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SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human Activity Recognition

SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning...

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2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Wang, Yijun Wang, Yong Xu, Chendong Yao, Shuai Wu, Qisong Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

ISBN: (纸本)9798350368741

Human Activity Recognition (HAR) such as fall detection has become increasingly critical due to the aging population, necessitating effective monitoring systems to prevent serious injuries and fatalities associated with falls. This study focuses on fine-tuning the Vision Transformer (ViT) model specifically for HAR using radar-based Time-Doppler signatures. Unlike traditional image datasets, these signals present unique challenges due to their non-visual nature and the high degree of similarity among various activities. Directly fine-tuning the ViT with all parameters proves suboptimal for this application. To address this challenge, we propose a novel approach that employs Low-Rank Adaptation (LoRA) fine-tuning in the weight space to facilitate knowledge transfer from pre-trained ViT models. Additionally, to extract fine-grained features, we enhance feature representation through the integration of a serial-parallel adapter in the feature space. Our innovative joint fine-tuning method, tailored for radar-based Time-Doppler signatures, significantly improves HAR accuracy, surpassing existing state-of-the-art methodologies in this domain. Our code is released at https://***/wangyijunlyy/SelaFD. © 2025 IEEE.

关键词： Fine-Tuning Human Activity Recognition Time-Doppler Vision Transformer

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Reversible Data Hiding in Encrypted image based on Block and Huffman Encoding 2nd

Reversible Data Hiding in Encrypted Image Based on Block and...

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2nd international conference on Security and Information Technologies with AI, internet Computing and Big-data Applications, SITAIBA 2023

作者： Yang, Cheng-Hsing Weng, Chi-Yao Huang, Bo-Han Huang, Wei-Qing Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung900 Taiwan Department of Computer Science and Information Engineering National Chiayi University Chiayi600 Taiwan

ISBN: (纸本)9789819777853

The paper presents a reversible data hiding method with image compression consisting of Huffman coding and block encrypted. In the proposed approach, the image is used to compressed for generating the specified space for hiding the secret messages. When the secret message is extracted from the image, the location of the secret message is obtained through the image information hiding structure and Huffman coding table. After message extracting and image decryption, the image could be recovered to the original image. Experimental results show that our proposed method can achieve image recovering and have better hiding payload than that of related works. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data hiding Huffman code image encryption Reversible data hiding

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image Interpolation Utilizing Neighboring Pixels as References in Conjunction with Multilayer Perceptron Neural Networks 2nd

Image Interpolation Utilizing Neighboring Pixels as Referenc...

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2nd international conference on Security and Information Technologies with AI, internet Computing and Big-data Applications, SITAIBA 2023

作者： Lu, Tzu Chuen Vo, Thanh Nhan Jana, Biswapati Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan Department of Computer Science Vidyasagar University Midnapore721102 India

ISBN: (纸本)9789819777853

This study seeks to investigate the utilization of neural networks for the generation of high-quality image interpolation. This introduction endeavors to deliver a comprehensive exploration of the foundational principles underpinning interpolation, coupled with an investigation into the substantial potential presented by neural networks in this field. We introduce an image interpolation approach and substantiate its exceptional performance through empirical evidence. Additionally, we develop methodologies for evaluating experimental interpolation outcomes and explore potential applications in other domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Interpolation

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Chaos Theory based Gravitational Search Algorithm For Medical image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： signal to noise ratio

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The Design of Spectrally-Constrained Sequences with Periodic Zero-Correlation Zone

The Design of Spectrally-Constrained Sequences with Periodic...

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2024 international conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Liu, Shicheng Peng, Xiuping Wang, Shide School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Hebei Province Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

Spectrally-constrained sequences (SCSs) are primarily used in emerging systems operating over non-contiguous spectrum such as cognitive radio and cognitive radar. In order to obtain more SCSs, firstly, a novel type of SCS called periodic zero-correlation zone spectrally-constrained sequence(PZCZ-SCS) is proposed in this paper. Then, based on time-frequency synthesis analysis, the conditions that the PZCZ-SCSs must satisfy in terms of "spectral constraint" are derived. Specifically, it is a divisible difference set with parametersλ1 = k and λ1 > λ2. Finally, the construction of PZCZ-SCSs is presented by using the Chinese remainder theorem. Through this research, a new class of SCS is provided for cognitive radio. © 2025 SPIE.

关键词： image coding

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Automated Programmable Logic Controller Memory Forensics Using RGB image Analysis and Deep Learning 18th

Automated Programmable Logic Controller Memory Forensics Usi...

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18th international conference on Critical Infrastructure Protection

作者： Awad, Rima Asmar Sprayberry, Michael Ahmed, Irfan Rogers, Michael Lopez, Juan Oak Ridge Natl Lab Oak Ridge TN 37830 USA Virginia Commonwealth Univ Richmond VA USA Tennessee Technol Univ Cookeville TN USA

ISBN: (纸本)9783031818905;9783031818882;9783031818875

The introduction of Industry 4.0 and internet-based technologies has enhanced industrial control system operations but have inadvertently increased their vulnerabilities to cyber attacks. When an industrial control system is compromised, security analysts need to identify the root cause quickly to start the recovery process and develop mitigation strategies. Memory forensics is critical in the incident analysis process to ascertain what occurred. Approaches for analyzing the persistent memory in industrial control devices are limited and almost nonexistent for volatile memory. This chapter proposes an automated methodology for programmable logic controller memory dump analysis using computer vision and deep learning techniques. The methodology converts the sequences of bytes in a programmable logic controller memory dump to red-green-blue pixels and employs a deep learning model that learns the underlying patterns and features of pre-labeled forensic artifacts in images and segments them into distinct regions. The trained model is employed to automatically segment new memory images and identify forensic artifacts. Evaluation of the methodology on a Schneider Electric Modicon M221 programmable logic controller under code injection and code modification attacks demonstrates its ability to detect attack artifacts in memory dumps.

关键词： Industrial Control systems Memory Forensics image Analysis Deep Learning

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18th international KES conference on Agents and Multi-Agent systems: Technologies and Applications, KES-AMSTA 2024

18th International KES Conference on Agents and Multi-Agent ...

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18th international KES conference on Agents and Multi-Agent systems: Technologies and Applications, KES-AMSTA 2024

ISBN: (纸本)9789819764686

The proceedings contain 34 papers. The special focus in this conference is on Agents and Multi-Agent systems: Technologies and Applications. The topics include: Fair Path Generation for Formation Control Combining Ant Colony and Particle Swarm Optimizations;preference Definition in a Multi-agent System Approach for User-Centric internet of Things Smart Environments;AI-Driven Nitrogen Stress Management in Cereal Crops via Drone technology;agent-based Simulation for Withdrawal of Energy-Inefficient Housing;resource Limitations of Business Process Simulations: Literature Study;data Augmentation for Business Process Alignment: Proof of Concept and Experimental Design;smart Scheduling in Urban Traffic: A Multi-Agent System for Dynamic Vanpooling;analysis of the Impact on Area Prices Using Propensity Score Matching, Considering the Proportions of Photovoltaic and Thermal Power Generation Simultaneously;consideration of Building a Simulator to Evaluate Methods to Maximize the Effectiveness of Advertising to Households;Human-AI-Collaboration SECI Model: The Knowledge Management Model of the Experts’ Tacit Knowledges with Augmented LLM-based AI;modeling Agent Awareness After Hierarchical Managerial Training;development of An Application for Extracting Needs Related to Actual Gas Use Using Text Mining;Impact of Intelligent systems in the Home Office on Employee Satisfaction—Lessons Learned from the COVID-19 Pandemic;agile Methodologies in Software Development: A Study of Effective Management Strategies for Companies in the Digital Era;predictive Analysis of Cyclical Sales Data;EMG signal Classification Using Machine Learning for Augmentative Assistance in Cerebral Palsy;wellnessWave—Platform for the Digitization of the Romanian Public Medical System;TRIAGE Predictive Model based on Artificial Neural Networks and Deep Learning;platform for Automated Assessment of Obstetric Ultrasounds, Using Machine Learning;identification of Fonts in Unstructured Environments Through Deep Lea

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15th EAI international conference on Bio-inspired Information and Communications Technologies, BICT 2024

15th EAI International Conference on Bio-inspired Informatio...

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15th EAI international conference on Bio-inspired Information and Communications Technologies, BICT 2024

ISBN: (纸本)9783031815980

The proceedings contain 16 papers. The special focus in this conference is on Bio-inspired Information and Communication Technologies. The topics include: Optimizing Drug Delivery Strategies by Pathway Analysis for Waveform Modulation-based Molecular Communication;Enhancing DNA-based IoBNT Throughput and Reducing Congestion with Yin-Yang Coding;a Molecular Communication Model Driven by Magnetic Field Force;CDM based on Izhikevich Neuron Model;multi-user Diffusive Molecular Communication systems with A Passive Relay Node: Receiver Design and Performance Analysis;machine Learning-based Detection Time Estimation for Molecular Communication;taming signal-Dependent Counting Noise with Machine Learning for Molecular Communication;energy-Efficient Transmitter Creation in Molecular Communication;a Method for Determining Relay Node Location in Molecular Communication;glycemic Oscillation Decomposition-based Personalized Blood Glucose Prediction with Continuous Glucose Monitoring;A Compact Bio-inspired Microstrip Antenna (Bi-MPA) for Medical Microwave Imaging Applications;image Encryption and Decryption Algorithm based on DNA Sequence: Performance Analysis of Channel Fusion Processing;Optimizing the Classification of SSVEP signals in Brain-Computer Interfaces: A Novel Sliding Window Data Segmentation Method based on Weighted Voting Mechanism;spontaneous Motion of Nanorobots Inspired Computational technology for Tumor Boundary Exploration.

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Smoke Segmentation Improvement based on Fast Segment Anything Model with YOLOv11 for a Wildfire Monitoring System 10

Smoke Segmentation Improvement Based on Fast Segment Anythin...

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10th international conference on internet of Things, Big Data and Security, IoTBDS 2025

作者： Bunpleng, Puchit Thunyatada, Puthtipong Aksornsuwan, Bhutharit Tungpimolrut, Kanokvate Murata, Ken T. Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand NECTEC National Science and Technology Development Agency Pathum Thani Thailand National Institute of Information and Communications Technology Tokyo Japan

ISBN: (纸本)9789897587504

Forests and wildlife are crucial parts of our ecosystem. Wildfires occurring in dry and hot regions represent a significant threat to these areas, particularly in ASEAN countries during the dry season. While human observers are often employed to detect wildfires, their scarcity and limited availability highlight the need for automated solutions. This study explores the use of machine learning, specifically computer vision, to enhance wildfire detection by segmenting smoke, an approach which potentially gives information regarding the size and the direction of the spread of the smoke, aiding mitigation efforts. We extend prior work by proposing a model to predict the errors and performance of segmentation masks without access to the ground truth, with the aim of facilitating iterative self-improvement of segmentation models. The FireSpot dataset is used to fine-tune a YOLOv11 model to predict bounding boxes of smoke successfully;subsequently, the outputs of this model are used as a prompt to refine a FastSAM model designed to segment the image into a proposed mask containing the smoke. The proposed mask and the corresponding original image are then used to train a machine learning model where the targets are metrics regarding the error rates of the masks. The results show that a gradient boosting model achieves good prediction performance in predicting some error metrics like the IoU (denoted TPP in this paper) between the proposed and actual segmentation masks with an MSE of 0.03 and R2 of 0.46, as well as the proportion of false positives over the union of the proposed and actual masks (denoted FPP in our paper) with an MSE of 0.0002 and R2 of 0.95, while a pre-trained deep learning model fails to learn the distribution, achieving considerably lower performance for IoU with an MSE of 0.05 and R2 of 0.06 and FPP with an MSE of 0.0002 and R2 of -1.15. These findings open the way to future work where the results of the error prediction model can be used as feedback to

关键词： Deep learning

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12th international conference on Recent Trends in Computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

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12th international conference on Recent Trends in Computing, ICRTC 2024

ISBN: (纸本)9789819789450

The proceedings contain 34 papers. The special focus in this conference is on Recent Trends in Computing. The topics include: Deep Learning-based Prediction of Human Ageing Related Proteins;the Insight of Clustering Algorithms for Emerging Networking Paradigms;Revolutionizing Cardiovascular Health: Exploring the Potential of AI-Driven Approaches in Diagnosing Arrhythmia Disorders;humor Detection in Telugu Social Media Text Using Cost-Sensitive Learning and Indian Language Embeddings;empirical Comparison of Sleep Disorder Prediction Using Machine Learning Techniques;securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural Networks;machine Learning-Powered Intrusion Detection for Network Security;secure Chat System: Harnessing the Power of Hybrid Encryption for Enhanced Security and Confidentiality;deep Neural Networks for Early Diabetes Mellitus Prediction;advancements in image Deblurring and Performance Metrics Using Deep Learning Technique;emotion-Levitating Music Recommendation System Through Real-Time Facial Recognition;smartVoice TrashMate: The Intelligent Voice-Controlled Trash Bin;an Innovative image Enhancement Algorithm for Color Filter Array images: A Novel Approach;Enhancing Secure and Efficient Communication in Free-Space Optical Networks Using Spectral Amplitude Coding - OCDMA technology;enhancing Citrus Fruit Classification and Quality Assessment Through Advanced Machine Learning Techniques;connecting Health for a Better Tomorrow Through internet of Medical Things;image Processing in Retail Marketing: Innovations for Customer Engagement, Sales, and Operational Efficiency;Leaf Checker: AI-based Plant Diagnostic Tool Using Alexnet and Mobilenet;ioT-Supported Indoor Navigation System Using Machine Learning Technique;a Customizable Virtual Assistant Enhanced with Vision and Speech Capabilities;An Unequal Cluster-based Routing Mechanism Using Lyrebird Optimization Algorithm to Achieve Network Longevity in WSNs.

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