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IEEE Conference on Decision and Control

作者： Ayda Kamalifar Carlo Cenedese Michele Cucuzzella Antonella Ferrara Department of Electrical Computer Science and Biomedical Engineering University of Pavia Pavia Italy Department of Information Technology and Electrical Engineering ETH Zürich Zürich Switzerland Center for Systems and Control ENTEG Faculty of Science and Engineering (FSE) University of Groningen (UG) Nijenborgh 4 Groningen the Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we propose a novel infrastructure-dependent ramp-metering control for the recently proposed METANET with service station (METANET-s) model, i.e., a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. We study the effect of a ramp-metering control scheme on a highway stretch with a service station and show that it is capable of actively regulate internal traffic demand attempting to exit the service station via its on-ramp, on top of contributing to decrease the traffic congestion on the mainstream. In fact, the proposed control scheme effectively prevents the backlog of vehicles attempting to merge back onto the mainstream. This dynamic control mechanism is further endowed by a route guidance control strategy increasing the share of vehicles stopping at the service station during main-stream congestion periods, e.g. via incentives. The combined effect of our control schemes allows to take full advantage of the presence of service stations, reducing the overall traffic congestion. Simulation results demonstrate the effectiveness of the proposed control strategies.

关键词： Simulation Merging Numerical models Vehicle dynamics Traffic congestion

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Cassava Root Disease Classification Using Deep Learning Techniques

Cassava Root Disease Classification Using Deep Learning Tech...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Seethalakshmi K Bhagyalakshmi A G Anitha S. Priyadarshini Pavithra Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Business Systems S.A Engineering College Chennai Tamil Nadu India Department of Information & Technology St Peter's Institute of Higher Education and Research Chennai Tamil Nadu India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Crop like cassava plays a crucial role in regions facing food insecurity, serving as a primary food source. However, the susceptibility of cassava to various diseases poses a significant threat to crop yields and livelihoods. In this methodology, the input images undergoes a series of preprocessing steps initially. Scaling and Gaussian smoothing filters are applied to cancel out noise. After preprocessing, the images are subjected to segmentation using the UNet architecture to remove background. Next, feature extraction is conducted utilizing Convolutional Neural Networks (CNNs) to capture relevant features. After future extraction, a dense network is used for classification, facilitating the identification of affected images. With cassava root dataset images, the system achieves an impressive accuracy rate of 99.25%, outperforming existing methods. This advancement not only aids in safeguarding cassava production but also holds promise for the cassava production and export industries by providing an efficient means of detecting diseased crops, thus enabling prompt action to mitigate losses and ensure food security. Additionally, the ease of disease detection offered by the proposed system facilitates quality control in cassava production and enhances the competitiveness of cassava exports in the global market.

关键词： Industries Accuracy Smoothing methods Food security Production Feature extraction Convolutional neural networks Crop yield Viruses (medical) Diseases

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Gram-Schmidt Methods for Unsupervised Feature Selection

Gram-Schmidt Methods for Unsupervised Feature Selection

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IEEE Conference on Decision and Control

作者： Bahram Yaghooti Netanel Raviv Bruno Sinopoli Department of Electrical and Systems Engineering Washington University in St. Louis St. Louis MO USA Department of Computer Science and Engineering Washington University in St. Louis St. Louis MO USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Unsupervised feature selection is a critical task in data analysis, particularly when faced with high-dimensional datasets and complex and nonlinear dependencies among features. In this paper, we propose a family of Gram-Schmidt feature selection approaches to unsupervised feature selection that addresses the challenge of identifying non-redundant features in the presence of nonlinear dependencies. Our method leverages probabilistic Gram-Schmidt (GS) orthogonalization to detect and map out redundant features within the data. By applying the GS process to capture nonlinear dependencies through a pre-defined, fixed family of functions, we construct variance vectors that facilitate the identification of high-variance features, or the removal of these dependencies from the feature space. In the first case, we provide information-theoretic guarantees in terms of entropy reduction. In the second case, we demonstrate the efficacy of our approach by proving theoretical guarantees under certain assumptions, showcasing its ability to detect and remove nonlinear dependencies. To support our theoretical findings, we experiment over various synthetic and real-world datasets, showing superior performance in terms of classification accuracy over state-of-the-art methods. Further, our information-theoretic feature selection algorithm strictly generalizes a recently proposed Fourier-based feature selection mechanism at significantly reduced complexity.

关键词： Data analysis Accuracy Numerical analysis Feature extraction Probabilistic logic Vectors Entropy Complexity theory Classification algorithms Information theory

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Exploring blockchain solutions: Integrating cryptocurrency with IoT for enhanced security

Exploring blockchain solutions: Integrating cryptocurrency w...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： M Shravan Shital Kakad Umar M Mulani Geeti Sharma Mohit Tiwari Mary Shiba C School of Business SR University Warangal Telangana India Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune Maharashtra KJ College of Engineering and Management Research Pune Faculty of Management Studies (CMS Business School) Jain Deemed to be University Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi Delhi Department of Computer Science and Business Systems R.M.K Engineering College Thiruvallur

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Blockchain, initially developed as the underlying technology for Bitcoin, has garnered significant attention for its applications beyond cryptocurrency, particularly in complex non-monetary domains. Utilizing cryptographic methods like hashing and asymmetric encryption, combined with a distributed consensus model, Blockchain-based ledgers achieve high levels of security and immutability, effectively removing the need for third-party intermediaries. Meanwhile, the rapid growth in Internet of Things (IoT) devices being connected to networks raises substantial concerns around security and privacy. Addressing these security challenges is essential for the expanding IoT landscape. This paper explores the blockchain solution by integrating cryptocurrency with IoT for enhanced security, reviewing recent studies and applications to evaluate Blockchain's role in IoT security, pinpoint challenges, and propose Blockchain-driven solutions to improve security in the IoT ecosystem.

关键词： Privacy Ecosystems Focusing Bitcoin Reliability engineering Hardware Blockchains Safety Internet of Things Security

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Cybersecurity Challenges and Solutions in the Digital Age: An Analysis

Cybersecurity Challenges and Solutions in the Digital Age: A...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Sargunavathi S Talluri Upender Sowmiya B Sundar R Rajasekaran E S.Sree Dharinya Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of CSE CMR College of Engineering and Technology Hyderabad Telangana Department of Computing Technologies SRM Institute of Science and Technology Department of CSE Sathyabama Institute of Science and Technology Department of Science and Humanities V.S.B Engineering College Karur India School of Computer Science Engineering and Information Systems. VIT University Vellore

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

In the digital age, cybersecurity has become a critical concern for individuals, organizations, and governments worldwide. The rapid advancement of technology and the increasing interconnectedness of systems have introduced numerous vulnerabilities, making cybersecurity a complex and ever-evolving challenge. This paper provides a comprehensive analysis of the current cybersecurity landscape, identifying key challenges such as sophisticated cyber-attacks, data breaches, and the growing threat of ransomware. Additionally, it explores innovative solutions and strategies to mitigate these risks, including the implementation of advanced threat detection systems, and the adoption of robust security protocols. Furthermore, the paper delves into the importance of cyber hygiene, the role of regulatory frameworks, and the necessity of continuous education and awareness programs to foster a culture of cybersecurity. It also examines the impact of emerging technologies like the Internet of Things (IoT) and 5G networks on cybersecurity, highlighting both opportunities and vulnerabilities. Special attention is given to the integration of blockchain technology for secure transactions and identity verification. The paper also addresses the significance of international collaboration and information sharing to combat global cyber threats. By examining both the threats and the potential countermeasures, this paper aims to offer valuable insights into safeguarding digital assets, ensuring the integrity and confidentiality of information, and promoting a proactive and resilient approach to cybersecurity in an increasingly digital world.

关键词： Training Protocols Government Data breach Information age Threat assessment Blockchains Ransomware computer security Cyberattack

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GPU-Based Efficient Parallel Heuristic Algorithm for High-Utility Itemset Mining in Large Transaction Datasets (Extended Abstract)

GPU-Based Efficient Parallel Heuristic Algorithm for High-Ut...

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International Conference on Data engineering

作者： Wei Fang Haipeng Jiang Hengyang Lu Jun Sun Xiaojun Wu Jerry Chun-Wei Lin Department of Computer Science and Technology Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China Department of Distributed Systems and IT Devices Silesian University of Technology Poland

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality.

关键词： Runtime Itemsets Heuristic algorithms Sociology Approximation algorithms Search problems Topology

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Pseudo-label Assisted Optimization of Multi-branch Network for Cross-domain Person Re-identification

Pseudo-label Assisted Optimization of Multi-branch Network f...

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IEEE International Conference on Mechatronics and Automation

作者： Zhengyang Wang Shuxiang Guo Xue Shang Xiufen Ye College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China Department of Electrical and Computer Engineering National University of Singapore Singapore School of Life science Beijing Institute of Technology Beijing China

Person re-identification plays an important role in the field of robot intelligence perception and public safety. Its main task is to identify person targets under cross-camera. However, the domain diversity between different datasets poses a clear challenge for adapting a model trained on one dataset to another. Currently, person re-identification methods based on domain adaptive learning and pseudo-label have made good progress on this problem. Unfortunately, inferior pseudo-labels and source domain noise affect the performance. In order to improve the quality of generated pseudo-labels and enhance the feature representation capability of the model, we propose a pseudo-label assisted optimization of a multi-branch person re-identification method. The multi-branch network is able to extract and represent more effective global and local features, and the generated pseudo-labels are optimized by using cosine similarity and DBSCAN clustering on the feature vectors, thus improving the consistency of the supervised information to enhance the cross-domain recognition performance. We also use a loss function combining cross-entropy loss and triplet loss to make the best feature learning. Experiments show that our method performs well in the Market-to-Duke and Duke-to-Market cross-domain recognition tasks.

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BMP-SD: Marrying Binary and Mixed-Precision Quantization for Efficient Stable Diffusion Inference

BMP-SD: Marrying Binary and Mixed-Precision Quantization for...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Gu, Cheng Li, Gang Lin, Xiaolong Ling, Jiayao Cheng, Jian Liang, Xiaoyao Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Casia The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Beijing China AiRiA Nanjing China

ISBN: (纸本)9783982674100

Stable Diffusion (SD) is an emerging deep neural network (DNN) model that has demonstrated impressive capabilities in generative tasks such as text-to-image generation. However, the iterative denoising stage of the SD model is extremely expensive in both computations and memory accesses, making it challenging for fast and energy-efficient edge deployment. To alleviate the overhead of denoising, in this paper we propose BMP-SD, a post-training quantization framework for hardware-efficient SD inference. BMP-SD employs binary weight quantization to significantly reduce the computational complexity and memory footprint of iterative denoising, along with dynamic step-aware mixed-precision activation quantization, based on the observation that not all denoising steps are equally important for a specific input prompt. Experiments on the text-to-image generation task show that BMP-SD achieves mixed-precision (W1.73A4.87) with minimal accuracy loss on MS-COCO 2014 dataset. We also evaluate the BMP-SD quantized model on three state-of-the-art bit-flexible DNN accelerators, results reveal that our method can deliver up to 5.14× performance and 3.85×energy efficiency improvements compared to W8A8 quantization. © 2025 EDAA.

关键词： Deep neural networks

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Hierarchical Multi-Agent DRL Based Dynamic Cluster Reconfiguration for UAV Mobility Management

arXiv

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arXiv 2024年

作者： Meer, Irshad A. Besser, Karl-Ludwig Ozger, Mustafa Schupke, Dominic Poor, H. Vincent Cavdar, Cicek School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Computer Engineering Princeton University United States Airbus Central Research and Technology Taufkirchen82024 Germany Department of Electronic Systems Aalborg University Copenhagen2450 Denmark

Multi-connectivity involves dynamic cluster formation among distributed access points (APs) and coordinated resource allocation from these APs, highlighting the need for efficient mobility management strategies for users with multi-connectivity. In this paper, we propose a novel mobility management scheme for unmanned aerial vehicles (UAVs) that uses dynamic cluster reconfiguration with energy-efficient power allocation in a wireless interference network. Our objective encompasses meeting stringent reliability demands, minimizing joint power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework, specifically tailored for dynamic clustering and power allocation. The edge cloud connected with a set of APs through low latency optical back-haul links hosts the high-level agent responsible for the optimal clustering policy, while low-level agents reside in the APs and are responsible for the power allocation policy. To further improve the learning efficiency, we propose a novel action-observation transition-driven learning algorithm that allows the low-level agents to use the action space from the high-level agent as part of the local observation space. This allows the lower-level agents to share partial information about the clustering policy and allocate the power more efficiently. The simulation results demonstrate that our proposed distributed algorithm achieves comparable performance to the centralized algorithm. Additionally, it offers better scalability, as the decision time for clustering and power allocation increases by only 10% when doubling the number of APs, compared to a 90% increase observed with the centralized approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Convergence of energy-based learning in linear resistive networks

arXiv

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arXiv 2025年

作者： Huijzer, Anne-Men Chaffey, Thomas Besselink, Bart van Waarde, Henk J. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands CogniGron - Groningen Cognitive Systems and Materials Center Netherlands Control Group Department of Engineering University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a first step in this direction by analysing a particular energy-based learning algorithm, Contrastive Learning, applied to a network of linear adjustable resistors. It is shown that, in this setup, Contrastive Learning is equivalent to projected gradient descent on a convex function, for any step size, giving a guarantee of convergence for the *** Codes 65K10, 68T05, 93B30, 93D99 © 2025, CC BY-SA.

关键词： Contrastive Learning

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