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 pract...
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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 als...
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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 ...
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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.
Most existing image super-resolution (SR) methods commonly assume that the degradation kernel is fixed and known. Blind SR aims to handle various unknown degradation processes closer to real-world applications and mor...
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ISBN:
(纸本)9798350363999;9798350364002
Most existing image super-resolution (SR) methods commonly assume that the degradation kernel is fixed and known. Blind SR aims to handle various unknown degradation processes closer to real-world applications and more generalizations. We propose a self-supervised cross-scale nonlocal attention network for blind SR (CNSR) which jointly models a blur kernel estimation module (KEM) based on a regularization model and a high-resolution image reconstruction module (HRM) based on a deep neural network. The low-resolution (LR) image is used as the supervision signal, and the blur kernel and high-resolution image are estimated simultaneously by iterating the two modules alternately. In HRM, we introduce a cross-scale nonlocal correspondence aggregation module (CNCAM) that uses the cross-scale self-similarity of images to provide additional information for image reconstruction. Experimental results show that CNSR can effectively improve image reconstruction performance.
The Industrial internet of Things offers modern industrial firms numerous opportunities for significant growth. The internet of Things technology utilized in the sector has developed into a large-scale network as a re...
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ISBN:
(纸本)9798350384901;9798350384895
The Industrial internet of Things offers modern industrial firms numerous opportunities for significant growth. The internet of Things technology utilized in the sector has developed into a large-scale network as a result of the increasing amount of data and devices. Industrial IoT networks are by nature susceptible to hacking and intrusions. Thus, in order to ensure the security of IIoT networks, intrusion detection system IDS development is essential. This study proposes an IDS to defeat a variety of cyberattacks in industrial internet of things environments. The suggested approach reduces the dimension of the data characteristics and enhances the performance of anomaly identification by using the kernel principal component analysis technique. For binary classification, we use the kernel extreme learning machine to identify if the traffic flow is benign or attack. We use it to multiclass classification in order to classify the set of attacks based on the specific type of attack. The performance results are assessed and analyzed using the X-IIoTDS dataset in order to prove the effectiveness of the suggested anomaly detection approach. The evaluation's findings show that the suggested anomaly detection strategy may greatly raise detection performance outcomes in terms of specificity, accuracy, sensitivity, and F1-score while also successfully increasing detection efficiency.
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 wat...
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This article explores the challenges and advancements in multi-view camera systems, 2D pose estimation, and 3D reconstruction for capturing and reconstructing live performances. It conducts a comparative analysis of m...
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Recently, the field of lightweight cryptography (LWC) has emerged in response to the security needs of low-cost, widely used technology. It is essential to implement an encryption approach with access control to give ...
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Recently, the field of lightweight cryptography (LWC) has emerged in response to the security needs of low-cost, widely used technology. It is essential to implement an encryption approach with access control to give less complex, more flexible, and safe access to sensitive data. In this work, a novel lightweight chaotic encryption approach with fuzzy access control is presented to encrypt light images in the IoT domain, while maintaining image quality. With the aid of multiplexer modeling and information shift register technology, the algorithm's design combines random and chaotic mapping approach based on a specific password key with a predetermined number of fuzzy logic shifts on the password key for the image pixels. Further, to extract the private key with complexity and boost defense against attacks, a shift register and logical xor combination is employed. The simulation of the proposed model for AVR microcontroller has been done under MATLAB software and the design of various encryption components has been used to implement lightweight mapping. The proposed system has been evaluated in terms of histogram analysis, adjacent pixel correlation analysis, contrast analysis, homogeneity analysis, energy analysis, NIST analysis, error mean square analysis, information entropy, pixel number change rate, integrated mean change intensity, peak signal-to-noise ratio, and time complexity. Remarkably, the proposed technique has demonstrated high efficiency. The simulation results show that the homogeneity, energy, contrast, NPCR, and UACI criteria have improved by 11.5%, 13.1%, 19%, 0.53%, and 0.12%, respectively, compared to other methods in other articles.
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 Int...
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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.
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 o...
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