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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

共 20446 条记录，以下是41-50 订阅

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Driving with Deep Learning: A Robust Traffic Sign Detection and Classification System 2

Driving with Deep Learning: A Robust Traffic Sign Detection ...

引用

2nd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2024

作者： Patel, Harshita Chippa, Ishaan Kumar, Devansh Maske, Vinay Ravindra Satish Bagal, Aakanksha Singh Rajput, Dharmendra School of Information Technology and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350327533

Intelligent Transport systems, such as self-driving automobiles, represent a prime example of applications utilizing traffic sign recognition technology. The increasing demand for autonomous vehicles highlights a significant challenge: Their ability to adhere to traffic regulations without human intervention for road safety. As technological advancements pave the way for increased automation, this study addresses the detection and identification of traffic sign boards under varying conditions, including lighting, direction, and diverse weather scenarios. Focusing on scenarios where the traffic sign is the sole object in an image with a minimal background, image processing techniques are employed for traffic sign recognition. Furthermore, the identified and classified sign is associated with its specific geospatial coordinates, using the device's location data, and subsequently stored in a database. This enables tracking of updates or changes to traffic signs along a designated route or area, with original information being replaced or overwritten upon any alteration at a particular location. © 2024 ieee.

关键词： CNN Geospatial coordinates Intelligent Transport systems Traffic signal Database

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A Review of Artificial Intelligence-Enhanced Security Solutions for the internet of Things 4

A Review of Artificial Intelligence-Enhanced Security Soluti...

引用

4th international conference on Artificial Intelligence, Virtual Reality and Visualization, AIVRV 2024

作者： Yue, Qingqing Tu, Shanshan Li, Wenlong Li, Hongchen Beijing University of Technology Faculty of Information Technology Beijing100124 China

ISBN: (纸本)9798331528744

The rapid development of the internet of Things (IoT) is fundamentally transforming various industries, with many IoT systems increasingly driven by artificial intelligence (AI). Advances in AI introduce new changes and breakthroughs in IoT, while the swift progress of IoT technology also presents significant security and privacy challenges. This paper reviews the major security issues and solutions within AI-driven IoT environments, focusing on key management protocols, intrusion detection systems, physical layer authentication, and data encryption. It examines the advantages and limitations of symmetric and asymmetric key management, the roles of network-based and host-based intrusion detection systems, and how physical layer authentication enhances device security through wireless signal characteristics. Additionally, the paper discusses the role of homomorphic encryption and privacy protection technologies, suggesting that integrating these methods represents an effective strategy for addressing IoT security challenges. Future research directions include developing scalable security protocols, advanced threat detection systems, and incorporating AI to enhance the resilience and efficiency of IoT security measures. © 2024 ieee.

关键词： Artificial Intelligence internet of Things security and privacy

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A Convolutional Neural Network-based Approach For image Analysis and Injection Detection 20

A Convolutional Neural Network-based Approach For Image Anal...

引用

20th ieee international conference on Advanced Video and signal-based Surveillance (AVSS)

作者： Titouna, Chafiq Nait-Abdesselam, Farid Gustave Eiffel Univ ESIEE Paris LIGM Paris France Univ Missouri Kansas City MO 64110 USA

ISBN: (纸本)9798350374292;9798350374285

Due to their usefulness in smart cities and other civilian uses, drones are becoming increasingly popular. With the ability to be organized into networks, they can be used to gather various kinds of data, including images and videos with multimedia characteristics, and then forward it to processing centers for additional handling. They also become a fresh target for a variety of attacks, such as GPS spoofing, denial of service, and false data injection. It is therefore vital and required to create new systems and protection mechanisms against these threats. In this research, we highlight the risk associated with the so-called False Data Injection (FDI) and present a deep learning-based approach for detecting it. An injection of misleading data into the data (images) gathered by the drones is regarded as a serious and potent attack that has the potential to significantly change a final judgment made by the processing center. Our strategy uses deep learning for image analysis and classification in order to thwart this attack. Using Nearest Neighbor Interpolation (NNI) to scale the incoming image to match the classifier, we next feed the image to a Convolutional Neural Network (CNN) for image classification. Finally, we use the Mahalanobis Distance to compare each class of classification results to a neighborhood. Our solution performs well, irrespective of image size, as evidenced by numerical findings on the current dataset, which show an accuracy of 97.71%, a precision of 96.69%, a recall of 94.33%, and an F-score of 0.941%.

关键词： Unmanned Aerial Vehicles Convolutional Neural Network False Data Injection

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FG-PIH: A Fusion of Fresnelet Transform and Gradient Directional Pattern for Perceptual image Hashing 2

FG-PIH: A Fusion of Fresnelet Transform and Gradient Directi...

引用

2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Meesala, Pavani Thounaojam, Dalton Meitei Computer Science and Engineering National Institute of Technology Silchar Computer Vision Laboratory Silchar India

ISBN: (纸本)9798350366570

Perceptual image hashing is pivotal in various image processing applications, including image authentication, content-based image retrieval, tampered image detection, and copyright protection. This paper proposes a novel approach for perceptual image hashing by combining the Fresnelet Transform with Gradient Directional Patterns. Using the FG-PIH technique, the proposed method achieves superior robustness against common image processing attacks while maintaining perceptual similarity for near-duplicate images. Experimental results on standard benchmark datasets demonstrate the effectiveness and efficiency of the proposed Fresnelet Transform-based perceptual image hashing scheme. Furthermore, comparative analysis against state-of-the-art methods underscores the competitiveness of our approach in terms of hash quality and computational complexity. © 2024 ieee.

关键词： Hamming distance

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Highly Accurate Deep Learning-based Lung Nodule Recognition Algorithm in the IoMT 8

Highly Accurate Deep Learning-Based Lung Nodule Recognition ...

引用

8th international conference on Smart internet of Things

作者： Deng, Zixin Wang, Lina Yang, Zijia Wen, Long Cai, Jijing Zhejiang Chinese Med Univ Hangzhou Peoples R China Zhejiang A&F Univ Hangzhou Peoples R China Macau Univ Sci & Technol Macau Peoples R China Hangzhou Dianzi Univ Hangzhou Peoples R China

ISBN: (纸本)9798350366457;9798350366440

Artificial Intelligence (AI) technology plays an important role in the internet of Medical Things. During the diagnosis of early stage lung cancer, medical image data is affected by interference between patient and device, which can lead to accuracy in lung nodule identification. To address these issues, we propose a deep learning-based interference-resistant lung nodule recognition model, Yolov8-SH, which aims to improve the robust detection ability of the model. In this paper, an attention mechanism is introduced to allow the recognition model to focus more on the key regions of the image, which helps to maintain the model's recognition stability when the lung CT image is disturbed. Introducing the Haar Wavelet Downsampling (HWD) to make the recognition model focus on the frequency components corresponding to the size and shape of the nodule, can enhance the visibility of the lung nodule and make it easier to detect and analyze. Deep learning techniques are used to achieve accurate recognition of lung nodules, which is of great help in the early diagnosis and treatment of the condition. Experimental comparisons with existing lung nodule recognition models showed that the Yolov8-SH model improved mAP50 and Precision by 9.6% and 4%, respectively.

关键词： Artificial Intelligence Yolov8 Attention internet of Medical Things Deep Learning Interference-Resistant

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IMPROVING image DE-RAINING USING REFERENCE-GUIDED TRANSFORMERS 31

IMPROVING IMAGE DE-RAINING USING REFERENCE-GUIDED TRANSFORME...

引用

2024 international conference on image Processing

作者： Ye, Zihao Cho, Jaehoon Oh, Changjae Queen Mary Univ London Sch Elect Engn & Comp Sci London England Hyundai Motor Co Seoul South Korea

ISBN: (纸本)9798350349405;9798350349399

image de-raining is a critical task in computer vision to improve visibility and enhance the robustness of outdoor vision systems. While recent advances in de-raining methods have achieved remarkable performance, the challenge remains to produce high-quality and visually pleasing de-rained results. In this paper, we present a reference-guided de-raining filter, a transformer network that enhances de-raining results using a reference clean image as guidance. We leverage the capabilities of the proposed module to further refine the images de-rained by existing methods. We validate our method on three datasets and show that our module can improve the performance of existing prior-based, CNN-based, and transformer-based approaches.

关键词： image de-raining transformers

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Research on Fire Recognition Algorithm based on Automatic image Recognition technology 6

Research on Fire Recognition Algorithm Based on Automatic Im...

引用

6th ieee international conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Zhao, Zhijian Jiangsu Union Technical Institute Nanjing Engineering Branch Nanjing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374315

Aiming at the research of automatic fire recognition technology, this paper discusses a fire recognition algorithm based on automatic image recognition technology. The algorithm makes full use of advanced technologies such as image processing, pattern recognition and machine learning, and effectively extracts fire signal information such as flame color and dynamic characteristics through real-time analysis of the acquired image sequence. In this paper, a deep learning model integrating multiple fire characteristics is proposed. By training large-scale fire sample data, the algorithm can improve its adaptability and recognition accuracy to different environments, lighting conditions and fire development stages. In addition, aiming at the problem of flame recognition in complex scenes, a method based on 3D convolutional neural network was designed to effectively distinguish real fire from interference factors by integrating spatiotemporal information between continuous frames. © 2024 ieee.

关键词： Premixed flames

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Emergency rescue system for chemical industrial parks based on IoT 4

Emergency rescue system for chemical industrial parks based ...

引用

4th international conference on Machine Learning and Intelligent systems Engineering (MLISE)

作者： Wei, Zhongxia Liu, Zeliang Xu, Qin Wang, Ying Jiangxi Bros Pharmaceut Co Ltd Jiujiang Peoples R China Jiujiang Univ Jiujiang Peoples R China

ISBN: (纸本)9798350375084;9798350375077

With the advancement of technology, the use of IoT technology for information management of safety production in chemical industrial parks is of great significance. This article designs an emergency rescue system for chemical industrial parks based on the internet of Things, which mainly consists of a main control module, infrared detection module, image acquisition module, voice prompt module, network transmission module, etc. This system adopts Zigbee wireless networking technology, which has the characteristics of easy operation, low power consumption, low cost, easy expansion, and easy maintenance. Practice has shown that in the social environment of energy conservation and emission reduction, the system has high application value.

关键词： Emergency rescue system internet of Things ZigBee Intelligent control

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Multi-DSP Joint SAR Real-Time Imaging System based on PFA Sub-Aperture Imaging Algorithm 2

Multi-DSP Joint SAR Real-Time Imaging System Based on PFA Su...

引用

2nd ieee international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Liu, Xueqing Song, Yue Li, Zhongyu Wu, Junjie Yang, Jianyu Du, Ping Zhang, Yueyue Dong, Mingji Miao, Xin Liu, Jiaxuan University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Shanghai Satellite Network Research Institute Co. Ltd. Shanghai Key Laboratory of Satellite Network Shanghai China

ISBN: (纸本)9798331515669

The Synthetic Aperture Radar(SAR) real-time imaging system designed with multi-DSP architecture has been widely used in both airborne and satellite platforms. In the context of real-time imaging systems, Polar Format Algorithm(PFA) fall short of meeting the system's real-time demands. This paper proposes a multi DSP architecture SAR imaging system based on sub-aperture PFA to meet the real-time imaging requirements. The system utilizes multiple sub-aperture echo data to design a "2+1"DSP C6678 hardware architecture, and implements the sub aperture PFA algorithm in parallel on the hardware to improve imaging efficiency. Furthermore, we performed phase gradient autofocus processing on the imaging results and achieved high-resolution images. The experimental results show that compared with traditional PFA, the proposed sub aperture PFA algorithm and hardware architecture reduce imaging time by 54%. © 2024 ieee.

关键词： image enhancement

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Neurological Disorder Diagnosis through Deep Residual Network-based EEG signal Analysis 7

Neurological Disorder Diagnosis through Deep Residual Networ...

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ieee 7th international conference on Advanced Technologies, signal and image Processing (ATSIP)

作者： Jridi, Afifa Djemal, Ridha Belwafi, Kais ENIS Natl Sch Engn ATMS Lab Sfax 3038 Tunisia ENIS Natl Sch Engn Dept Elect Engn Sfax 3038 Tunisia Univ Sarjah Dept Comp Engn Coll Comp & Informat Sarjah U Arab Emirates

ISBN: (纸本)9798350351491;9798350351484

Quantifying anomalies in brain signals can reveal various brain conditions and pathologies. Most recent studies on neurological disorders diagnosis such as epilepsy and autism spectrum disorder (ASD) based on electroencephalogram (EEG) rely on custom feature extraction techniques. Traditional methods of feature extraction approaches are time-consuming and provide limited accuracy. So, to balance accuracy and efficiency, more ability is required in the choice of such significant features. This study introduces a feature extraction model based on a deep residual network (ResNet) capable of automatically extracting representative features from EEG signal to address this issue. This proposed method consists of three steps: signal preprocessing using a static filtering method, hidden pattern extraction from EEG signals using the ResNet model, and classification using a SoftMax layer. EEG data sets from the UBonn University database and King Abdulaziz University (KAU) are used in this study. The ResNet model's results are evaluated using accuracy, sensitivity, and specificity. In this study, the proposed diagnostic system achieves a classification for 3 classes accuracy of 100% for Epilepsy in offline diagnosis and an accuracy of 95.5% for Autism in a three-class classification.

关键词： Residual network (ResNet) Electroencephalogram (EEG) Computer-Aided Decision systems (CADS) Feature extraction

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