Sorry, you need to enable JavaScript to visit this website.

Signal and System Modeling, Representation and Estimation

QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING


Large-scale networks of computing units, often characterised by the absence of central control, have become commonplace in many applications. To facilitate data processing in these large-scale networks, distributed signal processing is required. The iterative behaviour of distributed processing algorithms combined with energy, computational power, and bandwidth limitations imposed by such networks, place tight constraints on the transmission capacities of the individual nodes.

Paper Details

Authors:
Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens
Submitted On:
14 March 2017 - 5:54am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Schellekens 2017 - Poster - QUANTISATION EFFECTS IN PDMM A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING.pdf

(9 downloads)

Keywords

Subscribe

[1] Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens, "QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1764. Accessed: Apr. 28, 2017.
@article{1764-17,
url = {http://sigport.org/1764},
author = {Daan H. M. Schellekens; Thomas Sherson; Richard Heusdens },
publisher = {IEEE SigPort},
title = {QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING},
year = {2017} }
TY - EJOUR
T1 - QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING
AU - Daan H. M. Schellekens; Thomas Sherson; Richard Heusdens
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1764
ER -
Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens. (2017). QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING. IEEE SigPort. http://sigport.org/1764
Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens, 2017. QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING. Available at: http://sigport.org/1764.
Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens. (2017). "QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING." Web.
1. Daan H. M. Schellekens, Thomas Sherson, Richard Heusdens. QUANTISATION EFFECTS IN PDMM: A FIRST STUDY FOR SYNCHRONOUS DISTRIBUTED AVERAGING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1764

Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model


We introduce a new estimation algorithm specifically designed for the latent high-order autoregressive models. It implements the concept of the filter-based maximum likelihood. Our approach is fully deterministic and is less computationally demanding than the traditional Monte Carlo Markov chain techniques. The simulation experiments confirm the interest of our approach.

Paper Details

Authors:
Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski
Submitted On:
7 March 2017 - 6:12am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster.pdf

(23 downloads)

Keywords

Subscribe

[1] Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski, "Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1684. Accessed: Apr. 28, 2017.
@article{1684-17,
url = {http://sigport.org/1684},
author = {Ivan Gorynin; Emmanuel Monfrini; Wojciech Pieczynski },
publisher = {IEEE SigPort},
title = {Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model},
year = {2017} }
TY - EJOUR
T1 - Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model
AU - Ivan Gorynin; Emmanuel Monfrini; Wojciech Pieczynski
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1684
ER -
Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski. (2017). Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model. IEEE SigPort. http://sigport.org/1684
Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski, 2017. Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model. Available at: http://sigport.org/1684.
Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski. (2017). "Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model." Web.
1. Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski. Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1684

Energy Blowup for Truncated Stable LTI Systems


In this paper we analyze the convergence behavior of a sampling based system approximation process, where the time variable is in the argument of the signal and not in the argument of the bandlimited impulse response. We consider the Paley-Wiener space $PW_\pi^2$ of bandlimited signals with finite energy and stable linear time-invariant (LTI) systems, and show that there are signals and systems such that the approximation process diverges in the $L^2$-norm, i.e., the norm of the signal space. We prove that the sets of signals and systems creating divergence are jointly spaceable, i.e., there exists an infinite dimensional closed subspace of $PW_\pi^2$ and an infinite dimensional closed subspace of the space of all stable LTI systems, such that the approximation process diverges for any non-zero pair of signal and system from these subspaces.

Paper Details

Authors:
Holger Boche, Ullrich Mönich
Submitted On:
2 March 2017 - 5:57am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

icassp2017_energy_poster.pdf

(22 downloads)

Keywords

Subscribe

[1] Holger Boche, Ullrich Mönich, "Energy Blowup for Truncated Stable LTI Systems", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1580. Accessed: Apr. 28, 2017.
@article{1580-17,
url = {http://sigport.org/1580},
author = {Holger Boche; Ullrich Mönich },
publisher = {IEEE SigPort},
title = {Energy Blowup for Truncated Stable LTI Systems},
year = {2017} }
TY - EJOUR
T1 - Energy Blowup for Truncated Stable LTI Systems
AU - Holger Boche; Ullrich Mönich
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1580
ER -
Holger Boche, Ullrich Mönich. (2017). Energy Blowup for Truncated Stable LTI Systems. IEEE SigPort. http://sigport.org/1580
Holger Boche, Ullrich Mönich, 2017. Energy Blowup for Truncated Stable LTI Systems. Available at: http://sigport.org/1580.
Holger Boche, Ullrich Mönich. (2017). "Energy Blowup for Truncated Stable LTI Systems." Web.
1. Holger Boche, Ullrich Mönich. Energy Blowup for Truncated Stable LTI Systems [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1580

Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity


In this paper, we solve blind image deconvolution problem that is to remove blurs form a signal degraded image without any knowledge of the blur kernel. Since the problem is ill-posed, an image prior plays a significant role in accurate blind deconvolution. Traditional image prior assumes coefficients in filtered domains are sparse. However, it is assumed here that there exist additional structures over the sparse coefficients. Accordingly, we propose new problem formulation for the blind image deconvolution, which utilize the structural

Paper Details

Authors:
Deokyoung Kang, Suk I. Yoo
Submitted On:
13 March 2017 - 4:21am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

icassp2017_jeon_3855_IVMSP-P9.8.pdf

(18 downloads)

icassp2017_jeon_3855_IVMSP-P9.8.pdf

(11 downloads)

Keywords

Additional Categories

Subscribe

[1] Deokyoung Kang, Suk I. Yoo, "Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1577. Accessed: Apr. 28, 2017.
@article{1577-17,
url = {http://sigport.org/1577},
author = {Deokyoung Kang; Suk I. Yoo },
publisher = {IEEE SigPort},
title = {Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity},
year = {2017} }
TY - EJOUR
T1 - Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity
AU - Deokyoung Kang; Suk I. Yoo
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1577
ER -
Deokyoung Kang, Suk I. Yoo. (2017). Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity. IEEE SigPort. http://sigport.org/1577
Deokyoung Kang, Suk I. Yoo, 2017. Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity. Available at: http://sigport.org/1577.
Deokyoung Kang, Suk I. Yoo. (2017). "Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity." Web.
1. Deokyoung Kang, Suk I. Yoo. Blind Image Deconvolution Using Student’s-t Prior With Overlapping Group Sparsity [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1577

AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID


It is well known that the positive- and negative frequency components of a real sinusoid spectrally interact with each other; thus, introducing bias in frequency estimation based on the periodogram maximization. We propose to filter out the negative-frequency component. To that end, a coarse frequency estimation is obtained using the windowing approach, known to reduce the estimation bias, and then used to filter out the negative frequency component via modulation and discrete Fourier transform

Paper Details

Authors:
Submitted On:
28 February 2017 - 5:41am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster_ICASSP2017.pdf

(55 downloads)

Keywords

Subscribe

[1] , "AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1498. Accessed: Apr. 28, 2017.
@article{1498-17,
url = {http://sigport.org/1498},
author = { },
publisher = {IEEE SigPort},
title = {AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID},
year = {2017} }
TY - EJOUR
T1 - AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1498
ER -
. (2017). AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID. IEEE SigPort. http://sigport.org/1498
, 2017. AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID. Available at: http://sigport.org/1498.
. (2017). "AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID." Web.
1. . AN ACCURATE METHOD FOR FREQUENCY ESTIMATION OF A REAL SINUSOID [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1498

Fast and Stable Signal Deconvolution via Compressible State-Space Models


Objective: Common biological measurements are in
the form of noisy convolutions of signals of interest with possibly
unknown and transient blurring kernels. Examples include EEG
and calcium imaging data. Thus, signal deconvolution of these
measurements is crucial in understanding the underlying biological
processes. The objective of this paper is to develop fast and
stable solutions for signal deconvolution from noisy, blurred and
undersampled data, where the signals are in the form of discrete

Paper Details

Authors:
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi
Submitted On:
12 December 2016 - 9:35am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

FCSS_slides.pdf

(78 downloads)

Keywords

Additional Categories

Subscribe

[1] Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi, "Fast and Stable Signal Deconvolution via Compressible State-Space Models", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1438. Accessed: Apr. 28, 2017.
@article{1438-16,
url = {http://sigport.org/1438},
author = {Abbas Kazemipour; Ji Liu; Min Wu ; Patrick Kanold and Behtash Babadi },
publisher = {IEEE SigPort},
title = {Fast and Stable Signal Deconvolution via Compressible State-Space Models},
year = {2016} }
TY - EJOUR
T1 - Fast and Stable Signal Deconvolution via Compressible State-Space Models
AU - Abbas Kazemipour; Ji Liu; Min Wu ; Patrick Kanold and Behtash Babadi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1438
ER -
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi. (2016). Fast and Stable Signal Deconvolution via Compressible State-Space Models. IEEE SigPort. http://sigport.org/1438
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi, 2016. Fast and Stable Signal Deconvolution via Compressible State-Space Models. Available at: http://sigport.org/1438.
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi. (2016). "Fast and Stable Signal Deconvolution via Compressible State-Space Models." Web.
1. Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi. Fast and Stable Signal Deconvolution via Compressible State-Space Models [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1438

Fast Computations for Approximation and Compression in Slepian Spaces


The discrete prolate spheroidal sequences (DPSS's) provide an efficient representation for signals that are perfectly timelimited and nearly bandlimited. Unfortunately, because of the high computational complexity of projecting onto the DPSS basis -- also known as the Slepian basis -- this representation is often overlooked in favor of the fast Fourier transform (FFT). In this presentation, we show that there exist fast constructions for computing approximate projections onto the leading Slepian basis elements.

Paper Details

Authors:
Santhosh Karnik, Zhihui Zhu, Michael B. Wakin, Justin Romberg, Mark A. Davenport
Submitted On:
10 December 2016 - 2:11am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

GlobalSIP2016SSPC13.pdf

(63 downloads)

Keywords

Subscribe

[1] Santhosh Karnik, Zhihui Zhu, Michael B. Wakin, Justin Romberg, Mark A. Davenport, "Fast Computations for Approximation and Compression in Slepian Spaces", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1436. Accessed: Apr. 28, 2017.
@article{1436-16,
url = {http://sigport.org/1436},
author = {Santhosh Karnik; Zhihui Zhu; Michael B. Wakin; Justin Romberg; Mark A. Davenport },
publisher = {IEEE SigPort},
title = {Fast Computations for Approximation and Compression in Slepian Spaces},
year = {2016} }
TY - EJOUR
T1 - Fast Computations for Approximation and Compression in Slepian Spaces
AU - Santhosh Karnik; Zhihui Zhu; Michael B. Wakin; Justin Romberg; Mark A. Davenport
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1436
ER -
Santhosh Karnik, Zhihui Zhu, Michael B. Wakin, Justin Romberg, Mark A. Davenport. (2016). Fast Computations for Approximation and Compression in Slepian Spaces. IEEE SigPort. http://sigport.org/1436
Santhosh Karnik, Zhihui Zhu, Michael B. Wakin, Justin Romberg, Mark A. Davenport, 2016. Fast Computations for Approximation and Compression in Slepian Spaces. Available at: http://sigport.org/1436.
Santhosh Karnik, Zhihui Zhu, Michael B. Wakin, Justin Romberg, Mark A. Davenport. (2016). "Fast Computations for Approximation and Compression in Slepian Spaces." Web.
1. Santhosh Karnik, Zhihui Zhu, Michael B. Wakin, Justin Romberg, Mark A. Davenport. Fast Computations for Approximation and Compression in Slepian Spaces [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1436

POWER LINE DETECTION VIA BACKGROUND NOISE REMOVAL


Tiny target detections, especially power line detection, have received great attention due to its critical role in ensuring the
flight safety of low-flying unmanned aerial vehicles (UAVs). In this paper, an accurate and robust power line detection method is proposed, wherein background noise is mitigated by an embedded convolution neural network (CNN) classifier before conducting the final power line extractions. Our

Paper Details

Authors:
Chaofeng Pan, Xianbin Cao, Dapeng Oliver Wu
Submitted On:
9 December 2016 - 11:12am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

POWER LINE DETECTION VIA BACKGROUND NOISE REMOVAL.pptx

(48 downloads)

Keywords

Subscribe

[1] Chaofeng Pan, Xianbin Cao, Dapeng Oliver Wu, "POWER LINE DETECTION VIA BACKGROUND NOISE REMOVAL", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1431. Accessed: Apr. 28, 2017.
@article{1431-16,
url = {http://sigport.org/1431},
author = {Chaofeng Pan; Xianbin Cao; Dapeng Oliver Wu },
publisher = {IEEE SigPort},
title = {POWER LINE DETECTION VIA BACKGROUND NOISE REMOVAL},
year = {2016} }
TY - EJOUR
T1 - POWER LINE DETECTION VIA BACKGROUND NOISE REMOVAL
AU - Chaofeng Pan; Xianbin Cao; Dapeng Oliver Wu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1431
ER -
Chaofeng Pan, Xianbin Cao, Dapeng Oliver Wu. (2016). POWER LINE DETECTION VIA BACKGROUND NOISE REMOVAL. IEEE SigPort. http://sigport.org/1431
Chaofeng Pan, Xianbin Cao, Dapeng Oliver Wu, 2016. POWER LINE DETECTION VIA BACKGROUND NOISE REMOVAL. Available at: http://sigport.org/1431.
Chaofeng Pan, Xianbin Cao, Dapeng Oliver Wu. (2016). "POWER LINE DETECTION VIA BACKGROUND NOISE REMOVAL." Web.
1. Chaofeng Pan, Xianbin Cao, Dapeng Oliver Wu. POWER LINE DETECTION VIA BACKGROUND NOISE REMOVAL [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1431

DICTIONARY LEARNING FOR SPARSE REPRESENTATION USING WEIGHTED L1-NORM


An efficient algorithm for overcomplete dictionary learning with l_p-norm as sparsity constraint to achieve sparse representation from a set of known signals is presented in this paper. The special importance of the l_p-norm (0<p<1) has been recognized in recent studies on sparse modeling, which can lead to stronger sparsity-promoting solutions than the l_1-norm. The l_p-norm, however, leads to a nonconvex optimization problem that is difficult to solve efficiently.

Paper Details

Authors:
Haoli Zhao, Shuxue Ding, Yujie Li, Zhenni Li, Xiang Li, Benying Tan
Submitted On:
8 December 2016 - 1:50pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

GSIPPOSTER.pdf

(55 downloads)

Keywords

Subscribe

[1] Haoli Zhao, Shuxue Ding, Yujie Li, Zhenni Li, Xiang Li, Benying Tan, "DICTIONARY LEARNING FOR SPARSE REPRESENTATION USING WEIGHTED L1-NORM", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1423. Accessed: Apr. 28, 2017.
@article{1423-16,
url = {http://sigport.org/1423},
author = {Haoli Zhao; Shuxue Ding; Yujie Li; Zhenni Li; Xiang Li; Benying Tan },
publisher = {IEEE SigPort},
title = {DICTIONARY LEARNING FOR SPARSE REPRESENTATION USING WEIGHTED L1-NORM},
year = {2016} }
TY - EJOUR
T1 - DICTIONARY LEARNING FOR SPARSE REPRESENTATION USING WEIGHTED L1-NORM
AU - Haoli Zhao; Shuxue Ding; Yujie Li; Zhenni Li; Xiang Li; Benying Tan
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1423
ER -
Haoli Zhao, Shuxue Ding, Yujie Li, Zhenni Li, Xiang Li, Benying Tan. (2016). DICTIONARY LEARNING FOR SPARSE REPRESENTATION USING WEIGHTED L1-NORM. IEEE SigPort. http://sigport.org/1423
Haoli Zhao, Shuxue Ding, Yujie Li, Zhenni Li, Xiang Li, Benying Tan, 2016. DICTIONARY LEARNING FOR SPARSE REPRESENTATION USING WEIGHTED L1-NORM. Available at: http://sigport.org/1423.
Haoli Zhao, Shuxue Ding, Yujie Li, Zhenni Li, Xiang Li, Benying Tan. (2016). "DICTIONARY LEARNING FOR SPARSE REPRESENTATION USING WEIGHTED L1-NORM." Web.
1. Haoli Zhao, Shuxue Ding, Yujie Li, Zhenni Li, Xiang Li, Benying Tan. DICTIONARY LEARNING FOR SPARSE REPRESENTATION USING WEIGHTED L1-NORM [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1423

Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision


This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.

Paper Details

Authors:
Submitted On:
10 December 2016 - 3:39pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Slides_GlobalSIP.pdf

(57 downloads)

Keywords

Subscribe

[1] , "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1383. Accessed: Apr. 28, 2017.
@article{1383-16,
url = {http://sigport.org/1383},
author = { },
publisher = {IEEE SigPort},
title = {Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision},
year = {2016} }
TY - EJOUR
T1 - Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1383
ER -
. (2016). Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. IEEE SigPort. http://sigport.org/1383
, 2016. Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. Available at: http://sigport.org/1383.
. (2016). "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision." Web.
1. . Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1383

Pages