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作者： Qin, Shuying He, Jianping Fang, Chongrong Lam, James Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Beijing100816 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Shenzhen Institute of Research and Innovation Shenzhen518055 China The University of Hong Kong Department of Mechanical Engineering Hong Kong999077 Hong Kong

Differential privacy is a widely used framework for evaluating privacy loss in data anonymization. While the continuous noise-adding mechanism has been extensively studied, there is a dearth of research on discrete random mechanisms for discretely distributed data. This study addresses this gap by examining the primary differential privacy conditions and properties for general discrete random mechanisms, and investigating the trade-off between data privacy and data utility. We establish sufficient and necessary conditions for discrete ϵ-differential privacy and sufficient conditions for discrete (ϵδ)-differential privacy, with closed-form expressions for differential privacy parameters. These conditions can be applied to evaluate the differential privacy properties of discrete noise-adding mechanisms with various types of noise. Moreover, we propose an optimal discrete ϵ-differential private noise-adding mechanism under the utility-maximization framework. Here, the utility is characterized by the similarity of the statistical properties between the mechanism's input and output. Our findings suggest that the optimal class of discrete noise probability distributions in the mechanism is staircase-shaped. © 1991-2012 IEEE.

关键词： Probability distributions

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Dual-Band HRRP Fusion Recognition via Wavelet Decomposition Embedded Autoencoder 2

Dual-Band HRRP Fusion Recognition via Wavelet Decomposition ...

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2nd IEEE International Conference on Signal, Information and Data processing, ICSIDP 2024

作者： Wang, Weijia Qi, Zhuchang Wang, Lue Yang, Wei Zhang, Liang Wang, Yanhua Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

High-resolution range profile (HRRP) is a crucial technique for radar target recognition. Due to the coupling of azimuthal scattering information and aspect sensitivity, fine target recognition using HRRP remains challenging. One way to mitigate HRRP aspect sensitivity is through enhancing the dimensionality of radar information acquisition. Therefore, this paper proposes a Dual-Band HRRP Fusion Recognition Model via Wavelet Decomposition Embedded Autoencoder (DF-WD-AENet). The method we proposed mainly comprises two modules. Firstly, the autoencoder model based on wavelet decomposition (WD-AEM) achieves constraint on feature information, making it better suited for dual-band fusion. Next is the dual-band information fusion module (DFM), in which we achieved the interaction and fusion of feature information through convolutional block attention module (CBAM) and omni-dimensional dynamic convolution (ODConv), and used a fusion loss function to train the model. Experiment shows that the recognition rate of our method is 7.73% higher than that of single-band, proving the effectiveness of the proposed method. © 2024 IEEE.

关键词： Wavelet decomposition

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Temporally-Adaptive Models for Efficient Video Understanding

arXiv

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arXiv 2023年

作者： Huang, Ziyuan Zhang, Shiwei Pan, Liang Qing, Zhiwu Zhang, Yingya Liu, Ziwei Ang, Marcelo H. Advanced Robotics Centre National University of Singapore Singapore DAMO Academy Alibaba Group China S-Lab National Technological University Singapore Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science China

Spatial convolutions are extensively used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in different frames. This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modeling complex temporal dynamics in videos. Specifically, TAdaConv empowers spatial convolutions with temporal modeling abilities by calibrating the convolution weights for each frame according to its local and global temporal context. Compared to existing operations for temporal modeling, TAdaConv is more efficient as it operates over the convolution kernels instead of the features, whose dimension is an order of magnitude smaller than the spatial resolutions. Further, kernel calibration brings an increased model capacity. Based on this readily plug-in operation TAdaConv as well as its extension, i.e., TAdaConvV2, we construct TAdaBlocks to empower ConvNeXt and Vision Transformer to have strong temporal modeling capabilities. Empirical results show TAdaConvNeXtV2 and TAdaFormer perform competitively against state-of-the-art convolutional and Transformer-based models in various video understanding benchmarks. Our codes and models are released at: https://***/alibaba-mmai-research/TAdaConv. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Dual-Band HRRP Recognition via Wavelet Packet Decomposition and Redundancy Reduction Model 2

Dual-Band HRRP Recognition via Wavelet Packet Decomposition ...

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2nd IEEE International Conference on Signal, Information and Data processing, ICSIDP 2024

作者： Yang, Wei Qi, Zhuchang Wu, Heke Li, Yang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou450000 China Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

ISBN: (纸本)9798331515669

High resolution range profiles (HRRP) is widely used in radar automatic target recognition. Due to aspect sensitivity, HRRP of different targets can be similar under certain aspects, making it difficult for recognition via traditional methods. One way to mitigate it is to increase the frequency dimension, utilizing the differences of target frequency response to extract inter-class discriminative information. Therefore, we propose a HRRP recognition model based on redundancy reduction criterion and wavelet packet decomposition. Specifically, we use deep wavelet packet decomposition to perform multi-level decomposition of dual-band HRRP and adaptively extract discriminative features of sub-wavelets;we use redundancy reduction layer to cluster intra-class features and expand inter-class features, thereby reducing feature heterogeneity. Furthermore, we propose a deep discriminative correlation analysis for joint analysis and transformation of dual-band features to fuse complementary information. Experimental results show that proposed method has excellent recognition performance. Additionally, analysis study also verify the effectiveness of each submodule. © 2024 IEEE.

关键词： Wavelet decomposition

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Binary system modes of matrix-coupled multidimensional Kuramoto oscillators

arXiv

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arXiv 2023年

作者： Wang, Chongzhi Shao, Haibin Li, Dewei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Synchronization and desynchronization are the two ends on the spectrum of emergent phenomena that somehow often coexist in biological, neuronal, and physical networks. However, previous studies essentially regard their coexistence as a partition of the network units: those that are in relative synchrony and those that are not. In real-world systems, desynchrony bears subtler divisions because the interacting units are high-dimensional, like fish schooling with varying speeds in a circle, synchrony and desynchrony may occur to the different dimensions of the population. In this work, we show with an ensemble of multidimensional Kuramoto oscillators, that this property is generalizable to arbitrary dimensions: for a d dimensional population, there exist 2d system modes where each dimension is either synchronized or desynchronized, represented by a set of almost binary order parameters. Such phenomena are induced by a matrix coupling mechanism that goes beyond the conventional scalar-valued coupling by capturing the inter-dimensional dependence amongst multidimensional individuals, which arises naturally from physical, sociological and engineering systems. As verified by our theory, the property of the coupling matrix thoroughly affects the emergent system modes and the phase transitions toward them. By numerically demonstrating that these system modes are interchangeable through matrix manipulation, we also observe explosive synchronization/desynchronization that is induced without the conditions that are previously deemed essential. Our discovery provides theoretical analogy to the cerebral activity where the resting state and the activated state coexist unihemispherically, it also evokes a new possibility of information storage in oscillatory neural networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Synchronization

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Design and Implementation of Telemedicine System Using Light Fidelity and PIC16F877A Microcontroller 14

Design and Implementation of Telemedicine System Using Light...

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14th International Conference on Information Science and Technology, ICIST 2024

作者： Ezilarasan, M.R. Leung, Man-Fai Dai, Xiangguang Vel Tech Rangarajan Dr. Sagunthala R and D Instituteof Science and Technology Department of Electronics and Communication Engineering Chennai India School of Computing and Information Science Anglia Ruskin University Faculty of Science and Engineering Cambridge United Kingdom Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Key Laboratory of Intelligent Chongqing Wanzhou404100 China

ISBN: (纸本)9798350353334

Medical Body Area Networks (MBANs) increasingly utilize wireless communication technologies to enhance the freedom and convenience for patients and caregivers. Currently, radio frequency (RF) is the prevalent medium in healthcare applications;however, the potential for electromagnetic interference with precision medical equipment remains a concern. This study introduces a novel design and implementation of a medical healthcare information system using the newly developed Wireless Visible Light Communication (VLC) technology, also known as Light Fidelity (Li-Fi). Visible Light Emitting Diodes (LEDs), expected to soon surpass traditional incandescent and fluorescent bulbs as the primary light source due to their energy efficiency, facilitate this technology. In this research, patient health is monitored using sensors, with results displayed on an LCD. An LED lightbulb transmits the data as light signals, which are captured by a photo-detector at the receiver's end. This setup not only displays the output on an LCD but also alerts caregivers through an auditory signal, beeping every 15 seconds for respiration and every 30 seconds for heart rate monitoring. Moreover, these lighting fixtures serve dual functions: illumination and wireless data transmission, using the rapid switching capability of LEDs. This prototype VLC-based system offers a promising alternative for data services and monitoring in hospital areas where RF use is restricted. © 2024 IEEE.

关键词： Liquid crystal displays

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Preserving Topology of Network Systems: Metric, Analysis, and Optimal Design

arXiv

引用

arXiv 2023年

作者： Li, Yushan Wang, Zitong He, Jianping Chen, Cailian Guan, Xinping Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems (NSs), and adding random noises to the nodal states provides a promising way. Nevertheless, recent works have revealed that the topology cannot be preserved under i.i.d. noises in the asymptotic sense. How to effectively characterize the non-asymptotic preservation performance still remains an open issue. Inspired by the deviation quantification of concentration inequalities, this paper proposes a novel metric named trace-based variance-expectation ratio. This metric effectively captures the decaying rate of the topology inference error, where a slower rate indicates better non-asymptotic preservation performance. We prove that the inference error will always decay to zero asymptotically, as long as the added noises are non-increasing and independent (milder than the i.i.d. condition). Then, the optimal noise design that produces the slowest decaying rate for the error is obtained. More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the non-zero topology inference error in the asymptotic sense simultaneously. Extensions to a general class of noises with multi-lag time dependence are provided. Comprehensive simulations verify the theoretical findings. Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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Clustering-Based Genetic Offspring Generation for Solving Multi-Objective Optimization Problems with Intricate Pareto Sets

SSRN

引用

SSRN 2023年

作者： Li, Lianghao Lin, Jianqing He, Cheng Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

Distinct from conventional benchmarks, multiobjective optimization problems (MOPs) encountered in practical domains exhibit complex variable linkages, resulting in intricate Pareto sets (PSs), such as rotated or nonlinear shapes. Simulated binary crossover (SBX), a widely utilized genetic operator for solving MOPs, exhibits a substantial degeneration in performance when applied to MOPs with intricate PSs. The rotation-based SBX (RSBX) incorporates the rotational property into SBX to handle MOPs with linear but rotated PSs. Nevertheless, RSBX may encounter difficulties in solving MOPs with nonlinear PSs. In order to tackle this challenge, we employ a clustering-based mating restriction strategy to address MOPs with intricate PSs, and propose an MOEA called CRSBX. Generally, the clustering-based mating restriction strategy involves partitioning the parent population into approximately linearly distributed clusters. Subsequently, RSBX is applied to each cluster for effective offspring generation. Empirically, we investigate the impact of the clustering algorithm and its associated parameters on CRSBX. Ablation studies are also conducted to examine the efficacy of the clustering-based mating restriction strategy. Additionally, we compare CRSBX with other representative algorithms on benchmark problems and real-world applications with intricate PSs. Comparison results highlight the promising performance of CRSBX in effectively addressing MOPs with intricate PSs. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Identification of Prognostic Signature in Esophageal Cancer Based on Network Analysis 15th

Identification of Prognostic Signature in Esophageal Cancer ...

引用

15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Ma, Jianfei Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Department of Pathology Renmin Hospital of Wuhan University Wuhan430060 China

ISBN: (纸本)9789811613531

Esophageal cancer is the 6th most common cancer in the world with a five-year survival among 15 % to 25 % patients. It’s of great significance to identify prognostic signature for precise prediction of patient survival. In this work, we use a network-based approach to identify the disease genes of esophageal cancer. We construct the co-expression networks of normal and cancer samples by interpreting the gene expression profile and further divide the networks into inactivated subnetwork and enhanced subnetwork. Functional enrichment analysis shows that phosphoprotein and acetylation are both enriched in the inactivated genes and enhanced genes. Furthermore, 5 kinesin family members, KIF11, KIF23, KIF18A, KIF18B, and KIF2A, are all found to be significantly enhanced, suggesting that kinesin is crucial in promoting the formation of esophageal cancer. The 11 genes both inactivated and enhanced in the process of tumor development are found to be prognosis-associated and perform well in evaluating the survival of esophageal cancer patients. This study provides us fundamental recognition about the functional dysregulation in esophageal cancer and help to identify the biomarker for the further development of therapeutic targets. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Optimal Linear Attack in Cyber-Physical Systems with Periodic Detection

Optimal Linear Attack in Cyber-Physical Systems with Periodi...

引用

IEEE Conference on Decision and control

作者： Jia Qi Chongrong Fang Jianping He Dept. of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Security issues are of significant importance for cyber-physical systems (CPS), where the attack design is a major concern. Most related studies on attack design implicitly consider that the control period and detection period are the same. However, the two periods could be different in practical systems with remote detection such as supervisory control and data acquisition (SCADA) systems, which could lead to new vulnerabilities for attackers. In this paper, we consider the design of innovation-based linear attack strategies for CPS when the control period and detection period are inconsistent. Specifically, we propose an attack framework that consists of attack strategies for detection and non-detection instants under the period discrepancy. On this basis, we design the optimal stealthy innovation-based linear attack strategies for state estimation and LQG control to maximize the estimation error or control cost, respectively. Simulations are given to demonstrate the effectiveness of the proposed attack strategies.

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