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共 28211 条记录，以下是161-170 订阅

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Biometric and IoT-based Vehicle Security Systems: Advancements and Applications

Biometric and IoT-based Vehicle Security Systems: Advancemen...

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Advancements in Electrical, Electronics, Communication, computing and automation (ICAECA), international conference on

作者： S. Umamaheswari A.G. Mohandas Gandhi V.Jagannath Dept of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India Department of Mechanical Engineering RVS Technical Campus Coimbatore

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Biometric authentication is a safe and effective access control method used in today's vehicles. This work discusses an authentication system based on dual-modal verification with face and fingerprint recognition for improved security and convenience to the user. Deep learning is used to train recognition models with a publicly accessible biometric dataset. Preprocessing operations like noise removal, data augmentation, and class balancing are used to make the models robust. Experimental outcomes show that face recognition is found to have more accurate performance than fingerprint recognition, along with a discussion of factors contributing to this performance. To check real-world feasibility, we also test the system's real-time operation, such as time taken for processing and network latency in an IoT-based setup. Security issues, like spoofing attacks, are handled by liveness detection techniques. Comparative study with other biometric modalities, such as iris recognition, shows the efficacy of the presented system.

关键词： Access control Training Accuracy Face recognition Biological system modeling Scalability Authentication Fingerprint recognition Telecommunication computing Reliability

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SHWCIM: A Scalable Heterogeneous Workload computing-in-Memory Architecture

SHWCIM: A Scalable Heterogeneous Workload Computing-in-Memor...

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Design, automation and Test in Europe conference and Exhibition

作者： Yanfeng Yang Yi Zou Zhibiao Xue Liuyang Zhang School of Microelectronics South China University of Technology Guangzhou China National Key Laboratory of Spintronics Hungzhou International Innovation Institute Beihang University Hangzhou China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

This study introduces HWCIM, a SRAM-based computing-In-Memory core, and SHWCIM, a CIM-capable Coarse-Grained Reconfigurable Architecture, to enhance re-source utilization, multi-functionality, and on-chip memory size in SRAM-based CIM designs. Evaluated using the SMIC 55nm process, HWCIM achieves 1.6x lower power, 2.8x higher energy efficiency, and up to 4.1x smaller area compared to previous CIM and CGRA works. Additionally, SHWCIM delivers an average 105.9x speedup over existing CGRAs and consumes 2-5x less energy than the Nvidia A40 GPU on realistic workloads.

关键词： Graphics processing units Europe In-memory computing Common Information Model (computing) Energy efficiency Vectors System-on-chip Reconfigurable architectures Common Information Model (electricity) Resource management

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Occipital Versus Frontal Electrophysiological Signal and Macroscopic Cerebrospinal Fluid Flow in Early Sleep 11

Occipital Versus Frontal Electrophysiological Signal and Mac...

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11th Vienna international conference on Mathematical Modelling, MATHMOD 2025

作者： Heinrich, Paula Müller, Leander Nuttall, Rachel Zott, Benedikt Sorg, Christian Kreuzer, Matthias Schneider, Gerhard Zimmermann, Juliana TU Wien Institute of Analysis and Scientific Computing Wiedner Hauptstraße 8 Vienna1040 Austria TUM Universitätsklinikum Department for Anesthesiology and Intensive Care Medicine Ismaninger Straße 22 Munich81675 Germany TUM Universitätsklinikum Department for Neuroradiology Ismaninger Straße 22 Munich81675 Germany

The glymphatic hypothesis suggests that cerebrospinal fluid (CSF) bulk movement across the brain plays a major role in brain waste clearance. Previous studies using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) indicate that CSF flow and EEG delta activity (0.5-4 Hz) are coupled, especially during sleep. In our study we investigated this relationship focusing on spatial distinction between the occipital and frontal brain region as well as slow (0.5-2 Hz) and fast (2-4 Hz) delta sub-bands. For this, we used a public EEG-fMRI dataset from 29 healthy young adults during wakefulness and early sleep stages. Our results show only slight changes in the correlation between EEG delta activity and CSF flow as individuals transitioned into light sleep. However, we observed differences within regions and frequency bands during sleep: Occipital delta activity exhibits a stronger correlation with CSF flow than frontal delta activity, and slow delta correlates more strongly than the fast delta sub-band. These results align with power spectral density (PSD) analyses, which reveal higher relative power in lower frequencies in occipital regions, while frontal regions display greater power in higher frequencies. Copyright © 2025 The Authors.

关键词： Cerebrospinal fluid

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Smart Sensors and IoT in Mechanical Engineering: Enhancing Monitoring and control of Industrial processes 1

Smart Sensors and IoT in Mechanical Engineering: Enhancing M...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Annavarapu, Babu J Hareesha, N.G. Kacheru, Goutham Mohammad, Ashif Chin, Jacky Ghule, Gauri KLEF Deemed to be University Department of Management Andhra Pradesh Guntur India Dayananda Sagar College of Engineering Department of Aeronautical Engineering Bengaluru India Wilmington University Department of IST United States University of Dhaka Department of Institute of Energy Bangladesh Mercu Buana University Department of Industrial Engineering West Jakarta Indonesia Department of E and TC VIIT Pune India

ISBN: (纸本)9798331518578

Unmanned aerial vehicles (UAVs) are currently regarded as among the most effective remote sensing methods for collecting data across wide regions. Along with measuring output and aiding in decision-making, they are now employed in the industrial domain as sensing equipment for proactively resolving or avoiding numerous problems. UAVs offer an economical, functional, and reliable technical system for event monitoring and data collecting. Cloud computation has proven to be an essential instrument for addressing processing needs, and the Internet of Things (IoT) transmits industrial data from platforms that monitor and manage surroundings to data processing platforms. The IoTs port connects many items to the internet in fog computation. It supports many communication standards and can function as a joint portal for various systems. Much work has gone into creating UAVs and platforms with several UAVs. This work presents a smart IoT monitoring and control platform depending on a UAV. It utilizes fog computation as an interface between IoT tiers and cloud computation activities. Its ability to seamlessly connect UAVs to industrial control platforms via IoT port platforms and process and analyze UAV shots in real-time and in a structured manner in the cloud is what makes it distinctive. The outcomes show unequivocally that the suggested structure is feasible in offering a dependable and effective UAV distant control platform to enhance product grade and minimize waste. Hence this study investigated the communication delay in several IoT ports between the various IoT levels. © 2025 IEEE.

关键词： Fog computing

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Regularized Multi-LLMs Collaboration for Enhanced Score-Based Causal Discovery 25th

Regularized Multi-LLMs Collaboration for Enhanced Score-Bas...

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25th international conference on Web Information Systems Engineering, WISE 2024

作者： Li, Xiaoxuan Liu, Yao Wang, Ruoyu Yao, Lina University of New South Wales Sydney Australia School of Computer Science and Engineering Northeastern University Shenyang110169 China School of Computing Macquarie University Sydney Australia CSIRO’s Data61 Sydney Australia

ISBN: (纸本)9789819605729

As the significance of understanding the cause-and-effect relationships among variables increases in the development of modern systems and algorithms, learning causality from observational data has become a preferred and efficient approach over conducting randomized control trials. However, purely observational data could be insufficient to reconstruct the true causal graph. Consequently, many researchers tried to utilise some form of prior knowledge to improve causal discovery process. In this context, the impressive capabilities of large language models (LLMs) have emerged as a promising alternative to the costly acquisition of prior expert knowledge. In this work, we further explore the potential of using LLMs to enhance causal discovery approaches, particularly focusing on score-based methods, and we propose a general framework to utilise the capacity of not only one but multiple LLMs to augment the discovery process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Cloud-Based AI Robotics for Precision Pruning of Fruit Trees to Improve Sustainable Farming 3

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

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3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Ilambirai, R.C. Barkathulla, A. Mahalakshmi, G. Babu, J. Jagan Prabu, K. Srinivasan, C. SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The crucial agricultural practice of precision pruning fruit trees greatly impacts fruit production yield and quality. Traditional pruning techniques take a lot of time and money because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system intended to precisely prune fruit trees to address these issues. This system uses cutting-edge robotics fitted with Convolutional Neural Networks (CNNs) to carry out precise and effective tasks related to pruning. Cloud computing integration makes it possible to process and make real-time decisions, improving the system's flexibility in responding to changing tree conditions and pruning needs. The system's primary parts are robotic arms for accurate cuts, various sensors for analyzing tree structures, and CNN models for maximizing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy compared to conventional techniques. This study demonstrates how cloud-integrated AI robotics can transform agricultural practices and improve fruit farming's sustainability and productivity. © 2025 IEEE.

关键词： Agricultural automation Fruit Trees Real-Time Data processing Sensor Integration Sustainable Farming

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

4th International Conference on Numerical Computations: Theo...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

ISBN: (纸本)9783031812439

The proceedings contain 105 papers. The special focus in this conference is on Numerical Computations: Theory and Algorithms. The topics include: Resource Allocation via Bayesian Optimization: an Efficient Alternative to Semi-Bandit Feedback;multi-Objective and Multiple Information Source Optimization for Fair & Green Machine Learning;extended Optimal control Problem for Practical Application;explainable process Deviance Discovery with Data-Efficient Deep Learning;line Search Stochastic Gradient Algorithm with A-priori Rule for Monitoring the control of the Variance;a Machine Learning Approach to Speed up the Solution of the Distributor’s Pallet Loading Problem;combined First- and Second-Order Directions for Deep Neural Networks Training;constrained Global Optimization by Smoothing;the Unreasonable Effectiveness of Optimal Transport Distance in the Design of Multi-Objective Evolutionary Optimization Algorithms;an Improved Modified Jaya Optimization Algorithm: Application to the Solution of Nonlinear Equation Systems;GPU Acceleration of the Enhanced Jaya Optimization Algorithm for Solving Large Systems of Nonlinear Equations;effective Resistance Based Community Detection in Complex Networks;a Comparison of Formulations for Aircraft Deconfliction;optimal Recombination Problem in Genetic Programming for Boolean Functions;heuristics with Local Improvements for Two-processor Scheduling Problem with Energy Constraint and Parallelization;numerical Analysis of Optimal control of Assets and Liabilities by a Bank;optimal control for Stochastic Multi-agent Systems With the Use of Parallel Hybrid Genetic Algorithm;DC Optimization in Adversarial Sparse Support Vector Machine;A First-Order Optimality Condition in Nonsmooth Generalized Semi-infinite Programming (GSIP);miniaturisation of Binary Classifiers Through Sparse Neural Networks;Price Forecasting for Bitcoin: Linear Regression and SVM Approaches;inside the Box: 0–1 Linear Programming Under Interval Uncertainty;machine Learn

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

4th International Conference on Numerical Computations: Theo...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

ISBN: (纸本)9783031812408

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Performance Optimization of Double-Tail Dynamic controlled Comparator at 90 nm Technology

Performance Optimization of Double-Tail Dynamic Controlled C...

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Advancements in Electrical, Electronics, Communication, computing and automation (ICAECA), international conference on

作者： Jaspar Vinitha Sundari T Santhosh P Viveksriram A T Jairuban K K. Paramasivam Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Analog to digital conversion Units with low power consumption and fast speed have long been in demand for efficient conversion systems. With respect to this demand to increase power efficiency and speed, a double tailed dynamic controlled comparator that offers reduced delay is studied. During the meticulous study through various simulations, significant reduction in technology and sizing ratios in comparators offers less power consumption. The comparator under study [11] resimulated in 90 nm CMOS technology offers a power consumption of 3.60 µW, energy efficiency of 10.03 fJ and a delay of 68. 11 ps. process corner and Monte Carlo simulations with a 70.29 ps delay are used to confirm the comparator's resilience.

关键词： Semiconductor device modeling Power demand Monte Carlo methods Telecommunication traffic Energy efficiency Delays Telecommunication computing Analog-digital conversion Optimization Resilience

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DQN-Based Crypto Trading: A Hybrid Approach to Market-Aware Buy-Hold Decisions and Profit-Driven Selling

DQN-Based Crypto Trading: A Hybrid Approach to Market-Aware ...

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Advancements in Electrical, Electronics, Communication, computing and automation (ICAECA), international conference on

作者： C. Bharathi Priya Murugesan V Sivavishnu G Vignesh Meenakshi Sundaram Department of Computer Science & Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Cryptocurrencies have quickly become a significant part of the financial landscape, largely due to their potential for substantial returns. This study examines a trading model based on Deep Q-Network (DQN) that combines reinforcement learning with a hybrid decision-making approach. Unlike traditional RL models that treat buy, hold, and sell decisions as equal, this framework differentiates the decision-making process. It enables DQN to learn optimal strategies for buying and holding while using a rule-based mechanism focused on profit targets for selling. This hybrid method ensures the model effectively captures market trends and secures profits at set thresholds, thereby minimizing unnecessary risk exposure. By adapting to changing market conditions and automating trade execution, this approach seeks to enhance profitability while reducing the risks associated with cryptocurrency trading. Experimental results highlight the effectiveness of this method in volatile market conditions

关键词： Deep learning Adaptation models Sentiment analysis Electric potential Profitability Decision making Reinforcement learning Market research Cryptocurrency Telecommunication computing

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