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检索条件"任意字段=International Conference on Computer Engineering and Technology"

共 438191 条记录，以下是4921-4930 订阅

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Design Optimization of Low-Power Microelectronic Circuits Based on CMOS technology

Design Optimization of Low-Power Microelectronic Circuits Ba...

引用

Consumer Electronics and computer engineering (ICCECE), 2021 IEEE international conference on

作者： Yanzhuo Li Haoyi Chen Chongqing University Chongqing China Nanjing Tech University Nanjing China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

As a renewable energy technology, photovoltaic power transmission has important value in meeting energy demand and reducing environmental pollution. This paper optimizes and designs a low-power consumption and high-reliability transmission scheme based on CMOS integrated circuits. The combination of current-limiting NMOS transistors and additional bias voltage regulation enables precise control of circuit operating current, effectively reducing power consumption and improving energy conversion efficiency. In addition, this paper draws on the optimization method of low-power sample-and-hold circuits, and takes advantage of the characteristics of low leakage current and low quiescent power consumption of CMOS devices to optimize the voltage detection and energy storage module while ensuring system stability. Experimental results show that the optimized design can flexibly adjust the switching voltage and working current to ensure the efficient operation of the system under the condition of low power consumption, and is suitable for small equipment application scenarios such as implantable bioelectronic devices and IoT micronodes.

关键词： MOSFET Power demand Voltage Switches Stability analysis Circuit stability CMOS integrated circuits Voltage control Energy storage Switching circuits

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LSMR: Synergy Randomness in Liquid State Machine and RRAM-based Analog-digital Accelerator 24

LSMR: Synergy Randomness in Liquid State Machine and RRAM-ba...

引用

43rd international conference on computer-Aided Design, ICCAD 2024

作者： Lin, Ning Wang, Songqi Zhang, Xinyuan Wang, Shaocong He, Yangu Zhang, Woyu Wang, Bo Li, Jiankun Li, Mingzi Cui, Binbin Li, Yi Chen, Jia Xia, Chunwei Xuan, Wei Chen, Xiaoming Shang, Dashan Wang, Zhongrui Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong ACCESS – AI Chip Center for Emerging Smart Systems Hong Kong Science Park Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The Hong Kong University of Science and Technology Hong Kong School of Computing University of Leeds Leeds United Kingdom Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400710773

Bio-inspired event sensors are gaining popularity at the edge, such as in robots and wearable electronics. This trend necessitates learning vast amounts of sensory data on the edge, often in few-shot or even zero-shot scenarios, posing challenges in both software and hardware. This paper presents a novel software-hardware co-design to address these issues. Software-wise, we develop an SNN-ANN model, where the SNN encoder is a liquid state machine (LSM) that naturally processes events and significantly reduces learning complexity at the edge due to fixed random weights. The lightweight trainable ANN projection heads are optimized through contrastive learning, enabling zero-shot learning of multimodal events. Hardware-wise, we propose a hybrid analog (RRAM)-digital (CMOS) accelerator – LSMR. The analog in-memory computing core physically implements the LSM by leveraging RRAM stochasticity to generate fixed random weights. The digital core utilizes innovative reconfigurable systolic arrays to accelerate the contrastive learning of ANN projection heads. Extensive experimental outcomes from six neuromorphic datasets, encompassing visual, tactile, and auditory modalities, demonstrate that LSMR considerably improves energy efficiency by a range of 1.65× to 23.70×, in comparison to state-of-the-art edge devices. Simultaneously, it reduces training complexity by a range of 152.83× to 20,587.77× across various edge learning tasks. © 2024 Copyright is held by the owner/author(s).

关键词： Reconfigurable architectures

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Securing the Digital Frontier: An Analysis of Cybersecurity Strategies and Obstacles

Securing the Digital Frontier: An Analysis of Cybersecurity ...

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Computational, Communication and Information technology (ICCCIT), international conference on

作者： V. Saravanan Indumathi. G. S Sathish Kumar James V.Samuthira Pandi J. Shakila S. Banumathi Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of MBA St Joseph's College of Engineering OMR Chennai BNY Mellon Center Pittsburgh USA Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of CSE Jeppiaar Engineering College Rajiv Gandhi Salai Chennai Department of Computer Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

In an era where digital connectivity is ubiquitous, the imperative to safeguard cyberspace has never been more pressing. This research delves into the multifaceted landscape of cybersecurity, examining the strategies employed by governments, businesses, and individuals to protect sensitive information and critical infrastructure from cyber threats. By leveraging a mixed-methods approach, which includes a review of existing literature, case studies, and expert interviews, the research provides a nuanced understanding of the cybersecurity ecosystem. Key findings reveal that while technological advancements have bolstered defense mechanisms, including sophisticated malware, advanced persistent threats (APTs), and insider threats, continues to outpace traditional security measures. Moreover, the study underscores the critical role of human factors, such as user error and inadequate training, as significant vulnerabilities. The research concludes with a set of recommendations aimed at enhancing cybersecurity posture, including the adoption of a proactive, adaptive defense strategy, increased investment in cybersecurity education and awareness, and the fostering of international cooperation to combat transnational cybercrime. The insights gleaned from this analysis are intended to inform policymakers, industry leaders, and cybersecurity professionals, equipping them with the knowledge to fortify the digital frontier against evolving threats.

关键词： Training Reviews Pressing Malware Critical infrastructure Information and communication technology computer security Protection Interviews Investment

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Enhancing Kidney Stone Detection in CT scans with Image Processing and the DCNN-RELM Model

Enhancing Kidney Stone Detection in CT scans with Image Proc...

引用

Electronics and Renewable Systems (ICEARS), international conference on

作者： Manjula Prabakaran Jesmin Zakaria S Kannan Santhiya P Rajasekar P M V Rathnamma Department of CSE- (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Electronics & Communication Engineering (ECE) NSHM Knowledge Campus Durgapur West Bengal India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy Tamilnadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India Department of CSE KLM College of Engineering for Women Kadapa Andhra Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

CT of the urinary system, abdomen, and kidneys appears to improve kidney stone screening and identification. Segmentation and categorization are essential for kidney stone identification and management. This work developed a semi-automatic kidney stone detection system using geometric concepts and image processing. The main steps include model training, feature extraction, segmentation, embossing, and preprocessing. Preprocessing involves trimming the input image to remove unnecessary kidney regions to focus on critical CT scan locations. Relief embossing enhanced image details. Segmentation accurately classified veins and blood arteries to define kidney stone boundaries. CNN-RELM was our training model. Compared to CNN and ELM models, the recommended system performed better. Its 93.87% kidney stone detection and localization accuracy prove its reliability. The semi-automatic system's reliable kidney stone detection and segmentation method may improve clinical decision-making. Combining CNN-RELM modeling and enhanced image processing could lead to medical imaging applications.

关键词： Training Image segmentation Accuracy Three-dimensional displays Computed tomography Embossing Data preprocessing Reliability Kidney Biomedical imaging

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DemoCraft: Using In-Context Learning to Improve Code Generation in Large Language Models

DemoCraft: Using In-Context Learning to Improve Code Generat...

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), international conference

作者： Kapu Nirmal Joshua Mihit Sreejith Department of Electrical Engineering Indian Institute of Technology Kanpur Department of Computer Science and Engineering Indian Institute of Technology Guwahati

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well.

关键词： Measurement computer languages Adaptation models Codes Uncertainty Large language models Computational modeling Semantics

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Intelligent Recognition of computer Images Based on GA-BP Neural Network

Intelligent Recognition of Computer Images Based on GA-BP Ne...

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Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Hongfei Song Tianjin University of Technology Tianjin China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Focusing on a field of intelligent computer image recognition, a BP neural network model which is rooted in genetic algorithm (GA) fine-tuning is presented in my study. Aiming at the limitations faced by traditional image recognition methods when dealing with complex image data, we optimise the initial parameters and network structure of the BP neural network using the full-scale search capability of GA, aiming to refine the accuracy and responsiveness of image systems recognition. The experiments are validated using multiple standard image datasets for comparative analysis, and the outcome reveals that the GA-optimised BP neural network model achieves significant performance gains in image classification and recognition tasks. It is proved that the optimised model has faster convergence speed and higher recognition accuracy, which has more obvious application advantages. Ultimately, novel perspectives and methodologies for the advancement of image recognition technology were introduced in this study. Robust theoretical and practical support for the application of GA-BP neural networks in relevant domains were offered.

关键词： Image recognition Accuracy Face recognition Computational modeling Neural networks Robustness Power electronics Structural engineering Standards Genetic algorithms

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In-Storage Read-Centric Seed Location Filtering Using 3D-NAND Flash for Genome Sequence Analysis 25

In-Storage Read-Centric Seed Location Filtering Using 3D-NAN...

引用

30th Asia and South Pacific Design Automation conference, ASP-DAC 2025

作者： Zheng, You-Kai Wei, Ming-Liang Cheng, Hsiang-Yun Yang, Chia-Lin Tsai, Ming-Hsiang Chien, Chia-Chun Zhong, Yuan-Hao Tseng, Po-Hao Li, Hsiang-Pang Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Macronix International Co. Ltd. Hsinchu Taiwan

ISBN: (纸本)9798400706356

Read mapping is a critical bottleneck in genome sequence analysis, requiring costly approximate string matching to identify potential matches between reads and a reference genome. Pre-alignment filtering methods aim to mitigate this issue by filtering out unnecessary mapping locations, and implementing them with processing-in-memory (PIM) approaches offers potential benefits by offloading filtering from the computing unit. However, the sparse number of potential mapping locations for each read limits the utilization of PIM's parallel computing capabilities, thereby hindering the overlapping of filtering and sequence alignment to hide filtering latency overheads. In this paper, we propose a 3D NAND-based in-storage pre-alignment filtering approach. Leveraging the read depth property, we introduce a read-centric pre-alignment filtering method that enables parallel comparison of multiple reads. We co-design software and hardware for in-situ processing of read-centric pre-alignment filtering within the storage, capitalizing on 3D NAND Flash's approximate parallel search capability. When integrating with a representative read mapping accelerator, our design achieves an average 1.36x performance improvement with comparable energy consumption. Compared to the state-of-the-art (SOTA) PIM solution, our design results in 123.8x and 53.3x performance gain and energy efficiency improvement. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： In situ processing

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Decentralized Continuous Group Key Agreement for UAV Ad-Hoc Network 6th

Decentralized Continuous Group Key Agreement for UAV Ad-Hoc...

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6th international conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Hong, Shijia Zhou, Tianqi Yang, Huijie Shen, Mingdi Zheng, Wenying School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9789819645657

With the rise of the Industrial Internet of Things (IIoT), Unmanned Aerial Vehicles (UAVs) have emerged as the preferred sensing tools for data collection. However, resource-constrained UAVs often face challenges in handling complex group key agreement processes, while key management in the flying ad hoc network (FANET) is also susceptible to single points of failure. To address this issue, this paper proposes a blockchain-based continuous group key agreement protocol for FANET. The blockchain is collectively maintained by UAVs and serves to record dynamic changes within the FANET. Additionally, it employs a ratchet tree to integrate a pseudo-random function generator with an updatable public key encryption scheme, effectively mitigating the risk of single points of failure, reducing communication overhead, and enhancing security and self-healing capabilities. Simulation experiments demonstrate that this protocol outperforms existing key agreement schemes in terms of reduced delay and improved security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain

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Bees Swarm Optimization Based Prediction of Machined Fatigue Failure

Bees Swarm Optimization Based Prediction of Machined Fatigue...

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Automation and Computation (AUTOCOM), international conference on

作者： Sumithra M S. Sahebzathi Nikhat Parveen Arun K. K Ganesh Madhukar Dahane U Nagabalan Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Velammal College of Engineering and Technology Madurai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Mechanical Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India Dr. Vithalrao Vikhe Patil College of Engineering Ahmedhnagar Maharashtra India Department of Science and Humanities – General Engineering R.M.D. Engineering College Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Artificial intelligence approach is now able to anticipate fatigue failure of machined components based on elementary key numbers without the need for professional knowledge of evaluation processes. In this paper, we develop a meta-heuristic approach for the prediction of defects using Bees Swarm Optimisation (BSO) in machined components. The study mainly predicts the fatigue failure by training the BSO experimentally with the training datasets. The three most essential input variables are the load stress, the hardness of the material, and the size of the fatal flaw (KD). In the context of fatigue testing, the only outcomes that may be determined are failure or non-failure. In this investigation, a single data set is generated for each specimen that is examined. When a full fracture has occurred at a given stress level, fracture surface analysis estimates the size of the deadly flaw that has formed. The architecture and hyperparameters of the neural network are cross-validated using a K-fold technique in order to get the highest possible prediction accuracy. After all is said and done, a cautious mean fatigue failure prediction accuracy of 92% is achieved.

关键词： Training Accuracy Machine learning Fatigue Production facilities Wind turbines Surface cracks Particle swarm optimization Stress Testing

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Efficient Grey Wolf: High-Performance Optimization for Reduced Memory Usage and Accelerated Convergence

Efficient Grey Wolf: High-Performance Optimization for Reduc...

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Consumer Electronics and computer engineering (ICCECE), 2021 IEEE international conference on

作者： Lianghao Tan Zhuo Peng Xiaoyi Liu Weixi Wu Dong Liu Ruobing Zhao Huangqi Jiang W. P. Carey School of Business Arizona State University Tempe USA Ira A. Fulton Schools of Engineering Arizona State University Tempe USA Tandon School of Engineering New York University New York USA Department of Computer Science Yale University New Haven USA Department of Industrial and Systems Engineering Lamar University Beaumont USA College of Computing Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

This paper presents an efficient Grey Wolf Optimizer (EGWO) designed to address the limitations of the standard Grey Wolf Optimizer (GWO), focusing on reducing memory usage and accelerating convergence. The proposed method integrates Sinusoidal Mapping for enhanced population diversity and a Transverse Longitudinal Crossover strategy to balance global exploration and local exploitation. These innovations improve search efficiency and optimization precision while maintaining a lightweight computational footprint. Experimental evaluations on 16 benchmark functions demonstrate EGWO's superior performance in convergence speed, solution accuracy, and robustness. Its application to hyperparameter tuning of a Random Forest model for a housing price dataset confirms its practical utility, further supported by SHAP-based interpretability analysis.

关键词： Technological innovation Accuracy Computational modeling Memory management Benchmark testing Robustness Optimization Tuning Standards Convergence

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