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UAV -Based Object Tracking Using Siamese Networks and Kalman Filtering

UAV -Based Object Tracking Using Siamese Networks and Kalman...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), international conference on

作者： S.V Ramanan M. Rajasekar G. Shanmugapriya Sujitha R Akshya. J Mani Deepak Choudhry Department of Electronics and Communication Engineering P PG Institute of Technology Coimbatore Department of Computer Science and Engineering VSB college of Engineering Technical Campus Coimbatore Department of Computer Science and Business Systems Dr.N G.P Institute of Technology Coimbatore Department of Information Technology Christ The King Engineering College Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The ability to match features and keep track of objects in changing dynamic environments is still an important problem, particularly due to varying noise levels, diverse datasets, and high dimensional feature spaces. Euclidean Distance and Cosine Similarity based matching are existing methods that commonly do not generalize well, particularly in the case of cross domain scenarios, where noise and motion dynamics are powerful factors to limit the accuracy of tracking. However, Deep Metric Learning approaches can extract robust features at the cost of computational efficiency and are unsuitable for real-time applications. This research proposes a Siamese Network framework to solve these problems by using robust feature matching and multi-object tracking. Based on that, the proposed framework uses a dual-branch network architecture in which feature embeddings extracted from paired inputs are mapped through the same shared latent space. Furthermore, the adaptive learning rate leads to efficient convergence during the training. Experimental results show that the Siamese Network not only achieves better accuracy than the existing methods and the more such as Deep Metric Learning but also performs significantly more efficient computation. With an accuracy of 90.2 % at a precision of 88.5%, the framework's inference time is much faster than that of Deep Metric Learning at 2.0ms. Furthermore, the proposed method performs well on the generalization over datasets with a 15% improvement of cross-domain matching accuracy on Euclidean Distance. By striking this balance between accuracy, efficiency and adaptability, it shows its suitability for dynamic and noisy environments for real-time tracking. The proposed method fills existing gaps and achieves a new state of the art for feature matching and tracking performance.

关键词： Accuracy Dynamics Euclidean distance Feature extraction Real-time systems Robustness Object tracking Noise measurement Vehicle dynamics Noise level

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DTR: Dynamic Tree-Ring Watermarking Framework for Diffusion-Based Video Generation

DTR: Dynamic Tree-Ring Watermarking Framework for Diffusion-...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shunyang Zeng Linlin Yang Jin Yang Yezhen Wang Tianyu Gao School of Cyber Science and Engineering Sichuan University Chengdu China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China College of Information Science and Technology Tibet University Lhasa China School of Computing National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The growing capabilities of diffusion-based text-to-video models have raised significant concerns about copyright protection and the traceability of synthetic video content. To address these concerns, existing watermarking techniques have been developed to invisibly embed information within video content. However, many of these techniques lack resilience when faced with different distortions. This makes existing methods unsuitable for real-world applications, where videos frequently undergo compression, scaling, and other transformations. In response to this challenge, we propose DTR, a training-free Dynamic Tree-Ring watermarking framework designed to enhance the robustness of video watermarks while preserving video quality. Our DTR embeds the predefined user-specific key into the Fourier domain of the initial latent variables. Then, the invertible Fourier transformation ensures the invisible nature of such an insertion. To further improve video quality, the key is divided and embedded as discrete segments across different initial latents, thereby minimizing distribution shifts caused by the watermark insertion. Comprehensive experiments demonstrate the superiority of the DTR framework compared to traditional approaches in terms of robustness, especially under the H.264 and H.265 compression standards, while maintaining high video quality and inference efficiency.

关键词： Watermarking Copyright protection Robustness Acoustics Quality assessment Text to video Speech processing Video recording Standards Resilience

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Ulcerative Colitis Image Classification Using Federated Deep Learning 4th

Ulcerative Colitis Image Classification Using Federated Deep...

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4th international conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Al-Refai, Mohammed Alkhaza’leh, Shahed Alzu’bi, Ahmad Department of Computer Science Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid22110 Jordan

ISBN: (纸本)9783031821523

Ulcerative Colitis is a chronic inflammatory bowel disease characterized by varying degrees of disease severity, often measured using the Mayo endoscopic score. The handling of medical data, encompassing sensitive personal health records and clinical information, necessitates stringent privacy protections. This study introduces a federated deep learning framework that leverages fine-tuned deep learning models trained via transfer learning on a dataset of endoscopic images. Employing DenseNet-121 as the foundational architecture, this approach enables the extraction and encoding of generic descriptors from ulcerative colitis images across multiple clients. We evaluated the training efficiency of our federated learning model in comparison to traditional centralized model, where the federated learning model outperformed the centralized approach by achieving an F1-score of 78% and a Quadratic Weighted Kappa score of 71%, and enhancing convergence speeds in terms of the reduction in the number of training rounds required. The results affirm the model’s capability in accurately diagnosing ulcerative colitis while ensuring the confidentiality of patient data, underscoring the viability of federated learning in sensitive healthcare applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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FCS: A Fault-Tolerance Asynchronous Federated Learning System with Model Checkpoint Storage

FCS: A Fault-Tolerance Asynchronous Federated Learning Syste...

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Annual IEEE Systems conference

作者： Norwich Mungkalaton Songpon Srisawai Yodsawalai Chodpathumwan Wei Li Albert Zomaya School of Computer Science The University of Sydney NSW Australia TGGS King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Federated Learning (FL) is a decentralized machine learning framework that enables large-scale training across multiple devices while maintaining strong privacy guarantees. However, as the number of participating devices increases, communication heterogeneity creates significant challenges for the aggregation server, especially in maintaining reliable communication and guaranteeing training progress for all devices. To ensure FL training stability and relax the strict synchronous communication requirements, we propose the Federated Check-point Storage (FCS) protocol. FCS is a message-free coordination protocol that supports fully asynchronous FL model aggregation by leveraging model checkpointing storage to decouple the strict communication dependency of the device and server during the FL training process. Specifically, FCS isolates the communication instructions required by the server to manage the availability of the device's local training and weight submission from the server's global model aggregation process. We show that the FCS protocol better handles intermittent device availability and discuss its robustness property in avoiding communication deadlock. Experiment results demonstrate that the FCS protocol improves both the round-to-accuracy convergence rates and the time-to-target accuracy of the FL training tasks on FEMNIST and CIFAR-10 datasets. Compared to state-of-the-art synchronous FL protocols under intermittent device availability, we show that FCS could be a robust FL training system for heterogeneous device networks of any scale.

关键词： Training Fault tolerance Privacy Protocols Federated learning Fault tolerant systems Stability analysis Robustness Servers Edge computing

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Secure Dynamic Spectrum Access in Internet-of-Things Based on Machine Unlearning

Secure Dynamic Spectrum Access in Internet-of-Things Based o...

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international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Feng Li Shen Shui Kwok-Yan Lam Bowen Shen Li Wang School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore College of Marine Electrical Engineering Dalian Maritime University Dalian China

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Dynamic spectrum access (DSA) has become a key technology to improve spectrum efficiency in Internet-of-Things (IoT) networks. However, traditional DSA methods require collection of user behaviour data and location information, hence posing concerns for leakage of privacy data from the AI models. In scenarios where strict privacy regulations require the deletion of privacy-sensitive user data, existing DSA mechanisms only have the AI models and without access to the training processes or training data. This paper proposes a DSA method for IoT by integrating Deep Q-Network (DQN) reinforcement learning with zero-shot machine unlearning. This novel approach addresses the dual challenges of efficient spectrum utilization and user privacy protection in IoT environments. By leveraging on knowledge transfer techniques in machine learning, our solution can effectively protect user privacy without directly accessing the original training data while maintaining the model's effectiveness on retained data. Furthermore, this method optimizes the learning process, ensuring that learning efficiency and model convergence speed are comparable to, and in some cases superior to, traditional reinforcement learning methods. Through a series of experimental validations, this paper demonstrates the effectiveness and efficiency of the proposal in IoT DSA scenarios. This research provides a new solution for IoT spectrum management and opens up new opportunities for privacy-enhanced machine learning applications.

关键词： Training Data privacy Privacy Adaptation models Machine learning algorithms Dynamic spectrum access Training data Data models Protection Radio spectrum management

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Harnessing Elastic Adaptive Learning Network for Low-Light Image Enhancement 2nd

Harnessing Elastic Adaptive Learning Network for Low-Light I...

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2nd international conference on Applied Intelligence, ICAI 2024

作者： Xing, Meng Li, Xiaomei Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology NJ Ningbo315201 China Medical Innovation Center and State Key Laboratory of Cardiology Shanghai East Hospital School of Medicine Institute for Regenerative Medicine Tongji University Shanghai200123 China Department of Artificial Intelligence Institute Tianjin Normal University Tianjin300387 China

ISBN: (纸本)9789819619030

In recent years, curve estimation-based low-light image enhancement methods have gained increasing attention due to they do not need reference images for training. However, existing methods often encounter issues such as overexposure, color distortion, and artifacts when processing images with uneven luminance distribution. To address this issue, this paper proposes an approximate global convergence curve estimation based unsupervised low-light image enhancement method, called Harnessing Elastic Adaptive Learning Network (HEAL-Net). Specifically, we design an Approximate Global convergence Curve (AGC-Curve) to reduce the model's sensitivity to input variations. Additionally, we introduce a Dynamic Deep Feature Mapping (DDFM) branch, which further smooths the luminance distribution by capturing the correlations between color channels. Experimental results demonstrate that our method achieves advanced performance across multiple benchmarks and exhibits strong robustness to inputs with uneven luminance distribution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mapping

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A Novel Development in AI Technologies and Its Effects on Digital Education Field 6th

A Novel Development in AI Technologies and Its Effects on Di...

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6th international conference on Smart computing and Informatics, SCI 2024

作者： Akhmetshin, Elvir Kirillova, Elena Klochko, Elena Vijaya Kumar, K. Gangadhara, Kommu Economic Sciences Department of Economics and Management of Elabuga Institute Kazan Federal University Kazan Russian Federation Khorezm University of Economics Urgench Uzbekistan Department of Law Southwest State University Kursk Russian Federation Economic Sciences Department of Management Kuban State Agrarian University Named After I.T. Trubilin Krasnodar Russian Federation Department of Computer Science Engineering GITAM School of Technology Gitam University Hyderabad India Department of IT Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (纸本)9789819619801

A fundamental shift in methods of instruction and learning has been brought about by the convergence of technology and evolving skills in the rapidly evolving educational landscape. This abstract investigates the dynamic interactions between these two aspects and their implications for modern education. Information sharing and acquisition have undergone a paradigm shift as a result of technological advancements. The growth of digital platforms, immersive simulations, and artificial intelligence has pushed educators and learners to acquire new skills. Instead of just imparting knowledge, instructors are increasingly acting as facilitators, assisting students in making decisions amidst a deluge of information. In order to provide students with the skills they need to thrive in an era of abundant knowledge, the educational approach currently places a strong emphasis on competencies like technological literacy, information evaluation, and flexibility. The marriage of talent and cutting-edge technology is changing the face of education. This shift emphasizes the necessity for educators to support students’ proficiency and adaptability in these novel paradigms. In order for students to engage with the modern world both efficiently and wisely, they must simultaneously enhance their digital skills. As education transforms, a healthy merging of human understanding and technological advancements will pave the way for an all-encompassing and empowering learning environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： AI Emerging skills Mobile learning Teaching and learning

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Enhancing Industrial Automation Flexibility Through Neural Network-Empowered Machine Vision Applications 1st

Enhancing Industrial Automation Flexibility Through Neural N...

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1st international conference on Artificial Intelligence and its Applications, ICAIA 2023

作者： George, Navin M. Rosero, Evelyn Mayakannan, S. Shameem, A. Divya, S. Arul, N. Electronics and Communication Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Riobamba060155 Ecuador Department of Mechanical Engineering Vidya Vikas College of Engineering and Technology Namakkal Tamilnadu Tiruchengode India AMET Business School AMET University Tamilnadu Chennai India Erode Tamil Nadu Perundurai638057 India Department of Mechatronics Engineering Sona College of Technology Tamilnadu Salem India

ISBN: (纸本)9783031843969

Vision-based systems provide non-contact sensory input for processing and feedback, expanding the capabilities of industrial automation applications. Prediction using ANNs aids such conformities in navigating nonlinear computational landscapes. They map the plethora of potential outcomes or zones of uncertainty introduced by the system’s constituents onto a set of feasible options for action. A measure of intelligence is transferred to robotic systems through the use of trained networks. Two uses of machine vision are presented here. The 3 DOF robotic assembly enables precision cutting of fragile materials with the help of visual guiding and pixel elimination. The 6-degrees-of-freedom robot uses a combination of visual feedback from an onboard camera and supervision from an external camera to achieve its goals. Pick-and-place operations are executed with a switching control method. Both systems improved in terms of computing time and convergence once ANN was used to make the strategies intelligent. The retrieved scene image features are put to use by the networks in a variety of ways. The simulation and experimental results that back up the suggested approaches demonstrates the value of artificial neural network in machine vision applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robotic assembly

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AI-Enhanced Learning Assistant Platform: An Advanced System for Q&A Generation from Provided Content, Answer Evaluation and Roadmap Generation 1st

AI-Enhanced Learning Assistant Platform: An Advanced System ...

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1st international Research conference on computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Arunnehru, J. Vishnu, S. Ganapathyappan, Kavya Kumar, Deepak SRM Institute of Science and Technology Vadapalani Chennai India

ISBN: (纸本)9783031823855

In today’s educational landscape, personalized and efficient learning experiences are increasingly sought after. This project presents an AI-Enhanced Learning Assistant Platform designed to transform the learning process. The platform offers a comprehensive suite of features aimed at optimizing knowledge acquisition and retention. This study covers automatic question and answer generation from provided textual learning resources. Utilizing cutting-edge language models and document analysis techniques, the system extracts relevant information and crafts challenging questions to comprehensively assess understanding. Leveraging state-of-the-art natural language processing models, the platform autonomously creates tailored questions, facilitates self-assessment, pinpointing students’ weak areas and provides actionable insights into learning progress. It prioritizes scalability, performance, and security and ensures seamless operation and user data integrity. We deem the AI-Enhanced Learning Assistant Platform represents a significant advancement in learning technology, through the convergence of artificial intelligence and natural language processing. Our project aims to enhance the current existing models by providing a platform for a personalized learning experience, only generating necessary information, helps in keeping track of performance of an individual and validate their own performance on the given topics. We can change the static prompts recursively which will automate the student evaluation. By empowering learners with personalized, adaptive, and interactive learning experiences, our platform aims to enhance the effectiveness and engagement of education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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2018 4th international conference for convergence in technology, I2CT 2018

2018 4th International Conference for Convergence in Technol...

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4th international conference for convergence in technology, I2CT 2018

ISBN: (纸本)9781538652329

The proceedings contain 148 papers. The topics discussed include: improving performance of ceiling fan through EAC and energy saving by smart automation: a review;drain current model of reconfigurable ferroelectric field effect transistor (R-Fe-FET);a wideband Vivaldi antenna with fractal dielectric lens for imaging applications;multiple CAs based framework with ANN for load balancing in cloud computing environment;comparative study of resistive and bridge type superconducting fault current limiter;natural language inference as an evaluation measure for abstractive summarization;deterministic real time kernel for dependable WSN;and prediction of protein subcellular localization using machine learning.

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