检索结果-内蒙古大学图书馆

sparse coding of birdsong and receptive field structure in songbirds

NETWORK-COMPUTATION IN NEURAL SYSTEMS 2009年第3期20卷 162-177页

作者： Greene, Garrett Barrett, David G. T. Sen, Kamal Houghton, Conor Trinity Coll Dublin Sch Math Dublin Ireland UCL Gatsby Computat Neurosci Unit London WC1E 6BT England Boston Univ Dept Biomed Engn Hearing Res Ctr Boston MA 02215 USA Boston Univ Dept Biomed Engn Ctr Biodynam Boston MA 02215 USA

Auditory neurons can be characterized by a spectro-temporal receptive field, the kernel of a linear filter model describing the neuronal response to a stimulus. With a view to better understanding the tuning properties of these cells, the receptive fields of neurons in the zebra finch auditory fore-brain are compared to a set of artificial kernels generated under the assumption of sparseness;that is, the assumption that in the sensory pathway only a small number of neurons need be highly active at any time. The sparse kernels are calculated by finding a sparse basis for a corpus of zebra-finch songs. This calculation is complicated by the highly-structured nature of the songs and requires regularization. The sparse kernels and the receptive fields, though differing in some respects, display several significant similarities, which are described by computing quantative properties such as the seperability index and Q-factor. By comparison, an identical calculation performed on human speech recordings yields a set of kernels which exhibit widely different tuning. These findings imply that Field L neurons are specifically adapted to sparsely encode birdsong and supports the idea that sparsification may be an important element of early sensory processing.

关键词： Auditory system natural scenes sparse coding spectro-temporal receptive field

来源：评论

学校读者我要写书评

暂无评论

sparse coding and Selected Applications

引用

KUNSTLICHE INTELLIGENZ 2012年第4期26卷 349-355页

作者： Hocke, Jens Labusch, Kai Barth, Erhardt Martinetz, Thomas Univ Lubeck Inst Neuro & Bioinformat Ratzeburger Allee 160 D-23562 Lubeck Germany Acrolinx D-10178 Berlin Germany

sparse coding has become a widely used framework in signal processing and pattern recognition. After a motivation of the principle of sparse coding we show the relation to Vector Quantization and Neural Gas and describe how this relation can be used to generalize Neural Gas to successfully learn sparse coding dictionaries. We explore applications of sparse coding to image-feature extraction, image reconstruction and deconvolution, and blind source separation.

关键词： sparse coding Neural Gas K-SVD Image deconvolution Image reconstruction Digit recognition Blind source separation

来源：评论

学校读者我要写书评

暂无评论

SCoBeP: Dense image registration using sparse coding and belief propagation

引用

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2013年第2期24卷 137-147页

作者： Barzigar, Nafise Roozgard, Aminmohammad Cheng, Samuel Verma, Pramode Univ Oklahoma Dept Elect & Comp Engn Tulsa OK 74135 USA

Image registration as a basic task in image processing has been studied widely in the literature. It is an important preprocessing step in various applications such as medical imaging, super resolution, and remote sensing. In this paper, we proposed a novel dense registration method based on sparse coding and belief propagation. We used image blocks as features, and then we employed sparse coding to find a set of candidate points. To select optimum matches, belief propagation was subsequently applied on these candidate points. Experimental results show that the proposed approach is able to robustly register scenes and is competitive as compared to high accuracy optical flow Brox et al. (2004) [1], and SIFT flow Liu et al. [2]. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Dense image registration sparse coding Belief propagation Middlebury data set

来源：评论

学校读者我要写书评

暂无评论

Impulse feature extraction method for machinery fault detection using fusion sparse coding and online dictionary learning

引用

Chinese Journal of Aeronautics 2015年第2期28卷 488-498页

作者： Deng Sen Jing Bo Sheng Sheng Huang Yifeng Zhou Hongliang Aeronautics and Astronautics Engineering College Air Force Engineering University Unit 94371 of People's Liberation Army

Impulse components in vibration signals are important fault features of complex machines. sparse coding （SC） algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding （FSC） and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.

关键词： Dictionary learning Fault detection Impulse feature extraction Information fusion sparse coding

来源：评论

学校读者我要写书评

暂无评论

Hierarchical-Based Object Detection with Improved Locality sparse coding

引用

Chinese Journal of Electronics 2016年第2期25卷 290-295页

作者： ZHU Suguo DU Junping REN Nan LIANG Meiyu Beijing Key Laboratory of Intelligent Telecommunication software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications

This paper proposes to extend the hierarchical method to be adapted to sequential frames, aiming at detecting the moving object in dynamic scenes. A novel two-layer model is proposed, in which dictionaries are learned through three different stages and the locality constrained sparse representation is improved. This leads more significant improvement for performance of both static image classification and moving object detection. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the state-of-the-art classification methods, and also able to detect moving object in the sequential frames accurately.

关键词： Hierarchical detection sparse coding Classification Object detection

来源：评论

学校读者我要写书评

暂无评论

sparse coding-based feature extraction for biometric remote authentication in Internet of Things

引用

SN APPLIED SCIENCES 2019年第9期1卷 1098页

作者： Amintoosi, Haleh Taresh, Ali Jaber Ferdowsi Univ Mashhad Fac Engn Comp Engn Dept Mashhad Razavi Khorasan Iran

In recent years, Internet of Things (IoT) has attracted lots of attention. However, the security related issues such as authentication remain a challenge. The heterogeneity of IoT in terms of devices and communication makes most existing authentication mechanisms inapplicable. So, there is a need for a two-factor authentication mechanism to obtain an end-to-end authentication between IoT devices/applications. In this paper, we propose a sparse coding based feature extraction for biometric remote user authentication. The proposed scheme makes use of sparse codes hash operations and overcomplete dictionary to store/retrieve the biometric data efficiently. The performance analysis proves that the proposed method is robust against noise and able to obtain the accuracy of 0.97.

关键词： Internet of Things Security Authentication Biometric sparse coding

来源：评论

学校读者我要写书评

暂无评论

sparse coding Techniques for Adjustable Lossy Compression of Baseband Signals in Centralized and Virtualized Communication Systems 32

Sparse Coding Techniques for Adjustable Lossy Compression of...

引用

32nd European Signal Processing Conference (EUSIPCO)

作者： Piana, Thibault Aissa-El-Bey, Abdeldjalil Arzel, Matthieu Thomas, Alain Lab STICC Brest France Space & Commun Lab Les Ulis France IMT Atlantique CNRS UMR 6285 Lab STICC F-29238 Brest France Safran Data Syst Space & Commun Lab Les Ulis France

ISBN: (纸本)9789464593617;9798331519773

This paper presents an efficient lossy compression method based on sparse coding for received noisy signals (RX) in satellite communication systems. The emergence of ground station as a service (GSaaS) and centralized radio access networks (C-RAN) requires efficient data transfers between ground stations and datacenters. Our approach employs sparse coding to compress baseband signals via offline trained dictionaries. We analyze the loss introduced by compression and its impact on common modulation schemes across a Gaussian channel using the Bit Error Rate (BER) measurement. Additionally, the proposed method is compared to lossless linear prediction coding (LPC) and uniform quantization. Our study highlights the potential of lossy sparse coding to reduce bandwidth requirements while preserving signal integrity by offering a potential compression ratio of up to 8% higher than existing methods for the same degradation.

关键词： dictionary learning sparse coding lossy compression entropy coding C-RAN GSaaS

来源：评论

学校读者我要写书评

暂无评论

sparse coding for Improved Signal-to-Noise Ratio in MRI 21

引用

21st International Conference on Neural Information Processing (ICONIP)

作者： Razzaq, Fuleah A. Mohamed, Shady Bhatti, Asim Nahavandi, Saeid Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3217 Australia

ISBN: (纸本)9783319126432;9783319126425

Magnetic Resonance images (MRI) do not only exhibit sparsity but their sparsity take a certain predictable shape which is common for all kinds of images. That region based localised sparsity can be used to de-noise MR images from random thermal noise. This paper present a simple framework to exploit sparsity of MR images for image de-noising. As, noise in MR images tends to change its shape based on contrast level and signal itself, the proposed method is independent of noise shape and type and it can be used in combination with other methods.

关键词： Magnetic Resonance imaging(MRI) sparse coding Signal-to Noise Ratio (SNR) Additive White Gaussian Noise (AWGN)

来源：评论

学校读者我要写书评

暂无评论

Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2021年第4期235卷 660-675页

作者： Martin-del-Campo, Sergio Sandin, Fredrik Schnabel, Stephan Lulea Univ Technol EISLAB Lulea Sweden Lulea Univ Technol SKF LTU Univ Technol Ctr Lulea Sweden SKF Res & Technol Dev Diagnost & Prognost Lulea Sweden

We analyze vibration signals from wind turbines with dictionary learning and investigate the relation between dictionary distances and faults occurring in a wind turbine output shaft rolling element bearing and gearbox under different data and compute constraints. Dictionary learning is an unsupervised machine learning method for signal processing, which permits learning a set of signal-specific features that have been used to monitor the condition of rotating machines, including wind turbines. Dictionary distance is one such feature, and its effectiveness depends on an adequate selection of the dictionary learning hyperparameters and the data availability, which typically is constrained in condition monitoring systems for remotely located wind farms. Here we evaluate the characteristics of the dictionary distance feature under healthy and faulty conditions of the wind turbines using different options for the selection of the pretrained dictionary, the sparsity of the signal model which determines the compute requirements, and the interval between data samples. Furthermore, we compare the dictionary distance feature to the typical time-domain features used in condition monitoring. We find that the dictionary distance based feature of a faulty wind turbine deviates by a factor of two or more from the population distribution several weeks before the gearbox bearing fault was reported, using a data sampling interval as long as 24 h and a model sparsity as low as 2.5%.

关键词： Unsupervised feature learning dictionary learning sparse coding conditon monitoring wind turbine

来源：评论

学校读者我要写书评

暂无评论

Efficient sparse coding Using Hierarchical Riemannian Pursuit

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 4069-4084页

作者： Xue, Ye Lau, Vincent K. N. Cai, Songfu Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

sparse coding is a class of unsupervised methods for learning a sparse representation of the input data in the form of a linear combination of a dictionary and a sparse code. This learning framework has led to state-of-the-art results in various signal processing tasks. However, classical methods learn the dictionary and the sparse code based on alternating optimizations, usually without theoretical guarantees for either optimality or convergence due to the non-convexity of the problem. Recent works on sparse coding with a complete dictionary provide strong theoretical guarantees thanks to the development of non-convex optimization. However, initial non-convex approaches learned the dictionary in the sparse coding problem sequentially in an atom-by-atom manner, which led to a long execution time. More recent works have sought to directly learn the entire dictionary at once, which substantially reduces the execution time. However, the associated recovery performance is degraded with a finite number of data samples. In this paper, we propose an efficient sparse coding scheme with a two-stage optimization. The proposed scheme leverages the global and local Riemannian geometry of the two-stage optimization problem and facilitates fast implementation for superb dictionary recovery performance by a finite number of samples. We further prove that, with high probability, the proposed scheme can exactly recover any atom in the target dictionary with a finite number of samples. Experiments on both synthetic and real-world data verify the efficiency and robustness of the proposed scheme.(1)

关键词： Dictionaries Encoding Optimization Industrial Internet of Things Convergence Wireless sensor networks Geometry sparse coding dictionary learning orthogonal group efficient implementation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：