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Chinese Control Conference (CCC)

作者： Yanqiong Zhang Youcheng Lou Yiguang Hong Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing Beijing CN

In this paper, we propose an approximate projection algorithm for a multi-agent network to cooperatively compute the intersection of hollow convex sets with switching topologies. This problem is equivalent to a non-convex constrained intersection computation problem, which can be converted into a convex constrained weighted optimization problem. We allow the presence of some computation errors for each agent to compute projections with respect to its own small set. We show that, with some appropriately selected projection accuracy conditions, all agents converge to the same point in the intersection set of a group of hollow convex sets. In addition, we prove that this point is the optimal solution of the proposed convex constrained weighted optimization problem.

关键词： Optimization Accuracy projection algorithms Convergence Approximation algorithms Multi-agent systems Algorithm design and analysis

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The generalized projection algorithm for time-varying systems

The generalized projection algorithm for time-varying system...

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Chinese Control Conference (CCC)

作者： Feng Ding Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi China

In this paper, the projection identification algorithm is extended and the generalized projection algorithm is presented. The analysis indicates that the generalized projection algorithm can track the time-varying parameters and has the same properties as the forgetting factor least squares algorithms but has less computational effort. The way of choosing the data window length is stated so as to obtain the minimum upper bound of the parameter estimation errors.

关键词： projection algorithms Time-varying systems Algorithm design and analysis Signal processing algorithms Parameter estimation Information science Vectors

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SOLVING THE HYPERSPECTRAL UNMIXING PROBLEM WITH projection ONTO CONVEX SETS

SOLVING THE HYPERSPECTRAL UNMIXING PROBLEM WITH PROJECTION O...

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European Signal Processing Conference

作者： Rob Heylen Muhammad Awais Akhter Paul Scheunders IMinds-Visionlab University of Antwerp Universiteitsplein 1

An important problem in hyperspectral unmixing is solving the inversion problem, which determines the abundances of each endmember in each pixel, taking the constraints on these abundances into account. In this paper, we present a new geometrical method for solving this inversion problem, based on the equivalence with the simplex projection problem, and projection onto convex sets. By writing the simplex as an intersection of a plane and convex halfspaces, an alternating projection algorithm is constructed based on the Dykstra algorithm. We show that the resulting algorithm can be used to successfully solve the spectral unmixing problem, and yields results that are comparable to those obtained with state-of-the-art methods. The runtime required is very competitive, and the very simple nature of the algorithm allows for highly efficient implementations.

关键词： Runtime Hyperspectral imaging Vectors Approximation algorithms projection algorithms Noise Convergence

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SINGLE IMAGE SUPER-RESOLUTION USING ADAPTIVE DOMAIN TRANSFORMATION

SINGLE IMAGE SUPER-RESOLUTION USING ADAPTIVE DOMAIN TRANSFOR...

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IEEE International Conference on Image Processing

作者： Abhishek Singh Narendra Ahuja Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign

ISBN: (纸本)9781479923427

In this paper we propose a new image domain prior term for regularizing the super-resolution reconstruction algorithm. This term encourages preserving the local ramp structure around edges, in the reconstruction algorithm. Ramp at a pixel is defined as the steepest sequence of monotonically increasing (or decreasing) pixels among all feasible directions around the pixel. As described in previous work, ramp based modeling is a richer characterization of local image structure than conventional gradients. Our proposed ramp-preserving constraint image is obtained by first running an accurate segmentation algorithm (which is itself obtained by ramp based modeling) on the low resolution image. We then perform a domain transformation of the pixels belonging to the steepest ramps at the edge pixels, in order to preserve sharpness. The resulting non-uniformly spaced image is then upscaled to a uniform, high resolution grid, using an edge preserving non-uniform interpolation scheme. This image is then used both as the prior constraint as well as the initial guess for the iterative super-resolution reconstruction algorithm. Our results compare favorably to the classical back-projection algorithm as well as newer methods which use learning based gradient domain priors.

关键词： Super-resolution image prior segmentation regularization super-resolution Reconstruction algorithm pixel images segmentation RAMP Ramps edge pixel Low-resolution image Image Imagery (Psychotherapy) projection algorithms IMAGE STRUCTURE Segmentation Modeling

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Direct Phase projection and Transcranial Focusing of Ultrasound for Brain Therapy

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2012年第6期59卷 1149-1159页

作者： Pinton, Gianmarco F. Aubry, Jean-Francois Tanter, Mickael CNRS INSERM Ecole Super Phys & Chim Ind Inst Langevin Paris France

Ultrasound can be used to noninvasively treat the human brain with hyperthermia by focusing through the skull. To obtain an accurate focus, especially at high frequencies (>500 kHz), the phase of the transmitted wave must be modified to correct the aberrations introduced by the patient's individual skull morphology. Currently, three-dimensional finite-difference time-domain simulations are used to model a point source at the target. The outward-propagating wave crosses the measured representation of the human skull and is recorded at the therapy array transducer locations. The signal is then time reversed and experimentally transmitted back to its origin. These simulations are resource intensive and add a significant delay to treatment planning. Ray propagation is computationally efficient because it neglects diffraction and only describes two propagation parameters: the wave's direction and the phase. We propose a minimal method that is based only on the phase. The phase information is projected from the external skull surface to the array locations. This replaces computationally expensive finite-difference computations with an almost instantaneous direct phase projection calculation. For the five human skull samples considered, the phase distribution outside of the skull is shown to vary by less than lambda/20 as it propagates over a 5 cm distance and the validity of phase projection is established over these propagation distances. The phase aberration introduced by the skull is characterized and is shown to have a good correspondence with skull morphology. The shape of this aberration is shown to have little variation with propagation distance. The focusing quality with the proposed phase-projection algorithm is shown to be indistinguishable from the gold-standard full finite-difference simulation. In conclusion, a spherical wave that is aberrated by the skull has a phase propagation that can be accurately described as radial, even after it has been distorted. By

关键词： phase error treatment planning propagation distance projection algorithms Transducers Human Remains Skull Phase Ultrasonic Waves aberration

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Efficient iterative LP decoding of LDPC codes with alternating direction method of multipliers

Efficient iterative LP decoding of LDPC codes with alternati...

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IEEE International Symposium on Information Theory

作者： Xiaojie Zhang Paul H. Siegel Samsung R&D America Dallas TX USA University of California San Diego CA USA

ISBN: (纸本)9781479904440

In this paper, we propose an efficient message-passing algorithm to solve the LP decoding problem. This algorithm is based on the alternating direction method of multipliers (ADMM), a classic technique in convex optimization theory that is designed for parallel implementation. The computational complexity of ADMM-based LP decoding is largely determined by the method used to project a vector of real values to the parity polytope of a given parity check. The key contribution of this paper is a novel, efficient projection algorithm that can substantially improve the decoding speed of the ADMM-based LP decoder.

关键词： Decoding Vectors projection algorithms Iterative decoding Optimization TV

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Almost sure convergence of consensus algorithms by relaxed projection mappings

Almost sure convergence of consensus algorithms by relaxed p...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Ondrej Slučiak Markus Rupp Institute of Telecommunications University of Technology Vienna Austria

In this contribution we present a stronger notion of almost sure convergence for a large class of consensus algorithms including also asynchronous updates. We introduce the concept of the so-called relaxed projection algorithms and show that many consensus algorithms can be interpreted as such relaxed projection updates. It is well known that such algorithms converge to a solution lying in the intersection of the projections. The convergence of such algorithms is, however, guaranteed only for deterministic ordering of the projections. Since we are interested in random data exchanges, we analyze the convergence in case of random orderings of the projections and show that the algorithms converge in the underrelaxed case even for time-varying and individual mixing parameters.

关键词： Signal processing algorithms Convergence Wireless sensor networks Signal processing projection algorithms Hilbert space Conferences

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An efficient GSC VSS-APA beamformer with integrated log-energy based VAD for noise reduction in speech reinforcement systems

An efficient GSC VSS-APA beamformer with integrated log-ener...

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International Symposium on Signals, Circuits and Systems (ISSCS)

作者： Marius Rotaru Silviu Ciochină Felix Albu Dept. of Telecommunications University Politehnica of Bucharest Bucharest Romania Electrical Engineering Department Valahia University of Targoviste Targoviste Romania

This paper presents an efficient time-domain Generalized Sidelobe Canceller (GSC) with low signal distortion capabilities using the variable step size affine projection algorithm (VSS-APA) and a log-energy based voice activity detector (VAD). The performance of the proposed VSS-APA based GSC method with integrated log-energy VAD, is illustrated in the context of speech reinforcement application using different signals with low signal-to-noise ratio and different types of noise.

关键词： Speech Microphones Arrays projection algorithms Signal to noise ratio Adaptive filters

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A FAST COMPRESSIVE SENSING METHOD WITH APPLICATION TO NETWORK ECHO CANCELLATION

A FAST COMPRESSIVE SENSING METHOD WITH APPLICATION TO NETWOR...

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European Signal Processing Conference

作者： Pratik Shah Steven L. Grant Jacob Benesty Missouri University of Science and Technology Rolla Missouri INRS-EMT University of Quebec Montreal Canada

ISBN: (纸本)9781479936878

Compressive sensing methods have been effectively used for sparse system identification. Many methods have been proposed to exploit this sparsity to reduce the amount of data required for identification. Most though, have high computational complexity. Recently, an iterative method based on the proportionate affine projection algorithm with row action projections (iPAPA-RAP) has been shown to have good convergence properties with relatively low complexity. Here, we present extensions of that algorithm that significantly speed convergence and as a result lower overall computational complexity. The main improvement is the addition of a zero attractor step with a variable scale factor. Significantly, this scale factor is made to be a function of the sparsity of the estimated system parameters. This greatly improves the convergence behavior of the resulting algorithm. It is compared with iteratively reweighted least-squares (IRLS) and l_0 - zero attracting projections (l_0-ZAP). Results show that the proposed algorithm converges faster with lower overall complexity.

关键词： PAPA sparse IRLS compressed sensing adaptive filter ZiPR Compressed sensing adaptive filters IRL Scale factor PSTPIP1 gene CANCELLATIONS Converge ECHO projection algorithms

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AN EFFICIENT METHOD FOR FINDING INTERSECTIONS OF MANY MANIFOLDS WITH APPLICATION TO PATCH BASED IMAGE PROCESSING

AN EFFICIENT METHOD FOR FINDING INTERSECTIONS OF MANY MANIFO...

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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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