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A nonprofiled side-channel analysis based on variational lower bound related to mutual information

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Science China(information Sciences) 2023年第1期66卷 277-295页

作者： Chi ZHANG Xiangjun LU Pei CAO Dawu GU Zheng GUO Sen XU School of Electronic information and Electrical Engineering Shanghai Jiao Tong University Zhi Xun Crypto Testing and Evaluation Technology Co. Ltd. 3. Viewsource Information Science and Technology Co. Ltd.

In this paper, we attempt to improve the practical performance of the nonprofiled side-channel analysis(Non SCA) with the help of neural networks. We first derive a variational lower bound related to mutual information(VLBRMI) optimized for the context of Non SCA, which possesses a set of adjustable parameters and whose maximum value linearly depends on the mutual information. Then, we propose a new Non SCA method called neural mutual information analysis(NMIA) that exploits the maximum VLBRMI as the distinguisher. We present an estimator of the maximum VLBRMI, which uses neural networks to instantiate the VLBRMI and trains the neural networks to approximate the maximum VLBRMI so that we can implement the NMIA efficiently. Finally, we evaluate the NMIA on several datasets. The experimental results show that NMIA outperforms the correlation power analysis, the mutual information analysis(MIA)based on histograms, the MIA based on kernel density estimation, and the state-of-the-art Non SCA method based on neural networks.

关键词： side-channel analysis nonprofiled method variational lower bound mutual information neural networks

mutual information assessment of LOS MIMO systems with reconfigurable antenna arrays

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The Journal of China Universities of Posts and Telecommunications 2013年第5期20卷 51-56,70页

作者： GAO Jun-jun ZHANG Jian-hua TAO Xiao-feng Science and Technology on information Systems Engineering Laboratory Wireless Technology Innovation Institute Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communication Ministry of Education Beijing University of Posts and Telecommunications

In presence of line-of-sight （LOS） propagation, multiple-input multiple-output （MIMO） systems can achieve maximum capacity over the whole signal-to-noise ratio （SNR） region by deploying reconfigurable antenna arrays. In this paper, the moment generating function （MGF） formulas of the mutual information （MI） is obtained for LOS MIMO systems with reconfigurable arrays. Then the exact expression of the mean MI is derived in an easily evaluated form. The results show an excellent match between the theoretical curves and the Monte-Carlo simulations.

关键词： multiple-input multiple-output line-of-sight mutual information reconfigurable antenna array Wishart

mutual information Is Copula Entropy

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Tsinghua Science and Technology 2011年第1期16卷 51-54页

作者：马健孙增圻 State Key Laboratory on Intelligent Technology and Systems Department of Computer Science and TechnologyTsinghua University

mutual information （MI） is a basic concept in information theory. Therefore, estimates of the MI are fundamentally important in most information theory applications. This paper provides a new way of understanding and estimating the MI using the copula function. First, the entropy of the copula, named the copula entropy, is defined as a measure of the dependence uncertainty represented by the copula function and then the MI is shown to be equivalent to the negative copula entropy. With this equivalence, the MI can be estimated by first estimating the empirical copula and then estimating the entropy of the empirical copula. Thus, the MI estimate is an estimation of the entropy, which reduces the complexity and computational requirements. Tests show that the method is more effective than the traditional method.

关键词： copula entropy mutual information estimation empirical copula

Measuring causality by taking the directional symbolic mutual information approach

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Chinese Physics B 2013年第3期22卷 556-560页

作者：陈贵谢磊褚健 Institute of Cyber-System and Control Zhejiang University

We propose a novel measure to assess causality through the comparison of symbolic mutual information between the future of one random quantity and the past of the *** provides a new perspective that is different from the conventional *** on this point of view,a new causality index is derived that uses the definition of directional symbolic mutual *** measure presents properties that are different from the time delayed mutual information since the symbolization captures the dynamic features of the analyzed time *** addition to characterizing the direction and the amplitude of the information flow,it can also detect coupling *** method has the property of robustness,conceptual simplicity,and fast computational speed.

关键词： causality measure Bandt and Pompe method mutual information transfer entropy

An enhanced text categorization method based on improved text frequency approach and mutual information algorithm

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Progress in Natural Science:Materials International 2007年第12期17卷 1494-1500页

作者： Maurizio Marchese 38050-Povo (TN) Department of information and Communication Technology University of Trento Italy) Via Sommarive 14

Text categorization plays an important role in data mining. Feature selection is the most important process of text categorization. Focused on feature selection, we present an improved text frequency method for filtering of low frequency features to deal with the data preprocessing, propose an improved mutual information algorithm for feature selection, and develop an improved *** method for characteristic weights evaluation. The proposed method is applied to the benchmark test set Reuters-21578 Top10 to examine its effectiveness. Numerical results show that the precision, the recall and the value of F1 of the proposed method are all superior to those of existing conventional methods.

关键词： text categorization mutual information feature selection characteristic weights classifier.

Perceptual image quality assessment metric using mutual information of Gabor features

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Science China(information Sciences) 2014年第3期57卷 130-138页

作者： DING Yong ZHANG Yuan WANG Xiang YAN XiaoLang KRYLOV Andrey S. Institute of VLSI Design Zhejiang University Laboratory of Mathematical Methods of Image Processing Lomonosov Moscow State University

A good objective metric of image quality assessment(IQA)should be consistent with the subjective judgment of human *** this paper,a four-stage perceptual approach for full reference IQA is *** the first stage,the visual features are extracted by 2-D Gabor filter that has the excellent performance of modeling the receptive fields of simple cells in the primary visual *** in the second stage,the extracted features are post-processed by the divisive normalization transform to reflect the nonlinear mechanisms in human visual *** the third stage,mutual information between the visual features of the reference and distorted images is employed to measure the visual *** in the last pooling stage,the mutual information is converted to the final objective quality *** results show that the proposed metic has a high correlation with the subjective assessment and outperforms other state-of-the-art metrics.

关键词： image quality assessment mutual information Gabor features perceptual metric entropy

Multi-sensor image registration by combining local self-similarity matching and mutual information

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Frontiers of Earth Science 2018年第4期12卷 779-790页

作者： Xiaoping LIU Shuli CHEN Li ZHUO Jun LI Kangning HUANG Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and PlanningSun Yat-Sen UniversityGuangzhou 510275China School of Forestry and Environmental Studies Yale UniversityNew HavenCT 06511USA

Automatic multi-sensor image registration is a challenging task in remote sensing. Conventional image registration algorithms may not be applicable when common underlying visual features are not distinct. In this paper, we propose a novel image registration approach that integrates local self-similarity (LSS) and mutual information (MI) for multi-sensor images with rigid/nonrigid radiometric and geometric distortions. LSS is a well-performing descriptor that captures common, local internal layout features for multi-sensor images, whereas MI focuses on global intensity relationships. First, potential control points are identified by using the Harris algorithm and screened based on the self-similarity of their local surrounding internal layouts. Second, a Bayesian probabilistic model for matching the ensemble of the LSS features is introduced. Third, a particle swarm optimization (PSO) algorithm is adopted to optimize the point and region correspondences for maximum self-similarity and MI and, ultimately, a robust mapping function. The proposed approach is compared with several conventional image registration algorithms that are based on the sum of squared differences (SSD), scale-invariant feature transforms (SIFT), and speeded-up robust features (SURF) through the experimental registration of pairs of Landsat TM, SPOT, and RADARSAT SAR images. The results demonstrate that the proposed approach is efficient and accurate.

关键词： automatic registration multi-sensor images local self-similarity mutual information particle swarm optimization

An automatic extraction method for individual tree crowns based on self-adaptive mutual information and tile computing

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International Journal of Digital Earth 2015年第6期8卷 495-516页

作者： Ying Bao Qingjiu Tian Min Chen Hui Lin International Institute for Earth System Science Nanjing UniversityNanjingPR China Jiangsu Provincial Key Laboratory of Geographic information Science and Technology Nanjing UniversityNanjingPR China Institute of Space and Earth information Science The Chinese University of Hong KongShatinHong KongPR China Shenzhen Research Institute The Chinese University of Hong KongShenzhenPR China Department of Geography and Resource Management The Chinese University of Hong KongShatinHong KongPR China

Forest data acquisition,which is of crucial importance for modeling global biogeochemical cycles and climate,makes a contribution to building the ecological Digital Earth(DE).Due to the complex calculations and large volumes of data associated with high-resolution images of large areas,accurate and effective extraction of individual tree crowns remains *** this study,two GeoEye-1 panchromatic images of Beihai and Ningbo in China with areas of 5 and 25 km2,respectively,were used as experimental data to establish a novel method for the automatic extraction of individual tree crowns based on a self-adaptive mutual information(SMI)algorithm and tile computing technology(SMI-TCT).To evaluate the performance of the algorithm,four commonly used algorithms were also applied to extract the individual tree *** overall accuracy of the proposed method for the two experimental areas was superior to that of the four other algorithms,with maximum extraction accuracies of 85.7%and 63.8%.Moreover,the results also indicated that the novel method was suitable for individual tree crowns extraction in sizeable areas because of the multithread parallel computing technology.

关键词： individual tree crowns GeoEye-1 automatic extraction tile computing mutual information

Double Pruning Structure Design for Deep Stochastic Configuration Networks Based on mutual information and Relevance

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Instrumentation 2022年第4期9卷 26-39页

作者： YAN Aijun LI Jiale TANG Jian Faculty of information Technology Beijing University of TechnologyBeijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China Beijing Laboratory for Urban Mass Transit Beijing 100124China

Deep stochastic configuration networks(DSCNs)produce redundant hidden nodes and connections during training,which complicates their model *** at the above problems,this paper proposes a double pruning structure design algorithm for DSCNs based on mutual information and *** the training process,the mutual information algorithm is used to calculate and sort the importance scores of the nodes in each hidden layer in a layer-by-layer manner,the node pruning rate of each layer is set according to the depth of the DSCN at the current time,the nodes that contribute little to the model are deleted,and the network-related parameters are *** the model completes the configuration procedure,the correlation evaluation strategy is used to sort the global connection weights and delete insignificance connections;then,the network parameters are updated after pruning is *** experimental results show that the proposed structure design method can effectively compress the scale of a DSCN model and improve its modeling speed;the model accuracy loss is small,and fine-tuning for accuracy restoration is not *** obtained DSCN model has certain application value in the field of regression analysis.

关键词： Deep Stochastic Configuration Networks mutual information Relevance Hidden Node Double Pruning