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CNN-Based Circular Economy in E-Waste Management with Blockchain and IoT Integration

CNN-Based Circular Economy in E-Waste Management with Blockc...

引用

visual Analytics and Data visualization (ICVADV), International conference on

作者： P. Santhuja A Suresh Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Increasing electronic waste (e-waste) is a serious environmental and financial problem that calls for creative ideas for effective resource recovery and recycling. This paper proposes an IoT- and Blockchain-integrated e-waste management system based on Convolutional Neural Networks (CNNs) to boost automated waste classification and, thus, circular economy practices. While Blockchain guarantees open and safe tracking of waste transportation, IoT sensors and RFID tags allow real-time monitoring of e-waste. Training on a collection of 50,000 e-waste images, a CNN-based image classification model attained an accuracy of 96.2% in classifying components into recyclables, reusables, and hazardous items. Using automated decision-making, the system showed a 28% reduction in processing time and a 32% gain in sorting efficiency over conventional approaches. Blockchain incorporation enhanced traceability through 100% secure transaction records, lowering fraud and illegal disposal. Experimental data show that this method improves 23% resource recovery rates by promoting sustainable e-waste management. The proposed architecture minimizes environmental effects by encouraging ethical e-waste disposal and a closed-loop recycling system. These results show how Blockchain-secured, AI-driven IoT devices might help to advance circular economic ideas for world e-waste management.

关键词： Waste management Training Accuracy Electronic waste Real-time systems Recycling Blockchains Internet of Things Convolutional neural networks Sorting

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Enhancing Autonomous Driving through Dual-Process Learning with Behavior and Reflection Integration

Enhancing Autonomous Driving through Dual-Process Learning w...

引用

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

作者： Xiao Zhang Kangsheng Wang Tianyu Hu Huimin Ma School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Contemporary autonomous driving (AD) methodologies, which predominantly convert visual features into control directives, face long-tail challenges due to constraints imposed by limited data distribution. Conversely, human drivers exhibit proficiency in such conditions, underscoring the significance of emulating human cognition in AD systems. Therefore, we introduce Dual-Process Learning (D-PL) approach for cognitive-enhanced decision-making. Inspired by dual-process theory, the D-PL method combines Behavior Pattern Learning (BPL) and Self-Reflective Learning (SRL) to integrate quick, intuitive decisions with deliberate, analytical reasoning, constructing a hierarchical decision model for sophisticated trajectory planning. Our approach improves decision-making, enhances adaptability, and tackles the crucial open-world generalization challenge encountered by current AD methods. Comprehensive evaluations on the nuScenes dataset validate the robustness of our method, demonstrating its superior performance in navigating the intricacies of real-world contrasting with conventional models.

关键词： Adaptation models visualization Heavily-tailed distribution Trajectory planning Decision making Power line communications Cognition Robustness Speech processing Autonomous vehicles

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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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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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PeT-KeyStAtion: Parameter-efficient Transformer with Keypoint-guided Spatial-temporal Aggregation for Video-based Person Re-identification

PeT-KeyStAtion: Parameter-efficient Transformer with Keypoin...

引用

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

作者： Xingan Ma Jinhui Yi Juergen Gall University of Bonn Bonn Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Video-based Person Re-identification (ReID) is crucial in visual surveillance, focusing on matching video snippets of individuals across multiple non-overlapping cameras. Existing methods either conduct ReID at the image level without leveraging temporal information, or employ complex temporal information aggregation techniques, which results in substantial network size and reduced performance efficiency. Recent advances in Vision Transformer (ViT) architectures leverage diverse large-scale datasets alongside sophisticated architectures to achieve enhanced fine-grained feature discrimination. To fully explore the potential of ViT architectures without adding substantial additional modules for video-based ReID, we propose PeT-KeyStAtion: a Parameter-efficient Transformer with Keypoint- guided Spatial-temporal Aggregation using a Spatial-Temporal and Keypoint (STK) Module with lightweight adapters. Our framework effectively captures and aggregates spatial, temporal, and keypoint information with only 11% of the parameters compared to full fine-tuning. Extensive experiments show that our method outperforms state-of-the-art baselines on MARS and iLIDS-VID, and achieves promising performance on LS-VID.

关键词： Representation learning visualization Mars Computer vision Surveillance Focusing Signal processing Transformers Speech processing Identification of persons

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Soft Computing Based Finger Vein Approval Using Digital image processing Filter

Soft Computing Based Finger Vein Approval Using Digital Imag...

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2025 International conference on Intelligent Control, Computing and communications, IC3 2025

作者： Harsavarthini, K.R. Dawood, M.Sheik Prasanthi, Guttumukkala Shnain, Ammar H. Sravanthi, P. Kaliappan, S. Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Sethu Institute of Technology Department of Electrical Communication Engineering Pulloor Tamil Nadu Kariapatti626115 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India College of Ttechnical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Cmr College of Engineering & Technology Department of Cse Telangana Hyderabad India Lovely Professional University Division of Research and Development Jalandhar - Delhi G.T.Road Punjab Phagwara144411 India

ISBN: (纸本)9798331527495

The brain of humans is able to recognise and distinguish things in a photo with ease. The goal of artificial intelligence (AI) is to emulate or imitate how humans see going forward. Exposure to private information were restricted, therefore security has been preserved using fingertip vein-based identification of users. The poor accuracy of obtained photos due to irregular lighting, detector excellence, positioning deviations, and outside influences are the key problems in the hand vein inspection procedure. In the next section, we applied the Wiener filter to better the appeal of the photographs of thumb veins. Those devoid of noise photographs are offered for teaching the well-liked., had trained ConvNet topology used for thumb vein fingerprint user authentication. subsequently., in order to protect sensitive information and uphold silence, we evaluated the effectiveness of ConvNet algorithms (convolutional neural networks), which are names that include Alex Internet, Compression Net, Alphabet Net, Remix Net, Optimised Net, Smartphone Net, Residence Net, Density Net., as well as NASNet for hand vein-based particular validation. This study makes use of foot artery scans collected from the a Kaggle network. This study demonstrates resnet101's outstanding agility with 97.64 % efficiency over peering networks. © 2025 ieee.

关键词： Convolutional neural networks

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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...

引用

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

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

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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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