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14th International Conference on Graphics and Image Processing (ICGIP)

作者： Jiang, kun Liu, Zheng Zhu, Lei Xian Univ Technol Sch Comp Sci & Engn Xian Peoples R China

ISBN: (纸本)9781510666313;9781510666320

Recent years, analysis representation learning and its applications for classification have been well explored and applied, due to its flexible representation ability and low classification complexity. With a learned analysis dictionary, test samples can be transformed into a sparse subspace for classification efficiently. However, the underling locality of sample data has rarely been explored and reliably married with analysis representation learning to enhance the discriminative capability of the classifier. In this paper, we propose a novel adaptive locality sensitive analysis representation learning model for pattern classification (ALAR). It considers the intrinsic geometric properties by imposing adaptive weighted constrained graph regularization to uncover the geometric structure for the image data. Through the learned analysis dictionary, we transform the image to a new and compact space where the manifold assumption can be further guaranteed. Thus, the local geometrical structure of images can be preserved in sparse representation coefficients. Moreover, the ALAR model is iteratively solved by the synthesis k-svd and gradient technique. Experimental results on image classification validate the performance superiority of our ALAR model.

关键词： Geometry Structure Adaptive Weighted Error Analysis Representation Learning k-svd algorithm

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Wind farm clutter suppression for air surveillance radar based on a combined method of clutter map and k-svd algorithm

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IET RADAR SONAR AND NAVIGATION 2020年第9期14卷 1354-1364页

作者： He, Wei-kun Zhang, Xin Bi, Feng-Hua Yang, Lei Civil Aviat Univ China Tianjin Peoples R China Aircraft Design Inst Shanghai Shanghai Peoples R China

A clutter suppression method based on a combined method of clutter map and k-svd algorithm has been proposed, which aims at reducing false alarm rate of air surveillance radar caused by wind farm clutter. First, a clutter map, based on the fixed position and Doppler spectral width characteristics of wind turbines, is established using constant false alarm rate detection. The obtained clutter map is then used to process the traces detected by radar. To retain the target while the clutter is suppressed, the traces in the clutter area are processed using the k-svd algorithm, which is first proposed to mitigate wind turbine clutter in this study. The experimental results of measured data show that the false alarm caused by the wind turbines can be effectively reduced.

关键词： search radar wind power plants singular value decomposition wind turbines radar clutter radar signal processing clutter map k-svd algorithm clutter suppression method air surveillance radar wind turbines constant false alarm rate detection clutter area wind turbine clutter wind farm clutter suppression

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Sparse Representation of Network Topology with k-svd algorithm 43

Sparse Representation of Network Topology with K-SVD Algorit...

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43rd IEEE-Computer-Society Annual International Computers, Software and Applications Conference (COMPSAC)

作者： Matsuo, Ryotaro Nakamura, Ryo Ohsaki, Hiroyuki Kwansei Gakuin Univ Dept Informat Grad Sch Sci & Technol Nishinomiya Hyogo 6691337 Japan

ISBN: (纸本)9781728126074

In recent years, a statistical approach called sparse modeling has been studied extensively for estimating unobserved model parameters from a small number of observations using the sparsity of model parameters. Although sparse modeling has been applied to many practical problems in the fields of signal processing and image processing, to the best of our knowledge, few studies have applied it to the field of information networking. In this paper, we investigate whether a sparse representation of network topology can be obtained from a dictionary trained with a dictionary learning algorithm in sparse modeling. Specifically, we train a dictionary from a number of learning network topologies using the k-svd algorithm, which is one of conventional dictionary learning algorithms, and obtain a sparse representation of the network topology by solving an L-0-norm minimization problem for given network topology and the trained dictionary. Furthermore, through experiments, the effects of several factors-the network (i.e., topology and network size) and the dictionary (i.e., dictionary size)-on sparse representation of network topologies are investigated. Our finding includes that graphs whose structure is uniform (e.g., tree) and networks with cluster structure are suitable for sparse representation of network topologies.

关键词： Sparse Modeling Overcomplete Dictionary Network Topology Sparse Representation k-svd algorithm l(0)-norm Minimization Problem

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Immune k-svd algorithm for dictionary learning in speech denoising

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NEUROCOMPUTING 2014年 137卷 223-233页

作者： Zhou, Yan Zhao, Heming Shang, Li Liu, Tao Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Jiangsu Peoples R China Suzhou Vocat Univ Elect Informat Engn Coll Dept Commun Technol Suzhou 215104 Jiangsu Peoples R China

A speech denoising method based on the sparse representation over a redundant dictionary is proposed in this paper. This redundant dictionary is trained by a corpus of corrupted spectrogram patches using immune k-singular value decomposition (Ik-svd) algorithm, which is an efficient sparse representation method for speech signals. Since the traditional k-svd algorithm is an iterative method that alternates between sparse representation with respect to a given dictionary and a process of updating the dictionary atoms to better fit the data, it has the disadvantages of large calculation quantity, low approximation speed and accuracy. However, the immune algorithm has the characteristics of local fast convergence and global optimal, so it can optimize the k-svd learning by introducing immune mechanism to modify the pursuit algorithm. This immune optimized k-svd algorithm not only can accelerate the learning speed of k-svd, but also increases its approximation accuracy. In test, the University of Victoria speech database is exploited to test the speech denoising approach proposed here. And compared with original k-svd algorithm and Wavelet Transform (WT) denoising algorithm, experimental results show that the property of our algorithm behaves more stable and behaves better improvement signal-to-noise ratio (ISNR). (C) 2013 Elsevier B.V. All rights reserved.

关键词： k-svd algorithm Immune algorithm Speech denoising Sparse representation Redundant dictionary

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Learning a Common Dictionary for CSI Feedback in FDD Massive MU-MIMO-OFDM Systems

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2023年 4卷 530-544页

作者： Gadamsetty, Pavan kumar Hari, k. V. S. Hanzo, Lajos Indian Inst Sci Dept Elect Commun Engn Bengaluru 560012 India Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

In a transmit preprocessing aided frequency division duplex (FDD) massive multi-user (MU) multiple-input multiple-output (MIMO) scheme assisted orthogonal frequency-division multiplexing (OFDM) system, it is required to feed back the frequency domain channel transfer function (FDCHTF) of each subcarrier at the user equipment (UE) to the base station (BS). The amount of channel state information (CSI) to be fed back to the BS increases linearly with the number of antennas and subcarriers, which may become excessive. Hence we propose a novel CSI feedback compression algorithm based on compressive sensing (CS) by designing a common dictionary (CD) to reduce the CSI feedback of existing algorithms. Most of the prior work on CSI feedback compression considered single-UE systems. Explicitly, we propose a common dictionary learning (CDL) framework for practical frequency-selective channels and design a CD suitable for both single-UE and multi-UE systems. A set of two methods is proposed. Specifically, the first one is the CDL-k singular value decomposition (ksvd) method, which uses the k-svd algorithm. The second one is the CDL-orthogonal Procrustes (OP) method, which relies on solving the orthogonal Procrustes problem. The CD conceived for exploiting the spatial correlation of channels across all the subcarriers and UEs compresses the CSI at each UE, and upon reception reconstructs it at the BS. Our simulation results show that the proposed dictionary's estimated channel vectors have lower normalized mean-squared error (NMSE) than the traditional fixed Discrete Fourier Transform (DFT) based dictionary. The CSI feedback is reduced by 50%, and the memory reduction at both the UE and BS starts from 50% and increases with the number of subcarriers.

关键词： Dictionaries OFDM Matching pursuit algorithms Discrete Fourier transforms Correlation Massive MIMO Compressed sensing Wideband frequency domain channel transfer function (FDCHTF) channel state information (CSI) compressive sensing (CS) massive MIMO common dictionary learning (CDL) common dictionary (CD) orthogonal Procrustes (OP) problem k-svd algorithm

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Power User Behavior and Load Forecasting Based on Big Data 4

Power User Behavior and Load Forecasting Based on Big Data

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4th International Signal Processing, Communications and Engineering Management Conference, ISPCEM 2024

作者： Song, Jihong Li, Baohai Li, Hui State Grid Qinghai Electric Power Company Xining810000 China State Grid Qinghai Haibei Power Supply Company Tibetan Autonomous Prefecture of Haibei Qinghai812200 China

ISBN: (纸本)9798331528676

With the improvement of power system intelligence and the development of power collection technology, a huge amount of power data is generated. This data includes the number of times of electricity consumption, time of electricity consumption, electricity equipment and other users' electricity consumption behaviours. In this paper, a set of power consumption behavior analysis model based on power big data was constructed, which helped to deeply understand the characteristics and behavioral laws of users' power consumption, and provided important support for the energy-saving management and optimization of China's power grid enterprises. Traditional research on electricity consumption behaviour mainly relies on small-sample research and simple statistical analysis, which cannot completely and accurately grasp electricity consumption behaviour. However, the electric power consumption collection and analysis model based on electric power big data, with its large data scale, multiple dimensions, wide coverage and high accuracy, can study users' electricity consumption behaviour from a finer and more comprehensive perspective. In this paper, a user electricity consumption behaviour analysis model based on electricity big data was introduced, and the k-svd (k-Singular Value Decomposition) algorithm was used to decompose the user's electricity consumption patterns in order to extract the user's electricity consumption characteristics and behavioural patterns. The experimental results showed that the k-svd algorithm was able to predict the power consumption of users more accurately, and the prediction error was small, usually between 1-2 kWh. Load prediction based on the k-svd algorithm shows high efficiency and stability in practical applications, providing strong support for energy management and optimization. ©2024 IEEE.

关键词： Data Processing k-svd algorithm Load Forecasting Power Big Data User Behavior Analysis

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Image Reconstruction for ECT under Compressed Sensing Framework Based on an Overcomplete Dictionary

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Computer Modeling in Engineering & Sciences 2022年第3期130卷 1699-1717页

作者： Xuebin Qin Yutong Shen Jiachen Hu Mingqiao Li Peijiao Yang Chenchen Ji Xinlong Zhu School of Electrical and Control Engineering Xi’an University of Science and TechnologyXi’an710054China Shaanxi Coal and Shaanbei Mining Co. Ltd.Xi’an719000China

Electrical capacitance tomography(ECT)has great application potential inmultiphase processmonitoring,and its visualization results are of great significance for studying the changes in two-phase flow in closed *** this paper,compressed sensing(CS)theory based on dictionary learning is introduced to the inverse problem of ECT,and the k-svd algorithm is used to learn the overcomplete dictionary to establish a nonlinear mapping between observed capacitance and sparse *** the trained overcomplete dictionary has the property to match few features of interest in the reconstructed image of ECT,it is not necessary to rely on the sparsity of coefficient vector to solve the nonlinear mapping as most algorithms based on CS ***-phase flow distribution in a cylindrical pipe was modeled and simulated,and three variations without sparse constraint based on Landweber,Tikhonov,and Newton-Raphson algorithms were used to rapidly reconstruct a 2-D image.

关键词： Electrical capacitance tomography dictionary learning compressed sensing k-svd algorithm overcomplete dictionary two-phase flow

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Improved sparse representation algorithm for optical coherence tomography images

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022年第4期32卷 1286-1293页

作者： Miao, Hanyuan Zhou, Xiaohong Wang, Wei Jiang, Weiliang Jin, Tao Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Fudan Univ Childrens Natl Med Ctr Ophthalmol Childrens Hosp Shanghai Peoples R China Naval Med Ctr PLA Res Dept Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Gen Hosp Sch Med Shanghai Peoples R China

An improved sparse representation algorithm is proposed for reducing speckle noise in optical coherence tomography (OCT) images. To reduce the dictionary training time, the orthogonal matching pursuit algorithm (OMP) is replaced by a piecewise orthogonal matching pursuit algorithm (ST-OMP), and a threshold standard is set to train multiple atoms simultaneously to improve the tracking efficiency. Normal, diabetic macular edema, and fundus neovascularization membrane retina OCT images were tested using the proposed algorithm and other denoising algorithms. The results show that the proposed algorithm exhibits an excellent denoising ability. The details and edge information in the OCT images were well preserved by the proposed algorithm. Meanwhile, the image processing time was reduced by half with the proposed algorithm compared with the traditional k-svd algorithm.

关键词： k-svd algorithm optical coherence tomography piecewise orthogonal matching pursuit algorithm speckle noise

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Radar recognition of multiple micro-drones based on their micro-Doppler signatures via dictionary learning

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IET RADAR SONAR AND NAVIGATION 2020年第9期14卷 1310-1318页

作者： Zhang, Wenyu Li, Gang Baker, Chris Tsinghua Univ Dept Elect Engn Beijing Peoples R China Univ Birmingham Dept Elect Elect & Syst Engn Birmingham W Midlands England

Most existing work on radar classification of micro-drones assumes that the received signal is wholly reflected from a single micro-drone. However, when there are multiple micro-drones in the observed scene, the superimposition of their micro-Doppler signatures increases the classification difficulty. In particular, it is even more challenging to determine if a specific type of micro-drones exists. In this study, a method for recognition of multiple micro-drones based on their micro-Doppler signatures via dictionary learning is introduced. First, the dictionary is learnt for each type of micro-drone by using the k-svd algorithm on cadence-velocity diagrams (CVD) of training samples. The CVD is obtained by computing the Fourier transform of the time series of a complex time-frequency spectrogram. Subsequently, the sparse representation of the CVD of multiple micro-drones is obtained by the orthogonal matching pursuit algorithm with the learnt dictionary. Finally, a threshold detector is applied to the sparse solution in order to extract the components of multiple micro-drones. Experimental results using measured data, which are collected from hovering drones by a continuous-wave radar in an indoor environment, show that this dictionary-learning-based method achieves a recognition performance of 93% when half of the measured data are used for training.

关键词： radar signal processing singular value decomposition Doppler radar signal representation signal classification Fourier transforms radar target recognition learning (artificial intelligence) radar computing time series approximation theory CW radar multiple microdrones microDoppler signatures single microdrone radar recognition dictionary learning k-svd algorithm cadence-velocity diagrams Fourier transform time series complex time-frequency spectrogram orthogonal matching pursuit algorithm radar classification

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Interferenceless coded aperture correlation holography (I-COACH) adaptive compression imaging 11

Interferenceless coded aperture correlation holography (I-CO...

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11th International Conference on Information Optics and Photonics (CIOP)

作者： Liu, Chao Zia, Ali Wan, Yuhong Beijing Univ Technol Coll Appl Sci Beijing Peoples R China

ISBN: (数字)9781510631748

ISBN: (纸本)9781510631748

Interferenceless coded aperture correlation holography (I-COACH) is an incoherent digital holography technique without two-wave interference. In the system of I-COACH, the information of the corresponding depth of a three-dimensional object is reconstructed by the cross-correlation operation between a point hologram (PSH) and an object hologram (OH). Because the I-COACH needs to combine multi-step phase-shift technique and image averaging technique to suppress the bias terms and background noise in reconstructed images, the image time resolution is poor. In order to suppress the bias terms and background noise, in this paper, we proposed an adaptive learning dictionary reconstruction (ALDR) method of I-COACH system by combing non-linear reconstruction technique and sparse representation theory. By using the nonlinear reconstruction image as the training data of the model, the k-means singular value decomposition (k-svd) and orthogonal matching pursuit (OMP) algorithm are used to obtain a redundant adaptive learning dictionary for reconstructing image. The compression reconstruction is performed to achieve the adaptive noise-free reconstructed image in the I-COACH.

关键词： I-COACH sparse representation k-svd algorithm OMP algorithm adaptive learning dictionary

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