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Statistical Signal Processing

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.

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Authors:
Anna Guerra, Nicola Sparnacci, Davide Dardari, Petar M. Djuric
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20 June 2018 - 8:48am
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poster_SPAWC_2018_3.pdf

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[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: Aug. 21, 2018.
@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

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30 May 2018 - 5:31am
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poster_IEEE_2018.pdf

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[1] , "Uncertainty Quantification in Sunspot Counts", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3218. Accessed: Aug. 21, 2018.
@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.

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Authors:
Nir Halay, Koby Todros
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2 May 2018 - 3:30pm
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ICASSP_2018_POSTER_VER_3.pdf

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[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: Aug. 21, 2018.
@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.

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Authors:
Chenhao Li, Simon J. Godsill
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20 April 2018 - 4:06am
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Sequential Methods for Non-homogeneous Poisson Intensity Inference

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[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: Aug. 21, 2018.
@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.

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Authors:
Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi
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20 April 2018 - 2:58am
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ICASSP2018.pdf

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[1] Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi, "DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3088. Accessed: Aug. 21, 2018.
@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.

ICASSP.pdf

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Authors:
Thakshila Wimalajeewa, Pramod K. Varshney
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20 April 2018 - 1:55am
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ICASSP.pdf

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[1] Thakshila Wimalajeewa, Pramod K. Varshney, "Bayesian Sparse Signal Detection Exploiting Laplace Prior", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3081. Accessed: Aug. 21, 2018.
@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

On Sequential Random Distortion Testing of Non-Stationary Processes


Random distortion testing (RDT) addresses the problem of testing whether or not a random signal deviates by more than a specified tolerance from a fixed value. The test is non-parametric in the sense that the distribution of the signal under each hypothesis is assumed to be unknown. The signal is observed in independent and identically distributed (i.i.d) additive noise. The need to control the probabilities of false alarm and missed de- tection while reducing the number of samples required to make a decision leads to the SeqRDT approach.

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Authors:
Dominique Pastor, Vinod Sharma, Pramod K. Varshney
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20 April 2018 - 1:38am
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ICASSP18_Slides.pdf

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[1] Dominique Pastor, Vinod Sharma, Pramod K. Varshney, "On Sequential Random Distortion Testing of Non-Stationary Processes", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3079. Accessed: Aug. 21, 2018.
@article{3079-18,
url = {http://sigport.org/3079},
author = {Dominique Pastor; Vinod Sharma; Pramod K. Varshney },
publisher = {IEEE SigPort},
title = {On Sequential Random Distortion Testing of Non-Stationary Processes},
year = {2018} }
TY - EJOUR
T1 - On Sequential Random Distortion Testing of Non-Stationary Processes
AU - Dominique Pastor; Vinod Sharma; Pramod K. Varshney
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3079
ER -
Dominique Pastor, Vinod Sharma, Pramod K. Varshney. (2018). On Sequential Random Distortion Testing of Non-Stationary Processes. IEEE SigPort. http://sigport.org/3079
Dominique Pastor, Vinod Sharma, Pramod K. Varshney, 2018. On Sequential Random Distortion Testing of Non-Stationary Processes. Available at: http://sigport.org/3079.
Dominique Pastor, Vinod Sharma, Pramod K. Varshney. (2018). "On Sequential Random Distortion Testing of Non-Stationary Processes." Web.
1. Dominique Pastor, Vinod Sharma, Pramod K. Varshney. On Sequential Random Distortion Testing of Non-Stationary Processes [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3079

Locally Optimal Invariant Detector for Testing Equality of Two Power Spectral Densities

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19 April 2018 - 8:48pm
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Slides_ICASSP_Calgary.pdf

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[1] , "Locally Optimal Invariant Detector for Testing Equality of Two Power Spectral Densities", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3044. Accessed: Aug. 21, 2018.
@article{3044-18,
url = {http://sigport.org/3044},
author = { },
publisher = {IEEE SigPort},
title = {Locally Optimal Invariant Detector for Testing Equality of Two Power Spectral Densities},
year = {2018} }
TY - EJOUR
T1 - Locally Optimal Invariant Detector for Testing Equality of Two Power Spectral Densities
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3044
ER -
. (2018). Locally Optimal Invariant Detector for Testing Equality of Two Power Spectral Densities. IEEE SigPort. http://sigport.org/3044
, 2018. Locally Optimal Invariant Detector for Testing Equality of Two Power Spectral Densities. Available at: http://sigport.org/3044.
. (2018). "Locally Optimal Invariant Detector for Testing Equality of Two Power Spectral Densities." Web.
1. . Locally Optimal Invariant Detector for Testing Equality of Two Power Spectral Densities [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3044

HUMAN-MACHINE INFERENCE NETWORKS FOR SMART DECISION MAKING: OPPORTUNITIES AND CHALLENGES


The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines complementary cognitive strengths of humans and machines in an intelligent manner to tackle various inference tasks and achieves higher performance than either humans or machines by themselves. While inference performance optimization techniques for human-only or sensor-only networks are quite mature, HuMaINs require novel signal processing and machine learning solutions.

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Authors:
Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney
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19 April 2018 - 4:14pm
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ICASSP2018_ppt.pdf

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[1] Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney, "HUMAN-MACHINE INFERENCE NETWORKS FOR SMART DECISION MAKING: OPPORTUNITIES AND CHALLENGES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3018. Accessed: Aug. 21, 2018.
@article{3018-18,
url = {http://sigport.org/3018},
author = {Aditya Vempaty; Bhavya Kailkhura; Pramod K. Varshney },
publisher = {IEEE SigPort},
title = {HUMAN-MACHINE INFERENCE NETWORKS FOR SMART DECISION MAKING: OPPORTUNITIES AND CHALLENGES},
year = {2018} }
TY - EJOUR
T1 - HUMAN-MACHINE INFERENCE NETWORKS FOR SMART DECISION MAKING: OPPORTUNITIES AND CHALLENGES
AU - Aditya Vempaty; Bhavya Kailkhura; Pramod K. Varshney
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3018
ER -
Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney. (2018). HUMAN-MACHINE INFERENCE NETWORKS FOR SMART DECISION MAKING: OPPORTUNITIES AND CHALLENGES. IEEE SigPort. http://sigport.org/3018
Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney, 2018. HUMAN-MACHINE INFERENCE NETWORKS FOR SMART DECISION MAKING: OPPORTUNITIES AND CHALLENGES. Available at: http://sigport.org/3018.
Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney. (2018). "HUMAN-MACHINE INFERENCE NETWORKS FOR SMART DECISION MAKING: OPPORTUNITIES AND CHALLENGES." Web.
1. Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney. HUMAN-MACHINE INFERENCE NETWORKS FOR SMART DECISION MAKING: OPPORTUNITIES AND CHALLENGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3018

Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term


The Bayesian information criterion is generic in the sense that it does not include information about the specific model selection problem at hand. Nevertheless, it has been widely used to estimate the number of data clusters in cluster analysis. We have recently derived a Bayesian cluster enumeration criterion from first principles which maximizes the posterior probability of the candidate models given observations. But, in the finite sample regime, the asymptotic assumptions made by the criterion, to arrive at a computationally simple penalty term, are violated.

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Authors:
Frewweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir
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14 April 2018 - 7:52am
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Bayesian Cluster Enumeration Criterion

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[1] Frewweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir, "Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2820. Accessed: Aug. 21, 2018.
@article{2820-18,
url = {http://sigport.org/2820},
author = {Frewweyni K. Teklehaymanot; Michael Muma; Abdelhak M. Zoubir },
publisher = {IEEE SigPort},
title = {Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term},
year = {2018} }
TY - EJOUR
T1 - Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term
AU - Frewweyni K. Teklehaymanot; Michael Muma; Abdelhak M. Zoubir
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2820
ER -
Frewweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir. (2018). Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term. IEEE SigPort. http://sigport.org/2820
Frewweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir, 2018. Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term. Available at: http://sigport.org/2820.
Frewweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir. (2018). "Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term." Web.
1. Frewweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir. Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2820

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