Article
Computer Science, Hardware & Architecture
Jinrong Chen, Lin Liu, Rongmao Chen, Wei Peng, Xinyi Huang
Summary: This article introduces a method for preserving privacy in a context-aware recommendation system in a two-cloud model. The author adjusts the additive secret sharing scheme and designs secure comparison and division protocols to propose a secure and efficient recommendation system. Experimental results demonstrate the effectiveness of the scheme.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
Francis H. Shajin, P. Rajesh
Summary: Multiple-Input and Multiple-Output (MIMO) technology, motivated by the needs of 5G wireless communications, is an important subject. This dissertation suggests the implementation of RDHS using FPGA, Chaotic Key Generation-Based Paillier Cryptography, and LDPC with machine learning technique. The proposed method shows higher network throughput, network life, and lower delay rate compared to existing methods such as McEliece, Elgamal, and Elliptic curve cryptosystem.
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
(2022)
Article
Computer Science, Artificial Intelligence
Guohuai Lin, Hongyi Li, Choon Ki Ahn, Deyin Yao
Summary: This article focuses on the event-based finite-time neural attitude consensus control problem for the six-rotor unmanned aerial vehicle (UAV) systems with unknown disturbances. It addresses the issues of external disturbances and uncertain nonlinear dynamics using a disturbance observer and radial basis function neural networks (RBF NNs). The proposed finite-time command filtered (FTCF) backstepping method effectively manages the complexity explosion problem and an event-triggered mechanism is considered to alleviate the communication burden.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Zhou Su, Yuntao Wang, Qichao Xu, Ning Zhang
Summary: This article proposes a lightweight vehicular blockchain-enabled secure (LVBS) data sharing framework for UAV-aided IoV in disaster rescue. The framework utilizes the collaboration between UAVs and blockchain to enable data sharing and secure driving in disaster areas. The research shows that this framework improves the security of the consensus phase and promotes high-quality data sharing.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Liyong Fu, Zechao Li, Qiaolin Ye, Hang Yin, Qingwang Liu, Xiaobo Chen, Xijian Fan, Wankou Yang, Guowei Yang
Summary: In this article, a new robust discriminant subspace (RDS) learning method is presented for feature extraction. The method uses a different objective function formulation to ensure the subspace is both robust and discriminative. An efficient nongreedy iterative algorithm is proposed to solve the challenging optimization problem. The experimental results on image classification databases demonstrate the effectiveness of RDS.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
Summary: This article discusses the applications of deep learning in community detection, providing a classification of different methods and models. It introduces popular datasets, evaluation metrics, and open-source implementations, and discusses the practical applications of community detection in various domains. The article concludes with suggestions for future research directions in this growing field of deep learning.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Liang Huang, Xu Feng, Anqi Feng, Yupin Huang, Li Ping Qian
Summary: This paper proposes a deep learning-based algorithm to solve the offloading decision problem in mobile edge computing networks. By using multiple parallel DNNs to generate offloading decisions and utilizing a shared replay memory to further train and improve DNNs, near-optimal offloading decisions can be generated quickly.
MOBILE NETWORKS & APPLICATIONS
(2022)
Article
Computer Science, Hardware & Architecture
Dezhi Han, Nannan Pan, Kuan-Ching Li
Summary: The proposed CP-ABE scheme in this article achieves revocation, white-box traceability, and the application of hidden policy. The ciphertext is composed of two parts: the access policy encrypted by attribute value and the revocation information related to a binary tree. The scheme is proven to be IND-CPA secure, efficient, and promising in the standard model.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Danfeng Hong, Lianru Gao, Jing Yao, Naoto Yokoya, Jocelyn Chanussot, Uta Heiden, Bing Zhang
Summary: This paper proposes a deep learning approach called endmember-guided unmixing network (EGU-Net) to improve the efficiency and accuracy of hyperspectral unmixing. By utilizing a two-stream Siamese deep network and adding spectrally meaningful constraints, EGU-Net is able to extract endmembers better and achieve more accurate and interpretable unmixing solutions.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shuangming Yang, Jiang Wang, Xinyu Hao, Huiyan Li, Xile Wei, Bin Deng, Kenneth A. Loparo
Summary: The article introduces a biologically-inspired cognitive supercomputing system, BiCoSS, which integrates multiple GRs of SNNs to create a hybrid neuromorphic platform with efficient and scalable architecture and low power consumption. The system has successfully replicated various biological cognitive activities, demonstrating its high performance and potential applications.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Hairong Lin, Chunhua Wang, Cong Xu, Xin Zhang, Herbert H. C. Iu
Summary: In this article, a novel method for designing multistructure chaotic attractors in memristive neural networks is proposed. By utilizing a multipiecewise memristive synapse control in a Hopfield neural network (HNN), various complex multistructure chaotic attractors can be produced. Theoretical analysis and numerical simulation demonstrate that multiple multistructure chaotic attractors with different topologies can be generated by conducting the memristive synapse-control in different synaptic coupling positions. Meanwhile, the number of structures can be easily controlled with the memristor control parameters. Furthermore, a module-based analog memristive neural network circuit is designed, allowing the arbitrary number of multistructure attractors to be obtained by selecting corresponding control voltages. Finally, a novel image encryption cryptosystem with a permutation-diffusion structure is designed and evaluated, exhibiting its excellent encryption performances, especially the extremely high key sensitivity.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuxiang Zhang, Wei Li, Mengmeng Zhang, Ying Qu, Ran Tao, Hairong Qi
Summary: This article introduces a Topological structure and Semantic information Transfer network (TSTnet) for the problem of cross-scene hyperspectral image classification. The method uses a graph structure to characterize topological relationships and utilizes graph convolutional networks (GCN) to analyze the data. Experimental results demonstrate that TSTnet outperforms other domain adaptation methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yue Wu, Jiaheng Li, Yongzhe Yuan, A. K. Qin, Qi-Guang Miao, Mao-Guo Gong
Summary: An unsupervised change detection method is proposed using a convolutional autoencoder and a commonality autoencoder to extract common features in heterogeneous images, distinguishing changed and unchanged regions. Experimental results demonstrate the promising performance of this method compared to existing approaches.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hongjing Liang, Zhixu Du, Tingwen Huang, Yingnan Pan
Summary: This article investigates the problem of adaptive performance guaranteed tracking control for multiagent systems (MASs) with power integrators and measurement sensitivity. A new control approach is proposed to guarantee the convergence of the relative position error between neighboring agents within a preassigned finite time. By utilizing the Nussbaum gain technique and neural networks, a novel control scheme is developed to solve the unknown measurement sensitivity on the sensor, relaxing the restrictive condition. Based on the Lyapunov functional method, it is proven that the relative position error can converge into the prescribed boundary.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Liang Tan, Keping Yu, Fangpeng Ming, Xiaofan Cheng, Gautam Srivastava
Summary: This article proposes a HoneyNet approach for enhancing the security of AIoT by combining threat detection and situational awareness. Experimental results demonstrate the feasibility and effectiveness of the proposed solution.
IEEE CONSUMER ELECTRONICS MAGAZINE
(2022)
Article
Computer Science, Artificial Intelligence
Qingxin Meng, Xuzhi Lai, Ze Yan, Chun-Yi Su, Min Wu
Summary: This article discusses an uncertain two-link rigid-flexible manipulator with vibration amplitude constraint, and aims to achieve its position control through motion planning and adaptive tracking approach. The motion trajectories planning for the manipulator's two links can guarantee reaching desired angles and suppress vibration, while the adaptive tracking controller enables the two links to track the planned trajectories under various uncertainties. Simulation results confirm the effectiveness of the proposed control strategy and the superior performance of motion planning and tracking controller.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shuangming Yang, Jiang Wang, Nan Zhang, Bin Deng, Yanwei Pang, Mostafa Rahimi Azghadi
Summary: This article introduces a large-scale cerebellar network model and a cerebellum-inspired neuromorphic architecture, demonstrating improved biomimicry. Experimental results show that real-time operation can be achieved, with system throughput up to 4.70 times larger than previous works.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yongming Li, Jiaxin Zhang, Wei Liu, Shaocheng Tong
Summary: This work investigates an adaptive neural network optimized output-feedback control problem for a class of stochastic nonlinear systems with unknown nonlinear dynamics, input saturation, and state constraints. It proposes an optimized control strategy based on the backstepping technique and actor-critic architecture to prevent system violations of state constraints and ensure bounded signals in the closed-loop system.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Weixiang Zhou, Jun Fu, Huaicheng Yan, Xin Du, Yueying Wang, Hua Zhou
Summary: This article investigates the problem of path following for underactuated unmanned surface vehicles (USVs) subject to state constraints. A control algorithm is proposed that combines the backstepping technique, adaptive dynamic programming (ADP), and the event-triggered mechanism. The algorithm consists of three modules: guidance law, dynamic controller, and event triggering. By introducing the guidance-based path-following (GBPF) principle and using a critic neural network (NN) to approximate the cost function, the proposed approach can handle the "singularity" problem and guarantee approximate optimal performance. The simulation results and experimental validation demonstrate the effectiveness of the approach.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Thanh Thi Nguyen, Vijay Janapa Reddi
Summary: This article presents a survey of DRL approaches developed for cyber security, including vital aspects such as DRL-based security methods for cyber-physical systems and autonomous intrusion detection techniques. It also discusses multiagent DRL-based game theory simulations for defense strategies against cyberattacks. Future research directions and extensive discussions on DRL-based cyber security are provided.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
