ieee International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Pohl, Thomas M. Mecklenbräuker, Christoph F. Arthaber, Holger EMCE Technische Universität Wien Wien Austria Inst. of Telecommunications Technische Universität Wien Wien Austria

ISBN: (纸本)9798350368741

We report on the proximity detection of a moving object tagged with a passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) tag compared to a stationary tag and the recognition of predefined movement trajectories of tagged objects. Both methods are based on sequentially received tag responses with time and reader information. The recording system consists of a commercially available UHF RFID reader and a switched antenna system with eight antennas. We use supervised machine learning classification methods comprising convolutional layers, Decision Tree (DT), Random Forest (RF), and Ridge classifiers. The proximity detection is compared with two decision thresholds and shows accuracies of more than 83 percent. Evaluation of the trajectory recognition shows an accuracy of 87.5 percent. © 2025 ieee.

关键词： machine learning proximity recognition RFID tags UHF technology

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Empowering Federated learning With Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics

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ieee TRANSACTIONS ON signal processing 2025年 73卷 766-780页

作者： Xiang, Ming Ioannidis, Stratis Yeh, Edmund Joe-Wong, Carlee Su, Lili Northeastern Univ Dept ECE Boston MA 02115 USA Carnegie Mellon Univ Dept ECE Pittsburgh PA 15213 USA

Federated learning is a popular distributed learning approach for training a machine learning model without disclosing raw data. It consists of a parameter server and a possibly large collection of clients (e.g., in cross-device federated learning) that may operate in congested and changing environments. In this paper, we study federated learning in the presence of stochastic and dynamic communication failures wherein the uplink between the parameter server and client i is on with unknown probability p(i)(t) in round t. Furthermore, we allow the dynamics of p(i)(t) to be arbitrary. We first demonstrate that when the p(i)(t)'s vary across clients, the most widely adopted federated learning algorithm, Federated Average (FedAvg), experiences significant bias. To address this observation, we propose Federated Postponed Broadcast (FedPBC), a simple variant of FedAvg. It differs from FedAvg in that the parameter server postpones broadcasting the global model to the clients with active uplinks till the end of each training round. Despite uplink failures, we show that FedPBC converges to a stationary point of the original non-convex objective. On the technical front, postponing the global model broadcasts enables implicit gossiping among the clients with active links in round t. In spite of the time-varying nature of pti, we can bound the perturbation of the global model dynamics using techniques to control gossip-type information mixing errors. Extensive experiments have been conducted on real-world datasets over diversified unreliable uplink patterns to corroborate our analysis.

关键词： Servers Uplink Federated learning Vehicle dynamics Training Stochastic processes Distance learning Computer aided instruction Smart phones Convergence communication failures gossiping non-convex optimization fault-tolerance

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Enhancing Convolutional Models for Indoor Radio Mapping via Ray Marching

Enhancing Convolutional Models for Indoor Radio Mapping via ...

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2025 ieee International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wu, Mengfan Skocaj, Marco Boban, Mate Munich Research Center Huawei Technologies Germany School of Electrical Engineering and Computer Science TU Berlin Germany

ISBN: (纸本)9798350368741

In this paper, we present an enhanced convolutional model for indoor radio map generation, focusing on the integration of a novel ray-marching feature. We describe our machine learning pipeline developed for the ICASSP 2025 signal processing Grand Challenge, specifically the First Indoor Pathloss Radio Map Prediction Challenge. Our method incorporates a ray-marching feature that, combined with a UNet architecture enhanced by dilated convolution layers, significantly improves indoor pathloss prediction accuracy. Our approach achieved a 3rd-place ranking in the challenge with a weighted RMSE of 10.33 on the test dataset. © 2025 ieee.

关键词： feature engineering machine learning radio maps Wireless communications

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CSI Compression Method With Dual Differential Feedback for Next-Generation Wi-Fi Networks

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ieee WIRELESS COMMUNICATIONS LETTERS 2025年第2期14卷 475-478页

作者： Barannikov, Andrey Levitsky, Ilya Loginov, Vyacheslav Khorov, Evgeny Russian Acad Sci Inst Informat Transmiss Problems Wireless Networks Lab Moscow 141171 Russia

The multiple-input multiple-output (MIMO) technology improves Wi-Fi throughput by increasing the number of antennas. However, with more antennas and developing coordinated MIMO operations, the amount of channel state information (CSI) increases. As the CSI-induced overhead limits the performance of Wi-Fi networks, this letter proposes a new low-complexity dual differential CSI compression method that significantly reduces this overhead. Experimental and simulation results in diverse scenarios show that the proposed algorithm overcomes state-of-the-art algorithms of similar complexity, reducing the feedback size by up to 40%.

关键词： Wireless fidelity Array signal processing signal to noise ratio Matrix decomposition Time-domain analysis Standards Frequency-domain analysis Accuracy Throughput machine learning CSI compression MU-MIMO Wi-Fi differential feedback

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