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检索条件"主题词=software algorithms"

共 12474 条记录，以下是331-340 订阅

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An Extension of Vector Fitting to Weakly Nonlinear Circuits

An Extension of Vector Fitting to Weakly Nonlinear Circuits

引用

IEEE Workshop on Signal Propagation on Interconnects (SPI)

作者： Antonio Carlucci Ion Victor Gosea Stefano Grivet-Talocia Dept. Electronics and Telecommunications Politecnico di Torino Italy Max Planck Institute for Dynamics of Complex Technical Systems CSC group Magdeburg Germany

ISBN: (数字)9798331520618

ISBN: (纸本)9798331520625

This paper describes a macromodeling algorithm to build behavioral models of nonlinear systems and components in a data-driven manner. The main tool is an extension of the Vector Fitting algorithm to perform rational approximation of multivariate transfer functions that describe nonlinear systems according to Volterra series theory. In addition to the rational model, a realization in terms of differential equations is provided so as to enable time-domain simulation. The raw data required by this algorithm can be extracted by means of Harmonic Balance analysis or from X-parameters, both available in commercial software.

关键词： Translation Fitting software algorithms Transfer functions Approximation algorithms Mathematical models Vectors Numerical models Integrated circuit modeling Nonlinear systems

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PEARL: FPGA-Based Reinforcement Learning Acceleration with Pipelined Parallel Environments

PEARL: FPGA-Based Reinforcement Learning Acceleration with P...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Jiayi Li Hongxiao Zhao Wenshuo Yue Yihan Fu Daijing Shi Anjunyi Fan Yuchao Yang Bonan Yan Institute for Artificial Intelligence Peking University Beijing China Beijing Advanced Innovation Center for Integrated Circuits School of Integrated Circuits Peking University Beijing China School of Electronic and Computer Engineering Peking University Shenzhen China Chinese Institute for Brain Research Beijing China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Reinforcement learning (RL) is an effective machine learning approach that enables artificial intelligence agents to perform complex tasks and make decisions in dynamic situations. Training an RL agent demands its repetitive interaction with the environment to learn optimal policies. To efficiently collect training data, parallelizing environments is a widely used technique by enabling simultaneous interactions between multiple agents and environments. However, existing CPU-based RL software frameworks face a key challenge of slow multi-environmental update computation. To solve this problem, we present a novel FPGA-based RL accelerating framework-PEARL. PEARL instantiates multiple parallel environments and accelerates them with a carefully designed pipeline scheme to hide data transfer latency within the computation time. We evaluate PEARL on respective RL environments and achieve 4.36 x to 972.6 x speedup over the existing fastest software-based framework for parallel environment execution. When scaling the number of environments from 1024 to 43008 (42x) in CliffWalking benchmark, the power consumption increases marginally by 3%, while LUT and flip-flops utilization rise by 2.24 x and 3.08 x, respectively. This demonstrates efficient resource usage and power management in PEARL. Further, PEARL allows users to define and add their environments within the framework flexibly. We have established an open-source repository for users to utilize and expand. We also implement PEARL with the existing RL algorithm and save 7% -15% training time. All the source code is available online https://***/Selinaee/FPGA_Gym.

关键词： Training Source coding software algorithms Training data Reinforcement learning Benchmark testing Throughput software Table lookup Field programmable gate arrays

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Anaheim: Architecture and algorithms for Processing Fully Homomorphic Encryption in Memory

Anaheim: Architecture and Algorithms for Processing Fully Ho...

引用

IEEE Symposium on High-Performance Computer Architecture

作者： Jongmin Kim Sungmin Yun Hyesung Ji Wonseok Choi Sangpyo Kim Jung Ho Ahn Seoul National University

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Fully homomorphic encryption (FHE) is a promising solution for privacy-preserving cloud computing as it enables computations on sensitive data without any risk of data leakage. Despite the significant attention FHE has received, substantial computation and memory demands make it hardly practical for real-world applications. We propose a readily available and practical hardware solution to tackle this problem by using GPUs. GPUs have adequate computational and memory resources to handle complex operations in FHE, including number-theoretic transform (NTT), on which most prior work has deeply focused. However, through detailed analyses, we discover that the performance bottleneck on GPUs is primarily due to simpler element-wise operations, which are limited by off-chip memory (DRAM) bandwidth. Motivated by these observations, we develop Anaheim, a processing-in-memory (PIM) architecture for FHE. We develop optimized FHE execution flows and an end-toend software framework for using PIM with GPUs. Also, we design a versatile PIM unit that handles various modular integer arithmetic PIM instructions, along with an efficient data mapping and associated PIM execution algorithms that minimize data access overhead by leveraging the internal structure of DRAM. Our concerted efforts substantially enhance the performance and energy efficiency of various FHE workloads on GPUs.

关键词： software algorithms Layout Random access memory Computer architecture Bandwidth Transforms software Energy efficiency Hardware Homomorphic encryption

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Development of a Model for Texture Analysis of Machined Parts on CNC Machines

Development of a Model for Texture Analysis of Machined Part...

引用

International Russian Smart Industry Conference (SmartIndustryCon)

作者： Ilya Kovalev David Malykh Alexander Kiselev department of Computer Control Systems MSUT “STANKIN” Moscow Russia department of Robotics and Mechatronics MSUT “STANKIN” Moscow Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This work is devoted to the development of a software module for analyzing the textures of machined parts on CNC machines using computer vision methods. The main goal of the research is to create a tool capable of analyzing geometric parameters of products based on texture properties of materials, which is especially relevant in the conditions of high-volume production. The paper considers modern methods of texture analysis, as well as their robustness to various disturbances such as illumination changes, noise, blurring, rotation and image scaling. An analysis system that combines binocular and texture methods of image processing is proposed, which makes it possible to improve the accuracy of measuring distances to objects on the basis of binocular algorithm, by means of additional analysis of texture properties of surfaces of the processed part.

关键词： Computer vision Image analysis software algorithms Noise User interfaces software Robustness Classification algorithms Surface texture Surface treatment

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Analysis of Visual Information to Determine the Emotional State of People

Analysis of Visual Information to Determine the Emotional St...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Svetlana M. Kutsenko Elena A. Saltanaeva Tatiana V. Lapteva Nikolay S. Kiselev Information Technology and Information Systems Department Kazan State Power Engineering University Kazan Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The demand for emotion recognition software in various fields of human endeavour is undeniable. Research has focused on the application of various technologies and methods, including machine learning, neural networks and big data analytics. Available software in the field of emotion recognition is characterised by a number of drawbacks: high cost; not always transparent data privacy and ethics; dependence on data quality; and narrow specialisation of data processing. Thus, it is relevant to develop the software taking into account the minimisation of the above disadvantages. In implementing the application, data collection and preparation was carried out, a recognition model was developed using neural networks and machine learning, and the accuracy of the model was evaluated. The application implements user authorisation using encryption algorithms, file uploading, file analysis, saving and viewing the results. The developed software allows recognising six basic emotions: joy, sadness, anger, fear, surprise and neutral state.

关键词： Training Emotion recognition Visualization Analytical models Accuracy Text recognition Neural networks software algorithms Data models software

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Exoskeleton Upper Limb Robot: Classification, Key Technology and Research Direction

Exoskeleton Upper Limb Robot: Classification, Key Technology...

引用

Robotics, Control and Automation (ICRCA), International Conference on

作者： Yecheng Ruan Hefei University of Technology Hefei China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

As a human-computer interaction system, upper limb exoskeleton robots play an important role in rehabilitation medicine and human assistance by enhancing human strength and providing payload support. This article summarizes the functional classification, key technologies, and applications of upper limb exoskeleton robots. Firstly, the development status of upper limb exoskeletons at home and abroad was analyzed, and the classification of upper limb exoskeletons, including rehabilitation medicine and human assistance, was introduced. Typical exoskeleton robots with different driving and transmission modes were also detailed. Secondly, the key technologies of upper limb exoskeletons include control technology and drive technology. In terms of control technology, key technologies such as motion intention recognition, motion trajectory planning, and human-computer interaction were analyzed, and the application effects of different algorithms and methods were discussed. In terms of transmission technology, the advantages and disadvantages of electric motors, hydraulic and pneumatic modes, as well as direct transmission and cable transmission were compared. Finally, the future development of upper limb exoskeleton robots at the software and hardware levels was discussed, and improvement measures were proposed to enhance intent recognition intelligence, optimize trajectory planning, and enhance human-machine compatibility. This study provides theoretical basis and technical reference for the further development of upper limb exoskeleton robots.

关键词： Human computer interaction Robot motion Limbs Trajectory planning Exoskeletons software algorithms software Trajectory software measurement Robots

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Jagerman Approximation of Erlang Processes in On-Board Computing Systems Models

Jagerman Approximation of Erlang Processes in On-Board Compu...

引用

Systems of Signals Generating and Processing in the Field of on Board Communications

作者： Andrei G. Erokhin Margarita F. Vanina Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

The design of on-board computer systems associates with a large number of calculations and the implementation of complex algorithms. Analytical and simulation models develop for this purpose. These models must take into account a significant number of factors, which include environmental influences, load fluctuations, and others. Queuing theory methods widely uses in modeling on-board computer systems. Queuing network models may be either open or closed. It becomes possible to take into account the network users operation technology when calculating the characteristics of this a model in the latter case. It is necessary to use special software or develop your own programs using algorithmic programming languages in any case. The processes taking place in on-board computer networks describes by the Erlang processes. software implementation of it has been has quite complicated for a long time. In 1974 Jagerman develops a fast algorithm that implemented Erlang formula however. This paper provides examples of models and programs that allow you calculate the characteristics of on-board computer systems with a complex structure of user dialogue based on the Jagerman algorithm and Brown method for solution of nonlinear equations.

关键词： Analytical models Nonlinear equations Computational modeling software algorithms Approximation algorithms Mathematical models Numerical models Queueing analysis Load modeling Information systems

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Collaborative Diversified Caching Strategies for Enhanced Performance in Edge Server Networks

Collaborative Diversified Caching Strategies for Enhanced Pe...

引用

2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Yukang Guo Yongxuan Sang Bo Wang Yaoli Xu Software Engineering College Zhengzhou University of Light Industry Zhengzhou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Edge computing effectively mitigates the high network latency issues associated with central cloud architectures by deploying server resources in close proximity to users. However, the storage and other resources of edge servers are limited, making it essential to adopt reasonable cache replacement strategies to enhance cache hit rates for edge services, thereby reducing service latency and improving service quality. The spatiotemporal correlation in user service request distributions presents both opportunities and challenges for edge service caching. Existing research in this domain often neglects the collaboration between edge servers, which can lead to low cache hit rates and consequently reduce the efficiency of edge resource utilization and service quality. To address the cache hit problem in the context of multi-edge device collaboration, this paper proposes a diversified caching algorithm.

关键词： Performance evaluation Multi-access edge computing software algorithms Collaboration Network security Spatiotemporal phenomena Servers Resource management Internet of Things software engineering

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Training Neural Networks Based on PSO Algorithm and BP Algorithm

Training Neural Networks Based on PSO Algorithm and BP Algor...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Darong Gao The Electrical Engineering College Northwest Minzu University Lanzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Signal processing has made extensive use of neural networks. This study examines neural network training using the PSO and BP algorithms. To train the neural network, the PSO algorithm alone, the BP algorithm alone, and the PSO algorithm combined with BP algorithm are examined for the same signal. It is determined that the PSO algorithm in conjunction with the BP algorithm can enhance the network's learning efficiency and accelerate its convergence.

关键词： Training software algorithms Neural networks Signal processing algorithms Signal processing Approximation algorithms software Power electronics MATLAB Optimization

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IoT Based Monitoring System for Health During Menstruation

IoT Based Monitoring System for Health During Menstruation

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Chinmaya Khadse Shital Hajare Nishad Sheikh Roshni Dahule Priya Kate Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawani Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. Data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD.

关键词： Data privacy Ethics software algorithms Prediction algorithms software Internet of Things Biomedical monitoring Wearable devices Smart phones Monitoring

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