The Experts below are selected from a list of 7863 Experts worldwide ranked by ideXlab platform

Timothy J. Gardner - One of the best experts on this subject based on the ideXlab platform.

  • EUSIPCO - Stable time-frequency contours for Sparse Signal Representation
    2013
    Co-Authors: Yoonseob Lim, Barbara G. Shinn-cunningham, Timothy J. Gardner
    Abstract:

    Many Signals cannot be resolved in time and frequency with a single time-scale of analysis and multi-band Representations are needed that can adapt to the local Signal content. Using a newly developed contour-based Representation of Signals, we show that efficient multi-band Representations arise when long-range, structurally stable shapes are enhanced relative to background. For the examples provided here, resolution in time and frequency is distributed adaptively so that each component of a Signal is represented in its most parsimonious form. The resulting Representation is characterized by simple shapes in the time-frequency plane.

  • stable time frequency contours for Sparse Signal Representation
    European Signal Processing Conference, 2013
    Co-Authors: Yoonseob Lim, Barbara G Shinncunningham, Timothy J. Gardner
    Abstract:

    Many Signals cannot be resolved in time and frequency with a single time-scale of analysis and multi-band Representations are needed that can adapt to the local Signal content. Using a newly developed contour-based Representation of Signals, we show that efficient multi-band Representations arise when long-range, structurally stable shapes are enhanced relative to background. For the examples provided here, resolution in time and frequency is distributed adaptively so that each component of a Signal is represented in its most parsimonious form. The resulting Representation is characterized by simple shapes in the time-frequency plane.

Alan S Willsky - One of the best experts on this subject based on the ideXlab platform.

  • Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing
    IEEE Signal Processing Magazine, 2014
    Co-Authors: Mujdat Cetin, Sadegh Samadi, Kush R. Varshney, Ivana Stojanovic, Özben Naime Önhon, W.c. Karl, Alan S Willsky
    Abstract:

    This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. In particular, it reviews 1) the analysis and synthesis-based Sparse Signal Representation formulations for SAR image formation together with the associated imaging results, 2) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization, 3) sparsity-based methods for joint imaging and autofocusing from data with phase errors, 4) techniques for exploiting sparsity for SAR imaging of scenes containing moving objects, and 5) recent work on compressed sensing (CS)-based analysis and design of SAR sensing missions.

  • Sparse Signal Representation in Structured Overcomplete Dictionaries with Application to Synthetic Aperture Radar
    2007
    Co-Authors: Kush R. Varshney, Mujdat Cetin, John W. Fisher, Alan S Willsky
    Abstract:

    Sparse Signal Representations and approximations from overcomplete dictionaries have become an invaluable tool recently. In this paper, we develop a new, heuristic, graph-structured, Sparse Signal Representation algorithm for overcomplete dictionaries that can be decomposed into subdictionaries and whose dictionary elements can be arranged in a hierarchy. Around this algorithm, we construct a methodology for advanced image formation in wide-angle synthetic aperture radar (SAR), defining an approach for joint anisotropy characterization and image formation. Additionally, we develop a coordinate descent method for jointly optimizing a parameterized dictionary and recovering a Sparse Representation using that dictionary. The motivation is to characterize a phenomenon in wide-angle SAR that has not been given much attention before: migratory scattering centers, i.e. scatterers whose apparent spatial location depends on aspect angle. Finally, we address the topic of recovering solutions that are Sparse in more than one objective domain by introducing a suitable sparsifying cost function. We encode geometric objectives into SAR image formation through sparsity in two domains, including the normal parameter space of the Hough transform.

  • a Sparse Signal Representation based approach to image formation and anisotropy determination in wide angle radar
    Signal Processing and Communications Applications Conference, 2007
    Co-Authors: Kush R. Varshney, Mujdat Cetin, John W. Fisher, Alan S Willsky
    Abstract:

    We consider the problem of jointly forming images and determining anisotropy from wide-angle synthetic aperture radar (SAR) measurements. Conventional SAR image formation techniques assume isotropic scattering, which is not valid with wide-angle apertures. We present a method based on a Sparse Representation of aspect-dependent scattering with an overcomplete dictionary composed of elements with varying levels of angular persistence. Solved as an inverse problem, the result is a complex-valued, aspect-dependent response for each spatial location in a scene. Our formulation leads to an optimization problem for which we develop a tractable, graph-structured approximate algorithm. We present experimental results on realistic electromagnetic simulations demonstrating the effectiveness of the proposed approach.

  • homotopy continuation for Sparse Signal Representation
    International Conference on Acoustics Speech and Signal Processing, 2005
    Co-Authors: Dmitry Malioutov, Mujdat Cetin, Alan S Willsky
    Abstract:

    We explore the application of a homotopy continuation-based method for Sparse Signal Representation in overcomplete dictionaries. Our problem setup is based on the basis pursuit framework, which involves a convex optimization problem consisting of terms enforcing data fidelity and sparsity, balanced by a regularization parameter. Choosing a good regularization parameter in this framework is a challenging task. We describe a homotopy continuation-based algorithm to find and trace efficiently all solutions of basis pursuit as a function of the regularization parameter. In addition to providing an attractive alternative to existing optimization methods for solving the basis pursuit problem, this algorithm can also be used to provide an automatic choice for the regularization parameter, based on prior information about the desired number of non-zero components in the Sparse Representation. Our numerical examples demonstrate the effectiveness of this algorithm in accurately and efficiently generating entire solution paths for basis pursuit, as well as producing reasonable regularization parameter choices. Furthermore, exploring the resulting solution paths in various operating conditions reveals insights about the nature of basis pursuit solutions.

  • ICASSP (5) - Homotopy continuation for Sparse Signal Representation
    Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005
    Co-Authors: Dmitry Malioutov, Mujdat Cetin, Alan S Willsky
    Abstract:

    We explore the application of a homotopy continuation-based method for Sparse Signal Representation in overcomplete dictionaries. Our problem setup is based on the basis pursuit framework, which involves a convex optimization problem consisting of terms enforcing data fidelity and sparsity, balanced by a regularization parameter. Choosing a good regularization parameter in this framework is a challenging task. We describe a homotopy continuation-based algorithm to find and trace efficiently all solutions of basis pursuit as a function of the regularization parameter. In addition to providing an attractive alternative to existing optimization methods for solving the basis pursuit problem, this algorithm can also be used to provide an automatic choice for the regularization parameter, based on prior information about the desired number of non-zero components in the Sparse Representation. Our numerical examples demonstrate the effectiveness of this algorithm in accurately and efficiently generating entire solution paths for basis pursuit, as well as producing reasonable regularization parameter choices. Furthermore, exploring the resulting solution paths in various operating conditions reveals insights about the nature of basis pursuit solutions.

Thushara D. Abhayapala - One of the best experts on this subject based on the ideXlab platform.

  • Mode Domain Spatial Active Noise Control Using Sparse Signal Representation
    2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018
    Co-Authors: Yu Maeno, Yuki Mitsufuji, Thushara D. Abhayapala
    Abstract:

    Active noise control (ANC) over a sizeable space requires a large number of reference and error microphones to satisfy the spatial Nyquist sampling criterion, which limits the feasibility of practical realization of such systems. This paper proposes a mode-domain feedforward ANC method to attenuate the noise field over a large space while reducing the number of microphones required. We adopt a Sparse reference Signal Representation to precisely calculate the reference mode coefficients. The proposed system consists of circular reference and error microphone arrays, which capture the reference noise Signal and residual error Signal, respectively, and a circular loudspeaker array to drive the anti-noise Signal. Experimental results indicate that above the spatial Nyquist frequency, our proposed method can perform well compared to a conventional methods. Moreover, the proposed method can even reduce the number of reference microphones while achieving better noise attenuation.

  • ICASSP - Mode Domain Spatial Active Noise Control Using Sparse Signal Representation
    2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018
    Co-Authors: Yu Maeno, Yuki Mitsufuji, Thushara D. Abhayapala
    Abstract:

    Active noise control (ANC) over a sizeable space requires a large number of reference and error microphones to satisfy the spatial Nyquist sampling criterion, which limits the feasibility of practical realization of such systems. This paper proposes a mode-domain feedforward ANC method to attenuate the noise field over a large space while reducing the number of microphones required. We adopt a Sparse reference Signal Representation to precisely calculate the reference mode coefficients. The proposed system consists of circular reference and error microphone arrays, which capture the reference noise Signal and residual error Signal, respectively, and a circular loudspeaker array to drive the anti-noise Signal. Experimental results indicate that above the spatial Nyquist frequency, our proposed method can perform well compared to a conventional methods. Moreover, the proposed method can even reduce the number of reference microphones while achieving better noise attenuation.

Yoonseob Lim - One of the best experts on this subject based on the ideXlab platform.

  • EUSIPCO - Stable time-frequency contours for Sparse Signal Representation
    2013
    Co-Authors: Yoonseob Lim, Barbara G. Shinn-cunningham, Timothy J. Gardner
    Abstract:

    Many Signals cannot be resolved in time and frequency with a single time-scale of analysis and multi-band Representations are needed that can adapt to the local Signal content. Using a newly developed contour-based Representation of Signals, we show that efficient multi-band Representations arise when long-range, structurally stable shapes are enhanced relative to background. For the examples provided here, resolution in time and frequency is distributed adaptively so that each component of a Signal is represented in its most parsimonious form. The resulting Representation is characterized by simple shapes in the time-frequency plane.

  • stable time frequency contours for Sparse Signal Representation
    European Signal Processing Conference, 2013
    Co-Authors: Yoonseob Lim, Barbara G Shinncunningham, Timothy J. Gardner
    Abstract:

    Many Signals cannot be resolved in time and frequency with a single time-scale of analysis and multi-band Representations are needed that can adapt to the local Signal content. Using a newly developed contour-based Representation of Signals, we show that efficient multi-band Representations arise when long-range, structurally stable shapes are enhanced relative to background. For the examples provided here, resolution in time and frequency is distributed adaptively so that each component of a Signal is represented in its most parsimonious form. The resulting Representation is characterized by simple shapes in the time-frequency plane.

Mujdat Cetin - One of the best experts on this subject based on the ideXlab platform.

  • Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing
    IEEE Signal Processing Magazine, 2014
    Co-Authors: Mujdat Cetin, Sadegh Samadi, Kush R. Varshney, Ivana Stojanovic, Özben Naime Önhon, W.c. Karl, Alan S Willsky
    Abstract:

    This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. In particular, it reviews 1) the analysis and synthesis-based Sparse Signal Representation formulations for SAR image formation together with the associated imaging results, 2) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization, 3) sparsity-based methods for joint imaging and autofocusing from data with phase errors, 4) techniques for exploiting sparsity for SAR imaging of scenes containing moving objects, and 5) recent work on compressed sensing (CS)-based analysis and design of SAR sensing missions.

  • Sparse Representation-based SAR imaging
    2010
    Co-Authors: Sadegh Samadi, Mujdat Cetin, Mohammad Ali Masnadi-shirazi
    Abstract:

    There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation method which formulates the SAR imaging problem as a Sparse Signal Representation problem. Sparse Signal Representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. However, for problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the Representation scheme for effective Sparse Representation. Since we are usually interested in features of the magnitude of the SAR reflectivity field, our new approach is designed to Sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimization problem over the Representation of magnitude and phase of the underlying field reflectivities. We develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimization problem. Our experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high quality SAR images as well as exhibiting robustness to uncertain or limited data.

  • Sparse Signal Representation for Complex-Valued Imaging
    2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009
    Co-Authors: Sadegh Samadi, Mujdat Cetin, Mohammad Ali Masnadi-shirazi
    Abstract:

    We propose a Sparse Signal Representation-based method for complex-valued imaging. Many coherent imaging systems such as synthetic aperture radar (SAR) have an inherent random phase, complex-valued nature. On the other hand Sparse Signal Representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. For complex-valued problems, the key challenge is how to choose the dictionary and the Representation scheme for effective Sparse Representation. We propose a mathematical framework and an associated optimization algorithm for a Sparse Signal Representation-based imaging method that can deal with these issues. Simulation results show that this method offers improved results compared to existing powerful imaging techniques.

  • Sparse Signal Representation in Structured Overcomplete Dictionaries with Application to Synthetic Aperture Radar
    2007
    Co-Authors: Kush R. Varshney, Mujdat Cetin, John W. Fisher, Alan S Willsky
    Abstract:

    Sparse Signal Representations and approximations from overcomplete dictionaries have become an invaluable tool recently. In this paper, we develop a new, heuristic, graph-structured, Sparse Signal Representation algorithm for overcomplete dictionaries that can be decomposed into subdictionaries and whose dictionary elements can be arranged in a hierarchy. Around this algorithm, we construct a methodology for advanced image formation in wide-angle synthetic aperture radar (SAR), defining an approach for joint anisotropy characterization and image formation. Additionally, we develop a coordinate descent method for jointly optimizing a parameterized dictionary and recovering a Sparse Representation using that dictionary. The motivation is to characterize a phenomenon in wide-angle SAR that has not been given much attention before: migratory scattering centers, i.e. scatterers whose apparent spatial location depends on aspect angle. Finally, we address the topic of recovering solutions that are Sparse in more than one objective domain by introducing a suitable sparsifying cost function. We encode geometric objectives into SAR image formation through sparsity in two domains, including the normal parameter space of the Hough transform.

  • a Sparse Signal Representation based approach to image formation and anisotropy determination in wide angle radar
    Signal Processing and Communications Applications Conference, 2007
    Co-Authors: Kush R. Varshney, Mujdat Cetin, John W. Fisher, Alan S Willsky
    Abstract:

    We consider the problem of jointly forming images and determining anisotropy from wide-angle synthetic aperture radar (SAR) measurements. Conventional SAR image formation techniques assume isotropic scattering, which is not valid with wide-angle apertures. We present a method based on a Sparse Representation of aspect-dependent scattering with an overcomplete dictionary composed of elements with varying levels of angular persistence. Solved as an inverse problem, the result is a complex-valued, aspect-dependent response for each spatial location in a scene. Our formulation leads to an optimization problem for which we develop a tractable, graph-structured approximate algorithm. We present experimental results on realistic electromagnetic simulations demonstrating the effectiveness of the proposed approach.