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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381-390 订阅

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Scalable Spatial Queries in Big Data systems 23

Scalable Spatial Queries in Big Data Systems

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23rd IEEE International conference on Mobile Data Management (IEEE MDM)

作者： Abdelhafeez, Laila Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA Univ Calif Riverside Ctr Geospatial Sci Riverside CA 92521 USA

ISBN: (数字)9781665451765

ISBN: (纸本)9781665451765

The amount of data in the world is increasing exponentially, a large portion of this data comes from the interactions over mobile devices and the ubiquitous IoT applications. Improving our ability to extract information and insights from these large and complex datasets is crucial to a variety of applications. Our research focuses on scaling spatial queries in the context of big data systems, to be able to apply complex algorithms on large-scale spatial datasets in a timely manner. In particular, this paper studies two spatial queries: (a) spatial group-by polygon query which groups input data points by a given complex polygon set (e.g. world countries), and (b) polygonization query which polygonizes an input set of line strings (e.g. USA road network).

关键词： spatial big data group by join polygon query processing scalable aggregation polygonization doubly-connected edge list distributed systems

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Hybrid Image processing Device as Wearable Aide for Visually Impaired 8

Hybrid Image Processing Device as Wearable Aide for Visually...

引用

8th International conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Papanai, Ashish Kaushik, Harsh Maharaja Agrasen Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665408165

With the advent of technology and algorithms in image processing, many wearable aides are available in the market, and researchers across the globe are developing new solutions. The existing solutions and products fail to provide an economically viable and single solution to the problem of visually impaired people. This study introduces AIde, a novel system amalgamating optimized computer vision algorithms and wearable IoT devices to convert the information extracted from the camera mounted on the user to audio-based signals for assisted mobility. The proposed system has three user modes: indoor, outdoor, and hybrid, which can be modified using the buttons provided on the wearable device. The indoor mode can be used for object detection and classification to increase the accessibility of the objects inside a confined space, and the outdoor mode will provide the user with enhanced mobility and directional sense based on the highlighted path captured by the mounted camera. The hybrid model combines both the features of indoor and outdoor modes, which will detect objects and improve mobility simultaneously. The proposed model is optimized to work in a computationally efficient environment with an at par efficiency and accuracy as the state-of-art and costly market alternatives. © 2022 IEEE.

关键词： Edge computing

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Cluster routing algorithm based on agglomerative hierarchical clustering in wireless sensor networks

Cluster routing algorithm based on agglomerative hierarchica...

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2023 International conference on Intelligent systems, Communications, and Computer Networks, ISCCN 2023

作者： Li, Hongbing Cui, Hao Ou, Jun Wang, Yuning Key Laboratory of Intelligent Information Processing Control of Chongqing Three Gorges University China Chongqing Key Laboratory of Geological Environment Monitoring Disaster Early Warning in Three Gorges Reservoir Area China Internet of Things and Intelligent Control Technology Chongqing Engineering Research Center China

ISBN: (纸本)9781510666238

To save the energy and extend the lifetime of the network, Cluster Routing Algorithm based on Agglomerative Hierarchical Clustering (CRAAHC) is proposed in wireless sensor networks. WSN is divided into a certain quantity of ring areas based on the energy consumption model. Clustering process is optimized by constraining the distance between clusters and the diameter of clusters to improve the quality of clustering algorithm. Cluster head selection strategy is improved by considering the residual energy of the node and energy consumption in communication with other nodes in one cluster. Simulation results show that CRAAHC algorithm can achieve energy efficiency and routing reliability. © 2023 SPIE.

关键词： Clustering algorithms

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New information Extracting and Analysis Methodology for the Terminology Research Purposes: The Field of Biology 18

New Information Extracting and Analysis Methodology for the ...

引用

18th Italian Research conference on Digital Libraries, IRCDL 2022

作者： Jasmonts, Gints Svike, Silga Škirmante, Karina Ventspils University of Applied Sciences 1 Inženieru Street 101 VentspilsLV-3601 Latvia

This study is part of a larger research developing a database of organism names for preserving linguistic diversity and for terminological studies. This article addresses the issue of retrieving the names of organisms from digitized resources of specialized literature for subsequent collection in the new database. The study used image processing algorithms, optical Character Recognition (OCR) and language recognition algorithms, altogether creating a methodology that reflects the possibility of using data retrieval for the terms of the field of biology in Latvian and other local languages - the names of organisms along with their scientific or Latin name. Further on, these retrieved data are scrutinized and prepared for addition in the developed database. © 2022 Copyright for this paper by its authors.

关键词： Database systems

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Review on Advanced Vehicle Recognition System: OCR and REST API Integration for Efficient Results 5

Review on Advanced Vehicle Recognition System: OCR and REST ...

引用

5th IEEE International conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Rohit Kumar Saxena, Aumreesh Kumar Jhapate, Arun Shrivastava, Ritu Srivastava, Rajiv Sirt Bhopal India Csit Sirt Bhopal India Cse Sirt Bhopal India

ISBN: (纸本)9798350383522

Vehicle number plate recognition ranks first in traffic management, especially in densely populated areas like India. This paper presents a conceptual approach that integrates state-of-the-art technologies to accurately identify vehicles using video license plate scanning.[1] Powered by a Raspberry Pi and a set of advanced algorithms, the system is ideal for securing confined spaces including educational institutions and high-security roads Using the power of OpenCV, the system catches on and runs video programs to extract sensitive data. This study digs deep into the difficult areas of acceptable behavior and photo appearance, with license plate recognition being a major focus. A notable feature is the integration of optical Character Recognition (OCR) technology via a REST API, which provides the ability to quickly and accurately extract colors from license plates REST API continuous capture of license plates creates a plate image, which provides key information from these plates.[3]. Experimental findings confirm the capabilities of the system, showing recognition rates in standard with neural network-based strategies for number plate recognition The proposed model opens up possibilities for various applications, from regulatory requirements to checking forged vehicle registrations, extending to activity-monitoring, and encouraging electronic tax collection In together with OCR and REST API integration, this review shows a remarkable walk away progressive system detection vehicle. The convergence assures increased accuracy and skill in number plate recognition, and helps the system for safer, smarter and safer transportation systems. © 2024 IEEE.

关键词： Advanced Image processing Automated Number Plate Recognition Efficient Vehicle Identification License Plate Recognition OCR Technology optical Text Recognition REST API Integration Vehicle Detection System

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Robust adaptive beamforming based on sampling covariance matrix reconstruction and steering vector estimation 13

Robust adaptive beamforming based on sampling covariance mat...

引用

13th International conference on Signal processing systems, ICSPS 2021

作者： Shao, Jiangong Li, Peng Hu, Jingwei Sun, Dengbo Xie, Renhong Rui, Yibin School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

Traditional adaptive beamforming algorithms require high accuracy for steering vectors(SV), array models and desired signal(DS). However, the performance of the beamformer will seriously degrade when the DS is present in training snapshots. For the purpose of improving output performance of adaptive beamformer, a novel adaptive beamforming algorithm is proposed. This approach estimates the desired signal SV and reconstructs the sampling covariance matrix (CM) based on integrating over a undesired signal region. Furthermore, only a little prior knowledge is required, such as the approximate incident angle of the DS. The proposed algorithm remove not only the influence of the DS in the sampling covariance matrix, but also the effect of background noise perturbation, which is significantly improved compared with other methods. The results of data simulation experiments confirms that the beamformer has a excellent performance in output performance. © COPYRIGHT SPIE.

关键词： Covariance matrix

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A Framework for Tile processing on Edge Servers for Roadside Traffic Surveillance 7

A Framework for Tile Processing on Edge Servers for Roadside...

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7th International conference on algorithms, Computing and systems, ICACS 2023

作者： Jaipuria, Saumya Bhattacharya, Arani Banerjee, Ansuman Indian Statistical Institute Kolkata India Indraprastha Institute of Information Technology Delhi India

ISBN: (纸本)9798400709098

Roadside traffic monitoring is increasingly performed by deploying roadside high-resolution video cameras and then running Computer Vision (CV) models on the video data. Since computer vision models are compute-intensive as they utilize Deep Neural Networks (DNNs), the data is usually sent to one or more edge servers located adjacent to mobile base stations. Recent techniques propose running CV models on tiles of videos separately to detect and track small objects. Several CV models exist, each with different requirements of compute and memory. Since more compute and memory-intensive CV models provide higher accuracy, a key challenge of such techniques is to determine which vision model should be used on which tile. This becomes even more challenging if multiple videos are processed by the same edge server. In this paper, we first formulate this problem of model selection on edge devices as an Integer Linear Programming (ILP) problem, and then propose a heuristic to solve it. Our experiments show that it is quite effective in practice. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Edge computing

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Lightweight design and deployment of object detection model for low-light scenes 4

Lightweight design and deployment of object detection model ...

引用

4th International conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Sun, Kangjian Huo, Ju Jiang, Xiaona Gao, Yichao Cai, Chen School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China China FAW Corporation Limited Changchun China College of Design and Engineering National University of Singapore Singapore Signal and Communication Research Institute China Academy of Railway Sciences Beijing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. This paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. The complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. The proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. The deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based nighttime autonomous driving. © 2025 SPIE.

关键词： Object detection Deep learning Image processing

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Real-time Equalization for Bandwidth-Constrained optical Interconnects in Intra-Vehicle Networks

Real-time Equalization for Bandwidth-Constrained Optical Int...

引用

OptoElectronics and Communications conference, OECC

作者： Jinyun Chen Xinghao Lai Lin Sun Yi Cai Gangxiang Shen Gordon Ning Liu School of Electronic and Information Engineering Soochow University Suzhou China

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

In bandwidth-constrained intra-vehicle optical networks, a cost-effective scheme is proposed to improve the transmission data rate. Through an FPGA-GPU computation platform, effective equalization of partial response signals is realized and real-time transmission of 1.2 Gbps NRZ signal is demonstrated using optoelectronic devices with 3 dB bandwidth of 550 MHz.

关键词： optical interconnections Costs Bandwidth optical fiber networks Optoelectronic devices Real-time systems optical transmitters optical signal processing

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An Efficient Cluster-Based Asynchronous Activity Detection Algorithm for Cell-Free Massive MIMO systems

An Efficient Cluster-Based Asynchronous Activity Detection A...

引用

International conference on Wireless Communications and Signal processing (WCSP)

作者： Chenhao You Ziheng Li Tong Wang Lin Gao Yufei Jiang School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In this paper, we propose an efficient cluster-based activity detection algorithm for asynchronous grant-free random access (GFRA) cell-free massive multiple-input multiple-output (CF-mMIMO) systems. To address the uncertain delay challenge in the asynchronous transmission scenario, we aim to enhance the accuracy of active user devices detection and meanwhile reducing the computational complexity of the detection process by utilizing an efficient cluster-based block coordinate descent (BCD) activity detection algorithm. This challenge is heightened in the context of ultra-reliable low-latency communications (URLLC), where precise and timely detection of active users is crucial. To enhance the efficiency of the detection process, we investigate an active set strategy to reduce the number of potential active user devices requiring identification. The accuracy of active user device detection for asynchronous GFRA in CF-mMIMO systems is significantly enhanced by the implementation of the proposed cluster-based BCD algorithm with active set. Simulation results demonstrate that the proposed algorithm achieves superior detection performance with lower computational complexity compared to the traditional algorithms.

关键词： Wireless communication Performance evaluation Accuracy Simulation Signal processing algorithms Clustering algorithms Ultra reliable low latency communication Signal processing Detection algorithms Computational complexity

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