## Greedy Algorithm With Approximation Ratio For Sampling Noisy Graph Signals

We study the optimal sampling set selection problem in sampling a noisy $k$-bandlimited graph signal. To minimize the effect of noise when trying to reconstruct a $k$-bandlimited graph signal from $m$ samples, the optimal sampling set selection problem has been shown to be equivalent to finding a $m \times k$ submatrix with the maximum smallest singular value, $\sigma_{\min}$ \cite{chen2015discrete}. As the problem is NP-hard, we present a greedy algorithm inspired by a similar submatrix selection problem known in computer science and to which we add a local search refinement.

## main - Copy.pdf

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url = {http://sigport.org/2516},

author = {Changlong Wu; Wenxin Chen; June Zhang },

publisher = {IEEE SigPort},

title = {Greedy Algorithm With Approximation Ratio For Sampling Noisy Graph Signals},

year = {2018} }

T1 - Greedy Algorithm With Approximation Ratio For Sampling Noisy Graph Signals

AU - Changlong Wu; Wenxin Chen; June Zhang

PY - 2018

PB - IEEE SigPort

UR - http://sigport.org/2516

ER -

## SOLVING LINEAR INVERSE PROBLEMS USING GAN PRIORS: AN ALGORITHM WITH PROVABLE GUARANTEES

In recent works, both sparsity-based methods as well as learning-based methods have proven to be successful in solving several challenging linear inverse problems. However, sparsity priors for natural signals and images suffer from poor discriminative capability, while learning-based methods seldom provide concrete theoretical guarantees. In this work, we advocate the idea of replacing hand-crafted priors, such as sparsity, with a Generative Adversarial Network (GAN) to solve linear inverse problems such as compressive sensing.

## poster_icassp.pdf

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url = {http://sigport.org/2511},

author = {Viraj Shah; Chinmay Hegde },

publisher = {IEEE SigPort},

title = {SOLVING LINEAR INVERSE PROBLEMS USING GAN PRIORS: AN ALGORITHM WITH PROVABLE GUARANTEES},

year = {2018} }

T1 - SOLVING LINEAR INVERSE PROBLEMS USING GAN PRIORS: AN ALGORITHM WITH PROVABLE GUARANTEES

AU - Viraj Shah; Chinmay Hegde

PY - 2018

PB - IEEE SigPort

UR - http://sigport.org/2511

ER -

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- 13 April 2018 - 9:38am
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url = {http://sigport.org/2488},

author = { },

publisher = {IEEE SigPort},

title = {The Network Nullspace Property for Compressed Sensing of Big Data over Networks},

year = {2018} }

T1 - The Network Nullspace Property for Compressed Sensing of Big Data over Networks

AU -

PY - 2018

PB - IEEE SigPort

UR - http://sigport.org/2488

ER -

## Wavelet-Based Reconstruction for Unlimited Sampling

Self-reset analog-to-digital converters (ADCs) allow for digitization of a signal with a high dynamic range. The reset action is equivalent to a modulo operation performed on the signal. We consider the problem of recovering the original signal from the measured modulo-operated signal. In our formulation, we assume that the underlying signal is Lipschitz continuous. The modulo-operated signal can be expressed as the sum of the original signal and a piecewise-constant signal that captures the transitions. The reconstruction requires estimating the piecewise-constant signal.

## ICASSP_2018_Sunil.pdf

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url = {http://sigport.org/2461},

author = {Aniruddha Adiga; Basty Ajay Shenoy; Chandra Sekhar Seelamantula },

publisher = {IEEE SigPort},

title = {Wavelet-Based Reconstruction for Unlimited Sampling},

year = {2018} }

T1 - Wavelet-Based Reconstruction for Unlimited Sampling

AU - Aniruddha Adiga; Basty Ajay Shenoy; Chandra Sekhar Seelamantula

PY - 2018

PB - IEEE SigPort

UR - http://sigport.org/2461

ER -

## Wavelet-Based Reconstruction for Unlimited Sampling

Self-reset analog-to-digital converters (ADCs) allow for digitization of a signal with a high dynamic range. The reset action is equivalent to a modulo operation performed on the signal. We consider the problem of recovering the original signal from the measured modulo-operated signal. In our formulation, we assume that the underlying signal is Lipschitz continuous. The modulo-operated signal can be expressed as the sum of the original signal and a piecewise-constant signal that captures the transitions. The reconstruction requires estimating the piecewise-constant signal.

## ICASSP_2018_Sunil.pdf

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url = {http://sigport.org/2460},

author = {Aniruddha Adiga; Basty Ajay Shenoy; Chandra Sekhar Seelamantula },

publisher = {IEEE SigPort},

title = {Wavelet-Based Reconstruction for Unlimited Sampling},

year = {2018} }

T1 - Wavelet-Based Reconstruction for Unlimited Sampling

AU - Aniruddha Adiga; Basty Ajay Shenoy; Chandra Sekhar Seelamantula

PY - 2018

PB - IEEE SigPort

UR - http://sigport.org/2460

ER -

## DESIGN OF SAMPLING SET FOR BANDLIMITED GRAPH SIGNAL ESTIMATION

It is of particular interest to reconstruct or estimate bandlimited graph signals, which are smoothly varying signals defined over graphs, from partial noisy measurements. However, choosing an optimal subset of nodes to sample is NP-hard. We formularize the problem as the experimental design of a linear regression model if we allow multiple measurements on a single node. By relaxing it to a convex optimization problem, we get the proportion of sample for each node given the budget of total sample size. Then, we use a probabilistic quantization to get the number of each node to be sampled.

## GlobalSIP_Poster_XX_v2.pdf

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url = {http://sigport.org/2290},

author = {Xuan Xie; Hui Feng; Junlian Jia; Bo Hu },

publisher = {IEEE SigPort},

title = {DESIGN OF SAMPLING SET FOR BANDLIMITED GRAPH SIGNAL ESTIMATION},

year = {2017} }

T1 - DESIGN OF SAMPLING SET FOR BANDLIMITED GRAPH SIGNAL ESTIMATION

AU - Xuan Xie; Hui Feng; Junlian Jia; Bo Hu

PY - 2017

PB - IEEE SigPort

UR - http://sigport.org/2290

ER -

## Phase Retrieval Based Deconvolution Algorithm in Optical Systems

In an optical imaging system, the retrieved image of an object is blurred by the point spread function (PSF) of the system,and cannot exactly represent the object. Deconvolution is an effective method to recover the object from the blurred image and improve the resolution of the optical system. But in real optical system, the detector only measures the intensity of the light, not the phase.

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url = {http://sigport.org/2265},

author = {Shaohua Qin; Sebastian Berisha; David Mayerich and Zhu Han },

publisher = {IEEE SigPort},

title = { Phase Retrieval Based Deconvolution Algorithm in Optical Systems},

year = {2017} }

T1 - Phase Retrieval Based Deconvolution Algorithm in Optical Systems

AU - Shaohua Qin; Sebastian Berisha; David Mayerich and Zhu Han

PY - 2017

PB - IEEE SigPort

UR - http://sigport.org/2265

ER -

## REGULARIZED SELECTION: A NEW PARADIGM FOR INVERSE BASED REGULARIZED IMAGE RECONSTRUCTION TECHNIQUES

## ICIP2017_sigport.pdf

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- 15 February 2018 - 9:52am
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url = {http://sigport.org/2098},

author = {F. Kucharczak; C. Mory; O. Strauss; F. Comby; D. Mariano-Goulart },

publisher = {IEEE SigPort},

title = {REGULARIZED SELECTION: A NEW PARADIGM FOR INVERSE BASED REGULARIZED IMAGE RECONSTRUCTION TECHNIQUES},

year = {2017} }

T1 - REGULARIZED SELECTION: A NEW PARADIGM FOR INVERSE BASED REGULARIZED IMAGE RECONSTRUCTION TECHNIQUES

AU - F. Kucharczak; C. Mory; O. Strauss; F. Comby; D. Mariano-Goulart

PY - 2017

PB - IEEE SigPort

UR - http://sigport.org/2098

ER -

## Super-resolution delay-Doppler estimation for sub-Nyquist radar via atomic norm minimization

## ICASSP2017.pdf

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- 13 March 2017 - 12:25am
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url = {http://sigport.org/1754},

author = {Feng Xi; Shengyao Chen; Zhong Liu },

publisher = {IEEE SigPort},

title = {Super-resolution delay-Doppler estimation for sub-Nyquist radar via atomic norm minimization},

year = {2017} }

T1 - Super-resolution delay-Doppler estimation for sub-Nyquist radar via atomic norm minimization

AU - Feng Xi; Shengyao Chen; Zhong Liu

PY - 2017

PB - IEEE SigPort

UR - http://sigport.org/1754

ER -

## Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study

Compressive information acquisition is a natural approach for low-power hardware front ends, since most natural signals are sparse in some basis. Key design questions include the impact of hardware impairments (e.g., nonlinearities) and constraints (e.g., spatially localized computations) on the fidelity of information acquisition. Our goal in this paper is to obtain specific insights into such issues through modeling of a Large Area Electronics (LAE)-based image acquisition system.

## Comp_Info_Acq_ICASSP17_Poster.zip

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url = {http://sigport.org/1702},

author = {Tiffany Moy; Upamanyu Madhow; Naveen Verma },

publisher = {IEEE SigPort},

title = {Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study},

year = {2017} }

T1 - Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study

AU - Tiffany Moy; Upamanyu Madhow; Naveen Verma

PY - 2017

PB - IEEE SigPort

UR - http://sigport.org/1702

ER -