检索结果-内蒙古大学图书馆

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

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

来源：评论

学校读者我要写书评

暂无评论

Multi-Task Spiking Neural Network for Simultaneous Vapor Recognition and Concentration Estimation

引用

ieee ACCESS 2025年 13卷 29847-29863页

作者： Sartori Locatelli, Pedro Ait Fares, Salma Martini Colombo, Dalton El-Sankary, Kamal Freund, Michael S. Dalhousie Univ Dept Elect & Comp Engn Halifax NS B3J 1Z1 Canada InterTalk Crit Informat Syst Dartmouth NS B3B 0J5 Canada Univ Fed Minas Gerais Dept Elect Engn BR-31270901 Belo Horizonte Brazil Dalhousie Univ Dept Chem Halifax NS B3H 4R2 Canada

Neural networks have been instrumental in advancing machine olfaction systems, greatly enhancing their ability to process olfactory information. As the drive to integrate sensing and signal processing on chip continues, recent advancements emphasize the need for simple yet efficient pattern recognition systems. Spiking Neural Networks (SNNs) have gained significant attention in the realm of machine olfaction for their high computational efficiency and biological realism, closely mirroring the human olfactory system's approach to processing odors. Many studies have successfully applied SNN models for vapor classification;however, limited research exists on their use for concentration estimation. This article not only addresses this gap but also introduces a simple three-layer, multi-task shared SNN that simultaneously performs vapor recognition and concentration estimation using carbon black-polymer composite sensor arrays. Multi-task learning using SNNs proves particularly beneficial in this context by utilizing shared computations across tasks to optimize resource usage. Experimental results demonstrate that integrating both tasks into a single network enhances overall computational efficiency and reduces model complexity without sacrificing performance. Remarkably, this proof-of-concept design achieves a 33% reduction in the total number of neurons while performing 48% fewer synaptic operations per forward pass compared to two separate single-task SNNs. Logic synthesis further reveals that the shared approach consumes approximately 37% less power and occupies 37% less silicon area, showcasing the potential of multi-task learning with SNNs to propel artificial olfaction systems.

关键词： Polymers Multitasking Estimation Olfactory Sensor arrays Sensors Carbon Biology Biological neural networks Concentration estimation electronic nose gas recognition machine olfaction multi-task learning spiking neural network spiking neural network

来源：评论

学校读者我要写书评

暂无评论

Seismic Denoising Based on Dictionary learning With Double Regularization for Random and Erratic Noise Attenuation

引用

ieee TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Shekhar, Nakka Tejaswi, Dokku James, Abin Kuruguntla, Lakshmi Dodda, Vineela Chandra Mandpura, Anup Kumar Chinnadurai, Sunil Elumalai, Karthikeyan SRM Univ Dept Elect & Commun Engn Amaravati 522502 Andhra Pradesh India Koneru Lakshmaiah Educ Fdn KLEF Dept Elect & Commun Engn Vaddeswaram 522302 Andhra Pradesh India Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amaravati 522503 Andhra Pradesh India Delhi Technol Univ Dept Elect Engn New Delhi 110042 India

In seismic data processing, denoising is one of the essential steps to identifying the earth's subsurface layer information. The noise present in the seismic data is categorized into two types: random and erratic noise. The random noise is distributed uniformly over the seismic data. The erratic noise attenuation is always challenging due to the unknown distribution of high-amplitude peaks over seismic data. The existing double sparsity dictionary learning (DSDL) method performs with analytical and adaptive transforms;both the transforms include iterative algorithms with K-singular-value decomposition (SVD);it is computationally costly, and the dictionary is initialized with trained data. To address these limitations, we propose a novel method of dictionary learning with double regularization (DLDR) to denoise both random and erratic noise from seismic data. In double regularization, we used with l(1)-norm and nuclear norm. The denoised data is applied to the alternating direction method of multipliers (ADMMs) to improve denoising while preserving the signal features from seismic data while reducing the computational cost. We evaluated the performance of the proposed method using signal-to-noise ratio (SNR), mean squared error (MSE), and local similarity map. The numerical results demonstrated that the proposed method resulted in higher SNR, lower MSE, and less signal leakage from seismic data. The method gives precise interpretation from the denoised seismic data.

关键词： Noise Noise reduction machine learning Dictionaries Attenuation Matching pursuit algorithms Iterative methods Filtering Continuous wavelet transforms signal to noise ratio & ell (1)-norm alternating direction method of multiplier (ADMM) dictionary learning double regularization erratic noise nuclear norm random noise

来源：评论

学校读者我要写书评

暂无评论

Graph Neural Networks With Adaptive Structures

引用

ieee JOURNAL OF SELECTED TOPICS IN signal processing 2025年第1期19卷 181-194页

作者： Zhang, Zepeng Lu, Songtao Huang, Zengfeng Zhao, Ziping ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Ecole Polytech Fed Lausanne Lab Intelligent Maintenance & Operat Syst CH-1015 Lausanne Switzerland IBM Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA Fudan Univ Sch Data Sci Shanghai 200433 Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

Graph neural networks (GNNs) have made significant progress in various machine learning tasks. Despite their success, many existing GNN models are shown to be vulnerable to adversarial attacks, creating a stringent need to build robust GNN architectures. In this work, we introduce a novel interpretable message passing scheme over adaptive graph structures (ASMP) to enhance the resilience of GNNs against graph structural attacks. The ASMP layers are constructed through optimization steps that concurrently optimize node features and graph structures, allowing the message passing process to be conducted across dynamically adjusted graphs at different layers. This adaptability enables ASMP to handle noisy or perturbed graph structures more effectively, enhancing robustness. We also establish the theoretical convergence properties for the ASMP scheme. By integrating ASMP with neural networks, we introduce a new class of GNNs with adaptive structures (ASGNNs). Extensive experiments on semi-supervised node classification tasks demonstrate that ASGNN outperforms the state-of-the-art GNN architectures regarding classification accuracy when subjected to various adversarial attack scenarios.

关键词： Graph neural networks Message passing Adaptation models Perturbation methods Robustness Purification Optimization Noise measurement Heuristic algorithms Electronic mail Adversarial attacks graph neural network graph signal denoising graph signal processing graph structure learning

来源：评论

学校读者我要写书评

暂无评论

Audio-Based Kinship Verification Using Age Domain Conversion

引用

ieee signal processing LETTERS 2025年 32卷 301-305页

作者： Sun, Qiyang Akman, Alican Jing, Xin Milling, Manuel Schuller, Bjorn W. Imperial Coll London Dept Comp GLAM London SW7 2AZ England Tech Univ Munich CHI Chair Hlth Informat MRI D-81675 Munich Germany MDSI Munich Data Sci Inst D-85748 Munich Germany MCML Munich Ctr Machine Learning D-80539 Munich Germany

Audio-based kinship verification (AKV) is important in many domains, such as home security monitoring, forensic identification, and social network analysis. A key challenge in the task arises from differences in age across samples from different individuals, which can be interpreted as a domain bias in a cross-domain verification task. To address this issue, we design the notion of an "age-standardised domain" wherein we utilise the optimised CycleGAN-VC3 network to perform age-audio conversion to generate the in-domain audio. The generated audio dataset is employed to extract a range of features, which are then fed into a metric learning architecture to verify kinship. Experiments are conducted on the KAN_AV audio *** results demonstrate that the method markedly enhances the accuracy of kinship verification, while also offering novel insights for future kinship verification research.

关键词： Feature extraction Training Generators Videos Generative adversarial networks Vectors Hidden Markov models Data models Time-frequency analysis Recording Audio kinship verification deep learning GAN voice conversion

来源：评论

学校读者我要写书评

暂无评论

Four-Channel Pulse Extraction and Matching Method for Loose Particle Localization on Sealed Electronic Equipment

引用

ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhai, Guofu Sun, Zhigang Wang, Guotao Zhang, Min Harbin Inst Technol Inst Reliabil Elect Apparat & Elect Harbin 150001 Peoples R China Heilongjiang Univ Sch Elect & Elect Engn Harbin 150080 Peoples R China

The existing loose particle localization research for sealed electronic equipment (SEE) focuses on the selection and optimization of classifiers, processing and optimization of datasets, and extraction of multidomain signal features, while neglecting the processing of four-channel loose particle signals at the source link, and there are many problems that need to be solved. Therefore, in this article, for the first time, the authors shifted their research focus to four-channel loose particle signals and proposed a suitable pulse extraction and matching method, which includes two stages. In the first stage, the authors newly proposed a two-stage variable multithreshold (TSVMT) pulse extraction algorithm. It takes the average energy and average zero-crossing rate (ZCR) of all sampling points in the signal as a reference, sets multiple thresholds according to the 3 sigma distribution criterion of normal distribution, performs prescreen on pulses through peak energy threshold, and accurately extracts independent pulses through endpoint energy threshold and endpoint ZCR threshold. This effectively solves the problems of unclear threshold setting rules, unstable threshold reference objects, ignoring pulses with smaller amplitudes, low reliability of pulse extraction and not considering continuous multiple pulses in the existing research. In the second stage, the authors newly proposed a threshold-judgment-search (TJS) pulse-matching algorithm. It takes the loose particle signal collected by the farthest acoustic emission (AE) sensor as the signal reference object, determines the starting time threshold based on the loose particle signal collected by the closest AE sensor, and searches for the pulse group that satisfies the starting time threshold. This effectively solves the problems of unstable signal reference objects, unreasonable signal reference objects, and unstable and unreliable pulse matching in the existing research. Feature extraction and dataset construction a

关键词： Location awareness Feature extraction Data mining Accuracy Sun signal processing algorithms Electronic equipment Background noise Training Search problems Loose particle localization machine learning pulse extraction pulse matching sealed electronic equipment (SEE)

来源：评论

学校读者我要写书评

暂无评论

Deep learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments

引用

ieee ACCESS 2025年 13卷 35707-35732页

作者： Essaid, Billel Kheddar, Hamza Batel, Noureddine Chowdhury, Muhammad E. H. Univ MEDEA Elect Engn Dept LSEA Lab Medea 26000 Algeria Qatar Univ Dept Elect Engn Doha Qatar

Automatic speech recognition (ASR) and speech enhancement are essential tools in modern life, aiding not only in machine interaction but also in supporting individuals with hearing impairments. These processes begin with capturing speech in analog form and applying signal processing algorithms to ensure compatibility with devices like cochlear implants (CIs). However, CIs, with their limited number of electrodes, often cause speech distortion, and despite advancements in state-of-the-art signal processing techniques, challenges persist, particularly in noisy environments with multiple speech sources. The rise of artificial intelligence (AI) has introduced innovative strategies to address these limitations. This paper presents a novel deep learning (DL)-based technique that leverages attention mechanisms to improve speech intelligibility through noise suppression. The proposed approach includes two strategies: the first integrates temporal convolutional networks (TCNs) and multi-head attention (MHA) layers to capture both local and global dependencies within the speech signal, enabling precise noise filtering and improved clarity. The second strategy builds on this framework by additionally incorporating bidirectional gated recurrent units (Bi-GRU) alongside TCN and MHA layers, further refining sequence modeling and enhancing noise reduction. The optimal model configuration, using TCN-MHA-Bi-GRU with a kernel size of 16, achieved a compact model size of 788K parameters and recorded training, and validation losses of 0.0350 and 0.0446, respectively. Experimental results on the TIMIT and Harvard Sentences datasets, enriched with diverse noise sources from the DEMAND database, yielded high intelligibility scores with a short-time objective intelligibility (STOI) of 0.8345, word recognition score (WRS) of 99.2636, and an near correlation coefficient (LCC) of 0.9607, underscoring the model's capability to enhance speech perception in noisy CI environments, ensuring a bala

关键词： Speech enhancement Noise measurement Noise reduction Noise Convolutional neural networks Autoencoders Biological system modeling Training Real-time systems Feature extraction Cochlear implant deep learning sound coding strategy speech enhancement transformer

来源：评论

学校读者我要写书评

暂无评论

Normalizing Flow-Based Differentiable Particle Filters

引用

ieee TRANSACTIONS ON signal processing 2025年 73卷 493-507页

作者： Chen, Xiongjie Li, Yunpeng Kings Coll London Fac Dent Oral & Craniofacial Sci Ctr Oral Clin & Translat Sci London SE1 9RT England

Recently, there has been a surge of interest in incorporating neural networks into particle filters, e.g. differentiable particle filters, to perform joint sequential state estimation and model learning for nonlinear non-Gaussian state-space models in complex environments. Existing differentiable particle filters are mostly constructed with vanilla neural networks that do not allow density estimation. As a result, they are either restricted to a bootstrap particle filtering framework or employ predefined distribution families (e.g. Gaussian distributions), limiting their performance in more complex real-world scenarios. In this paper we present a differentiable particle filtering framework that uses (conditional) normalizing flows to build its dynamic model, proposal distribution, and measurement model. This not only enables valid probability densities but also allows the proposed method to adaptively learn these modules in a flexible way, without being restricted to predefined distribution families. We derive the theoretical properties of the proposed filters and evaluate the proposed normalizing flow-based differentiable particle filters' performance through a series of numerical experiments.

关键词： Particle filters Neural networks State-space methods Particle measurements Parameter estimation Monte Carlo methods Atmospheric measurements Proposals Maximum likelihood estimation Bayes methods Sequential Monte Carlo differentiable particle filters normalizing flows parameter estimation machine learning

来源：评论

学校读者我要写书评

暂无评论

Convergence and Privacy of Decentralized Nonconvex Optimization With Gradient Clipping and Communication Compression

引用

ieee JOURNAL OF SELECTED TOPICS IN signal processing 2025年第1期19卷 273-282页

作者： Li, Boyue Chi, Yuejie Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

Achieving communication efficiency in decentralized machine learning has been attracting significant attention, with communication compression recognized as an effective technique in algorithm design. This paper takes a first step to understand the role of gradient clipping, a popular strategy in practice, in decentralized nonconvex optimization with communication compression. We propose PORTER, which considers two variants of gradient clipping added before or after taking a mini-batch of stochastic gradients, where the former variant PORTER-DP allows local differential privacy analysis with additional Gaussian perturbation, and the latter variant PORTER-GC helps to stabilize training. We develop a novel analysis framework that establishes their convergence guarantees without assuming the stringent bounded gradient assumption. To the best of our knowledge, our work provides the first convergence analysis for decentralized nonconvex optimization with gradient clipping and communication compression, highlighting the trade-offs between convergence rate, compression ratio, network connectivity, and privacy.

关键词： signal processing algorithms Convergence Optimization Stochastic processes Privacy Differential privacy Perturbation methods Linear programming Vectors Training Communication compression convergence rate gradient clipping local differential privacy

来源：评论

学校读者我要写书评

暂无评论

FSRS: A Frequency Space Random Search Approach for Efficient Opaque-Box Adversarial Attacks

引用

ieee TRANSACTIONS ON RELIABILITY 2025年

作者： Tsai, Min Han Huang, Qi Xian Sun, Hung Min Natl Tsing Hua Univ Inst Informat Syst & Applicat Hsinchu 300044 Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu 300044 Taiwan

The imperative of ensuring machine learning safety has escalated concomitantly with its pervasive deployment. Among the myriad of potential threats, adversarial examples represent a particularly insidious form of attack, wherein malefactors introduce meticulously engineered perturbations to a model's input, thereby skewing its output. Recent developments have underscored the feasibility of such attacks even in the absence of intimate knowledge regarding the model's architecture, leveraging the model's output to iteratively refine these perturbations. Notwithstanding, the opacity inherent in opaque-box models-characterized by the unavailability of gradient information-necessitates a prodigious number of queries to mount a successful attack, thereby diminishing their efficacy. In response to these challenges, this study introduces the frequency space random search (FSRS) attack methodology. Distinct from conventional opaque-box strategies that operate within the spatial domain, the FSRS method innovatively manipulates the low-frequency domain, thereby curtailing the requisite search space. Furthermore, it harnesses the inherent efficiencies of random search algorithms to minimize the query footprint. Empirical validation of our approach reveals its superior performance in terms of query efficiency and success rate, all the while ensuring minimal perceptual distortion when benchmarked against contemporary opaque-box adversarial techniques.

关键词： Perturbation methods Frequency-domain analysis Discrete wavelet transforms Optimization signal processing algorithms Classification algorithms Sun Safety Computational modeling machine learning algorithms Adversarial attacks deep learning discrete wavelet transform (DWT) random search

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：