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Sequential learning; sequential decision methods (MLR-SLER)

MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION


This study introduces a machine hearing system for robot audition, which enables a robotic agent to pro-actively minimize the uncertainty of sound source location estimates through motion. The proposed system is based on an active exploration approach, providing a means to model and predict effects of the agent's future motions on localization uncertainty in a probabilistic manner. Particle filtering is used to estimate the posterior probability density function of the source position from binaural measurements, enabling to jointly assess azimuth and distance of the source.

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Authors:
Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa
Submitted On:
1 March 2017 - 5:16am
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poster_icassp2017.pdf

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[1] Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa, "MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1543. Accessed: Sep. 21, 2017.
@article{1543-17,
url = {http://sigport.org/1543},
author = {Christopher Schymura; Juan Diego Rios Grajales; Dorothea Kolossa },
publisher = {IEEE SigPort},
title = {MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION},
year = {2017} }
TY - EJOUR
T1 - MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION
AU - Christopher Schymura; Juan Diego Rios Grajales; Dorothea Kolossa
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1543
ER -
Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa. (2017). MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION. IEEE SigPort. http://sigport.org/1543
Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa, 2017. MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION. Available at: http://sigport.org/1543.
Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa. (2017). "MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION." Web.
1. Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa. MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1543

Distributed Sequence Prediction: A consensus+innovations approach


This paper focuses on the problem of distributed sequence
prediction in a network of sparsely interconnected agents,
where agents collaborate to achieve provably reasonable
predictive performance. An expert assisted online learning
algorithm in a distributed setup of the consensus+innovations
form is proposed, in which the agents update their weights
for the experts’ predictions by simultaneously processing the
latest network losses (innovations) and the cumulative losses
obtained from neighboring agents (consensus). This paper

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Authors:
Anit Kumar Sahu, Soummya Kar
Submitted On:
6 December 2016 - 3:37am
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GlobalSIP_talk.pdf

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[1] Anit Kumar Sahu, Soummya Kar, "Distributed Sequence Prediction: A consensus+innovations approach", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1357. Accessed: Sep. 21, 2017.
@article{1357-16,
url = {http://sigport.org/1357},
author = {Anit Kumar Sahu; Soummya Kar },
publisher = {IEEE SigPort},
title = {Distributed Sequence Prediction: A consensus+innovations approach},
year = {2016} }
TY - EJOUR
T1 - Distributed Sequence Prediction: A consensus+innovations approach
AU - Anit Kumar Sahu; Soummya Kar
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1357
ER -
Anit Kumar Sahu, Soummya Kar. (2016). Distributed Sequence Prediction: A consensus+innovations approach. IEEE SigPort. http://sigport.org/1357
Anit Kumar Sahu, Soummya Kar, 2016. Distributed Sequence Prediction: A consensus+innovations approach. Available at: http://sigport.org/1357.
Anit Kumar Sahu, Soummya Kar. (2016). "Distributed Sequence Prediction: A consensus+innovations approach." Web.
1. Anit Kumar Sahu, Soummya Kar. Distributed Sequence Prediction: A consensus+innovations approach [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1357

Kernel-based low-rank feature extraction on a budget for Big data streams

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Authors:
Dimitrios Berberidis, Georgios B. Giannakis
Submitted On:
23 February 2016 - 1:44pm
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Globalsip2015.pdf

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[1] Dimitrios Berberidis, Georgios B. Giannakis, "Kernel-based low-rank feature extraction on a budget for Big data streams", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/434. Accessed: Sep. 21, 2017.
@article{434-15,
url = {http://sigport.org/434},
author = {Dimitrios Berberidis; Georgios B. Giannakis },
publisher = {IEEE SigPort},
title = {Kernel-based low-rank feature extraction on a budget for Big data streams},
year = {2015} }
TY - EJOUR
T1 - Kernel-based low-rank feature extraction on a budget for Big data streams
AU - Dimitrios Berberidis; Georgios B. Giannakis
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/434
ER -
Dimitrios Berberidis, Georgios B. Giannakis. (2015). Kernel-based low-rank feature extraction on a budget for Big data streams. IEEE SigPort. http://sigport.org/434
Dimitrios Berberidis, Georgios B. Giannakis, 2015. Kernel-based low-rank feature extraction on a budget for Big data streams. Available at: http://sigport.org/434.
Dimitrios Berberidis, Georgios B. Giannakis. (2015). "Kernel-based low-rank feature extraction on a budget for Big data streams." Web.
1. Dimitrios Berberidis, Georgios B. Giannakis. Kernel-based low-rank feature extraction on a budget for Big data streams [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/434

GlobalSIP_slides_AEK


This paper considers unconstrained convex optimiza- tion problems with time-varying objective functions. We propose algorithms with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and correction steps, while sampling the problem data at a constant rate of 1{h, where h is the length of the sampling interval. The prediction step is derived by analyzing the iso-residual dynamics of the optimality conditions.

globalsip.pdf

PDF icon globalsip.pdf (928 downloads)

Paper Details

Authors:
Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro
Submitted On:
23 February 2016 - 1:44pm
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globalsip.pdf

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[1] Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro, "GlobalSIP_slides_AEK", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/367. Accessed: Sep. 21, 2017.
@article{367-15,
url = {http://sigport.org/367},
author = {Andrea Simonetto; Aryan Mokhtari; Geert Leus; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {GlobalSIP_slides_AEK},
year = {2015} }
TY - EJOUR
T1 - GlobalSIP_slides_AEK
AU - Andrea Simonetto; Aryan Mokhtari; Geert Leus; Alejandro Ribeiro
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/367
ER -
Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro. (2015). GlobalSIP_slides_AEK. IEEE SigPort. http://sigport.org/367
Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro, 2015. GlobalSIP_slides_AEK. Available at: http://sigport.org/367.
Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro. (2015). "GlobalSIP_slides_AEK." Web.
1. Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro. GlobalSIP_slides_AEK [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/367

CRH: A Simple Benchmark Approach to Continuous Hashing


gsip_mc.pdf

PDF icon gsip_mc.pdf (341 downloads)

gsip_mc.pdf

PDF icon gsip_mc.pdf (222 downloads)

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Authors:
Submitted On:
23 February 2016 - 1:43pm
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gsip_mc.pdf

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gsip_mc.pdf

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[1] , "CRH: A Simple Benchmark Approach to Continuous Hashing", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/248. Accessed: Sep. 21, 2017.
@article{248-15,
url = {http://sigport.org/248},
author = { },
publisher = {IEEE SigPort},
title = {CRH: A Simple Benchmark Approach to Continuous Hashing},
year = {2015} }
TY - EJOUR
T1 - CRH: A Simple Benchmark Approach to Continuous Hashing
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/248
ER -
. (2015). CRH: A Simple Benchmark Approach to Continuous Hashing. IEEE SigPort. http://sigport.org/248
, 2015. CRH: A Simple Benchmark Approach to Continuous Hashing. Available at: http://sigport.org/248.
. (2015). "CRH: A Simple Benchmark Approach to Continuous Hashing." Web.
1. . CRH: A Simple Benchmark Approach to Continuous Hashing [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/248

A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation


Precise estimation of synchronization parameters is essential for reliable
data detection in digital communications and phase errors can
result in significant performance degradation. The literature on estimation
of synchronization parameters, including the carrier frequency
offset, are based on approximations or heuristics because
the optimal estimation problem is analytically intractable for most
cases of interest. We develop an online Bayesian inference procedure
for blind estimation of the frequency offset, for arbitrary signal

Paper Details

Authors:
Keith W. Forsythe
Submitted On:
23 February 2016 - 1:43pm
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Type:
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SeqBayesInfBlindFreqEst_sigport.pdf

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[1] Keith W. Forsythe, "A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/213. Accessed: Sep. 21, 2017.
@article{213-15,
url = {http://sigport.org/213},
author = {Keith W. Forsythe },
publisher = {IEEE SigPort},
title = {A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation},
year = {2015} }
TY - EJOUR
T1 - A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation
AU - Keith W. Forsythe
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/213
ER -
Keith W. Forsythe. (2015). A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation. IEEE SigPort. http://sigport.org/213
Keith W. Forsythe, 2015. A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation. Available at: http://sigport.org/213.
Keith W. Forsythe. (2015). "A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation." Web.
1. Keith W. Forsythe. A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/213