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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yevgen Matviychuk Shannon M. Hughes Department of Electrical Computer and Energy Engineering University of Colorado at Boulder

ISBN: (纸本)9781479903573

Solving inverse problems in signal processing often involves making prior assumptions about the signal being reconstructed. Here the appropriateness of the chosen model greatly determines the quality of the final result. Recently it has been proposed to model images by representing them as sets of smaller patches arising from an underlying manifold. This model has been shown to be surprisingly effective in tasks such as denoising, inpainting, and superresolution. However, such a representation is fraught with the difficulty of finding the intersection of many presumably non-linear manifolds in high-dimensional space, which precludes much of its potential use. This paper proposes an efficient method of solving this problem using a kernel methods variant of the projection onto convex sets algorithm to quickly find the intersection of many manifolds while learning their non-linear structure. Indeed the final solution can even be expressed in closed form. We foresee our method allowing a patch-based regularization to be applied across a wide variety of inverse problems, including compressive sensing, inpainting, deconvolution, etc. Indeed, as a proof of concept of our approach, we show how it can be employed in the regularization of an image denoising problem. Here, it even outperforms a state-of-the-art de-noising technique, non-local means.

关键词： Patch-based image processing Inverse problems Kernel methods Manifold models projection algorithms Inverse method Image processing Manifolds Intersection projection algorithms kernel method Noise reduction Patches

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AN EFFICIENT KERNEL ADAPTIVE FILTERING ALGORITHM USING HYPERPLANE projection ALONG AFFINE SUBSPACE

AN EFFICIENT KERNEL ADAPTIVE FILTERING ALGORITHM USING HYPER...

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20th European Signal Processing Conference (EUSIPCO)

作者： Yukawa, Masahiro Ishii, Ryu-ichiro Niigata Univ Dept Elect & Elect Engn Niigata 95021 Japan

ISBN: (纸本)9781467310680

We propose a novel kernel adaptive filtering algorithm that selectively updates a few coefficients at each iteration by projecting the current filter onto the zero instantaneous-error hyperplane along a certain time-dependent affine subspace. Coherence is exploited for selecting the coefficients to be updated as well as for measuring the novelty of new data. The proposed algorithm is a natural extension of the normalized kernel least mean squares algorithm operating iterative hyperplane projections in a reproducing kernel Hilbert space. The proposed algorithm enjoys low computational complexity. Numerical examples indicate high potential of the proposed algorithm.

关键词： kernel adaptive filter projection algorithms reproducing kernel Hilbert space normalized kernel least mean square algorithm

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DOUBLE PSEUDO AFFINE projection ALGORITHM FOR SPEECH ENHANCEMENT AND ACOUSTIC NOISE REDUCTION

DOUBLE PSEUDO AFFINE PROJECTION ALGORITHM FOR SPEECH ENHANCE...

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20th European Signal Processing Conference (EUSIPCO)

作者： Djendi, Mohamed Scalart, Pascal Univ Saad Dahlab Signal Proc & Image Lab LATSI Route SoumaaBP 270 Blida 09000 Algeria ENSSAT IRISA CAIRN Lannion France

ISBN: (纸本)9781467310680

In this paper, we consider the speech enhancement problem in a moving car through a blind source separation scheme involving two closely spaced microphones. We propose the use of a new double pseudo affine projection DPAP algorithm to estimate and suppress coherent noise components from speech. The proposed DPAP algorithm is applied to the forward blind source separation FBSS structure and combined with a new whitening scheme of its two inputs. In order to avoid the use of a manual voice activity detector in the new DPAP algorithm, we have adapted then applied the technique proposed in [1] to the FBSS structure and for the new proposed algorithm. The simulation results show that the DPAP algorithm, when controlled by the proposed technique is able to fully cancel the correlated noise components from speech.

关键词： Speech enhancement Noise reduction projection algorithms Voice activity detectors Pseudo Affine In-law Acoustic noise Blind source separation Speech

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Quantitative Analysis and Interpretation of Transient Electromagnetic Data via Principal Component Analysis

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2012年第5期50卷 1910-1918页

作者： Kass, M. Andy Li, Yaoguo Colorado Sch Mines Ctr Grav Elect & Magnet Studies Golden CO 80401 USA

Transient electromagnetic (TEM) data in applied geophysics are invariably contaminated by random and coherent noise associated with acquisition, geology, and the environment. Advanced unexploded ordnance (UXO) detection and discrimination using TEM data require the suppression of the random noise and reliable separation of UXO signal from other coherent signals. The random noise is usually easier to remove, with coherent signal being more difficult to identify as well as separate. We have developed a method based on principal component analysis (PCA) to achieve both objectives. As a data-adaptive linear transformation, PCA is a fast and reliable method for the removal of random uncorrelated noise as well as for the separation of coherent undesired signals from those due to UXO and UXO-like anomalies. In this paper, we outline the PCA method, including the choice of data organization, construction of covariance matrices, and choice of principal components in reconstruction. We then show both synthetic and field data as examples of the efficacy of the method.

关键词： Classification algorithms electromagnetics geophysical signal processing principal component analysis (PCA) projection algorithms

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Processing of Smart Meters Data Based on Random projections

Processing of Smart Meters Data Based on Random Projections

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IEEE PES Innovative Smart Grid Technologies Conference

作者： Andre Dieb Martins Edmar C. Gurjao Independent Researcher Joao Pessoa PB. Brazil. Departamento de Engenharia Eletrica Universidade Federal de Campina Grande Campina Grande PB. Brazil.

ISBN: (纸本)9781467352727

Placed at the core of Smart Grids metering infrastructure are the Smart Meters, devices that not only measure electric energy consumption of a customer but also report data back to the utility. This process effectivelly sets a stage for a two-way communications link between meters and the utility, which may be used to improve not only the planning but also the operation of the grid. Data submitted goes through the Advanced Metering Infrastructure and is finally delivered to the utility at the edge of the topology. Once at the utility, the streamed data must be processed for billing and monitoring of the grid. Though, given the large volume of the data (e.g. billions of meters), usual processing may be costly both in time and resources, fact that motivates a search for lower complexity processing. In this paper we apply dimensionality reduction via random projection to obtain a reduced version (sketch) of Meters' original data, thus increasing the processing throughput of the utility. Using real smart meters measurements, we show that processing using sketches sized 50% smaller than original data can achieve a 2% average relative error while presenting greater data rates.

关键词： Data Processing Event Detection Meter Reading projection algorithms

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A VARIATIONAL MULTIPHASE MODEL FOR SIMULTANEOUS MR IMAGE SEGMENTATION AND BIAS CORRECTION

A VARIATIONAL MULTIPHASE MODEL FOR SIMULTANEOUS MR IMAGE SEG...

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19th IEEE International Conference on Image Processing (ICIP)

作者： Zhang, Haili Chen, Yunmei Ye, Xiaojing Univ Florida Dept Math Gainesville FL 32611 USA Georgia Inst Technol Sch Math Atlanta GA 30332 USA

ISBN: (纸本)9781467325332;9781467325349

In this paper, we present a multiphase segmentation model for MR images in the presence of strong intensity inhomogeneity. The problem is formalized as a constraint min-max optimization problem that consists both primal and dual variables. We use the primal dual hybrid gradient (PDHG) algorithm to alternately solve for the optimal solutions. The proposed algorithm is quite efficient in that all the subproblems have closed form solutions. Moreover, the computational complexity is shown to be linear with respect to the size of the image. Numerical experiments on various images demonstrated that our algorithm outperforms recently developed methods in terms of efficiency and accuracy.

关键词： Image segmentation Maximum a posteriori estimation Optimization PDHG projection algorithms

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Convex Optimization of Subspace Fitting Autofocus for Stripmap SAR Images

Convex Optimization of Subspace Fitting Autofocus for Stripm...

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IEEE Radar Conference (RADAR)

作者： West, Roger Gunther, Jacob Moon, Todd Utah State Univ Dept Elec & Comp Engn Logan UT 84322 USA

ISBN: (纸本)9781467306584

There are many different autofocus algorithms for correcting phase errors in stripmap SAR images. In this paper, we develop a mathematically principled model-based phase error estimation method for stripmap SAR images. The proposed phase estimation method uses classical subspace fitting techniques that are well known in the array processing literature. The novelty in this paper is the development of the proposed autofocus method and how the phase estimates are found using convex optimization. It is shown that the proposed method is non-iterative in the sense that iterations between the image domain and the range compressed domain to obtain our phase error estimates are not needed. It is also shown that the phase error estimates can be applied while simultaneously forming the SAR image using the convolution back-projection algorithm.

关键词： auto-focus phase error Convex function Synthetic aperture radar Radar polarimetry projection algorithms Subspace Convolution MACHINE SHOP PRACTICE iterative methods

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A Perturbed projection Algorithm with Inertial Technique for Split Feasibility Problem

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JOURNAL OF APPLIED MATHEMATICS 2012年第unknown期000卷 1-10页

作者： Dang, Yazheng Gao, Yan Han, Yanli Shanghai Univ Sci & Technol Sch Management Shanghai 200093 Peoples R China Henan Polytech Univ Dept Math & Informat Sci Jiaozuo City 454001 Henan Peoples R China

This paper deals with the split feasibility problem that requires to find a point closest to a closed convex set in one space such that its image under a linear transformation will be closest to another closed convex set in the image space. By combining perturbed strategy with inertial technique, we construct an inertial perturbed projection algorithm for solving the split feasibility problem. Under some suitable conditions, we show the asymptotic convergence. The results improve and extend the algorithms presented in Byrne (2002) and in Zhao and Yang (2005) and the related convergence theorem.

关键词： Convexity linear transformations Inertial projection algorithms application conditions

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The variable step size regularized block exact affine projection algorithm

The variable step size regularized block exact affine projec...

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International Symposium on Electronics and Telecommunications (ISETC)

作者： Felix Albu Dinu Coltuc Danilo Comminiello Michele Scarpiniti Electrical Engineering Department Valahia University of Targoviste Targoviste Romania Information and Communication Department ‘Sapienza’ University of Rome Rome Italy

This paper presents several block exact affine projection algorithms (BEAPA) with a variable regularization factor and/or variable step size. The performance of the algorithms is investigated for the acoustic echo cancellation (AEC) and noise reduction applications. It is shown that the variable step size regularized BEAPA whose regularization factor and variable step size are adjusted according to the square of a time-averaging estimate of the autocorrelation of a priori and a posteriori errors is a possible choice for AEC and noise reduction systems.

关键词： projection algorithms Speech Signal to noise ratio Adaptive filters Vectors Acoustics

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Nonlinear acoustic echo cancellation based on a parallel-cascade kernel affine projection algorithm

Nonlinear acoustic echo cancellation based on a parallel-cas...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jose M. Gil-Cacho Toon van Waterschoot Marc Moonen Søren Holdt Jensen Department ESAT-SISTA KU Leuven Leuven Belgium Department Electrical System University of Aalborg Aalborg Denmark

In acoustic echo cancellation (AEC) applications, oftentimes an acoustic path from a loudspeaker to a microphone is estimated by means of a linear adaptive filter. However, loudspeakers introduce nonlinear distortions which may strongly degrade the adaptive filter performance, thus nonlinear filters have to be considered. This paper proposes two adaptive algorithms namely the parallel and cascade sliding-window kernel based affine projection algorithm (PSW-KAPA and CSW-KAPA) to solve the problem of nonlinear AEC (NLAEC) while keeping the computational complexity low. They are based on a leaky KAPA which employs the theory and algorithms of kernel methods. The basic concept is to perform adaptive filtering in a linear space that is nonlinearly related to the original input space. A kernel specifically designed for acoustic applications is proposed, which consists in a weighted sum of the linear and the Gaussian kernels. The motivation is basically to separate the problem into linear and nonlinear subproblems. The weights in the kernel also impose different forgetting mechanisms in the sliding window which in turn translates to a more flexible regularization. Simulation results show that PSW-KAPA and CSW-KAPA consistently outperform the linear NLMS, and generalize well both in high and low linear to nonlinear ratio (LNLR).

关键词： Kernel Vectors Echo cancellers projection algorithms Computational complexity

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