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检索条件"主题词=software algorithms"

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Research on Lightweight Object Detection and Image Recognition Based on Convolutional Neural Networks

Research on Lightweight Object Detection and Image Recogniti...

引用

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

作者： Wen Ha Jiahui Yang School of Computer Engineering Technology Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

With the rapid development of deep learning technology, Convolutional Neural Networks (CNNs) have achieved remarkable success in the field of computer vision. However, traditional CNN models often feature complex network structures and a large number of parameters, which limits their application on resource-constrained devices such as mobile devices and embedded systems. Therefore, researching lightweight convolutional neural networks is of great significance for promoting the popularization of computer vision technology on these devices. This paper first outlines the research background and significance of lightweight convolutional neural networks, and then introduces in detail the MobileNet series models, the RepViT model, and a lightweight convolutional neural network called FastEdgeCNN for image object recognition. Finally, experiments are conducted to verify the effectiveness of these lightweight models in object detection and image recognition tasks.

关键词： Computer vision Image recognition Computational modeling software algorithms Object detection Network security Mobile handsets Convolutional neural networks Object recognition software engineering

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The Hidden Challenges of Merging: A Tool-Based Exploration

The Hidden Challenges of Merging: A Tool-Based Exploration

引用

IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Luciana De Q. L. Gomes Melina Mongiovi Sabrina Souto Everton L. G. Alves Systems and Computer Department Federal University of Campina Grande Campina Grande PB Brazil Computer Science Department State University of Campina Grande Campina Grande PB Brazil

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Merging is common in collaborative software development, often leading to conflicts. Code modifications, such as refactorings, may contribute to merge conflicts depending on the approach employed by the merging tools. In this paper, we investigate how code changes over time influence merge conflicts and examine how different merge tools affect their frequency. We analyzed 507,411 merge actions from GitHub Java projects using three distinct merge tools: Git, jFSTMerge, and IntelliMerge. Our findings reveal that nearly 43 percent of Git's conflicting scenarios involved refactorings, and resolving these conflicts required significantly more time. We found that refactorings increase the likelihood of conflicts by roughly 10 times in Git, 12 times in jFSTMerge, and 9 times in IntelliMerge. Additionally, out of 62 refactoring types executed, 33.8 percent were consistently associated with conflicts across all three tools. Regarding the number of developers involved in the history of a conflict, jFSTMerge and IntelliMerge were 28 times and 8 times more likely to result in merge conflicts, respectively. These in-sights may be employed to enhance merging algorithms to better handle these specific types of changes and to guide development teams in mitigating risks by coordinating refactorings, potentially reducing the overall rate of conflicts.

关键词： Time-frequency analysis Java Codes Collaborative software Merging software algorithms Collaboration software History software development management

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Fault Model Establishment and Hybrid Neural Network Diagnosis for Manipulator

Fault Model Establishment and Hybrid Neural Network Diagnosi...

引用

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

作者： Yongbo Chen Zhang Ting Department of Mechanical and Electronic Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Due to solve the problem of imbalanced and insufficient fault diagnosis data for manipulator, an effective joint fault model is established and the fault diagnosis analyses are proposed with an optimized hybrid neural network in the paper. the terminal acceleration data is gained by the Co-simulation analysis in Ansys and Adams software. The two-population algorithm is improved to optimize the hybrid network model with three shallow networks. In addition, after preprocessing the acceleration data, the hybrid network is used for training, learning and diagnosing fault. Finally, the network achieves a high fault-diagnosis accuracy with an average of 94.8%. The result indicates that not only the simulation model is established correctly, but also the fault features of acceleration data for the optimized network model are effective.

关键词： Fault diagnosis Solid modeling Accuracy Neural networks software algorithms Feature extraction Manipulators Data models Mathematical models software

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Research on System Integration Development and Experiment for a Tomato Harvesting Robot

Research on System Integration Development and Experiment fo...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Bingyuan Zhang Xinyu Lu Lingli Zhou Meina Zhang Ni Ren Jizhan Liu Key Laboratory of Modern Agricultural Equipment and Technology Jiangsu University Zhenjiang China Institute of Agricultural Information Jiangsu Academy of Agricultural Sciences Zhenjiang China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Selective harvesting of fresh-market tomatoes is a labor-intensive task and one of the most cost-intensive stages in tomato production. Therefore, “machine substitution” is being an effective way to achieve automated harvesting for urgent need. To address the harvesting requirements of tomatoes grown on overhead suspension systems in greenhouse facilities, an automated tomato harvesting robot was developed by integrating RGB-D cameras, LiDAR sensor, a four-wheel-drive and four-wheel-steering(4WID4WIS) mobile chassis, a 6DOF collaborative robotic arm, and an integrated picking-recovery mechanism. A software system was developed based on the ROS framework to enable an autonomous “move-observe-harvest” workflow. Tomato detection and localization were implemented by using the YOLOv5 algorithm framework, while motion trajectory control of the robotic arm was achieved through a combination of hand-eye calibration, the RRT-Connect algorithm, and key waypoint strategies. Field tests of the harvesting robot were conducted in a greenhouse production environment. The results showed that the average harvesting cycle was 12.48 seconds per tomato, the harvesting success rate was 84%. These findings provide valuable insights for the development and industrial application of intelligent harvesting equipment tailored to tomatoes cultivated on overhead suspension systems in greenhouse facilities.

关键词： Location awareness Service robots Trajectory planning Robot kinematics Green products software algorithms Robot vision systems Production Manipulators Trajectory

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Optimal Methods of Primary Information Processing of the CCD-based Emission Spectrometer Registration System

Optimal Methods of Primary Information Processing of the CCD...

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

作者： Vladimir S. Sukhomlinov Rimma S. Zaripova Anna N. Popova Saint-Petersburg State University St. Petersburg Russia Kazan State Power Engineering University Kazan Russia Empress Catherine II Saint-Petersburg Mining University St. Petersburg Russia

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The paper presents a nonlinear theory solving the problem of accounting for the influence of «third» elements on the results of determining the concentration of a particular element, developed with respect to atomic emission spectral analysis on the basis of first principles. The authors present the corresponding mathematical software algorithm, successfully tested in the form of a plug-in block of the digital node of atomic emission spectrometers, which allows to upload data for the construction of calibration dependencies and subsequent calibration of spectrometric equipment, which sufficiently increases the economic efficiency of the spectral-analytical laboratory.

关键词： Atomic measurements Power measurement software algorithms Time measurement software Calibration Steel Chemical elements Spectral analysis Standards

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TidalMesh: Topology-Driven AllReduce Collective Communication for Mesh Topology

TidalMesh: Topology-Driven AllReduce Collective Communicatio...

引用

IEEE Symposium on High-Performance Computer Architecture

作者： Dongkyun Lim John Kim KAIST

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

In deep learning workloads, collective communication across multiple nodes is a critical component in determining overall performance. AllReduce (as well as ReduceScatter and AllGather) is a commonly used collective communication for not only training but also inference. The performance of AllReduce depends on the algorithm utilized (or the “logical” topology) as well as the physical topology of the system that interconnects the nodes together. There has been many work on improving AllReduce performance but prior work have often been topologyaware approach where existing AllReduce algorithms were optimized for a given physical topology. In this work, we propose a topology-driven approach where the topology characteristics are exploited to propose a novel AllReduce collective communication algorithm; thus, the logical topology of the algorithm maps well to the physical topology. In particular, as 2D mesh topology is widely used in various scale-out systems, we propose TidalMesh AllReduce algorithm - a novel approach that exploits the inherent characteristics of the physical 2D mesh topology by pushing flows between the endpoint nodes, similar to a tidal wave, to achieve near-optimal performance for AllReduce. We propose how Sparse TidalMesh AllReduce minimizes bandwidth overhead of TidalMesh with no loss in performance. In addition, we demonstrate how collective communication unrolling can be exploited to enable “software pipelining” of collective communication while exploiting the unique opportunity of superimposing different phases of AllReduce. As a result, TidalMesh results in up to $\mathbf{2 4 \%}$ improvement in AllReduce performance across various deep learning models on a 64 -node $8 \times 8$ 2D mesh, compared to the state-of-the-art while maintaining the simplicity of a logical ring algorithm.

关键词： Deep learning Training software algorithms Computer architecture Bandwidth software Inference algorithms Topology Pipeline processing

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Real-Time, Multistatic RFID Localization with Commodity, Unsynchronized Radios

Real-Time, Multistatic RFID Localization with Commodity, Uns...

引用

IEEE International Conference on RFID

作者： Dimitrios Angelou Aggelos Bletsas School of Electrical and Computer Engineering Technical University of Crete Chania Greece Dept. of Electrical and Computer Engineering Rutgers WINLAB The State University of New Jersey

ISBN: (数字)9798331509057

ISBN: (纸本)9798331509064

Passive RFID tag localization has been mostly focused on monostatic and/or synchronized setups. This work moves a few steps further and performs real-time localization with distributed, i.e., multistatic radios, which are also unsynchronized at the carrier level. It succeeds by exploiting the elliptical direction of arrival (EllDoA) algorithm with a “playback” carrier frequency offset (CFO) mitigation method, showing localization feasibility at a small error cost, even with very cheap software defined radios. The inherent carrier phase offset (CPO) of such distributed setups is addressed, and it is shown that the calibration step needs to be performed only once, and not for further reruns of the experiment; thus, the proposed method is suitable for many real-world and real-time applications, which has not been shown before, to the best of our knowledge.

关键词： Location awareness Three-dimensional displays Prevention and mitigation Scalability software algorithms Real-time systems Calibration Synchronization Radiofrequency identification software radio

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Proactive Platform for Active Health Services (AHS) Considering Data Securities

Proactive Platform for Active Health Services (AHS) Consider...

引用

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

作者： Peiping Wu Wangni Xu Xin Yu China Fine Art Academy Hangzhou China New York University New York USA

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

In order to meet the high requirements of active health service (AHS) platform for data security and privacy protection, this study innovates from many aspects and proposes a new design scheme of active health service platform. The innovations of this study mainly include three aspects: (1) This study designs and develops a new health monitoring device that integrates blood pressure, blood oxygen and pulse monitoring functions from the hardware and software levels. (2) This study implements a new cloud data encryption method based on a hybrid chaotic mapping algorithm. The algorithm integrates chaotic information to improve data transmission security. (3) This study proposes a system algorithm for the multi-factor authentication and access control to further enhance data privacy protection capabilities. In terms of data security, the proposed hybrid chaotic encryption algorithm combines Logistic and Rossler mapping to achieve a balance between complexity and encryption efficiency. Experimental results show that this platform can effectively support active health management and protect data security to the greatest extent.

关键词： Multi-factor authentication Chaotic mapping Data security software algorithms Blood pressure Encryption Protection Monitoring Blood Logistics

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Evaluating Code Emulating Upsets on Microcontroller Devices

Evaluating Code Emulating Upsets on Microcontroller Devices

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IEEE Southeastcon

作者： Cebron Williams Daniel B. Limbrick John Kelly Christopher Doss Department of Electrical and Computer Engineering North Carolina Agricultural and Technical State University Greensboro NC United States

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Fault attacks pose a significant threat to the functionality and reliability of embedded systems, particularly microcontrollers that are widely used in critical applications such as consumer electronics, medical devices, industrial systems, and recently machine learning models. Given the nature of fault injection attacks, the cost for setting up and running fault injection campaigns can be inaccurate, time consuming and expensive. Because of this, alternative methods such as software, GDB based and RTL model fault injection have been developed. This paper compares these fault injection methods and proposes a high-level, standalone method of injecting faults into the ARM Cortex M0+ microcontroller with high accuracy and customization, while exploring the vulnerability of various algorithms.

关键词： Embedded systems Medical devices Costs Microcontrollers software algorithms Machine learning Computer crashes software Reliability Consumer electronics

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Identification Based on the Optimal Information Structure of Non-Stationary Objects

Identification Based on the Optimal Information Structure of...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Isroil I. Jumanov Sunatillo M. Xolmonov Olimjon I. Djumanov Department of Artificial Intelligence and Information Systems Samarkand state university named after Sh. Rashidov Samarkand Uzbekistan

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Scientific and methodological foundations have been developed for optimizing the identification of non-stationary objects based on the optimal information structure with mechanisms for selecting an adequate model, reducing excess space, highlighting, dividing random time series, clustering, and forecasting, subject to a priori limitations and parametric uncertainty of processes. The effectiveness of using identifiers based on dynamic models combined with a neural network has been studied. algorithms for combined statistical and dynamic models and neural networks of various architectures have been implemented. Tools for modeling processes and presenting statistical, dynamic and specific characteristics were obtained. The computational schemes of network components with mechanisms for setting variables, optimization, and adaptive network training have been improved.v A cognitive analysis of variables has been implemented taking into account structural complexity, stochasticity of element connections based on a stochastic regression model, by tracking the ambiguity of dynamics. Optimization problems based on the probabilistic risk function and sliding control were solved. A generalized algorithm for identifying non-stationary objects is constructed based on tools for identifying boundaries, the general range of element values, selecting informative elements, and generating training sets. An identification software package was implemented in C++ in the parallel computing environment “CUDA”.

关键词： Training Adaptation models software packages Computational modeling Heuristic algorithms software algorithms Stochastic processes Data models Object recognition Optimization

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