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

ICASSP 2018

ICASSP is the world's largest and most comprehensive technical conference on signal processing and its applications. It provides a fantastic networking opportunity for like-minded professionals from around the world. ICASSP 2018 conference will feature world-class presentations by internationally renowned speakers and cutting-edge session topics. Visit ICASSP 2018.

Teaching Signals & Systems in a Project-Based Environment


Project-based learning is a form of active learning where large-scale projects provide context for technical learning. Along with background information, this paper examines teaching and learning of signals and systems in the context of two ABET accredited project-based learning programs. Examples of projects, deep learning activities and classroom activities are provided.

Paper Details

Authors:
Robert Sleezer, Eleanor Leung, Rebecca Bates
Submitted On:
21 April 2018 - 3:17pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Teaching Signals & Systems in a Project-Based Environment

(0)

Keywords

Subscribe

[1] Robert Sleezer, Eleanor Leung, Rebecca Bates, "Teaching Signals & Systems in a Project-Based Environment", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3124. Accessed: Apr. 21, 2018.
@article{3124-18,
url = {http://sigport.org/3124},
author = {Robert Sleezer; Eleanor Leung; Rebecca Bates },
publisher = {IEEE SigPort},
title = {Teaching Signals & Systems in a Project-Based Environment},
year = {2018} }
TY - EJOUR
T1 - Teaching Signals & Systems in a Project-Based Environment
AU - Robert Sleezer; Eleanor Leung; Rebecca Bates
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3124
ER -
Robert Sleezer, Eleanor Leung, Rebecca Bates. (2018). Teaching Signals & Systems in a Project-Based Environment. IEEE SigPort. http://sigport.org/3124
Robert Sleezer, Eleanor Leung, Rebecca Bates, 2018. Teaching Signals & Systems in a Project-Based Environment. Available at: http://sigport.org/3124.
Robert Sleezer, Eleanor Leung, Rebecca Bates. (2018). "Teaching Signals & Systems in a Project-Based Environment." Web.
1. Robert Sleezer, Eleanor Leung, Rebecca Bates. Teaching Signals & Systems in a Project-Based Environment [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3124

Identification of Bilinear Forms with the Kalman Filter

Paper Details

Authors:
Laura Dogariu, Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Pablo Piantanida
Submitted On:
21 April 2018 - 12:05pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Identification_of_Bilinear_Forms_with_the_Kalman_Filter_ICASSP_2018.pdf

(3 downloads)

Keywords

Additional Categories

Subscribe

[1] Laura Dogariu, Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Pablo Piantanida, "Identification of Bilinear Forms with the Kalman Filter", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3123. Accessed: Apr. 21, 2018.
@article{3123-18,
url = {http://sigport.org/3123},
author = {Laura Dogariu; Constantin Paleologu; Silviu Ciochina; Jacob Benesty; Pablo Piantanida },
publisher = {IEEE SigPort},
title = {Identification of Bilinear Forms with the Kalman Filter},
year = {2018} }
TY - EJOUR
T1 - Identification of Bilinear Forms with the Kalman Filter
AU - Laura Dogariu; Constantin Paleologu; Silviu Ciochina; Jacob Benesty; Pablo Piantanida
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3123
ER -
Laura Dogariu, Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Pablo Piantanida. (2018). Identification of Bilinear Forms with the Kalman Filter. IEEE SigPort. http://sigport.org/3123
Laura Dogariu, Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Pablo Piantanida, 2018. Identification of Bilinear Forms with the Kalman Filter. Available at: http://sigport.org/3123.
Laura Dogariu, Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Pablo Piantanida. (2018). "Identification of Bilinear Forms with the Kalman Filter." Web.
1. Laura Dogariu, Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Pablo Piantanida. Identification of Bilinear Forms with the Kalman Filter [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3123

AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION

Paper Details

Authors:
Sharad Joshi, Mohit Lamba, Vivek Goyal, Nitin Khanna
Submitted On:
21 April 2018 - 8:37am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP_Poster.pdf

(910 downloads)

Keywords

Subscribe

[1] Sharad Joshi, Mohit Lamba, Vivek Goyal, Nitin Khanna, "AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3122. Accessed: Apr. 21, 2018.
@article{3122-18,
url = {http://sigport.org/3122},
author = {Sharad Joshi; Mohit Lamba; Vivek Goyal; Nitin Khanna },
publisher = {IEEE SigPort},
title = {AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION},
year = {2018} }
TY - EJOUR
T1 - AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION
AU - Sharad Joshi; Mohit Lamba; Vivek Goyal; Nitin Khanna
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3122
ER -
Sharad Joshi, Mohit Lamba, Vivek Goyal, Nitin Khanna. (2018). AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION. IEEE SigPort. http://sigport.org/3122
Sharad Joshi, Mohit Lamba, Vivek Goyal, Nitin Khanna, 2018. AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION. Available at: http://sigport.org/3122.
Sharad Joshi, Mohit Lamba, Vivek Goyal, Nitin Khanna. (2018). "AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION." Web.
1. Sharad Joshi, Mohit Lamba, Vivek Goyal, Nitin Khanna. AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3122

RATE-DISTORTION OPTIMIZED ILLUMINATION ESTIMATION FOR WAVELET-BASED VIDEO CODING


We propose a rate-distortion optimized framework for estimating
illumination changes (lighting variations, fade in/out
effects) in a highly scalable coding system. Illumination
variations are realized using multiplicative factors in the image
domain and are estimated considering the coding cost
of the illumination field and input frames which are first
subject to a temporal Lifting-based Illumination Adaptive
Transform (LIAT). The coding cost is modelled by an L1-
norm optimization problem which is derived to approximate

Paper Details

Authors:
Maryam Haghighat, Reji Mathew, Aous Naman, Sean Young and David Taubman
Submitted On:
21 April 2018 - 1:54am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2018

(2 downloads)

Keywords

Subscribe

[1] Maryam Haghighat, Reji Mathew, Aous Naman, Sean Young and David Taubman, "RATE-DISTORTION OPTIMIZED ILLUMINATION ESTIMATION FOR WAVELET-BASED VIDEO CODING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3121. Accessed: Apr. 21, 2018.
@article{3121-18,
url = {http://sigport.org/3121},
author = {Maryam Haghighat; Reji Mathew; Aous Naman; Sean Young and David Taubman },
publisher = {IEEE SigPort},
title = {RATE-DISTORTION OPTIMIZED ILLUMINATION ESTIMATION FOR WAVELET-BASED VIDEO CODING},
year = {2018} }
TY - EJOUR
T1 - RATE-DISTORTION OPTIMIZED ILLUMINATION ESTIMATION FOR WAVELET-BASED VIDEO CODING
AU - Maryam Haghighat; Reji Mathew; Aous Naman; Sean Young and David Taubman
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3121
ER -
Maryam Haghighat, Reji Mathew, Aous Naman, Sean Young and David Taubman. (2018). RATE-DISTORTION OPTIMIZED ILLUMINATION ESTIMATION FOR WAVELET-BASED VIDEO CODING. IEEE SigPort. http://sigport.org/3121
Maryam Haghighat, Reji Mathew, Aous Naman, Sean Young and David Taubman, 2018. RATE-DISTORTION OPTIMIZED ILLUMINATION ESTIMATION FOR WAVELET-BASED VIDEO CODING. Available at: http://sigport.org/3121.
Maryam Haghighat, Reji Mathew, Aous Naman, Sean Young and David Taubman. (2018). "RATE-DISTORTION OPTIMIZED ILLUMINATION ESTIMATION FOR WAVELET-BASED VIDEO CODING." Web.
1. Maryam Haghighat, Reji Mathew, Aous Naman, Sean Young and David Taubman. RATE-DISTORTION OPTIMIZED ILLUMINATION ESTIMATION FOR WAVELET-BASED VIDEO CODING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3121

ORTHOGONALLY REGULARIZED DEEP NETWORKS FOR IMAGE SUPER-RESOLUTION

Paper Details

Authors:
HOJJAT SEYED MOUSAVI, VISHAL MONGA
Submitted On:
21 April 2018 - 1:45am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:

Document Files

poster_ORDSR_2.pdf

(3 downloads)

Keywords

Subscribe

[1] HOJJAT SEYED MOUSAVI, VISHAL MONGA, "ORTHOGONALLY REGULARIZED DEEP NETWORKS FOR IMAGE SUPER-RESOLUTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3120. Accessed: Apr. 21, 2018.
@article{3120-18,
url = {http://sigport.org/3120},
author = {HOJJAT SEYED MOUSAVI; VISHAL MONGA },
publisher = {IEEE SigPort},
title = {ORTHOGONALLY REGULARIZED DEEP NETWORKS FOR IMAGE SUPER-RESOLUTION},
year = {2018} }
TY - EJOUR
T1 - ORTHOGONALLY REGULARIZED DEEP NETWORKS FOR IMAGE SUPER-RESOLUTION
AU - HOJJAT SEYED MOUSAVI; VISHAL MONGA
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3120
ER -
HOJJAT SEYED MOUSAVI, VISHAL MONGA. (2018). ORTHOGONALLY REGULARIZED DEEP NETWORKS FOR IMAGE SUPER-RESOLUTION. IEEE SigPort. http://sigport.org/3120
HOJJAT SEYED MOUSAVI, VISHAL MONGA, 2018. ORTHOGONALLY REGULARIZED DEEP NETWORKS FOR IMAGE SUPER-RESOLUTION. Available at: http://sigport.org/3120.
HOJJAT SEYED MOUSAVI, VISHAL MONGA. (2018). "ORTHOGONALLY REGULARIZED DEEP NETWORKS FOR IMAGE SUPER-RESOLUTION." Web.
1. HOJJAT SEYED MOUSAVI, VISHAL MONGA. ORTHOGONALLY REGULARIZED DEEP NETWORKS FOR IMAGE SUPER-RESOLUTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3120

Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS

Paper Details

Authors:
Danqi Jin, Jie Chen, Cedric Richard, Jingdong Chen
Submitted On:
20 April 2018 - 11:54pm
Short Link:
Type:
Event:

Document Files

Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS.pdf

(5 downloads)

Keywords

Subscribe

[1] Danqi Jin, Jie Chen, Cedric Richard, Jingdong Chen, "Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3119. Accessed: Apr. 21, 2018.
@article{3119-18,
url = {http://sigport.org/3119},
author = {Danqi Jin; Jie Chen; Cedric Richard; Jingdong Chen },
publisher = {IEEE SigPort},
title = {Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS},
year = {2018} }
TY - EJOUR
T1 - Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS
AU - Danqi Jin; Jie Chen; Cedric Richard; Jingdong Chen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3119
ER -
Danqi Jin, Jie Chen, Cedric Richard, Jingdong Chen. (2018). Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS. IEEE SigPort. http://sigport.org/3119
Danqi Jin, Jie Chen, Cedric Richard, Jingdong Chen, 2018. Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS. Available at: http://sigport.org/3119.
Danqi Jin, Jie Chen, Cedric Richard, Jingdong Chen. (2018). "Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS." Web.
1. Danqi Jin, Jie Chen, Cedric Richard, Jingdong Chen. Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3119

Whole Sentence Neural Language Model


Recurrent neural networks have become increasingly popular for the task of language modeling achieving impressive gains in state-of-the-art speech recognition and natural language processing (NLP) tasks. Recurrent models exploit word dependencies over a much longer context window (as retained by the history states) than what is feasible with n-gram language models.

Paper Details

Authors:
Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran
Submitted On:
20 April 2018 - 10:30pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

whole sentence neural language model

(4 downloads)

Keywords

Additional Categories

Subscribe

[1] Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran, "Whole Sentence Neural Language Model ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3118. Accessed: Apr. 21, 2018.
@article{3118-18,
url = {http://sigport.org/3118},
author = {Abhinav Sethy; Kartik Audhkhasi; Bhuvana Ramabhadran },
publisher = {IEEE SigPort},
title = {Whole Sentence Neural Language Model },
year = {2018} }
TY - EJOUR
T1 - Whole Sentence Neural Language Model
AU - Abhinav Sethy; Kartik Audhkhasi; Bhuvana Ramabhadran
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3118
ER -
Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran. (2018). Whole Sentence Neural Language Model . IEEE SigPort. http://sigport.org/3118
Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran, 2018. Whole Sentence Neural Language Model . Available at: http://sigport.org/3118.
Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran. (2018). "Whole Sentence Neural Language Model ." Web.
1. Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran. Whole Sentence Neural Language Model [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3118

Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors


Sampling of smooth spatiotemporally varying fields is a well studied topic in the literature. Classical approach assumes that the field is observed at known sampling locations and known timestamps ensuring field reconstruction. In a first, in this work the sampling and reconstruction of a spatiotemporal bandlimited field is addressed, where the samples are obtained by a location-unaware, time-unaware mobile sensor. The spatial and temporal order of samples is assumed to be known. It is assumed that the field samples are affected by measurement-noise.

Paper Details

Authors:
Animesh Kumar
Submitted On:
20 April 2018 - 6:42pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Sensors Poster

(5 downloads)

Keywords

Subscribe

[1] Animesh Kumar, "Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3116. Accessed: Apr. 21, 2018.
@article{3116-18,
url = {http://sigport.org/3116},
author = {Animesh Kumar },
publisher = {IEEE SigPort},
title = {Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors},
year = {2018} }
TY - EJOUR
T1 - Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors
AU - Animesh Kumar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3116
ER -
Animesh Kumar. (2018). Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors. IEEE SigPort. http://sigport.org/3116
Animesh Kumar, 2018. Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors. Available at: http://sigport.org/3116.
Animesh Kumar. (2018). "Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors." Web.
1. Animesh Kumar. Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3116

MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION


Active noise control (ANC) over a sizeable space requires a large number of reference and error microphones to satisfy the spatial Nyquist sampling criterion, which limits the feasibility of practical realization of such systems. This paper proposes a mode-domain feedforward ANC method to attenuate the noise field over a large space while reducing the number of microphones required.

Paper Details

Authors:
Yuki Mitsufuji, Thushara Abhayapala
Submitted On:
20 April 2018 - 5:10pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2018_poster.pdf

(4 downloads)

Keywords

Subscribe

[1] Yuki Mitsufuji, Thushara Abhayapala, "MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3115. Accessed: Apr. 21, 2018.
@article{3115-18,
url = {http://sigport.org/3115},
author = {Yuki Mitsufuji; Thushara Abhayapala },
publisher = {IEEE SigPort},
title = {MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION},
year = {2018} }
TY - EJOUR
T1 - MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION
AU - Yuki Mitsufuji; Thushara Abhayapala
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3115
ER -
Yuki Mitsufuji, Thushara Abhayapala. (2018). MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION. IEEE SigPort. http://sigport.org/3115
Yuki Mitsufuji, Thushara Abhayapala, 2018. MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION. Available at: http://sigport.org/3115.
Yuki Mitsufuji, Thushara Abhayapala. (2018). "MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION." Web.
1. Yuki Mitsufuji, Thushara Abhayapala. MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3115

ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements

Paper Details

Authors:
Submitted On:
20 April 2018 - 4:30pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP_MW.pdf

(5 downloads)

Keywords

Subscribe

[1] , "ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3113. Accessed: Apr. 21, 2018.
@article{3113-18,
url = {http://sigport.org/3113},
author = { },
publisher = {IEEE SigPort},
title = {ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements},
year = {2018} }
TY - EJOUR
T1 - ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3113
ER -
. (2018). ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements. IEEE SigPort. http://sigport.org/3113
, 2018. ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements. Available at: http://sigport.org/3113.
. (2018). "ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements." Web.
1. . ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3113

Pages