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Sampling and Reconstruction

VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING


In recent years approximate message passing algorithms have gained a lot of attention and different versions have been proposed for coping with various system models. This paper focuses on vector approximate message passing (VAMP) for generalized linear models. While this algorithm is originally derived from a message passing point of view, we will review it from an estimation theory perspective and afterwards adapt it for a quantized compressed sensing application. Finally, numerical results are presented to evaluate the performance of the algorithm.

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Authors:
Daniel Franz, Volker Kuehn
Submitted On:
21 November 2018 - 4:49am
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[1] Daniel Franz, Volker Kuehn, "VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3698. Accessed: Apr. 22, 2019.
@article{3698-18,
url = {http://sigport.org/3698},
author = {Daniel Franz; Volker Kuehn },
publisher = {IEEE SigPort},
title = {VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING},
year = {2018} }
TY - EJOUR
T1 - VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING
AU - Daniel Franz; Volker Kuehn
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3698
ER -
Daniel Franz, Volker Kuehn. (2018). VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING. IEEE SigPort. http://sigport.org/3698
Daniel Franz, Volker Kuehn, 2018. VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING. Available at: http://sigport.org/3698.
Daniel Franz, Volker Kuehn. (2018). "VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING." Web.
1. Daniel Franz, Volker Kuehn. VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3698

Sketching Discrete Valued Sparse Matrices


The problem of recovering a sparse matrix X from its sketchAXB T is referred to as the matrix sketching problem. Typically, the sketch is a lower dimensional matrix compared to X, and the sketching matrices A and B are known. Matrix sketching algorithms have been developed in the past to recover matrices from a continuous valued vectorspace (e.g., R^N×N ). However, employing such algorithms to recover discrete valued matrices may not be optimal. In this paper, we propose two novel algorithms that can efficiently recover a discrete valued sparse matrix from its sketch.

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18 November 2018 - 12:01pm
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[1] , "Sketching Discrete Valued Sparse Matrices", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3677. Accessed: Apr. 22, 2019.
@article{3677-18,
url = {http://sigport.org/3677},
author = { },
publisher = {IEEE SigPort},
title = {Sketching Discrete Valued Sparse Matrices},
year = {2018} }
TY - EJOUR
T1 - Sketching Discrete Valued Sparse Matrices
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3677
ER -
. (2018). Sketching Discrete Valued Sparse Matrices. IEEE SigPort. http://sigport.org/3677
, 2018. Sketching Discrete Valued Sparse Matrices. Available at: http://sigport.org/3677.
. (2018). "Sketching Discrete Valued Sparse Matrices." Web.
1. . Sketching Discrete Valued Sparse Matrices [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3677

An Interior Point Method for Nonnegative Sparse Signal Reconstruction


We present a primal-dual interior point method (IPM) with a novel preconditioner to solve the ℓ1-norm regularized least square problem for nonnegative sparse signal reconstruction. IPM is a second-order method that uses both gradient and Hessian information to compute effective search directions and achieve super-linear convergence rates. It therefore requires many fewer iterations than first-order methods such as iterative shrinkage/thresholding algorithms (ISTA) that only achieve sub-linear convergence rates.

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Authors:
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld
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7 October 2018 - 5:05pm
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[1] Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld, "An Interior Point Method for Nonnegative Sparse Signal Reconstruction", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3603. Accessed: Apr. 22, 2019.
@article{3603-18,
url = {http://sigport.org/3603},
author = {Xiang Huang; Kuan He; Seunghwan Yoo; Oliver Cossairt; Aggelos Katsaggelos; Nicola Ferrier; and Mark Hereld },
publisher = {IEEE SigPort},
title = {An Interior Point Method for Nonnegative Sparse Signal Reconstruction},
year = {2018} }
TY - EJOUR
T1 - An Interior Point Method for Nonnegative Sparse Signal Reconstruction
AU - Xiang Huang; Kuan He; Seunghwan Yoo; Oliver Cossairt; Aggelos Katsaggelos; Nicola Ferrier; and Mark Hereld
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3603
ER -
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. (2018). An Interior Point Method for Nonnegative Sparse Signal Reconstruction. IEEE SigPort. http://sigport.org/3603
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld, 2018. An Interior Point Method for Nonnegative Sparse Signal Reconstruction. Available at: http://sigport.org/3603.
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. (2018). "An Interior Point Method for Nonnegative Sparse Signal Reconstruction." Web.
1. Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. An Interior Point Method for Nonnegative Sparse Signal Reconstruction [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3603

ITERATIVE OPTIMIZATION OF QUARTER SAMPLING MASKS FOR NON-REGULAR SAMPLING SENSORS


Non-regular sampling can reduce aliasing at the expense of noise.
Recently, it has been shown that non-regular sampling can be carried
out using a conventional regular imaging sensor when the surface of
its individual pixels is partially covered. This technique is called
quarter sampling (also 1/4 sampling), since only one quarter of each
pixel is sensitive to light. For this purpose, the choice of a proper
sampling mask is crucial to achieve a high reconstruction quality. In
the scope of this work, we present an iterative algorithm to improve

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Authors:
Simon Grosche, Jürgen Seiler, Andre Kaup
Submitted On:
5 October 2018 - 7:44am
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[1] Simon Grosche, Jürgen Seiler, Andre Kaup, "ITERATIVE OPTIMIZATION OF QUARTER SAMPLING MASKS FOR NON-REGULAR SAMPLING SENSORS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3535. Accessed: Apr. 22, 2019.
@article{3535-18,
url = {http://sigport.org/3535},
author = {Simon Grosche; Jürgen Seiler; Andre Kaup },
publisher = {IEEE SigPort},
title = {ITERATIVE OPTIMIZATION OF QUARTER SAMPLING MASKS FOR NON-REGULAR SAMPLING SENSORS},
year = {2018} }
TY - EJOUR
T1 - ITERATIVE OPTIMIZATION OF QUARTER SAMPLING MASKS FOR NON-REGULAR SAMPLING SENSORS
AU - Simon Grosche; Jürgen Seiler; Andre Kaup
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3535
ER -
Simon Grosche, Jürgen Seiler, Andre Kaup. (2018). ITERATIVE OPTIMIZATION OF QUARTER SAMPLING MASKS FOR NON-REGULAR SAMPLING SENSORS. IEEE SigPort. http://sigport.org/3535
Simon Grosche, Jürgen Seiler, Andre Kaup, 2018. ITERATIVE OPTIMIZATION OF QUARTER SAMPLING MASKS FOR NON-REGULAR SAMPLING SENSORS. Available at: http://sigport.org/3535.
Simon Grosche, Jürgen Seiler, Andre Kaup. (2018). "ITERATIVE OPTIMIZATION OF QUARTER SAMPLING MASKS FOR NON-REGULAR SAMPLING SENSORS." Web.
1. Simon Grosche, Jürgen Seiler, Andre Kaup. ITERATIVE OPTIMIZATION OF QUARTER SAMPLING MASKS FOR NON-REGULAR SAMPLING SENSORS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3535

Multilevel Illumination Coding for Fourier Transform Interferometry in Fluorescence Spectroscopy


Fourier Transform Interferometry (FTI) is an interferometric procedure for acquiring HyperSpectral (HS) data. Recently, it has been observed that the light source highlighting a (biologic) sample can be coded before the FTI acquisition in a procedure called Coded Illumination-FTI (CI-FTI). This turns HS data reconstruction into a Compressive Sensing (CS) problem regularized by the sparsity of the HS data. CI-FTI combines the high spectral resolution of FTI with the advantages of reduced-light-exposure imaging in biology.

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Authors:
Amirafshar Moshtaghpour, Laurent Jacques
Submitted On:
4 October 2018 - 9:29am
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Compressive Fourier Transform Interferometry

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[1] Amirafshar Moshtaghpour, Laurent Jacques, "Multilevel Illumination Coding for Fourier Transform Interferometry in Fluorescence Spectroscopy", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3405. Accessed: Apr. 22, 2019.
@article{3405-18,
url = {http://sigport.org/3405},
author = {Amirafshar Moshtaghpour; Laurent Jacques },
publisher = {IEEE SigPort},
title = {Multilevel Illumination Coding for Fourier Transform Interferometry in Fluorescence Spectroscopy},
year = {2018} }
TY - EJOUR
T1 - Multilevel Illumination Coding for Fourier Transform Interferometry in Fluorescence Spectroscopy
AU - Amirafshar Moshtaghpour; Laurent Jacques
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3405
ER -
Amirafshar Moshtaghpour, Laurent Jacques. (2018). Multilevel Illumination Coding for Fourier Transform Interferometry in Fluorescence Spectroscopy. IEEE SigPort. http://sigport.org/3405
Amirafshar Moshtaghpour, Laurent Jacques, 2018. Multilevel Illumination Coding for Fourier Transform Interferometry in Fluorescence Spectroscopy. Available at: http://sigport.org/3405.
Amirafshar Moshtaghpour, Laurent Jacques. (2018). "Multilevel Illumination Coding for Fourier Transform Interferometry in Fluorescence Spectroscopy." Web.
1. Amirafshar Moshtaghpour, Laurent Jacques. Multilevel Illumination Coding for Fourier Transform Interferometry in Fluorescence Spectroscopy [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3405

Super-Resolution Pulse-Doppler Radar Sensing via One-Bit Sampling


This paper investigates the delay-Doppler estimation problem of a pulse-Doppler radar which samples and quantizes the noisy echo signals to one-bit measurements.By applying a multichannel one-bit sampling scheme, we formulate the delay-Doppler estimation as a structured low-rank matrix recovery problem.Then the one-bit atomic norm soft-thresholding method is proposed to recover the low-rank matrix, in which a surrogate matrix is properly designed to evaluate the proximity of the recovered data to the sampled one.With the recovered low-rank matrix, the delays and Doppler frequencies can be d

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Authors:
Feng Xi; Shengyao Chen
Submitted On:
4 July 2018 - 10:53pm
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[1] Feng Xi; Shengyao Chen, "Super-Resolution Pulse-Doppler Radar Sensing via One-Bit Sampling", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3352. Accessed: Apr. 22, 2019.
@article{3352-18,
url = {http://sigport.org/3352},
author = {Feng Xi; Shengyao Chen },
publisher = {IEEE SigPort},
title = {Super-Resolution Pulse-Doppler Radar Sensing via One-Bit Sampling},
year = {2018} }
TY - EJOUR
T1 - Super-Resolution Pulse-Doppler Radar Sensing via One-Bit Sampling
AU - Feng Xi; Shengyao Chen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3352
ER -
Feng Xi; Shengyao Chen. (2018). Super-Resolution Pulse-Doppler Radar Sensing via One-Bit Sampling. IEEE SigPort. http://sigport.org/3352
Feng Xi; Shengyao Chen, 2018. Super-Resolution Pulse-Doppler Radar Sensing via One-Bit Sampling. Available at: http://sigport.org/3352.
Feng Xi; Shengyao Chen. (2018). "Super-Resolution Pulse-Doppler Radar Sensing via One-Bit Sampling." Web.
1. Feng Xi; Shengyao Chen. Super-Resolution Pulse-Doppler Radar Sensing via One-Bit Sampling [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3352

Non-negative Super-resolution is Stable


We consider the problem of localizing point sources on an interval from possibly noisy measurements. In the absence of noise, we show that measurements from Chebyshev sys- tems are an injective map for non-negative sparse measures, and therefore non-negativity is sufficient to ensure unique- ness for sparse measures. Moreover, we characterize non- negative solutions from inexact measurements and show that any non-negative solution consistent with the measurements is proportionally close to the solution of the system with ex- act measurements.

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Authors:
Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi
Submitted On:
29 May 2018 - 7:34am
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[1] Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi, "Non-negative Super-resolution is Stable", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3212. Accessed: Apr. 22, 2019.
@article{3212-18,
url = {http://sigport.org/3212},
author = {Armin Eftekhari; Jared Tanner; Andrew Thompson; Bogdan Toader; Hemant Tyagi },
publisher = {IEEE SigPort},
title = {Non-negative Super-resolution is Stable},
year = {2018} }
TY - EJOUR
T1 - Non-negative Super-resolution is Stable
AU - Armin Eftekhari; Jared Tanner; Andrew Thompson; Bogdan Toader; Hemant Tyagi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3212
ER -
Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi. (2018). Non-negative Super-resolution is Stable. IEEE SigPort. http://sigport.org/3212
Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi, 2018. Non-negative Super-resolution is Stable. Available at: http://sigport.org/3212.
Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi. (2018). "Non-negative Super-resolution is Stable." Web.
1. Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi. Non-negative Super-resolution is Stable [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3212

Unlimited Sampling of Sparse Signals


In a recent paper [1], we introduced the concept of “Unlimited Sampling”. This unique approach circumvents the clipping or saturation problem in conventional analog-to-digital converters (ADCs) by considering a radically different ADC architecture which resets the input voltage before saturation. Such ADCs, also known as Self-Reset ADCs (SR-ADCs), allow for sensing modulo samples.

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Authors:
Felix Krahmer, Ramesh Raskar
Submitted On:
30 April 2018 - 2:45am
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[1] Felix Krahmer, Ramesh Raskar, "Unlimited Sampling of Sparse Signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3188. Accessed: Apr. 22, 2019.
@article{3188-18,
url = {http://sigport.org/3188},
author = {Felix Krahmer; Ramesh Raskar },
publisher = {IEEE SigPort},
title = {Unlimited Sampling of Sparse Signals},
year = {2018} }
TY - EJOUR
T1 - Unlimited Sampling of Sparse Signals
AU - Felix Krahmer; Ramesh Raskar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3188
ER -
Felix Krahmer, Ramesh Raskar. (2018). Unlimited Sampling of Sparse Signals. IEEE SigPort. http://sigport.org/3188
Felix Krahmer, Ramesh Raskar, 2018. Unlimited Sampling of Sparse Signals. Available at: http://sigport.org/3188.
Felix Krahmer, Ramesh Raskar. (2018). "Unlimited Sampling of Sparse Signals." Web.
1. Felix Krahmer, Ramesh Raskar. Unlimited Sampling of Sparse Signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3188

ON COMPRESSIVE SENSING OF SPARSE COVARIANCE MATRICES USING DETERMINISTIC SENSING MATRICES

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Authors:
Alihan Kaplan, Volker Pohl, Dae Gwan Lee
Submitted On:
24 April 2018 - 9:19am
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[1] Alihan Kaplan, Volker Pohl, Dae Gwan Lee, "ON COMPRESSIVE SENSING OF SPARSE COVARIANCE MATRICES USING DETERMINISTIC SENSING MATRICES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3149. Accessed: Apr. 22, 2019.
@article{3149-18,
url = {http://sigport.org/3149},
author = {Alihan Kaplan; Volker Pohl; Dae Gwan Lee },
publisher = {IEEE SigPort},
title = {ON COMPRESSIVE SENSING OF SPARSE COVARIANCE MATRICES USING DETERMINISTIC SENSING MATRICES},
year = {2018} }
TY - EJOUR
T1 - ON COMPRESSIVE SENSING OF SPARSE COVARIANCE MATRICES USING DETERMINISTIC SENSING MATRICES
AU - Alihan Kaplan; Volker Pohl; Dae Gwan Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3149
ER -
Alihan Kaplan, Volker Pohl, Dae Gwan Lee. (2018). ON COMPRESSIVE SENSING OF SPARSE COVARIANCE MATRICES USING DETERMINISTIC SENSING MATRICES. IEEE SigPort. http://sigport.org/3149
Alihan Kaplan, Volker Pohl, Dae Gwan Lee, 2018. ON COMPRESSIVE SENSING OF SPARSE COVARIANCE MATRICES USING DETERMINISTIC SENSING MATRICES. Available at: http://sigport.org/3149.
Alihan Kaplan, Volker Pohl, Dae Gwan Lee. (2018). "ON COMPRESSIVE SENSING OF SPARSE COVARIANCE MATRICES USING DETERMINISTIC SENSING MATRICES." Web.
1. Alihan Kaplan, Volker Pohl, Dae Gwan Lee. ON COMPRESSIVE SENSING OF SPARSE COVARIANCE MATRICES USING DETERMINISTIC SENSING MATRICES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3149

On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems

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Authors:
Oguzhan Teke, P. P. Vaidyanathan
Submitted On:
22 April 2018 - 12:28am
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[1] Oguzhan Teke, P. P. Vaidyanathan, "On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3127. Accessed: Apr. 22, 2019.
@article{3127-18,
url = {http://sigport.org/3127},
author = {Oguzhan Teke; P. P. Vaidyanathan },
publisher = {IEEE SigPort},
title = {On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems},
year = {2018} }
TY - EJOUR
T1 - On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems
AU - Oguzhan Teke; P. P. Vaidyanathan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3127
ER -
Oguzhan Teke, P. P. Vaidyanathan. (2018). On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems. IEEE SigPort. http://sigport.org/3127
Oguzhan Teke, P. P. Vaidyanathan, 2018. On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems. Available at: http://sigport.org/3127.
Oguzhan Teke, P. P. Vaidyanathan. (2018). "On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems." Web.
1. Oguzhan Teke, P. P. Vaidyanathan. On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3127

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