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Online Extraction of Electrochemical Impedance Spectroscopy Pattern Based on EV Load Profile and Short Time Fourier Transform for Diagnosis of Lithium-ion Battery Safety

Online Extraction of Electrochemical Impedance Spectroscopy ...

引用

Annual ieee conference on Applied Power Electronics conference and Exposition (APEC)

作者： Miyoung Lee Dongcheol Lee Youngmin Bae Jongchan An Garam Yang Woonki Na Jonghoon Kim Department of Electrical Engineering Chungnam National University Daejeon Republic of Korea Department of Electrical and Computer Engineering California State University Fresno United States

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Batteries which convert chemical energy into electrical energy and play an important role in our daily lives and across various industries. The chemical reaction that occurs within a battery during operation of an electric vehicle (EV) depends on environmental conditions such as temperature, load, and humidity, and these variables greatly affect battery performance and life. However, there is challenging to analyze these electrochemical phenomena with only simple electric signals, such as direct current (DC) signal. For compensation, passive electrochemical impedance spectroscopy (PEIS) is derived by applying a signal processing algorithm that converts time series signals into frequency-domain signals in this study. Based on this, we propose to learn a siamese convolutional neural network (SCNN) algorithm on the converted PEIS pattern to the image and to effectively diagnose the state of the battery through similarity evaluation.

关键词： Lithium-ion batteries Heuristic algorithms Frequency-domain analysis Time series analysis Signal processing algorithms Feature extraction Vectors Convolutional neural networks Vehicle dynamics Electrochemical impedance spectroscopy

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Revelio: A Real-World Screen-Camera Communication System with visually Imperceptible Data Embedding

Revelio: A Real-World Screen-Camera Communication System wit...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Abbaas Alif Mohamed Nishar Shrinivas Kudekar Bernard Kintzing Ashwin Ashok Georgia State University Atlanta Georgia USA Revelio Communications Inc. Atlanta Georgia USA Bernard Kintzing LLC Helena Montana USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We present ‘Revelio’, a real-world screen-camera communication system leveraging temporal flicker fusion in the OKLAB color space. Using spatially-adaptive flickering and encoding information in pixel region shapes, Revelio achieves visually imperceptible data embedding while remaining robust against noise, asynchronicity, and distortions in screen-camera channels, ensuring reliable decoding by standard smartphone cameras. The decoder, driven by a two-stage neural network, uses a weighted differential accumulator for precise frame detection and symbol recognition. Initial experiments demonstrate Revelio’s effectiveness in interactive television, offering an unobtrusive method for meta-information transmission.

关键词： visualization Shape image color analysis Communication systems Neural networks Symbols Encoding Decoding Payloads Videos

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A New Model for Prototype-based Continual Learning in Hyperspherical Space

A New Model for Prototype-based Continual Learning in Hypers...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yixin Ren Yewei Xia Li Ke Longtao Huang Hui Xue Shuigeng Zhou School of Computer Science Fudan University Shanghai China Alibaba Group Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The continuous emergence of new objects in the visual world poses a serious challenge to deep object recognition methods, which sparks the increasing study on continual or incremental learning. However, learning new tasks faces the tough catastrophic forgetting problem, i.e., dramatic performance degradation on old tasks. A good continual learning model should be robustly adapted to the upcoming tasks while effectively handling catastrophic forgetting. In this paper, we focus on the class-incremental learning (CIL) task, and propose a novel prototype-based continual learning model C-HPN that projects the visual features into a hypersphere geometric space, where continual learning is conducted. C-HPN features two-fold contributions. On the one hand, instead of using the popular cross-entropy loss, we develop an instance-prototype compact loss to obtain well-clustered hyperspherical embeddings and a prototype-prototype separability loss to boost the model’s generalization by introducing large angle distance inductive bias between prototypes in the hyperspherical space. On the other hand, prototype construction and adaptation strategies are designed for effectively adapting new classes, and an instance-prototype relationship preservation distillation mechanism is introduced to overcome catastrophic forgetting. Extensive experiments on several image datasets validate the effectiveness of the proposed method.

关键词： Continuing education Hands Adaptation models visualization Incremental learning Prototypes Signal processing Sparks Object recognition Speech processing

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Imperceptible and Targeted Physical Attacks on Deep Learning-Based Speech Semantic communications

Imperceptible and Targeted Physical Attacks on Deep Learning...

引用

ieee conference on Wireless communications and Networking

作者： Yuhao Hua Yang Xu Chen Lyu Jia Liu Yulong Shen Weidong Yang Norio Shiratori Hangzhou Institute of Technology Xidian University Hangzhou China School of Computer Science and Technology Xidian University Xi'an China MoE Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China Center for Strategic Cyber Resilience Research and Development National Institute of Informatics Tokyo Japan Research and Development Initiative Chuo University Tokyo Japan

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The deep learning-based semantic communication system (DeepSC) is designed to improve the efficiency and accuracy of information transmission, by leveraging joint source-channel coding techniques to extract relevant semantic features. However, existing research on attack methods targeting DeepSC has primarily focused on text and image domains, leaving the speech domain largely unexplored. To this end, this paper proposes Iterative Semantic Gradient Update (ISGU), a novel approach for crafting physical layer adversarial attacks on DeepSC for speech transmission (DeepSC-ST). Specifically, we introduce a joint loss that combines the semantic similarity loss with Connectionist Temporal Classification loss to expedite the generation process of targeted attacks against the DeepSC-ST. In addition, we design an algorithm to generate adversarial examples, enhancing their imperceptibility by meticulously controlling the perturbation power added to the input speech. Extensive experiments indicate that ISGU is capable of rapidly generating highly covert adversarial examples, with a notably high attack success rate.

关键词： Training Perturbation methods Oral communication Information processing Speech enhancement Semantic communication Physical layer Feature extraction Security Iterative methods

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17th Asian conference on Intelligent Information and Database Systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent Information and Database Systems, ACIIDS 2025

ISBN: (纸本)9789819660049

The proceedings contain 29 papers. The special focus in this conference is on Intelligent Information and Database Systems. The topics include: GAMER-Pong: Game Adjustment by Monitoring Emotional Response;Machine Learning Algorithms Comparison for Hand sEMG-Recorded Movements Classification;boosting Face Super-Resolution and Identification with Knowledge Distillation and Frequency Domain Loss;improving Efficiency image Captioning by Using Attention Mechanism Combined with Knowledge Graph;integrating Topological Data Analysis and Deep Learning: A Case Study in Cardiovascular Disease Prediction at Thu Duc Hospital;boosting Lightweight Multi-branch Network by Integrating Segment, Skeleton, and Attribute Information;continuous Recognition of Mouth Patterns in Japanese Sign Language for visual Communication;Towards an UAV visual Navigation System Based on Map processing and Spatial Matching Techniques: A Literature Review;image Enhancement with Boosted Schrödinger Bridge;kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset;estimation of Distribution Algorithms with Overlapped Subpopulations;energy-Efficient Edge Query processing for Smart City Using Query Prediction;influence Maximization with Fairness Cost on Groups in Online Social Networks;TMMP: Efficient Continual Learning via Mixture of LoRA Experts and Top Maximum Magnitude Parameter Fusion;ADMEdge: Accelerating Edge Computing Through Co-design for Enhanced Data Mobility;the Recursive Scheme of Clustering;TOPSIS-Inspired Socio-cognitive Mutation Operator for Metaheuristics;feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis;a Feature Transformation Technique for Improving Ensemble Learning Systems;explainable Artificial Intelligence to Improve Interpretability in Predictive Mutation Testing;an Analysis of Class Imbalance Challenges in Predictive Mutation Testing;efficient Vietnamese Name Detection Using Highly Discriminative N-Gra

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FuzzyMIL: Decoupling Pathological Phenotypes through Deep Fuzzy Clustering for Efficient Whole Slide image Analysis

FuzzyMIL: Decoupling Pathological Phenotypes through Deep Fu...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Anran Liu Tong Li Jing Cai Srinivasa Sampath Veer Vajrala Department of Health Technology and Informatics Hong Kong Polytechnic University Hong Kong SAR China Division of Computational & Data Sciences Washington University in St.Louis St.Louis USA Department of Electronics and Communications Engineering Indian Institute of Information Technology Dharwad Dharwad India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In Multiple Instance Learning (MIL) for Whole Slide image (WSI) analysis, attention mechanisms are often employed to weigh the importance of different instances. However, global attention may lead to feature homogenization and overlook tissue differences. Adding local attention can capture these variations but is more parameter-intensive. To tackle the above challenges, in this work, we propose a deep fuzzy clustering framework (FuzzyMIL) based on a learnable Fuzzy C-means variant named FCM to analyze WSIs in a compact and efficient manner. By iteratively updating the fuzzy clustering centers, FCM decouples the morphological features of WSIs, resulting in more distinct and less correlated phenotypes. In this learning process, FCM compresses the feature representation space of WSI, guiding the features to gradually converge toward the representation prototypes. These prototypes, influenced by the soft assignment mechanism, take into account all updated features, enabling the model to retain both global information and local awareness. We evaluated our approach using three public datasets for diagnosis and sub-typing. Experimental results show that our approach achieves competitive performance while significantly reducing the downstream task framework parameters, striking a good balance between accuracy and model complexity. We release our code at https://***/Liuanana/FuzzyMIL.

关键词： Pathology Phenotypes image analysis Codes Attention mechanisms Prototypes Signal processing Acoustics Complexity theory Speech processing

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17th Asian conference on Intelligent Information and Database Systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent Information and Database Systems, ACIIDS 2025

ISBN: (纸本)9789819660070

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3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Speaker Verification and Diarization

3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Sp...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yafeng Chen Siqi Zheng Hui Wang Luyao Cheng Tinglong Zhu Rongjie Huang Chong Deng Qian Chen Shiliang Zhang Wen Wang Xihao Li Speech Lab Alibaba Group Carnegie Mellon University Zhejiang University University of North Carolina Chapel Hill

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We introduce 3D-Speaker-Toolkit, an open-source toolkit for multimodal speaker verification and diarization, designed for meeting the needs of academic researchers and industrial practitioners. The 3D-Speaker-Toolkit adeptly leverages the combined strengths of acoustic, semantic, and visual data, seamlessly fusing these modalities to offer robust speaker recognition capabilities. The acoustic module extracts speaker embeddings from acoustic features, employing both fully-supervised and self-supervised learning approaches. The semantic module leverages advanced language models to comprehend the substance and context of spoken language, thereby augmenting the system’s proficiency in distinguishing speakers through linguistic patterns. The visual module applies image processing technologies to scrutinize facial features, which bolsters the precision of speaker diarization in multi-speaker environments. Collectively, these modules empower the 3D-Speaker-Toolkit to achieve substantially improved accuracy and reliability in speaker-related tasks. With 3D-Speaker-Toolkit, we establish a new benchmark for multimodal speaker analysis. The toolkit also includes a handful of open-source state-of-the-art models and a large-scale dataset containing over 10,000 speakers. The toolkit is publicly available at https://***/modelscope/3D-Speaker.

关键词： visualization Adaptation models Runtime Semantics Self-supervised learning Signal processing Feature extraction Acoustics Speaker recognition Speech processing

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SSRMamba: Efficient visual State Space Model for Spectral Super-Resolution

SSRMamba: Efficient Visual State Space Model for Spectral Su...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Baisong Li Xingwang Wang Haixiao Xu College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Krowledge Engineering of Ministry of Education Jilin University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computational cost of Transformer structures, existing deep learning (DL)-based methods struggle to balance spectral reconstruction quality and computational efficiency. Recently, Mamba methods base on state-space models (SSM) show great potential in modeling long-range dependencies with linear complexity. Therefore, we introduce the Mamba model into spectral super-resolution (SSR) task. Specifically, we propose a three-stage SSR network base on Mamba, called SSRMamba. We design SpaMamba, SSMamba, and SpeMamba modules for shallow spatial information extraction, mixed information encoding, and spectral information reconstruction, respectively. Extensive experimental results demonstrate that SSRMamba not only surpasses existing methods in terms of quantification and quality, achieving state-of-the-art (SOTA) performance, but also significantly reduces model size and computational cost. The source code of SSRMamba is available at: https://***/Baisonm-Li/SSRMamba.

关键词： visualization Computational modeling Superresolution Information retrieval Transformers Computational efficiency State-space methods Speech processing image reconstruction Hyperspectral imaging

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Frequency-Domain Guided Multiple Parallel Kernels Network for Low-Light Remote Sensing image Enhancement

Frequency-Domain Guided Multiple Parallel Kernels Network fo...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jingxuan Zhou Hao Li Jinlong Wang Xiongxin Tang Fanjiang Xu Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to dark environments, optical aberrations, etc, the remote sensing images are often submerged under low contrast degradation, which greatly hinders their practical applications for agricultural management and other related tasks. The surface features of remote sensing images are often continuously distributed in space, thus, the sizes of the network’s receptive fields and its ability to learn long-range dependencies are crucial for restoring low-light remote sensing images. Existing methods based on CNN provide limited receptive fields, while Transformer-based methods are constrained by their quadratic computational complexity. To cope with these issues, we propose a novel low-light remote sensing image enhancement network that combines multi-scale receptive fields with frequency-domain attention. Specifically, this network employs multiple parallel kernels of varying sizes to learn multi-scale local features in the spatial domain and complements frequency-domain information to learn global long-range correlations, which achieves local-global feature extraction and further facilitates subsequent degraded images enhancement. We have conducted extensive experiments to demonstrate that our network outperforms existing methods quantitatively and achieves exceptional visual performance, which fully highlights the effectiveness and superiority of our method in enhancing low-light remote sensing images.

关键词： visualization Frequency-domain analysis Transforms Speech enhancement Feature extraction Transformers image restoration Kernel Remote sensing image enhancement

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