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

Statistical Signal Processing

First-order optimal sequential subspace change-point detection


We consider the sequential change-point detection problem of detecting changes that are characterized by a subspace structure. Such changes are frequent in high-dimensional streaming data altering the form of the corresponding covariance matrix. In this work we present a Subspace-CUSUM procedure and demonstrate its first-order asymptotic optimality properties for the case where the subspace structure is unknown and needs to be simultaneously estimated.

Paper Details

Authors:
Liyan Xie, George V. Moustakides, Yao Xie
Submitted On:
21 November 2018 - 7:42pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

GlobalSIP2018.pdf

Subscribe

[1] Liyan Xie, George V. Moustakides, Yao Xie, "First-order optimal sequential subspace change-point detection", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3706. Accessed: Mar. 22, 2019.
@article{3706-18,
url = {http://sigport.org/3706},
author = {Liyan Xie; George V. Moustakides; Yao Xie },
publisher = {IEEE SigPort},
title = {First-order optimal sequential subspace change-point detection},
year = {2018} }
TY - EJOUR
T1 - First-order optimal sequential subspace change-point detection
AU - Liyan Xie; George V. Moustakides; Yao Xie
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3706
ER -
Liyan Xie, George V. Moustakides, Yao Xie. (2018). First-order optimal sequential subspace change-point detection. IEEE SigPort. http://sigport.org/3706
Liyan Xie, George V. Moustakides, Yao Xie, 2018. First-order optimal sequential subspace change-point detection. Available at: http://sigport.org/3706.
Liyan Xie, George V. Moustakides, Yao Xie. (2018). "First-order optimal sequential subspace change-point detection." Web.
1. Liyan Xie, George V. Moustakides, Yao Xie. First-order optimal sequential subspace change-point detection [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3706

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.

Paper Details

Authors:
Daniel Franz, Volker Kuehn
Submitted On:
21 November 2018 - 4:49am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

sample_poster.pdf

Subscribe

[1] Daniel Franz, Volker Kuehn, "VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3698. Accessed: Mar. 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

Statistical detection and classification of transient signals in low-bit sampling time-domain signals


We investigate the performance of the generalized Spectral Kurtosis (SK) estimator in detecting and discriminating natural and artificial very short duration transients in the 2-bit sampling time domain Very-Long-Baseline Interferometry (VLBI) data. We demonstrate that, after a 32-bit FFT operation is performed on the 2-bit time domain voltages, these two types of transients become distinguishable from each other in the spectral domain.

Paper Details

Authors:
Gelu M. Nita, Aard Keimpema, Zsolt Paragi
Submitted On:
18 November 2018 - 4:48pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Presentation slides

Subscribe

[1] Gelu M. Nita, Aard Keimpema, Zsolt Paragi, "Statistical detection and classification of transient signals in low-bit sampling time-domain signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3682. Accessed: Mar. 22, 2019.
@article{3682-18,
url = {http://sigport.org/3682},
author = {Gelu M. Nita; Aard Keimpema; Zsolt Paragi },
publisher = {IEEE SigPort},
title = {Statistical detection and classification of transient signals in low-bit sampling time-domain signals},
year = {2018} }
TY - EJOUR
T1 - Statistical detection and classification of transient signals in low-bit sampling time-domain signals
AU - Gelu M. Nita; Aard Keimpema; Zsolt Paragi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3682
ER -
Gelu M. Nita, Aard Keimpema, Zsolt Paragi. (2018). Statistical detection and classification of transient signals in low-bit sampling time-domain signals. IEEE SigPort. http://sigport.org/3682
Gelu M. Nita, Aard Keimpema, Zsolt Paragi, 2018. Statistical detection and classification of transient signals in low-bit sampling time-domain signals. Available at: http://sigport.org/3682.
Gelu M. Nita, Aard Keimpema, Zsolt Paragi. (2018). "Statistical detection and classification of transient signals in low-bit sampling time-domain signals." Web.
1. Gelu M. Nita, Aard Keimpema, Zsolt Paragi. Statistical detection and classification of transient signals in low-bit sampling time-domain signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3682

MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS


This paper presents an empirical Bayesian method to estimate regularisation parameters in imaging inverse problems. The method calibrates regularisation parameters directly from the observed data by maximum marginal likelihood estimation, and is useful for inverse problems that are convex. A main novelty is that maximum likelihood estimation is performed efficiently by using a stochastic proximal gradient algorithm that is driven by two proximal Markov chain Monte Carlo samplers, intimately combining modern optimisation and sampling techniques.

Paper Details

Authors:
Marcelo Pereyra
Submitted On:
4 October 2018 - 12:19pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster ICIP Vidal 3oct18.pdf

Keywords

Additional Categories

Subscribe

[1] Marcelo Pereyra, "MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3436. Accessed: Mar. 22, 2019.
@article{3436-18,
url = {http://sigport.org/3436},
author = {Marcelo Pereyra },
publisher = {IEEE SigPort},
title = {MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS},
year = {2018} }
TY - EJOUR
T1 - MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS
AU - Marcelo Pereyra
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3436
ER -
Marcelo Pereyra. (2018). MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS. IEEE SigPort. http://sigport.org/3436
Marcelo Pereyra, 2018. MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS. Available at: http://sigport.org/3436.
Marcelo Pereyra. (2018). "MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS." Web.
1. Marcelo Pereyra. MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3436

Collaborative Target-Localization and Information-based Control in Networks of UAVs


In this paper, we study the capacity of UAV networks for high-accuracy localization of targets. We address the problem of designing a distributed control scheme for UAV navigation and formation based on an information-seeking criterion maximizing the target localization accuracy. Each UAV is assumed to be able to communicate and collaborate with other UAVs that are within a neighboring region, allowing for a feasible distributed solution which takes into account a trade-off between localization accuracy and speed of convergence to a suitable localization of the target.

Paper Details

Authors:
Anna Guerra, Nicola Sparnacci, Davide Dardari, Petar M. Djuric
Submitted On:
20 June 2018 - 8:48am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_SPAWC_2018_3.pdf

Subscribe

[1] Anna Guerra, Nicola Sparnacci, Davide Dardari, Petar M. Djuric, "Collaborative Target-Localization and Information-based Control in Networks of UAVs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3242. Accessed: Mar. 22, 2019.
@article{3242-18,
url = {http://sigport.org/3242},
author = {Anna Guerra; Nicola Sparnacci; Davide Dardari; Petar M. Djuric },
publisher = {IEEE SigPort},
title = {Collaborative Target-Localization and Information-based Control in Networks of UAVs},
year = {2018} }
TY - EJOUR
T1 - Collaborative Target-Localization and Information-based Control in Networks of UAVs
AU - Anna Guerra; Nicola Sparnacci; Davide Dardari; Petar M. Djuric
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3242
ER -
Anna Guerra, Nicola Sparnacci, Davide Dardari, Petar M. Djuric. (2018). Collaborative Target-Localization and Information-based Control in Networks of UAVs. IEEE SigPort. http://sigport.org/3242
Anna Guerra, Nicola Sparnacci, Davide Dardari, Petar M. Djuric, 2018. Collaborative Target-Localization and Information-based Control in Networks of UAVs. Available at: http://sigport.org/3242.
Anna Guerra, Nicola Sparnacci, Davide Dardari, Petar M. Djuric. (2018). "Collaborative Target-Localization and Information-based Control in Networks of UAVs." Web.
1. Anna Guerra, Nicola Sparnacci, Davide Dardari, Petar M. Djuric. Collaborative Target-Localization and Information-based Control in Networks of UAVs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3242

Uncertainty Quantification in Sunspot Counts

Paper Details

Authors:
Submitted On:
30 May 2018 - 5:31am
Short Link:
Type:
Event:
Document Year:
Cite

Document Files

poster_IEEE_2018.pdf

Subscribe

[1] , "Uncertainty Quantification in Sunspot Counts", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3218. Accessed: Mar. 22, 2019.
@article{3218-18,
url = {http://sigport.org/3218},
author = { },
publisher = {IEEE SigPort},
title = {Uncertainty Quantification in Sunspot Counts},
year = {2018} }
TY - EJOUR
T1 - Uncertainty Quantification in Sunspot Counts
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3218
ER -
. (2018). Uncertainty Quantification in Sunspot Counts. IEEE SigPort. http://sigport.org/3218
, 2018. Uncertainty Quantification in Sunspot Counts. Available at: http://sigport.org/3218.
. (2018). "Uncertainty Quantification in Sunspot Counts." Web.
1. . Uncertainty Quantification in Sunspot Counts [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3218

PLUG-IN MEASURE-TRANSFORMED QUASI-LIKELIHOOD RATIO TEST FOR RANDOM SIGNAL DETECTION


Recently, we developed a robust generalization of the Gaussian quasi-likelihood ratio test (GQLRT). This generalization, called measure-transformed GQLRT (MT-GQLRT), operates by selecting a Gaussian model that best empirically fits a transformed probability measure of the data. In this letter, a plug-in version of the MT-GQLRT is developed for robust detection of a random signal in nonspherical noise. The proposed detector is derived by plugging an empirical measure-transformed noise covariance, ob- tained from noise-only secondary data, into the MT-GQLRT.

Paper Details

Authors:
Nir Halay, Koby Todros
Submitted On:
2 May 2018 - 3:30pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP_2018_POSTER_VER_3.pdf

Subscribe

[1] Nir Halay, Koby Todros, "PLUG-IN MEASURE-TRANSFORMED QUASI-LIKELIHOOD RATIO TEST FOR RANDOM SIGNAL DETECTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3200. Accessed: Mar. 22, 2019.
@article{3200-18,
url = {http://sigport.org/3200},
author = {Nir Halay; Koby Todros },
publisher = {IEEE SigPort},
title = {PLUG-IN MEASURE-TRANSFORMED QUASI-LIKELIHOOD RATIO TEST FOR RANDOM SIGNAL DETECTION},
year = {2018} }
TY - EJOUR
T1 - PLUG-IN MEASURE-TRANSFORMED QUASI-LIKELIHOOD RATIO TEST FOR RANDOM SIGNAL DETECTION
AU - Nir Halay; Koby Todros
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3200
ER -
Nir Halay, Koby Todros. (2018). PLUG-IN MEASURE-TRANSFORMED QUASI-LIKELIHOOD RATIO TEST FOR RANDOM SIGNAL DETECTION. IEEE SigPort. http://sigport.org/3200
Nir Halay, Koby Todros, 2018. PLUG-IN MEASURE-TRANSFORMED QUASI-LIKELIHOOD RATIO TEST FOR RANDOM SIGNAL DETECTION. Available at: http://sigport.org/3200.
Nir Halay, Koby Todros. (2018). "PLUG-IN MEASURE-TRANSFORMED QUASI-LIKELIHOOD RATIO TEST FOR RANDOM SIGNAL DETECTION." Web.
1. Nir Halay, Koby Todros. PLUG-IN MEASURE-TRANSFORMED QUASI-LIKELIHOOD RATIO TEST FOR RANDOM SIGNAL DETECTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3200

SEQUENTIAL INFERENCE METHODS FOR NON-HOMGENEOUS POISSON PROCESSES WITH STATE-SPACE PRIOR


The non-homogeneous Poisson process (NHPP) is a point process with time-varying intensity across its domain, the use of which arises in numerous domains in signal processing, machine learning and many other fields. However, its applications are largely limited by the intractable likelihood and the high computational cost of existing inference schemes. We present an online inference framework that utilises generative Poisson data and sequential Markov Chain Monte Carlo (SMCMC) algorithm, which achieves improved performance in both synthetic and real datasets.

Paper Details

Authors:
Chenhao Li, Simon J. Godsill
Submitted On:
20 April 2018 - 4:06am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Sequential Methods for Non-homogeneous Poisson Intensity Inference

Subscribe

[1] Chenhao Li, Simon J. Godsill, "SEQUENTIAL INFERENCE METHODS FOR NON-HOMGENEOUS POISSON PROCESSES WITH STATE-SPACE PRIOR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3092. Accessed: Mar. 22, 2019.
@article{3092-18,
url = {http://sigport.org/3092},
author = {Chenhao Li; Simon J. Godsill },
publisher = {IEEE SigPort},
title = {SEQUENTIAL INFERENCE METHODS FOR NON-HOMGENEOUS POISSON PROCESSES WITH STATE-SPACE PRIOR},
year = {2018} }
TY - EJOUR
T1 - SEQUENTIAL INFERENCE METHODS FOR NON-HOMGENEOUS POISSON PROCESSES WITH STATE-SPACE PRIOR
AU - Chenhao Li; Simon J. Godsill
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3092
ER -
Chenhao Li, Simon J. Godsill. (2018). SEQUENTIAL INFERENCE METHODS FOR NON-HOMGENEOUS POISSON PROCESSES WITH STATE-SPACE PRIOR. IEEE SigPort. http://sigport.org/3092
Chenhao Li, Simon J. Godsill, 2018. SEQUENTIAL INFERENCE METHODS FOR NON-HOMGENEOUS POISSON PROCESSES WITH STATE-SPACE PRIOR. Available at: http://sigport.org/3092.
Chenhao Li, Simon J. Godsill. (2018). "SEQUENTIAL INFERENCE METHODS FOR NON-HOMGENEOUS POISSON PROCESSES WITH STATE-SPACE PRIOR." Web.
1. Chenhao Li, Simon J. Godsill. SEQUENTIAL INFERENCE METHODS FOR NON-HOMGENEOUS POISSON PROCESSES WITH STATE-SPACE PRIOR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3092

DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION


In this paper, we propose a fully distributed approximate message passing (AMP) algorithm, which reconstructs an unknown vector from its linear measurements obtained at nodes in a network. The proposed algorithm is a distributed implementation of the centralized AMP algorithm, and consists of the local computation at each node and the global computation using communications between nodes. For the global computation, we propose a distributed algorithm named summation propagation to calculate a summation required in the AMP algorithm.

Paper Details

Authors:
Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi
Submitted On:
20 April 2018 - 2:58am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2018.pdf

Subscribe

[1] Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi, "DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3088. Accessed: Mar. 22, 2019.
@article{3088-18,
url = {http://sigport.org/3088},
author = {Ryo Hayakawa; Ayano Nakai; Kazunori Hayashi },
publisher = {IEEE SigPort},
title = {DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION},
year = {2018} }
TY - EJOUR
T1 - DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION
AU - Ryo Hayakawa; Ayano Nakai; Kazunori Hayashi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3088
ER -
Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi. (2018). DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION. IEEE SigPort. http://sigport.org/3088
Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi, 2018. DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION. Available at: http://sigport.org/3088.
Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi. (2018). "DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION." Web.
1. Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi. DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3088

Bayesian Sparse Signal Detection Exploiting Laplace Prior


In this paper, we consider the problem of sparse signal detection with compressed measurements in a Bayesian framework. Multiple nodes in the network are assumed to observe sparse signals. Observations at each node are compressed via random projections and sent to a centralized fusion center. Motivated by the fact that reliable detection of the sparse signals does not require complete signal reconstruction, we propose two computationally efficient methods for constructing decision statistics for detection.

Paper Details

Authors:
Thakshila Wimalajeewa, Pramod K. Varshney
Submitted On:
20 April 2018 - 1:55am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP.pdf

Keywords

Additional Categories

Subscribe

[1] Thakshila Wimalajeewa, Pramod K. Varshney, "Bayesian Sparse Signal Detection Exploiting Laplace Prior", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3081. Accessed: Mar. 22, 2019.
@article{3081-18,
url = {http://sigport.org/3081},
author = {Thakshila Wimalajeewa; Pramod K. Varshney },
publisher = {IEEE SigPort},
title = {Bayesian Sparse Signal Detection Exploiting Laplace Prior},
year = {2018} }
TY - EJOUR
T1 - Bayesian Sparse Signal Detection Exploiting Laplace Prior
AU - Thakshila Wimalajeewa; Pramod K. Varshney
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3081
ER -
Thakshila Wimalajeewa, Pramod K. Varshney. (2018). Bayesian Sparse Signal Detection Exploiting Laplace Prior. IEEE SigPort. http://sigport.org/3081
Thakshila Wimalajeewa, Pramod K. Varshney, 2018. Bayesian Sparse Signal Detection Exploiting Laplace Prior. Available at: http://sigport.org/3081.
Thakshila Wimalajeewa, Pramod K. Varshney. (2018). "Bayesian Sparse Signal Detection Exploiting Laplace Prior." Web.
1. Thakshila Wimalajeewa, Pramod K. Varshney. Bayesian Sparse Signal Detection Exploiting Laplace Prior [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3081

Pages