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Hyperspectral image clustering via sparse dictionary-based anchored regression

IET IMAGE PROCESSING 2019年第2期13卷 261-269页

作者： Huang, Nan Xiao, Liang Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

Clustering for hyperspectral images (HSIs) is a very challenging task because HSIs usually have large spectral variability, high dimensionality, and complex structures. The main issue of this study is to develop an improved sparse subspace clustering (SSC) method for HSIs. As an extension of spectral clustering, SSC algorithm has achieved great success;however, the direct self-representation dictionary which is created by raw samples has poor representation power and also the widely used dictionary learning (DL) such as K-Singular Value Decomposition (K-SVD) faces with the problems of high computational complexity. In this study, the authors propose a novel HSI clustering method based on sparse DL and anchored regression. The proposed method follows three stages: (i) sparse DL;(ii) anchored subspace construction and regression;and (iii) representation-based spectral clustering. Specifically, we adopt a fast sparse DL method under a double sparsity constrained optimising model to capture the intrinsic HSIs. To establish a compact subspace for collaborative representation, we present an anchored subspace construction method by using atoms clustering and grouping methods. Owing to the anchored subspace, we can fast compute the representation coefficients with a predefined projection matrix. Experimental results demonstrate that the proposed method achieves the best performance for the HSIs clustering.

关键词： image representation hyperspectral imaging pattern clustering optimisation matrix algebra learning (artificial intelligence) sparse dictionary-based anchored regression hyperspectral images spectral variability high dimensionality complex structures improved sparse subspace clustering method SSC algorithm nature images low-dimensional data direct self-representation dictionary poor representation power high computational complexity representation-based spectral clustering fast sparse DL method intrinsic hyperspectral signatures compact subspace collaborative representation anchored subspace construction method hyperspectral data sets HSIs clustering task

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Fast algorithm for moving target localisation using FDA-MIMO radar

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JOURNAL OF ENGINEERING-JOE 2019年第19期2019卷 5749-5752页

作者： Xu, Jian Wang, Wen-Qin Cui, Can Huang, Bang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China Nanjing Univ Sci & Technol Sch Elect Engn & Optoelect Technol Nanjing Jiangsu Peoples R China

Target localisation using frequency diverse array (FDA) has drawn much attention owing to the additional degree of freedom in the range dimension. However, the Doppler effect was often ignored in previous FDA works because of the stationary target assumption, and in contrast, even stationary target localisation could be of quite a high computational complexity. In this study, a method is proposed that can jointly estimate range, angle, and Doppler for FDA multiple-input-multiple-output radar with low computational complexity. First, Doppler is estimated independently by utilising unstructured maximum likelihood method. Next, traditional two-dimensional (2D) MUSIC is divided into multiple one-dimensional (1D) MUSIC to estimate the angle. Then substituting the estimated angle into the former 2D MUSIC results in a 1D searching over range dimension to get the range estimation. The advantage of the proposed method was verified by simulation results.

关键词： computational complexity direction-of-arrival estimation MIMO radar Doppler effect radar signal processing MIMO communication maximum likelihood estimation fast algorithm FDA-MIMO radar frequency diverse array range dimension Doppler effect previous FDA works stationary target assumption stationary target localisation high computational complexity FDA multiple-input-multiple-output radar low computational complexity maximum likelihood method two-dimensional MUSIC one-dimensional MUSIC estimated angle 2D MUSIC results range estimation

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Software Architecture of high Efficiency Video Coding for Many-Core Systems with Power-Efficient Workload Balancing

Software Architecture of High Efficiency Video Coding for Ma...

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Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Khan, Muhammad Usman Karim Shafique, Muhammad Henkel, Joerg Karlsruhe Inst Technol CES D-76021 Karlsruhe Germany

ISBN: (纸本)9783981537024

The high Efficiency Video Coding (HEVC) standard aims at providing similar to 50% better compression compared to its predecessor (H.264) at the cost of high computational complexity. To enable HEVC video encoding in real-time scenarios, special coding support for parallelization is provided in HEVC that can be exploited by many-core systems. In this work, we present a HEVC software architecture where a video frame is adaptively divided into independent video frame regions (i.e. so-called video tiles) which are processed concurrently on multiple cores. By balancing the workload of each video tile mapped to a particular core, the total power consumption of a system is reduced (through dynamically scaling the operating frequency) under a given frame-rate constraint. We also exploit user tolerance to further curtail the HEVC workload with insignificant video quality degradation. Experimental results illustrate that the proposed approach results in similar to 43% power savings on a many-core system.

关键词： data compression software architecture video codecs video coding H.264 standard HEVC software architecture HEVC standard frame-rate constraint high computational complexity high efficiency video coding standard independent video frame regions many-core systems power consumption power-efficient workload balancing video quality degradation video tiles complexity theory Encoding Hardware Power demand Quality assessment Tiles Video recording

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A Learning Scheme to Fuzzy C-Means based on a Compromise in Updating Membership Degrees

A Learning Scheme to Fuzzy C-Means based on a Compromise in ...

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IEEE International Conference on Fuzzy Systems

作者： Wu, Shang-Lin Lin, Yang-Yin Liu, Yu-Ting Chen, Chih-Yu Lin, Chin-Teng Natl Chiao Tung Univ Inst Elect Control Engn Hsinchu 300 Taiwan Natl Chiao Tung Univ Brain Res Ctr Hsinchu 300 Taiwan Natl Chiao Tung Univ Inst Biomed Engn Hsinchu 300 Taiwan

ISBN: (纸本)9781479920723

Fuzzy C-Means (FCM) clustering is the most well-known clustering method according to fuzzy partition for pattern classification. However, there are some disadvantages of using that clustering method, such as computational complexity and execution time. Therefore, to solve these drawbacks of FCM, the two-phase FCM procedure has been proposed in this study. Compared with the conventional FCM, the usage of a compromised learning scheme makes more adaptive and effective. By performing the proposed approach, the unknown data could be rapidly clustered according to the previous information. A synthetic data set with two dimensional variables is generated to estimate the performance of the proposed method, and to further demonstrate that our method not only reduces computational complexity but economizes execution time compared with the conventional FCM in each example.

关键词： Fuzzy C-Means (FCM) Clustering Data classification high computational complexity Long execution time

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A Joint Decoding Algorithm for Turbo Product Codes Applied in PCM/FM Telemetry System

A Joint Decoding Algorithm for Turbo Product Codes Applied i...

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International Congress on Image and Signal Processing

作者： Li Wang Manxi Wang Liandong Wang Fu Yuan State Key Lab. of Complex Electromagn. Environ. Effects on Electron. & Inf. Syst. Luoyang China

ISBN: (纸本)9781479958368

For decreasing the high computational complexity of Turbo product code in PCM/FM telemetry system, a joint decoding algorithm based on syndrome-based decoding and a correlation operation to substitute for searching the competing codeword was proposed. Simulation results show that the proposed algorithm can effectively reduce the decoding complexity with less coding gain degradation.

关键词： computational complexity telemetry turbo codes PCM-FM telemetry system correlation operation decoding complexity high computational complexity joint decoding algorithm syndrome-based decoding turbo product codes Algorithm design and analysis complexity theory Decoding Frequency modulation Iterative decoding Phase change materials Product codes Chase decoding PCM/FM telemetry system Turbo product code

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A Learning Scheme to Fuzzy C-Means based on a Compromise in Updating Membership Degrees

A Learning Scheme to Fuzzy C-Means based on a Compromise in ...

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IEEE International Conference on Fuzzy Systems

作者： Shang-Lin Wu Yang-Yin Lin Yu-Ting Liu Chih-Yu Chen Chin-Teng Lin Institute of Electrical Control Engineering National Chiao Tung University Brain Research Center National Chiao Tung University Institute of Biomedical Engineering National Chiao Tung University

ISBN: (纸本)9781479920747

关键词： Fuzzy C-Means (FCM) Clustering Data classification high computational complexity Long execution time

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An ElGamal-Based Efficient and Privacy-Preserving Data Aggregation Scheme for Smart Grid

An ElGamal-Based Efficient and Privacy-Preserving Data Aggre...

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IEEE Global Communications Conference

作者： Xiaolei Dong Jun Zhou Khalid Alharbi Xiaodong Lin Zhenfu Cao Department of Computer Science and Engineering Shanghai Jiao Tong University Faculty of Business and Information Technology University of Ontario Institute of Technology

ISBN: (纸本)9781479935130

Smart Grid technologies are ever-increasingly being adopted by nations and governments, primarily for a huge technological advancement in the areas of power management and information and communications. The smart meters deployed in a household can monitor electricity usage in real-time, and aggregate the data for further analysis and control;however, the existing solutions mainly depend on Paillier's additive homomorphic encryptions, which are of high computational complexity. This inefficiency makes them impracticable in smart grid, which embraces thousands of users and requires frequent data aggregation. In this paper, we propose a privacy-preserving aggregation framework, followed by a concrete construction using Elgamal encryption, which is secure under chosen plaintext attack (CPA) but not chosen ciphertext attack (CCA). Then, we further extend the concrete construction into a CCA-secure counterpart. In addition to efficient data aggregation, the proposed scheme can protect the user's meter data from sophisticated attacks, which are sponsored by the community gateway and the users. The formal security proof and performance evaluations illustrate the efficiency and the practicability of our scheme as well as its strong security.

关键词： computational complexity cryptography data privacy power system security smart meters smart power grids telecommunication security CCA CPA ElGamal-based efficient data aggregation Paillier additive homomorphic encryptions chosen ciphertext attack chosen plaintext attack community gateway formal security proof high computational complexity performance evaluations power management privacy-preserving data aggregation scheme smart grid technology user meter data protection Communities Companies Cryptography Logic gates Power demand Smart grids

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Robust compressive multi-input-multi-output imaging

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IET RADAR SONAR AND NAVIGATION 2013年第3期7卷 233-245页

作者： Zhao, Guanghui Wang, Qi Shen, Fangfang Li, Xiaoming Shi, Guangming Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Aviat Key Lab Sci & Technol AISSS Wuxi 214063 Peoples R China

Canonical multi-input-multi-output (MIMO) imaging methods suffer from limited resolution, poor robustness against noise and high computational complexity, especially when the array aperture is limited (affecting angular resolution) and the number of snapshots is limited (affecting Doppler resolution). In this study, the authors discuss a new range-angle-Doppler monostatic MIMO imaging method through adaptive estimation of the generalised Cauchy prior distribution (GCD). The superiority of GCD-based model over existing l p-norm-based model (which actually assumes the prior as general Gaussian distribution) is theoretically verified through the issue of signal compressibility. The authors adapt a reweighted l(2)-norm iterative algorithm to solve the model. In our model the authors do not pre-define the scaling factor of the prior distribution and, during the iteration, the authors use a novel 'quantile-of-OS' method to adaptively estimate the scaling parameter of the prior distribution, enhancing the robustness of the method. Simulation results verify the image quality and speed advantages of the proposed method.

关键词： adaptive estimation communication complexity Doppler radar Gaussian distribution radar imaging radar resolution MIMO radar image quality quantile-of-OS method â„“2-norm iterative algorithm signal compressibility â„“p-norm-based model GCD-based model generalised Cauchy prior distribution adaptive estimation range-angle-Doppler monostatic MIMO imaging method snapshots number high computational complexity poor robustness canonical MIMO imaging methods robust compressive multiinput-multioutput imaging

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A Low-Delay Low-complexity EKF Design for Joint Channel and CFO Estimation in Multi-User Cognitive Communications

A Low-Delay Low-Complexity EKF Design for Joint Channel and ...

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54th Annual IEEE Global Telecommunications Conference (GLOBECOM)

作者： Zhao, Pengkai Shen, Cong Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90024 USA Qualcomm Inc San Diego CA USA

ISBN: (纸本)9781424492688

Parameter estimation in cognitive communications can be formulated as a multi-user estimation problem, which is solvable under maximum likelihood solution but involves high computational complexity. This paper presents a time-sharing and interference mitigation based EKF (Extended Kalman Filter) design for joint CFO (carrier frequency offset) and channel estimation at multiple cognitive users. The key objective is to realize low implementation complexity by decomposing high-dimensional parameters into multiple separate low-dimensional estimation problems, which can be solved in a time-shared manner via pipelining operation. We first present a basic EKF design that estimates the parameters from one TX user to one RX antenna. Then such basic design is time-shared and reused to estimate parameters from multiple TX users to multiple RX antennas. Meanwhile, we use interference mitigation module to cancel the co-channel interference at each RX sample. In addition, we further propose adaptive noise variance tracking module to improve the estimation performance. The proposed design enjoys low delay and low buffer size (because of its online real-time processing), as well as low implementation complexity (because of time-sharing and pipeling design). Its estimation performance is verified to be close to Cramer-Rao bound.

关键词： Antennas CFO estimation Channel estimation complexity theory Cramer Rao bound Estimation Interference Kalman filters Noise measurement OFDM carrier frequency offset channel estimation channel estimation cognitive radio computational complexity extended Kalman filter high computational complexity joint channel low delay low complexity maximum likelihood estimation maximum likelihood solution multi user estimation problem multiuser channels multiuser cognitive communications parameter estimation time sharing

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‘Next-generation’ genome wide association studies

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Hereditary Cancer in Clinical Practice 2012年第2期10卷 1-1页

作者： J Hopper E Makalic D Schmidt M Bui J Stone M Kapuscinski D Park M Jenkins M Southey Departments of Public Health and Pathology The University of Melbourne Australia

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