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Machine Learning for Signal Processing

Sparse Modeling


Sparse Modeling in Image Processing and Deep LearningSparse approximation is a well-established theory, with a profound impact on the fields of signal and image processing. In this talk we start by presenting this model and its features, and then turn to describe two special cases of it – the convolutional sparse coding (CSC) and its multi-layered version (ML-CSC).  Amazingly, as we will carefully show, ML-CSC provides a solid theoretical foundation to … deep-learning.

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Authors:
Michael Elad
Submitted On:
22 December 2017 - 1:26pm
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ICIP_KeyNote_Talk_small size.pdf

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[1] Michael Elad, "Sparse Modeling ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2260. Accessed: Apr. 07, 2020.
@article{2260-17,
url = {http://sigport.org/2260},
author = {Michael Elad },
publisher = {IEEE SigPort},
title = {Sparse Modeling },
year = {2017} }
TY - EJOUR
T1 - Sparse Modeling
AU - Michael Elad
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2260
ER -
Michael Elad. (2017). Sparse Modeling . IEEE SigPort. http://sigport.org/2260
Michael Elad, 2017. Sparse Modeling . Available at: http://sigport.org/2260.
Michael Elad. (2017). "Sparse Modeling ." Web.
1. Michael Elad. Sparse Modeling [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2260

Training machine learning on JPEG compressed images

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Authors:
Maxime Pistono, Gouenou Coatrieux, Jean-Claude Nunes, Michel Cozic
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1 April 2020 - 2:44am
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[1] Maxime Pistono, Gouenou Coatrieux, Jean-Claude Nunes, Michel Cozic, "Training machine learning on JPEG compressed images", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5063. Accessed: Apr. 07, 2020.
@article{5063-20,
url = {http://sigport.org/5063},
author = {Maxime Pistono; Gouenou Coatrieux; Jean-Claude Nunes; Michel Cozic },
publisher = {IEEE SigPort},
title = {Training machine learning on JPEG compressed images},
year = {2020} }
TY - EJOUR
T1 - Training machine learning on JPEG compressed images
AU - Maxime Pistono; Gouenou Coatrieux; Jean-Claude Nunes; Michel Cozic
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5063
ER -
Maxime Pistono, Gouenou Coatrieux, Jean-Claude Nunes, Michel Cozic. (2020). Training machine learning on JPEG compressed images. IEEE SigPort. http://sigport.org/5063
Maxime Pistono, Gouenou Coatrieux, Jean-Claude Nunes, Michel Cozic, 2020. Training machine learning on JPEG compressed images. Available at: http://sigport.org/5063.
Maxime Pistono, Gouenou Coatrieux, Jean-Claude Nunes, Michel Cozic. (2020). "Training machine learning on JPEG compressed images." Web.
1. Maxime Pistono, Gouenou Coatrieux, Jean-Claude Nunes, Michel Cozic. Training machine learning on JPEG compressed images [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5063

Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks

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23 March 2020 - 4:25pm
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[1] , "Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5024. Accessed: Apr. 07, 2020.
@article{5024-20,
url = {http://sigport.org/5024},
author = { },
publisher = {IEEE SigPort},
title = {Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks},
year = {2020} }
TY - EJOUR
T1 - Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5024
ER -
. (2020). Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks. IEEE SigPort. http://sigport.org/5024
, 2020. Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks. Available at: http://sigport.org/5024.
. (2020). "Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks." Web.
1. . Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5024

Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients


Segmenting a document image into text-lines and words finds applications in many research areas of DIA(Document Image Analysis) such as OCR, Word Spotting, and document retrieval. However, carrying out segmentation operation directly in the compressed document images is still an unexplored and challenging research area. Since JPEG is most widely accepted compression algorithm, this research paper attempts to segment a JPEG compressed printed text document image into text-lines and words, without fully decompressing the image.

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Authors:
Mohammed Javed, P Nagabhushan, Watanabe Osamu
Submitted On:
7 April 2020 - 5:04am
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DCC2020 Paper ID 181

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[1] Mohammed Javed, P Nagabhushan, Watanabe Osamu, "Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5001. Accessed: Apr. 07, 2020.
@article{5001-20,
url = {http://sigport.org/5001},
author = {Mohammed Javed; P Nagabhushan; Watanabe Osamu },
publisher = {IEEE SigPort},
title = {Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients},
year = {2020} }
TY - EJOUR
T1 - Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients
AU - Mohammed Javed; P Nagabhushan; Watanabe Osamu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5001
ER -
Mohammed Javed, P Nagabhushan, Watanabe Osamu. (2020). Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients. IEEE SigPort. http://sigport.org/5001
Mohammed Javed, P Nagabhushan, Watanabe Osamu, 2020. Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients. Available at: http://sigport.org/5001.
Mohammed Javed, P Nagabhushan, Watanabe Osamu. (2020). "Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients." Web.
1. Mohammed Javed, P Nagabhushan, Watanabe Osamu. Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5001

Improved Subspace K-Means Performance via a Randomized Matrix Decomposition


Subspace clustering algorithms provide the capability
to project a dataset onto bases that facilitate clustering.
Proposed in 2017, the subspace k-means algorithm simultaneously
performs clustering and dimensionality reduction with the goal
of finding the optimal subspace for the cluster structure; this
is accomplished by incorporating a trade-off between cluster
and noise subspaces in the objective function. In this study,
we improve subspace k-means by estimating a critical transformation

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Authors:
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley
Submitted On:
14 November 2019 - 7:39pm
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[1] Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley, "Improved Subspace K-Means Performance via a Randomized Matrix Decomposition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4958. Accessed: Apr. 07, 2020.
@article{4958-19,
url = {http://sigport.org/4958},
author = {Trevor Vannoy; Jacob Senecal; Veronika Strnadova-Neeley },
publisher = {IEEE SigPort},
title = {Improved Subspace K-Means Performance via a Randomized Matrix Decomposition},
year = {2019} }
TY - EJOUR
T1 - Improved Subspace K-Means Performance via a Randomized Matrix Decomposition
AU - Trevor Vannoy; Jacob Senecal; Veronika Strnadova-Neeley
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4958
ER -
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley. (2019). Improved Subspace K-Means Performance via a Randomized Matrix Decomposition. IEEE SigPort. http://sigport.org/4958
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley, 2019. Improved Subspace K-Means Performance via a Randomized Matrix Decomposition. Available at: http://sigport.org/4958.
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley. (2019). "Improved Subspace K-Means Performance via a Randomized Matrix Decomposition." Web.
1. Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley. Improved Subspace K-Means Performance via a Randomized Matrix Decomposition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4958

Poster: Generative-Discriminative Crop Type Identification using Satellite Images


Crop type identification refers to distinguishing certain crop from other landcovers, which is an essential and crucial task in agricultural monitoring. Satellite images are good data input for identifying different crops since satellites capture relatively wider area and more spectral information. Based on prior knowledge of crop phenology, multi-temporal images are stacked to extract the growth pattern of varied crops.

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Authors:
Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu
Submitted On:
9 November 2019 - 7:23pm
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Poster: Generative-Discriminative Crop Type Identification using Satellite Images

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[1] Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu, "Poster: Generative-Discriminative Crop Type Identification using Satellite Images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4942. Accessed: Apr. 07, 2020.
@article{4942-19,
url = {http://sigport.org/4942},
author = {Nan Qiao; Yi Zhao; Ruei-Sung Lin; Bo Gong; Zhongxiang Wu; Mei Han; Jiashu Liu },
publisher = {IEEE SigPort},
title = {Poster: Generative-Discriminative Crop Type Identification using Satellite Images},
year = {2019} }
TY - EJOUR
T1 - Poster: Generative-Discriminative Crop Type Identification using Satellite Images
AU - Nan Qiao; Yi Zhao; Ruei-Sung Lin; Bo Gong; Zhongxiang Wu; Mei Han; Jiashu Liu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4942
ER -
Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu. (2019). Poster: Generative-Discriminative Crop Type Identification using Satellite Images. IEEE SigPort. http://sigport.org/4942
Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu, 2019. Poster: Generative-Discriminative Crop Type Identification using Satellite Images. Available at: http://sigport.org/4942.
Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu. (2019). "Poster: Generative-Discriminative Crop Type Identification using Satellite Images." Web.
1. Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu. Poster: Generative-Discriminative Crop Type Identification using Satellite Images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4942

A deep network for single-snapshot direction of arrival estimation


This paper examines a deep feedforward network for beamforming with the single--snapshot Sample Covariance Matrix (SCM). The Conventional beamforming formulation, typically quadratic in the complex weight space, is reformulated as real and linear in the weight covariance and SCM. The reformulated SCMs are used as input to a deep feed--forward neural network (FNN) for two source localization. Simulations demonstrate the effect of source incoherence and performance in a noisy tracking example.

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Authors:
Peter Gerstoft, Emma Ozanich, Haiqiang Niu
Submitted On:
28 October 2019 - 10:56am
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[1] Peter Gerstoft, Emma Ozanich, Haiqiang Niu, "A deep network for single-snapshot direction of arrival estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4898. Accessed: Apr. 07, 2020.
@article{4898-19,
url = {http://sigport.org/4898},
author = {Peter Gerstoft; Emma Ozanich; Haiqiang Niu },
publisher = {IEEE SigPort},
title = {A deep network for single-snapshot direction of arrival estimation},
year = {2019} }
TY - EJOUR
T1 - A deep network for single-snapshot direction of arrival estimation
AU - Peter Gerstoft; Emma Ozanich; Haiqiang Niu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4898
ER -
Peter Gerstoft, Emma Ozanich, Haiqiang Niu. (2019). A deep network for single-snapshot direction of arrival estimation. IEEE SigPort. http://sigport.org/4898
Peter Gerstoft, Emma Ozanich, Haiqiang Niu, 2019. A deep network for single-snapshot direction of arrival estimation. Available at: http://sigport.org/4898.
Peter Gerstoft, Emma Ozanich, Haiqiang Niu. (2019). "A deep network for single-snapshot direction of arrival estimation." Web.
1. Peter Gerstoft, Emma Ozanich, Haiqiang Niu. A deep network for single-snapshot direction of arrival estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4898

Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks


We focus on a wireless sensor network powered with an energy beacon, where sensors send their measurements to the sink using the harvested energy. The aim of the system is to estimate an unknown signal over the area of interest as accurately as possible. We investigate optimal energy beamforming at the energy beacon and optimal transmit power allocation at the sensors under non-linear energy harvesting models. We use a deep reinforcement learning (RL) based approach where multi-layer neural networks are utilized.

Paper Details

Authors:
Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava, Anders Ahlen
Submitted On:
16 October 2019 - 8:16am
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[1] Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava, Anders Ahlen, "Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4875. Accessed: Apr. 07, 2020.
@article{4875-19,
url = {http://sigport.org/4875},
author = {Ayca Ozcelikkale; Mehmet Koseoglu; Mani Srivastava; Anders Ahlen },
publisher = {IEEE SigPort},
title = {Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks},
year = {2019} }
TY - EJOUR
T1 - Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks
AU - Ayca Ozcelikkale; Mehmet Koseoglu; Mani Srivastava; Anders Ahlen
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4875
ER -
Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava, Anders Ahlen. (2019). Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks. IEEE SigPort. http://sigport.org/4875
Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava, Anders Ahlen, 2019. Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks. Available at: http://sigport.org/4875.
Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava, Anders Ahlen. (2019). "Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks." Web.
1. Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava, Anders Ahlen. Deep Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4875

DYNAMIC SYSTEM IDENTIFICATION FOR GUIDANCE OF STIMULATION PARAMETERS IN HAPTIC SIMULATION ENVIRONMENTS

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Authors:
Andac Demir, Safaa Eldeeb, Murat Akçakaya, Deniz Erdoğmuş
Submitted On:
14 October 2019 - 7:55am
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[1] Andac Demir, Safaa Eldeeb, Murat Akçakaya, Deniz Erdoğmuş, "DYNAMIC SYSTEM IDENTIFICATION FOR GUIDANCE OF STIMULATION PARAMETERS IN HAPTIC SIMULATION ENVIRONMENTS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4869. Accessed: Apr. 07, 2020.
@article{4869-19,
url = {http://sigport.org/4869},
author = {Andac Demir; Safaa Eldeeb; Murat Akçakaya; Deniz Erdoğmuş },
publisher = {IEEE SigPort},
title = {DYNAMIC SYSTEM IDENTIFICATION FOR GUIDANCE OF STIMULATION PARAMETERS IN HAPTIC SIMULATION ENVIRONMENTS},
year = {2019} }
TY - EJOUR
T1 - DYNAMIC SYSTEM IDENTIFICATION FOR GUIDANCE OF STIMULATION PARAMETERS IN HAPTIC SIMULATION ENVIRONMENTS
AU - Andac Demir; Safaa Eldeeb; Murat Akçakaya; Deniz Erdoğmuş
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4869
ER -
Andac Demir, Safaa Eldeeb, Murat Akçakaya, Deniz Erdoğmuş. (2019). DYNAMIC SYSTEM IDENTIFICATION FOR GUIDANCE OF STIMULATION PARAMETERS IN HAPTIC SIMULATION ENVIRONMENTS. IEEE SigPort. http://sigport.org/4869
Andac Demir, Safaa Eldeeb, Murat Akçakaya, Deniz Erdoğmuş, 2019. DYNAMIC SYSTEM IDENTIFICATION FOR GUIDANCE OF STIMULATION PARAMETERS IN HAPTIC SIMULATION ENVIRONMENTS. Available at: http://sigport.org/4869.
Andac Demir, Safaa Eldeeb, Murat Akçakaya, Deniz Erdoğmuş. (2019). "DYNAMIC SYSTEM IDENTIFICATION FOR GUIDANCE OF STIMULATION PARAMETERS IN HAPTIC SIMULATION ENVIRONMENTS." Web.
1. Andac Demir, Safaa Eldeeb, Murat Akçakaya, Deniz Erdoğmuş. DYNAMIC SYSTEM IDENTIFICATION FOR GUIDANCE OF STIMULATION PARAMETERS IN HAPTIC SIMULATION ENVIRONMENTS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4869

Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis


Semi-continuous data have a point mass at zero and are continuous with positive support. Such data arise naturally in several real-life situations like signals in a blind source separation problem, daily rainfall at a location, sales of durable goods among many others. Therefore, efficient estimation of the underlying probability density function is of significant interest.

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Authors:
Sai K. Popuri, Zois Boukouvalas
Submitted On:
13 October 2019 - 4:45pm
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[1] Sai K. Popuri, Zois Boukouvalas, "Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4865. Accessed: Apr. 07, 2020.
@article{4865-19,
url = {http://sigport.org/4865},
author = {Sai K. Popuri; Zois Boukouvalas },
publisher = {IEEE SigPort},
title = {Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis},
year = {2019} }
TY - EJOUR
T1 - Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis
AU - Sai K. Popuri; Zois Boukouvalas
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4865
ER -
Sai K. Popuri, Zois Boukouvalas. (2019). Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis. IEEE SigPort. http://sigport.org/4865
Sai K. Popuri, Zois Boukouvalas, 2019. Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis. Available at: http://sigport.org/4865.
Sai K. Popuri, Zois Boukouvalas. (2019). "Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis." Web.
1. Sai K. Popuri, Zois Boukouvalas. Efficient Parameter Estimation for Semi-Continuous Data: An Application to Independent Component Analysis [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4865

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