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

共 3200 条记录，以下是181-190 订阅

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The design of a method for recovering degraded images of atmospheric turbulence based on deep learning

The design of a method for recovering degraded images of atm...

引用

2022 International conference on optical Technology, Semiconductor Materials, and devices, OTSMD 2022

作者： Li, Xiangxi Ma, Haotong Chu, Junqiu School of Optoelectronics University of Chinese Academy of Sciences Beijing101408 China Institute of Optics and Electronics Chinese Academy of Sciences Chengdu610209 China Key Laboratory of Optical Engineering Chinese Academy of Sciences Chengdu610209 China University of Chinese Academy of Sciences Beijing101408 China

ISBN: (纸本)9781510663015

When imaging long-range targets using ground-based optical systems, the presence of atmospheric turbulence can cause blurring, dithering, and other degradations in the observed images. In previous research work, the research objects are mostly point targets, and the research work on the recovery of extended targets is yet to be perfected. With the rapid development of deep learning, neural networks driven by data can be used to obtain the recovered images directly by establishing a nonlinear mapping relationship between the degraded and original images. Therefore, the use of wavefront phase detection devices can be avoided by deep learning algorithms. The neural network directly reconstructs the original target of the turbulent degraded image that solves the problem of image blurring due to dynamic turbulence. In this paper, we propose DeblurNet, which employs the global self-attentive module. It improves channel and spatial information extraction, reduces information loss between network layers and global interaction representation to improve the performance of deep neural networks. DeblurNet is used to minimize the effect of turbulence on images and is validated on the NWPU-RESISC45 dataset. We refer to two image evaluation criteria, PSNR and SSIM. From the results, the direct reconstruction of the original target image by deep learning has a good recovery effect. © 2023 SPIE.

关键词： Image reconstruction

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Solid-state optical chemosensor array devices for real-sample analysis 15

Solid-state optical chemosensor array devices for real-sampl...

引用

conference on Frontiers in Biological Detection - From Nanosensors to systems XV

作者： Minami, Tsuyoshi Univ Tokyo Inst Ind Sci 4-6-1 KomabaMeguro Ku Tokyo 1538505 Japan

ISBN: (纸本)9781510658998;9781510659001

Supramolecular optical chemosensors are useful tools in analytical chemistry for the visualization of molecular recognition information. One advantage is that they can be utilized for array systems to detect multiple analytes. However, chemosensor arrays have been evaluated mainly in the solution phase, which limits a wide range of practical applications. Thus, appropriate solid support materials such as polymer gels and paper substrates are required to broaden the scope of the application of chemosensors as on-site analytical tools. Herein, the author proposes the actual approaches for the fabrication of solid-state chemosensor arrays combined with powerful data processing techniques and portable digital recorders for real-world applications.

关键词： supramolecular chemistry chemosensor array pattern recognition imaging analysis paper polymer gel

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Enabling Smart Road Safety: Constrained Device Utilization for Accident Prevention in Traffic Congestion

Enabling Smart Road Safety: Constrained Device Utilization f...

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2024 Multimedia University Engineering conference, MECON 2024

作者： Khatoon, Amna Ullah, Asad Ahmed, Ishfaq Aslam, Ayesha Roslee, Mardeni Bin Ahmad, Shabeer School of Information Engineering Chang'An University Xi'an710064 China School of Information Engineering Xi'an Eurasia University Shaanxi Xi'an710065 China School of Marine Science Northwestern Polytechnical University Xi'an China Multimedia University University Faculty of Engineering Multimedia Cyberjaya63100 Malaysia School of Electronic Engineering Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798331530747

Computer Vision (CV) analysis on constrained devices plays a crucial role in enhancing road safety and mitigating traffic congestion. This study demonstrates the integration of image segmentation and classification techniques into traffic monitoring systems on devices with limited computational power, such as dashboard cameras, smartphones, and IoT sensors. These technologies enable efficient real-time data collection and processing in densely populated urban areas, facilitating the immediate detection of irregular traffic patterns and potential collision points by leveraging edge computing and optimized algorithms. By implementing these analyses directly on constrained devices strategically placed within traffic systems, our approach provides timely responses to dynamic conditions, significantly reducing accident risks through early warnings and adaptive route suggestions. Our research also shows that CV systems can monitor driver behavior, detect attention or weariness, and give timely signals to improve driver response times. The data supports improving traffic signal timings, which might reduce vehicle idle times and improve traffic flow, thus increasing road safety and reducing environmental impact. The study provides a comprehensive methodology for implementing high-quality CV technologies in resource-constrained environments, highlighting advancements in edge computing and energy-efficient hardware. These advances underscore the importance of CV in building sustainable, resilient transport networks and its versatility in municipal and vehicle traffic management, enabling proactive measures to reduce accidents. © 2024 IEEE.

关键词： Traffic congestion

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Differential Protection for Distribution Grids Based on Dual Compressed Sensing Theory

Differential Protection for Distribution Grids Based on Dual...

引用

2024 International conference Optoelectronic information and optical Engineering, OIOE 2024

作者： Duan, Yahui Wu, Tonghua Chen, Wei Dai, Wei Cao, Yuhan Cao, Wenjun School of China EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks Nari Technology Co. Ltd. Jiangsu211106 China The State Key Laboratory of Advanced Electromagnetic Technology Huazhong University of Science and Technology Wuhan430074 China State Grid Hebei Electronic Power Research Institute Hebei250002 China State Grid Shandong Electronic Power Research Institute Shandong050011 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

With the increasing integration of distributed renewable energy sources into distribution networks, the characteristics of fault currents in distribution networks have undergone significant changes. Traditional three-stage current protection is no longer sufficient to meet the protection requirements. In theory, a differential protection scheme can better fulfill the requirements of rapid and accurate fault detection and clearance in distribution networks. Differential protection relies on real-time communication for information exchange between devices, and many researchers have incorporated wireless networks such as 5G into distribution network differential protection. Given the exponential growth of data in distribution networks and the security considerations of wireless communication like 5G, this paper proposes a secure protection technique for distribution network differential protection based on dual compressed sensing theory. This technique employs two compressed sensing schemes to compress the data before transmission, reducing the amount of data transferred while enhancing security. Subsequently, secure determinations are made based on the data at both ends processed through the compressed sensing algorithm, ensuring the security of communication in distribution network differential protection while achieving the same level of protection performance as traditional differential protection schemes. © 2025 SPIE.

关键词： Compressed sensing Data transmission Matrices Data communications Wireless communications Network security Reconstruction algorithms Computer security Signal processing Signal to noise ratio

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Enhancing Vehicle Analysis with Fine Estimation on Video Streams Using Image processing Techniques

Enhancing Vehicle Analysis with Fine Estimation on Video Str...

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2023 Annual International conference on Emerging Research Areas: International conference on Intelligent systems, AICERA/ICIS 2023

作者： Radha, R. Reddy, Kotha Bhargavi Yeshwanth, R. Alliance University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350303452

Enhancing Vehicle Analysis with fine Estimation on video streams time object detection and tracking, and OCR for license plate recognition. By leveraging YOLO, the project extracts precise vehicle information from video streams, enabling comprehensive data collection for traffic analysis. Additionally, OCR algorithms automatically identify license plate numbers, facilitating law enforcement and fine estimation. This integrated approach enhances vehicle analysis, providing valuable insights for traffic optimization, infrastructure planning, and law enforcement strategies. The project aims to create safer, more efficient cities by harnessing the power of image processing techniques like YOLO and OCR in realtime vehicle analysis. © 2023 IEEE.

关键词： optical character recognition

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Application research of the improved Apriori algorithm based on frequent itemsets in intelligent greenhouses

Application research of the improved Apriori algorithm based...

引用

2023 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Wen, Yan Li, Min Ye, Yu School of Mechanical and Electrical Information Chengdu Agricultural College Chengdu Sichuan China

ISBN: (纸本)9781510677685

In the promotion and application of smart agriculture, how can we reasonably utilize and process the massive environmental data acquired from the growth process of crops by smart terminal devices? This is key to whether smart agriculture can be widely applied and promoted. This paper applies the Apriori algorithm to analyze the data of the crop production process. The itemsets generated by the Apriori algorithm use the frequent itemset mining algorithm for reduction. Based on this, the Apriori algorithm is improved by optimizing specific cluster partitioning and parallel algorithms, thus reducing the time and space wastage of the traditional Apriori algorithm. Finally, the improved Apriori algorithm based on frequent itemsets is applied and validated in the intelligent greenhouse of Chengdu Agricultural Science and Technology Vocational College. Using data mining techniques, the correlation between parameters such as temperature during the growth process of greenhouse vegetables and vegetable yield is unearthed. This provides intelligent decision-making and scientific basis for the precision management of vegetable production in the greenhouse, thereby achieving the goal of increasing vegetable yields. © 2024 SPIE.

关键词： Vegetables

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Deep Reinforcement Learning algorithms for Low Latency Edge Computing systems 3

Deep Reinforcement Learning algorithms for Low Latency Edge ...

引用

3rd International conference on Artificial Intelligence and Signal processing, AISP 2023

作者： Kumaran, K. Sasikala, E. SRM Institute of Science and Technology Chennai India Department of Information Technology Easwari Engineering College Chennai India SRM Institute of Science and Technology Kattankulathur Department of Data Science & Business Systems Chennai India

ISBN: (纸本)9798350320749

Nowadays, due to the increase of technological development in smart devices, more computational capabilities are needed with better performance. The maximization of the cloud computing resources used in mobile networks and Internet of Things makes good results. The data processing along with delay in the communication network will be easily handled by edge computing technology. The edge server brought as much as close to the end devices to avoid the frequent cloud access. In edge servers, the processing of the computational task is time consuming, which causes latency. The inference of deep learning along with reinforcement models makes the better resource allocation and task scheduling for complex systems. In this work, deep reinforcement models like Q learning, Double Q learning, Deep Q network, Double Deep Q learning has been compared. The developed Deep Reinforcement learning model has been deployed in the edge-based system. As a result, it is inferred that Double Deep Q learning is having the ability to make better decisions in resource allocation by maximizing the gains and rewards. The simulation result shows that Double Deep Q learning performs better with decreased latency and increases the performance of the edge systems. © 2023 IEEE.

关键词： Reinforcement learning

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Using Image processing and optical Character Recognition to Recognise ID cards in the Online Process of Onboarding

Using Image Processing and Optical Character Recognition to ...

引用

2022 International conference on Smart information systems and Technologies, SIST 2022

作者： Gulnara, Bektemyssova Yerassyl, Akhmer International Information Technology University Almaty Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

A broad range of computer vision studies have been conducted on the recognition of identity documents using mobile devices. A portfolio of techniques and algorithms for solving problems like face recognition, document detection and correction, text field recognition, and so on has been developed, and the lack of data sets has become a critical challenge. The electronic ID card has been commonly used to verify or recognize an individual since its discovery in 2011. Take note of several issues, such as high requirements for detecting the id-card field and challenges recognizing the data in the id-card. In this research, we realized an approach for identifying electronic ID cards using image detection and optical Character Recognition (OCR). This paper provides a short outline of how banking institutions and other fintech companies utilize highly developed OCR tools to automate their processes, enhancing not just service quality, as well as the precision, speed, and protection of their transaction data. As an outcome of our image processing and OCR techniques, we can achieve 99 percent ID detection. This research was based on the interface of a mobile application, such as those owned by one of Kazakhstan's banks. © 2022 IEEE.

关键词： optical character recognition

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Research on intrusion detection method of party building network in colleges and universities based on machine learning

Research on intrusion detection method of party building net...

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2024 International conference on Signal processing and Communication Security, ICSPCS 2024

作者： Fan, Chaozhi Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9781510681699

The rapid development of the Internet and the intelligent information age have provided more possibilities for data leakage, network attacks and other behaviors of malicious persons. The network security of party building in colleges and universities faces severe challenges. Intrusion detection is a new information security technology used to detect intrusions in computer network systems. Intrusion detection methods based on machine learning can better identify unknown anomalies than rule-based methods. The essence of the intrusion detection problem is a classification problem. There are many classification methods in machine learning, including convolutional neural networks, naive Bayes, SVM, etc. These algorithms have good classification performance on balanced data sets. How to extract more representative attribute features from complex data sets to more accurately describe normal behavior and intrusion behavior has become an important breakthrough point in current intrusion detection solutions based on machine learning. In order to solve the problem of network intrusion for party building in universities, this paper proposes to apply the convolutional neural network algorithm and random forest algorithm in machine learning to the intrusion detection model of party building network in universities, which effectively improves the overall classification accuracy of the model and the recognition rate of minority classes. © 2024 SPIE.

关键词： Intrusion detection

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Mobile-CANet: a new high-precision and lightweight method for rice pest classification

Mobile-CANet: a new high-precision and lightweight method fo...

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2023 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Wei, Junyu Wang, Yuxin Wu, Xinrui Shi, Liang Lai, Hanyin Li, Ju College of Information Engineering Sichuan Agricultural University Ya'an625000 China College of Mechanical and Electrical Engineering Sichuan Agricultural University Ya'an625000 China

ISBN: (纸本)9781510677685

Rice pests can adversely impact the quality and productivity of rice, and proper identification of pests is beneficial for growers to taking prompt steps to manage pests. Although the current crop pest identification model has achieved a high recognition accuracy in recent years with the application of deep learning, it is challenging to deploy on the edge devices with limited computing resources and low performance, and it cannot meet the needs of practical agricultural production. Therefore, we propose an effective real-time lightweight pest detection method, Mobile-CANet, which is based on the convolutional neural networks. This study develops a new network structure to enhance the feature extraction capabilities in order to address the issue of location information being lost during feature extraction. The capacity to extract features is enhanced by this network structure, which also incorporates transfer learning to enhance generalization ability. The results of the experiments show that this method's accuracy and number of parameters are superior to those of the existing picture classification models. On the publicly available dataset of rice pests, it can reach 85.3% accuracy with only 3.312M parameters, which can offer technical assistance for the implementation of mobile algorithms in the future. © 2024 SPIE.

关键词： Feature extraction

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