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Study on Initial Alignment Under Large Misalignment Angle

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Journal of information Hiding and Privacy Protection 2019年第3期1卷 95-108页

作者： Qi Wang Changsong Yang Shaoen Wu School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Science Ball State UniversityMuncieUSA School of Automation Nanjing University of Information Science and TechnologyNanjing210044China

Misalignment angle error model describing the SINS mathematical platform error is presented in this paper following the idea of small misalignment angle error model and large azimuth misalignment angle error *** can be considered that the three misalignment angles are independent of the rotational sequence in the misalignment error model,but not suitable in the large misalignment error *** error angle of Euler platform is used to represent the three misalignment angles from theoretical navigation coordinate system to computational navigation coordinate *** Euler platform error angle is utilized to represent the mathematical platform error and its physical meaning is very *** SINS nonlinear error model is deduced by using the error angle of Euler platform and is simplified under the condition of large azimuth error and small *** simplified results are more comprehensive and accurate than the large azimuth misalignment error *** damping SINS algorithm and its error model are proposed to change the structure of the strapdown inertial navigation algorithm by using the external damping *** accuracy of SINS error model of large Euler platform error angle is simulated,and has strong practicability in initial alignment and is conducive to reducing the amount of calculation.

关键词： Misalignment error model large misalignment angle Euler platform error angle SINS.

Modal regression based structured low-rank matrix recovery for multi-view learning

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arXiv 2020年

作者： Xu, Jiamiao Wang, Fangzhao Peng, Qinmu You, Xinge Wang, Shuo Jing, Xiao-Yuan Philip Chen, C.L. School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute China State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau 99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR). Different from previous LMvSL based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to efficiently optimize MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

On the Privacy-Preserving Outsourcing Scheme of Reversible Data Hiding over Encrypted Image Data in Cloud Computing

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computers, Materials & Continua 2018年第6期55卷 523-539页

作者： Lizhi Xiong Yunqing Shi School of Computer and Software Nanjing University of Information Science and TechnologyNo.219Ningliu RoadNanjing 210044China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyUniversity Heights NewarkNew Jersey 07102USA

Advanced cloud computing technology provides cost saving and flexibility of services for *** the explosion of multimedia data,more and more data owners would outsource their personal multimedia data on the *** the meantime,some computationally expensive tasks are also undertaken by cloud ***,the outsourced multimedia data and its applications may reveal the data owner’s private information because the data owners lose the control of their ***,this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia *** this paper,two reversible data hiding schemes are proposed for encrypted image data in cloud computing:reversible data hiding by homomorphic encryption and reversible data hiding in encrypted *** former is that additional bits are extracted after decryption and the latter is that extracted before ***,a combined scheme is also *** paper proposes the privacy-preserving outsourcing scheme of reversible data hiding over encrypted image data in cloud computing,which not only ensures multimedia data security without relying on the trustworthiness of cloud servers,but also guarantees that reversible data hiding can be operated over encrypted images at the different *** analysis confirms the correctness of the proposed encryption model and justifies the security of the proposed *** computation cost of the proposed scheme is acceptable and adjusts to different security levels.

关键词： Cloud data security re-encryption reversible data hiding cloud computing privacy-preserving.

Regularization with multilevel non-stationary tight framelets for image restoration

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arXiv 2021年

作者： Li, Yan-Ran Chan, Raymond H.F. Shen, Lixin Zhuang, Xiaosheng College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Department of Mathematics City University of Hong Kong Tat Chee Avenue Kowloon Tong Hong Kong Department of Mathematics Syracuse University SyracuseNY13244 United States

Variational regularization models are one of the popular and efficient approaches for image restoration. The regularization functional in the model carries prior knowledge about the image to be restored. The prior knowledge, in particular for natural images, are the first-order (i.e. variance in luminance) and second-order (i.e. contrast and texture) information. In this paper, we propose a model for image restoration, using a multilevel non-stationary tight framelet system that can capture the image's first-order and second-order information. We develop an algorithm to solve the proposed model and the numerical experiments show that the model is effective and efficient as compared to other higher-order models. Copyright © 2021, The Authors. All rights reserved.

关键词： Image reconstruction

Luna-TX: An Optimized Transactional Mechanism for Persistent Memory

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Luna-TX: An Optimized Transactional Mechanism for Persistent...

IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA)

作者： Hongping Shu Hao Liu Hongyu Chen Youyou Lu Jiwu Shu School of Software Engineering Chengdu University of Information Technology Chengdu China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781728138558

At present, most existing persistent memory programming libraries use write-ahead-logging(WAL) technology to ensure the consistency of memory allocating and updating process. In the application where persistent memory is frequently updated, this approach will bring a serious impact on system performance. In this work, we carefully analyzed the actual requirements of the applications and propose an optimized release mechanism for persistent memory called Luna_TX. In the improved mechanism, we remove the memory releasing phases of logs and objects from the critical path and generating in the process of update and release of transactions is delayed to the execution out of the transaction, which reduces the performance overhead on the critical paths. The experimental results have shown that the proposed mechanism can reduce the transaction delay significantly and boost up the whole transaction performance at 59%.

关键词： Resource management Libraries Nonvolatile memory Instruction sets Memory management Programming computer crashes

Extreme feature regions for image matching 26

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Extreme feature regions for image matching

26th Pacific Conference on computer Graphics and Applications, Pacific Graphics 2018

作者： Fan, Baijiang Rao, Yunbo Pu, Jiansu Deng, Jianhua School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783038680734

Extreme feature regions are increasingly critical for many image matching applications on affine image-pairs. In this paper, we focus on the time-consumption and accuracy of using extreme feature regions to do the affine-invariant image matching. Specifically, we proposed novel image matching algorithm using three types of critical points in Morse theory to calculate precise extreme feature regions. Furthermore, Random Sample Consensus (RANSAC) method is used to eliminate the features of complex background, and improve the accuracy of the extreme feature regions. Moreover, the saddle regions is used to calculate the covariance matrix for image matching. Extensive experiments on several benchmark image matching databases validate the superiority of the proposed approaches over many recently proposed affine-invariant SIFT algorithms. © 2018 The Author(s).

关键词： Image matching

Joint Minimization of Wired and Wireless Traffic for Content Delivery by Multicast Pushing

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Joint Minimization of Wired and Wireless Traffic for Content...

作者： Chen, Zhao Tao, Xiaoming Jiang, Chunxiao Leung, Victor C.M. Beijing National Research Center for Information Science and Technology Department of Electronics Engineering Tsinghua University Beijing100084 China Beijing Innovation Center for Future Chip Tsinghua University Beijing100084 China Tsinghua Space Center Tsinghua University Beijing100084 China BNRist Tsinghua University Beijing100084 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

As more mobile users become subscribers of content services, their subscribed content can be directly pushed from the content provider into the user equipment after the content is generated. In current and future network paradigms, a joint wired and wireless transmission design for this pushing is needed to guarantee the user experience without the extra deployment of communication infrastructures or consumption of resources. In this paper, we investigate a joint wired and wireless content delivery system that incorporates wired and wireless multicast. The users in the same group are served by wireless multicast from a base station (BS), while the BSs of the same content form a multicast tree in a backbone wired network. The sum of wired and wireless traffic is minimized by a joint design of user grouping, subchannel allocation, wired routing, and wired link usage. Exploiting the monotonicity of wired and wireless traffic with regard to the wired hop count, the original problem is converted for searching the optimal hop count vector that achieves the minimum sum of both types of traffic, which is solved by a monotonic optimization (MO)-based iterative algorithm. Compared with existing schemes and according to the numerical results, a reduction in total traffic of 43% can be achieved by our approach. © 2019 IEEE.

关键词： Iterative methods

Hyperspectral image denoising and anomaly detection based on low-rank and sparse representations

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arXiv 2021年

作者： Zhuang, Lina Gao, Lianru Zhang, Bing Fu, Xiyou Bioucas-Dias, José M. Department of Mathematics Hong Kong BaptistUniversity Hong Kong Hong Kong Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Instituto de Telecomunicações Instituto Superior Técnico Universidade de Lisboa Lisbon1049-001 Portugal

Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels. This enables objects to be detected and the identification of materials that have subtle differences between them. However, the increase in spectral resolution often means that there is a decrease in the number of photons received in each channel, which means that the noise linked to the image formation process is greater. This degradation limits the quality of the extracted information and its potential applications. Thus, denoising is a fundamental problem in hyperspectral image (HSI) processing. As images of natural scenes with highly correlated spectral channels, HSIs are characterized by a high level of self-similarity and can be well approximated by low-rank representations. These characteristics underlie the state-of-the-art methods used in HSI denoising. However, where there are rarely occurring pixel types, the denoising performance of these methods is not optimal, and the subsequent detection of these pixels may be compromised. To address these hurdles, in this paper, we introduce RhyDe (Robust hyperspectral Denoising), a powerful HSI denoiser, which implements explicit low-rank representation, promotes self-similarity, and, by using a form of collaborative sparsity, preserves rare pixels. The denoising and detection effectiveness of the proposed robust HSI denoiser is illustrated using semi-real and real data. A MATLAB demo of this work is available at https://***/LinaZhuang for the sake of reproducibility. Copyright © 2021, The Authors. All rights reserved.

关键词： Anomaly detection

EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: The MonuMAI cultural heritage use case

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arXiv 2021年

作者： Díaz-Rodríguez, Natalia Lamas, Alberto Sanchez, Jules Franchi, Gianni Donadello, Ivan Tabik, Siham Filliat, David Cruz, Policarpo Montes, Rosana Herrera, Francisco U2IS ENSTA Institut Polytechnique Paris Inria Flowers Palaiseau91762 France Free University of Bozen-Bolzano 39100 Italy DaSCI Andalusian Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Art History University of Granada Granada18071 Spain Department of Computer Science and Artificial Intelligence University of Granada Granada18071 Spain Department of Software Engineering University of Granada Granada18071 Spain

The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are black-box methods hard to debug, interpret, and certify. DL alone cannot provide explanations that can be validated by a non technical audience such as end-users or domain experts. In contrast, symbolic AI systems that convert concepts into rules or symbols –such as knowledge graphs– are easier to explain. However, they present lower generalisation and scaling capabilities. A very important challenge is to fuse DL representations with expert knowledge. One way to address this challenge, as well as the performance-explainability trade-off is by leveraging the best of both streams without obviating domain expert knowledge. In this paper, we tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph. We present the eXplainable Neural-symbolic learning (X-NeSyL) methodology, designed to learn both symbolic and deep representations, together with an explainability metric to assess the level of alignment of machine and human expert explanations. The ultimate objective is to fuse DL representations with expert domain knowledge during the learning process so it serves as a sound basis for explainability. In particular, X-NeSyL methodology involves the concrete use of two notions of explanation, both at inference and training time respectively: 1) EXPLANet: Expert-aligned eXplainable Part-based cLAssifier NETwork Architecture, a compositional convolutional neural network that makes use of symbolic representations, and 2) SHAP-Backprop, an explainable AI-informed training procedure that corrects and guides the DL process to align with such symbolic representations in form of knowledge graphs. We showcase X-NeSyL methodology using MonuMAI dataset for monument facade image classification, and demonstrate that with our approach, it is possible to i

关键词： Classification (of information)