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

共 6193 条记录，以下是11-20 订阅

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2024 16th international conference on Wireless Communications and signal Processing Physical Layer Authentication based on CNN-GAN

2024 16th International Conference on Wireless Communication...

引用

international conference on Wireless Communications and signal Processing (WCSP)

作者： Wei Xie Hongjun Wang Zimo Feng Chunlai Ma College of Electronic Engineering National University of Defense Technology Hefei China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

The rapid growth of the internet of Things (loT) has created significant opportunities for the future of telecommunications. Research on physical layer authentication with channel features holds promise to improve wireless network security for loT devices. In this paper, we exploit Channel State Information (CSI) phase extracted from the receiver with multiple antennas to enhance the channel spatial diversity. Then Convolutional Neural Network and Generative Adversarial Network (CNN-GAN) model is designed for physical layer authentication. Outlier score from model is introduced to authenticate device identity. Finally, we carry out extensive experimental evaluations about the authentication performance and robustness of the proposed scheme by data collected from Wi-Fi signal based on IEEE 802.11n protocol. The results have successfully demonstrated that the proposed scheme exhibits outstanding performance.

关键词： Performance evaluation Heating systems Authentication Receivers signal processing Physical layer Feature extraction MIMO Convolutional neural networks Antennas

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Design of Artificial Intelligence image Detection System based on internet of Things 24

Design of Artificial Intelligence Image Detection System Bas...

引用

5th international conference on Computer Information and Big Data Applications, CIBDA 2024

作者： Wu, Zhengnan Wuhan Donghu University Hubei Wuhan430212 China

ISBN: (纸本)9798400718106

In the rapid development of the internet of Things technology, image recognition and detection technology is used in all walks of life. In order to solve the limitations of traditional image detection methods in practical application, such as slow efficiency, low precision and lack of in-depth analysis, this study will design an artificial intelligence image detection system based on the internet of Things, starting from the requirements of synthetic signal images, image features acquisition and cloud image processing functions. Complete the design of image analysis system, image feature acquisition system and image integration system. Finally, the artificial intelligence image detection system is simulated and debugging. The test results prove that the artificial intelligence image detection system based on the internet of Things has the advantages of fast detection speed, high recognition accuracy and big data integration analysis, and can meet the stable operation of any configuration computer platform, providing new ideas for the development and research of image detection system. © 2024 ACM.

关键词： internet of things

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An image encryption algorithm based on non-degenerate chaos and dynamic Bi-directional permutation 16

An image encryption algorithm based on non-degenerate chaos ...

引用

16th international conference on signal Processing systems, ICSPS 2024

作者： Liao, Yunlong Xu, Qingyang Zhang, Qiyuan Xing, Zheng Yuan, Xiaochen Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781510689251

In the wake of the accelerated advancement of internet of Things(IoT) technology, a significant volume of multimedia information is emerging as the dominant component of IoT applications. However, this information also led to big security risks. Therefore, a secure and reliable image encryption scheme based on non-degenerate hyper chaos and dynamic bidirectional permutation is proposed for the characteristic of digital images which the primary information carrier in the IoT communication environment. The scheme uses non-degenerate discrete chaos to generate pseudo-random sequences and applies them to the encryption process involving image confusion, dynamic permutation, and dynamic diffusion. Through theoretical analysis and experimental verification, the algorithm exhibits high sensitivity to both the plaintext images and the ciphertext images. Concurrently, it can effectively resist common noise attacks and cryptographic attacks. In conclusion, the proposed scheme offers superior encryption capabilities and stands as a secure solution, compatible with a diverse range of IoT environments. © 2025 SPIE.

关键词： Chaos theory

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Skeleton-based Human Activities Fine-grained Recognition with RFID technology

Skeleton-Based Human Activities Fine-grained Recognition wit...

引用

9th international conference on signal and image Processing (ICSIP)

作者： Liu, Meng Chen, Yihong Zheng, Hao Huang, Lingbo Zhao, Kun Zhao, Yilin China West Normal Univ Internet Things Percept & Big Data Anal Key Lab N Nanchong Peoples R China

ISBN: (纸本)9798350350920

Human activity recognition(HAR) has received increasing attention and has been applied in multiple fields such as healthcare and human-computer interaction. Previous activity recognition methods have problems such as privacy leakage, strong intrusion on users, and coarse detection granularity. Therefore, we propose a privacy protection, easy-to-use, identifiability, lightweight, and fine-grained human activity recognition method based on bound-RFID technology. Firstly, we utilize the fundamental physical characteristics of radio frequency signals, such as doppler frequency (DF), received signal strength indicator (RSSI), and phase, which reflect human activity. Analyze the correlation between these data and human activities, establish a graphical relationship between data changes and activities, and generate human postures through posture time windows. Secondly, we integrate tags information to model human activities by establishing spatio-temporal skeleton graphs with temporal and spatial information. Finally, we model the spatio-temporal skeleton graph convolutional neural network to classify these graphs. As far as we know, this is the most refined HAR based on bound-RFID tags.

关键词： RFID GCN HAR Skeleton-based

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Robust image watermarking scheme based on orientation of local image texture 16

Robust image watermarking scheme based on orientation of loc...

引用

16th international conference on signal Processing systems, ICSPS 2024

作者： Li, Qiutong Zhang, Qiyuan Yuan, Xiaochen Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781510689251

With the rapid development of big data and internet of Things (IoT), more and more digital products are emerging. However, this has also brought about a growing problem of copyright violation. Digital image robust watermarking technology can effectively resist various attacks and protect image copyright. Most of the existing robust watermarking schemes generally adopt the global embedding method and often ignore the texture orientation of the cover image itself leading to poor imperceptibility and robustness. In this paper, we propose an innovative digital image robust watermarking scheme. Firstly, we conduct the Oriented FAST and Rotated BRIEF (ORB) and Speeded Up Robust Features (SURF) feature point detection algorithms, combined with the texture complexity of the cover image to select the most complex texture region as the local embedding location of the watermark. Secondly, to further improve the robustness of the watermarked image, we conduct the Discrete Cosine Transform (DCT) adaptive inter-block correlation algorithm. We embed the watermark into the cover image by adjusting the difference between the image embedding block and the adjacent block. We have tested a dataset containing multiple images, in which various common attacks are simulated and multiple cover images are tested. By comparing the experimental results, our scheme performs better regarding robustness and imperceptibility. © 2025 SPIE.

关键词： Cosine transforms

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REINFORCEMENT LEARNING-based SECURE VIDEO TRANSMISSION FOR IOV systems 31

REINFORCEMENT LEARNING-BASED SECURE VIDEO TRANSMISSION FOR I...

引用

2024 international conference on image Processing

作者： Liu, Lixin Liu, Zhibo Lu, Luxiaozhen Bu, Yanling Han, Bin Xiao, Liang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommun Sch Communicat & Informat Engn Nanjing Peoples R China Xiamen Univ Dept Informat & Commun Engn Xiamen Peoples R China

ISBN: (纸本)9798350349405;9798350349399

The rapid growth in the number of vehicles and types of services such as video transmission improves the quality-of-service (QoS) requirements and increases the difficulty in resisting eavesdropping attacks on the internet of Vehicles (IoV). Existing video transmission schemes that either ignore the impact of eavesdropping attacks or have the full knowledge of the attack model have performance degradation in highly dynamic IoV systems. In this paper, we propose a reinforcement learning-based secure video transmission scheme for IoV systems, which jointly optimizes the access control policy for each vehicle (i.e., the selection of access nodes such as the base stations or unmanned aerial vehicles) and the corresponding transmit power level against active eavesdropping. This scheme uses the QoS and eavesdropping rate as the criteria to evaluate the long-term risk of each state-action pair, which is estimated by a designed deep Q-network to avoid the risky access control policies that cause severe data leakage or video transmission failure. Simulation results show that our scheme reduces the energy consumption, transmission latency, and eavesdropping rate compared with the benchmark.

关键词： internet of Vehicles video transmission eavesdropping reinforcement learning

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Performance Evaluation of Machine Learning Algorithms for a Cluster-based Crop Recommendation System 17

Performance Evaluation of Machine Learning Algorithms for a ...

引用

17th international conference on signal-image technology and internet-based systems, SITIS 2023

作者： Muhammed, Dalhatu Ahvar, Ehsan Ahvar, Shohreh Trocan, Maria Paris France Nokia Massy France

ISBN: (纸本)9798350370911

A cluster-based crop recommendation system categorizes the crop candidates into several groups or classes (e.g., based on soil and environment parameters similarities). After receiving a request from a farmer, it recommends the most appropriate group of crops to the farmer. Proposing a group of crops (i.e., more than one crop) can allow farmers to consider their personal interests as well. In addition, the cluster-based crop recommendation system can reduce the complexity and utilized resources (e.g., compute resource). As the main contribution of this paper, we evaluate the performance of different Machine Learning (ML) algorithms to find the most appropriate one for using in the cluster-based crop recommendation system. © 2023 IEEE.

关键词： Crops

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A Machine Learning-based Intelligent ID System for the internet of Things

A Machine Learning-based Intelligent ID System for the Inter...

引用

2024 IEEE international conference on Advanced systems and Emergent Technologies, IC_ASET 2024

作者： Sawssen, Bacha Noureeddine, Liouane Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Tunisia

ISBN: (纸本)9798350384895

The internet of Things, or loT, is a rapidly expanding field that has been integrated into numerous different industries. Thanks to this technology, devices may send, receive, and analyze data without the assistance of a human. loT security and privacy concerns continue to be a significant obstacle, despite the fact that it has gained widespread acceptance in a number of important domains due to its ability to simplify human life and enhance service quality. To protect loT networks from different attacks, an anomaly-based intrusion detection system (IDS) can be included as a security feature. In order to combat various cyberattacks in internet of Things environments, this study suggests an anomaly-based intrusion detection system (IDS). The suggested approach makes use of in order to enhance anomaly identification performance and reduce the dimension of the data characteristics.. © 2024 IEEE.

关键词： Intrusion detection

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AoI-oriented Trajectory Planning based on Multi-agent Noisy Dueling Double Deep Q Network for UAVs-assisted internet of Things

AoI-oriented Trajectory Planning Based on Multi-agent Noisy ...

引用

9th international conference on signal and image Processing (ICSIP)

作者： Zhang, Huimin Du, Chaoyang Chen, Zhaofeng Si, Qintuya Liu, Yang Inner Mongolia Univ Coll Elect Informat Engn Hohhot Peoples R China Yancheng Inst Technolog 1 Middle Hope Ave Yancheng Peoples R China

ISBN: (纸本)9798350350920

The emergence of the internet of Things (IoT) has led to the development of Unmanned Aerial Vehicle (UAV) trajectory planning, aiming to enhance service quality in remote areas. However, traditional trajectory planning algorithms heavily rely on accurate environmental models, which pose challenges in achieving strategy convergence and minimizing the age of information (AoI). To address this issue, we propose an AoI-oriented UAV trajectory planning algorithm based on multi-agent noise dueling double deep Q-Network (MAND3QN). Considering data freshness as a priority, we initially model the multi-UAV trajectory planning problem as a problem of minimizing the average AoI. Subsequently, the Dueling structure is introduced to decompose the Q value into the value function of states and the dominance function of state-action pairs, which improves the learning efficiency and allows for a more accurate estimation of Q-values. Additionally, by incorporating noise parameters through NoisyNet implementation in neural networks, we introduce randomness to network output values and improve exploration capabilities within the state space. Simulation results demonstrate that our algorithm achieves stable and rapid convergence while significantly reducing AoI for UAVs-assisted IoT systems.

关键词： IoT UAVs trajectory planning Age of information multi-intelligence deep reinforcement learning

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A Dataset of Infrared images for Deep Learning based Drone Detection 17

A Dataset of Infrared Images for Deep Learning based Drone D...

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17th international conference on signal-image technology and internet-based systems, SITIS 2023

作者： Bhattacharya, Purbaditya Nowak, Patrick Ahlers, Daniel Zolzer, Udo Helmut Schmidt University Dept. of Signal Proc. and Comm. Hamburg Germany

ISBN: (纸本)9798350370911

Recently, drone detection has become a topic of interest due to the widespread usage of drones in various applications, particularly for recreational purposes. Such detection tasks are usually performed by deep learning models which require different kinds of image datasets to be trained on. Hence, a dataset of infrared images for drone detection is introduced in this paper. In order to generate the dataset, videos of drones are captured initially with multiple cameras at two different locations. The video frames are then extracted and the drones are annotated with the help of an annotation tool and an automated script. A comprehensive analysis of the dataset is provided and multiple configurations of a selection of CNN models are trained on a fraction of the dataset. The trained models are employed on the test dataset and their performance is evaluated. © 2023 IEEE.

关键词： Drones

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