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检索条件"任意字段=Conference on Algorithms, Devices, and Systems for Optical Information Processing"

共 3212 条记录，以下是561-570 订阅

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Online Optimization of Offloading Video Analytics Tasks to Multiple Edges for Accuracy Maximization

Online Optimization of Offloading Video Analytics Tasks to M...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yu Liang Sheng Zhang Jie Wu School of Computer and Electronic Information and the School of Artificial Intelligence Nanjing Normal University State Key Laboratory for Novel Software Technology Nanjing University Center for Networked Computing Temple University US

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time video analytics (VA) presents challenges due to its computational intensity and latency sensitivity, especially when processed on mobile devices with limited local resources. We propose to offload VA tasks to edge servers with diverse computational capabilities. We present a "detect + track" approach with on-device object tracking and edge-assisted object detection. We formulate a long-term nonlinear integer programming to maximize the overall accuracy within detection frequency and latency constraints. We then design a queue-based online optimization algorithm to solve it: relax the original problem from the integer domain to the real domain, then employ a queue-based adaptation and randomized rounding strategy. Via rigorous proof, both dynamic regret regarding detection accuracy and the real-time requirement are ensured. Evaluation results also demonstrate the effectiveness of our approach.

关键词： Accuracy Image edge detection Visual analytics Signal processing algorithms Streaming media Signal processing Real-time systems Servers Speech processing Optimization

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Network Traffic Co-Movement Assessment via Oriented Basis Signal processing and Ensemble Decision Trees

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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International conference on Intelligent Transportation

作者： Mohammad Reza Mohebbi Mohammad Javad Hassan Zada Majid Rostami-Shahrbabaki Mario Döller Iuliia Yamnenko Josef Ressel Center Vision2Move project University of Applied Sciences Kufstein Tirol Austria Department of Computer Science University of Passau Germany Chair of Traffic Engineering and Control Technical University of Munich Germany Department of Electronic Devices and Systems National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost.

关键词： Wavelet transforms Radio frequency Accuracy Machine learning algorithms Urban areas Signal processing algorithms Detectors Prediction algorithms Decision trees Random forests

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RTT Algorithm Optimization with Out-of-Order Retransmission Detection

RTT Algorithm Optimization with Out-of-Order Retransmission ...

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International conference on Wavelet Active Media Technology and information processing (ICWAMTIP)

作者： Jiang Wei Zhang Bin Wang Wenyong Zhu Qixun School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan China China Telecom Sichuan Branch Chengdu Sichuan China International Institute of Next Generation Internet Macau University of Science and Technology Macau Sichuan Communication Research Planning & Designing Co. Ltd

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Traditional Round-Trip Time (RTT) algorithms often struggle with maintaining accuracy under conditions of packet retransmissions and disorder, resulting in RTT estimations that fall outside the expected range. This can lead to misinterpretations of network health and performance. In response to this challenge, our research introduces an enhanced RTT calculation method designed for implementation within network devices. Our approach involves a bidirectional monitoring mechanism for detecting packet retransmissions during the process of traffic interception. By doing so, we aim to refine the precision of RTT estimations through meticulous packet inspection. Our novel algorithm is rigorously tested under simulated environments that mimic fluctuating network conditions. The outcomes clearly indicate that our methodology successfully screens out anomalous RTT values, which are typically introduced by packet retransmissions or disorder. Consequently, this leads to a substantial enhancement in the accuracy of RTT calculations. Our findings suggest that the adoption of this optimized RTT algorithm could significantly improve the reliability of network performance monitoring systems, making it a promising solution for real-world network management applications. The comparative experimental results of RTT optimization before and after simulating abnormal network environments show that the peak value of sudden abnormal RTT over the family side and the network side after optimization is reduced by about 75.8% and 99.5%, respectively.

关键词： Out of order Accuracy Refining Estimation Market research optical network units Real-time systems Reliability Optimization Monitoring

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Analysis of NIST Lightweight Cryptographic algorithms Performance in IoT Security Environments based on MQTT

Analysis of NIST Lightweight Cryptographic Algorithms Perfor...

引用

IEEE conference on Wireless Communications and Networking

作者： Volodymyr Voloshyn Mohammad S. Khan Gautam Srivastava Darshan M Department of Math and Computer Science Brandon University Canada Department of Computing East Tennessee State University Johnson City TN USA Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this vision paper, we analyze the protocols used in the Internet of Things (IoT), encryption methods, and their combination for exploitation. The Internet of Things (IoT) is an important paradigm of modern technology that connects physical objects and devices into a single network where they can exchange data and interact without direct human intervention. The Internet of Things is used in a variety of areas, from controlling household appliances to monitoring the condition of objects in industry and agriculture. The MQTT (Message Queuing Telemetry Transport) protocol was used in this work, which is a lightweight protocol for transmitting messages in IoT networks. It allows for efficient data exchange between devices, ensuring low energy consumption and minimizing bandwidth. The following ciphers were used to ensure the security of information in IoT networks: ASCON and Grain128-AEAD. ASCON is used to encrypt and authenticate data, ensuring its confidentiality and integrity. Grain128-AEAD is also used for data protection, providing a high level of security and encryption. This research work simulates an IoT environment based on the MQTT communication protocol and tests the performance of lightweight cryptographic algorithms. As evident from the results, encryption is an essential part of security for IoT -based communication systems and such lightweight algorithms could help in boosting the overall performance with low to none cases of failure. This paper looks to envision the suitability of these lightweight cryptographic (LWC) security and privacy solutions for IoT and Cyber-Physical systems (CPS).

关键词： Transport protocols Performance evaluation Privacy Protocols information processing Time measurement Encryption

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An Optimized Physical Reservoir Computing Through Genetic Algorithm 12

An Optimized Physical Reservoir Computing Through Genetic Al...

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12th International conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Wen, Kaiqiang Zhou, Wenli Zhuang, Yan Li, Chengzhu Gong, Qingfeng Sheng, Zhoujun Li, Zhuoyu School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory of Optoelectronics Wuhan430074 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Reservoir Computing (RC), a compact recurrent neural network (RNN), is an efficient artificial neural network suitable for processing timing signals. In our previous work on a physical reservoir consisting of single-walled carbon nanotubes (SWCNTs) network, its computing performance was presented to be improved by phosphomolybdic acid-modification on CNTs. In this paper, a genetic algorithm was used to search some hyperparameters related to its computing architecture, including input gain, input position, auxiliary input, regularization coefficient and leak rate, for two test benches, i.e., NARMA10 time series prediction and epileptic seizure EEG classification. The parametric setting for improved computing capability of this physical system were found. The best performances were achieved at normalized root-mean-square error of 0.0877 for NARMA10 and an accuracy of 98.33% for EEG classification, respectively. © 2022 IEEE.

关键词： Genetic algorithms

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Research on Design and Optimization Methods of Intelligent Sensors Based on Optoelectronic Detection Technology

Research on Design and Optimization Methods of Intelligent S...

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Electrical Drives, Power Electronics & Engineering (EDPEE), International conference on

作者： Dinghe Zhou Junbo Huang Harbin Engineering University Harbin Heilongjiang China Hebei University of Technology Tianjin China

ISBN: (数字)9798350395631

ISBN: (纸本)9798350395648

In optoelectronic information science and engineering technology, optoelectronic detection technology is a very important technology. Photoelectric sensor (PS) refers to the process of using a photoelectric element as the basic detection component, converting the measured changes into changes in other signals, and then using the photoelectric element to convert the optical signal into an electrical signal. With the gradual improvement of modern science and technology, optoelectronic detection technology has further integrated modern technologies such as automation control systems and digital information processing technology, effectively combining optical and electronic technologies, greatly improving the accuracy and speed of optoelectronic detection. This article designs an intelligent sensor based on photoelectric detection technology. Through in-depth research on the basic principles of photoelectric detection technology, combined with microelectronics technology and integrated circuit design, a miniaturized and low-power intelligent sensor is achieved. Meanwhile, utilizing deep learning (DL) algorithms to optimize and calibrate sensors, improving their performance and accuracy. The experimental results show that the sensor designed in this article has high measurement accuracy and good application prospects.

关键词： Integrated circuit synthesis Performance evaluation Biomedical optical imaging Optimization methods optical variables measurement Market research Microelectronics

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Measurement Methods for Software Execution Time on Heterogeneous Edge devices

Measurement Methods for Software Execution Time on Heterogen...

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IEEE International conference on Industrial Informatics (INDIN)

作者： Bernhard Rupprecht Birgit Vogel-Heuser Eva-Maria Neumann Institute of Automation and Information Systems School of Engineering and Design Technical University of Munich

Due to emerging data-driven approaches in factory automation in the course of Industry 4.0, automated Production systems must incorporate additional algorithms for data collection and processing tasks. However, strict real-time requirements, resource constraint devices, such as Programmable Logic Controllers or low-power edge devices, and network bandwidth limitations pose a challenge to selecting suitable algorithms for specific edge devices and vice-versa, also known as software-hardware co-design. Measuring the execution time of an algorithm or code snippet is therefore a crucial part of algorithm and hardware assessment and is incorporated in numerous benchmarks. However, this is not trivial since most existing time measurement methods are designed with specific devices in mind with limited portability to different hardware platforms. Thus this paper provides an overview of the properties of selected execution time measurement methods to support their feasible deployment in edge computing, including legacy systems. A time measurement code snippet for Beckhoff Programmable Logic Controllers and recommendations for implementing software-based timing functions for heterogeneous devices help shorten development times. Besides, comparing execution time measurement methods highlights the challenges of creating a generic cross-platform benchmark in future research.

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Development and Application of Intelligent Traffic Toll Data System Based on Blockchain

Development and Application of Intelligent Traffic Toll Data...

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Electronics and devices, Computational Science (ICEDCS), International conference on

作者： Yu Huang Jinrui Hu Jingming Huang College of Art & Design Nanning University Nanning China Guangxi Transportation Science and Technology Group CO. LTD. Nanning China The Scientific and Technical Information Institute Guangxi Zhuang Autonomous Region Nanning China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

This paper focuses on the research and development of intelligent traffic toll data system based on blockchain, aiming to improve the accuracy and efficiency of highway toll inspection. The system integrates blockchain technology and the Internet of Vehicles (IoV), builds a new trust management mechanism, realizes cross-platform data sharing and interaction, and solves the problem of data isolation and trust in the traditional toll system. This paper presents an algorithm based on Bayesian inference model, which is used to predict and analyze charging data under uncertain environment to ensure the reliability and accuracy of data processing. Through the system simulation, the performance of the designed algorithm and model under different working conditions is deeply analyzed. The simulation results show that the proposed system can greatly improve the efficiency and accuracy of charge audit while ensuring data security and privacy. This paper analyzes the simulation data precisely, verifies the feasibility and superiority of the system in practical application, and provides important technical support and application reference for the future development of intelligent transportation system.

关键词： Road transportation Technological innovation Accuracy Prediction algorithms Inference algorithms Data models Blockchains systems simulation Intelligent transportation systems Internet of Vehicles

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Fuzzy Morphological processing Algorithm of Blood Image

Fuzzy Morphological Processing Algorithm of Blood Image

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Annual Siberian Russian Workshop on Electron devices and Materials (EDM)

作者： Abrorjon M. Turgunov Temur N. Narzullaev Marks Ya. Matyakubov Munira T. Payzieva Sevara J. Kamolova Nilufar N. Rakhmonova Department of Algorithms and mathematical modeling Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

This article, a morphological image processing algorithm is developed for early detection of viral hepatitis from microscopic blood images. It is mentioned that it can be used to accurately determine each boundary detail in the fuzzy morphological processing of the gray image of the blood image. Boundary detection leads to higher recognition results in neural network training. Compared to the work of a number of scholars, our method offers such performance, but it offers improved dynamics and flexibility in the formulation of linguistic boundary criteria, which can be a leading factor in the design of image processing systems with dynamic and flexible rules, such as Type 2 allows fuzzy rules, which offers an interesting alternative to currently common deep learning applications. The validated experimental results show the reliability of the modified method compared to the Chaira method. Mean, entropy and standard deviation were calculated. It turned out to be higher than the values of the Chaira method.

关键词： Training Image processing Refining Medical services Reliability Blood X-ray imaging

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RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking Dataset And A Benchmark

RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking D...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Heng Yu Ruiheng Zhang Haoyang Sun Zhe Cao Biwen Yang Jin Zhang Guanyu Liu Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

High-quality perception is crucial in autonomous driving and monitoring systems, where millimeter-wave radar and infrared cameras play important roles due to their robustness and reliability under harsh conditions. Both technologies can serve as low-cost supplements to optical image detection, improving overall system robustness. However, there is currently a lack of widely applicable feature-level fusion methods and multimodal datasets to effectively integrate visible light with these two heterogeneous data types for multiple tasks. In this work, we collect a new multimodal dataset, RADCI8, which synchronizes data from a camera, an infrared camera, and a radar for target detection and tracking. The dataset includes 2D image annotations, radar RAD tensor data with distance, angle, and Doppler information, as well as target ID annotations in both data formats. In addition, to address the incomplete use of radar data in previous fusion algorithms, we propose a detection method that fuses image and radar features using feature concatenation and an attention mechanism. Our proposed algorithm achieves 51.5% AP with an IOU of 50:95 on 2D bounding box prediction, significantly improving average detection accuracy over vision-based methods and maintaining robustness even when a single sensor degrades.

关键词： Tensors Radar detection Signal processing algorithms Radar imaging Radar tracking Cameras Prediction algorithms Robustness Doppler radar Synchronization

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