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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13662 条记录，以下是1201-1210 订阅

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Malware Combination Detection in Industrial Control Terminal Collaborative Response System Using Multiple algorithms Based on Deep Learning

Malware Combination Detection in Industrial Control Terminal...

引用

Communication, image and Signal processing (CCISP), International conference on

作者： Bo Hu Weiyong Yang Jian Zhou Jianye Yu Zi Wang Xiaoming Zhou State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang China NARI Group Corporation/State Grid Electric Power Research Institute Nanjing NARI Information and Communication Technology Co Ltd. Nanjing China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

Because of its relevance and complexity in industry, the industrial control terminal collaborative response system is popular among app developers and users, as well as the main target of malevolent criminals. The rapid spread of malware has caused significant harm to users of the industrial control terminal collaborative response system. In this study, we build a model by combining dynamic and static feature detection, compiling test software output information using Fuzz technology, and training with a method that combines dynamic and static interest-based expert recommendation model (DSIERM) algorithm with Bi-LSTM algorithm. Modeling enables specific analysis and classification of test results. Based on this, the TaintBench suite and framework are introduced to compensate for the lack of relevant content for static taint analysis of applications in industrial control terminal collaborative response systems and to increase detection efficiency.

关键词： Deep learning Analytical models Heuristic algorithms Industrial control Software algorithms Signal processing algorithms Collaboration Static analysis Feature extraction Malware

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A Cloud-Edge Integrated Water Extraction Using Superpixel Segmentation 18th

A Cloud-Edge Integrated Water Extraction Using Superpixel Se...

引用

18th International conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Zhang, Qingmin Chen, Chen Zhou, Yang Wang, Zhiyi Jin, Fan Lu, Haitao Wan, Shaohua School of Communication Engineering Xidian University Xi’an China Xidian Hangzhou Institute of Technology Hangzhou311231 China The State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun130000 China National Key Laboratory of Science and Technology on Space Microwave CAST Xi’an710100 China Ningbo Joynext Technology Corporation Ningbo361000 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

ISBN: (纸本)9783031714665

With the development of the Industrial Internet of Things (IIoT) and the proposal of smart water conservancy, the integration of the Internet of Things (IoT), edge computing, and computer vision for hydrological information monitoring has become a trend in water conservancy. Difficult to provide comprehensive hydrological data from hydrological observation stations, hydrological forecasting in uninformative basins has become one of the research hotspots. water extraction can provide support for many hydrological monitoring. However, The water is difficult to extract when the water in the near-water image has reflection and water surface ripples. We propose a multi-condition water extraction method based on superpixel segmentation under cloud-edge integration architecture. Firstly, based on the color characteristics of the water and the surrounding vegetation, we propose a reflection processing mechanism. Secondly, we propose a mechanism based on superpixel segmentation for the effect of reflection on water extraction. Then, we propose a contour correction mechanism. Finally, the flood-filling algorithm is used to extract the water. We compare the traditional image segmentation algorithm with our proposed segmentation method. The results show that the proposed method outperforms other algorithms in all evaluation metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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A Formally Verified Leader Election Algorithm for Autonomous Driving systems

A Formally Verified Leader Election Algorithm for Autonomous...

引用

IEEE International conference on Embedded and Real-Time Computing systems and Applications (RTCSA)

作者： Ryuta Kambe Benjamin Gilby Yuuki Takano TIER IV Inc. Tokyo Japan

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

As autonomous driving systems mature, the importance of fault tolerance to provide safe operation even in hazardous conditions becomes a key issue in deployment. In an autonomous driving system composed of redundant devices, it is necessary to maintain consistency across the system regarding which devices should currently be handling processing. In this paper, we present a new leader election algorithm for autonomous driving systems that guarantees consistency in the presence of the failure of any single system component. To make the algorithm practical for autonomous driving systems, we design it to both handle network link failures and guarantee liveness in the presence of a single failure. Due to the safety-critical nature of our environment and the known difficulty of testing distributed algorithms, we use formal verification to validate our proposed algorithm. Our experiments show that the algorithm completes with reasonable time in a simulated environment of an autonomous driving system.

关键词： Fault tolerance Voting Fault tolerant systems Real-time systems Distributed algorithms Autonomous vehicles Formal verification Testing

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Automatic target detection utilizing an Edge IR vision transformer (EIR-ViT) 33

Automatic target detection utilizing an Edge IR vision trans...

引用

conference on Automatic Target Recognition XXXIII

作者： Adams, Ethan R. Depoian, Arthur C., II Kurz, Aidan G. Bailey, Colleen P. Guturu, Parthasarathy Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510661561;9781510661578

The detection and recognition of targets within imagery and video analysis is vital for military and commercial applications. The development of infrared sensor devices for tactical aviation systems imagery has increased the performance of target detection. Due to the advancements of infrared sensors capabilities, their use for field operations such as visual operations (visops) or reconnaissance missions that take place in a variety of operational environments have become paramount. Many techniques implemented stretch back to 1970, but were limited due to computational power. The AI industry has recently been able to bridge the gap between traditional signal processing tools and machine learning. Current state of the art target detection and recognition algorithms are too bloated to be applied for on ground or aerial mission reconnaissance. Therefore, this paper proposes Edge IR Vision Transformer (EIR-ViT), a novel algorithm for automatic target detection utilizing infrared images that is lightweight and operates on the edge for easier deployability.

关键词： Automatic Target Detection Vision Transformer Infrared Imaging Object Detection FLIR image FLIR dataset Edge Computing

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Smart Oral Health Monitor

Smart Oral Health Monitor

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International conference

作者： Spandana Anilkumar Kamkar Srushti Sanjay Vaishnavi P S Karthik S Dept. of Computer Science and Engineering BNM Institute of Technology Affiliated to VTU Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder.

关键词： Deep learning image processing Teeth Cameras Real-time systems Mobile applications Monitoring Diseases

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Enhanced DSP Blocks for Full High-Definition Video Bridging Application on FPGAs

Enhanced DSP Blocks for Full High-Definition Video Bridging ...

引用

International conference on Communications, Circuits and systems (ICCCAS)

作者： Panfeng Wang Yici Cai Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350386271

ISBN: (纸本)9798350386288

FPGA chips are widely used in video bridging applications. The DSP (Digital Signal processing) in FPGA can effectively improve the efficiency of video image processing algorithms. The 10 bits of pixel depth is common for Full High-Definition (FHD). However, in the existing commercial FPGAs, the DSP does not naturally support 10x10 multiplication. This brings some waste in area and power consumption. This paper proposes a new architecture of embedded DSP. It increases the maximum number of 10x10 multiplications in a DSP block to 8. It is also compatible with common working modes such as multiplication (18x18, 18x9), and accumulation. Meanwhile, introduce power gate and clock gate units in the DSP block to achieve fine-grained control of the power consumption. Users can dynamically control the power supply through their own logic. The entire circuit was successfully designed and implemented in an industrial 22nm process. The experimental results show that the static power consumption of the DSP block is about 20uW, the dynamic power consumption is about 200uW. And the maximum operating frequency can reach 670MHz. The effective area utilization of our DSP in 10x10 mode is 2.2 and 2.5 times higher than that in Intel Arria 10 series and Xilinx UltraScale series FPGAs, respectively.

关键词： Power demand Circuits and systems image processing Signal processing algorithms Digital signal processing Logic gates Logic

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Fast Object Detection Algorithm using Edge-based Operation Skip Scheme with Viola-Jones Method

Fast Object Detection Algorithm using Edge-based Operation S...

引用

IEEE International conference on Artificial Intelligence Circuits and systems (AICAS)

作者： Cheol-Ho Choi Joonhwan Han Jeongwoo Cha Jungho Shin Hyun Woo Oh Pangyo Research and Development (R&D) Center Hanwha Systems Co. Ltd. Seongnam Republic of Korea

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

Machine learning approaches are preferred over deep learning in embedded systems due to their resource efficiency. The widely adopted Viola-Jones method and related algorithms are selected for their high detection accuracy and reasonable processing speed. However, a limitation arises as processing time increases with additional classification iterations based on sub-window operations. To address this issue, we propose an enhanced object detection algorithm that incorporates the Viola-Jones method with edge component calibration and an edge-based operation skip scheme. The introduction of edge component calibration ensures detection performance comparable to conventional methods. This scheme, relying on edge values, significantly reduces unnecessary computations in the background, leading to a marked decrease in classification operations compared to conventional methods. Visual comparisons in experimental results demonstrate that our method increases the detection precision factor while maintaining recall. In terms of classification operations, our approach reduces their number by 31.38% to 85.78% compared to conventional methods. In simpler terms, our method improves processing speed by minimizing classification operations, making it well-suited for embedded systems with limited resource utilization.

关键词： Visualization Embedded systems Machine learning algorithms image edge detection Object detection Transforms Classification algorithms

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A Study on Staircase Artifacts in Total Variation image Restoration 7

A Study on Staircase Artifacts in Total Variation Image Rest...

引用

7th IEEE International conference on Signal and image processing Applications, ICSIPA 2021

作者： Adam, Tarmizi Hassan, Mohd Fikree Paramesran, Raveendran School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Malaysia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia

ISBN: (纸本)9781665435925

The total variation (TV) regularization is used in various image processing domains such as image super-resolution, reconstruction, compressed sensing, and restoration mainly due to its edge-preserving capabilities. However, the main problem when using the TV regularization is the staircase artifacts. For image restoration, the staircase artifacts manifest themselves by producing a smeared and blocky restored image, especially when the noise level is high. This problem has been a long-standing problem, and various improvements to TV regularization have been proposed. This paper studies the effects of the staircase artifacts produced by two different noises;Gaussian noise and salt-and-pepper noise. For this purpose, we compare three well-known algorithms, the alternating direction method of multipliers (ADMM), alternating minimization (AM), and accelerated AM, and observe the effects of staircase artifacts produced between the three algorithms. As a by-product, the accelerated AM tested for the salt-and-pepper noise can be seen as a new extension of the existing accelerated AM method. Results show that it is interesting to study further the effects of different types of noise and the algorithms to mitigate the staircase artifacts produced. © 2021 IEEE

关键词： image reconstruction

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Raindrop Removal for In-Vehicle Camera images with Generative Adversarial Network

Raindrop Removal for In-Vehicle Camera Images with Generativ...

引用

2022 IEEE International conference on systems, Man, and Cybernetics, SMC 2022

作者： Zhao, Zihao Jiang, Min Guo, Jia Yang, Xiaoyu Hu, Yudie Zhou, Xianlong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Raindrop removal for in-vehicle camera images is useful for surveillance and analysis system such as intelligent driving or case diagnosis. As in rainy days, images taken by in-vehicle cameras such as monitor or drive recorder often suffer from noticeable image degradation, and make it difficult to identify the objects on the road. With the uncertainty of raindrop distribution and the complexity of raindrop status, the attached raindrops of in-vehicle camera images can have different effects. And the large-diameter raindrop will greatly reduce the image quality. However, most of the popular deraining algorithms have been successful in recovering images with small raindrop noise and tiny image distortion, but fails to restore those with raindrops covering large areas. We propose a single image raindrop removal network based on Generative Adversarial Network for in-vehicle camera images. We incorporate two overlapping attention layers into the deraining network, which adopt task-driven visual attention and content perception mechanism. The former obtains features by recursive neural network to guide network more interested in the raindrop regions and the surrounding structures, while the content perception schema extracts the features far from raindrops. Moreover, we collect a new benchmark for In-Vehicle Camera image Deraining (ivCRID), it chooses in-vehicle camera images from various deraining dataset. The experimental results show that the proposed deraining network outperforms other state-of-the-art raindrop removal methods in image recovering test on the ivCRID dataset and also has the best performance on the experiments of traffic objects detection. © 2022 IEEE.

关键词： Generative adversarial networks

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A Low-Cost Deployment Case Study for Industrial IoT Data Collection and processing

A Low-Cost Deployment Case Study for Industrial IoT Data Col...

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Intelligent Technology and Embedded systems (ICITES), International conference on

作者： Jiajun Zhang Biao Hu College of Engineering China Agricultural University Beijing China

ISBN: (数字)9798331506292

ISBN: (纸本)9798331506308

Despite the widespread implementation of SCADA systems in factories for centralized data management, their functionality is restricted to devices equipped with sensors. Manual readings are still prevalent for critical instruments like temperature and pressure gauges, lacking interfaces, leading to inefficiencies and errors. Upgrading production line sensors with IoT-enabled instruments is costly and disruptive. To tackle this, we propose a cost-effective data acquisition system that leverages edge acquisition devices and deep learning algorithms without altering the existing equipment and processes. Our solution involves installing new sensors or cameras in critical areas and employing image recognition algorithms alongside indirect measurement techniques. This approach enhances the real-time accuracy of data collection, significantly boosts the overall efficiency of the production process, and reduces operating costs.

关键词： Temperature sensors Temperature measurement Deep learning Accuracy image edge detection Data acquisition Data collection Production facilities Sensors Edge computing

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