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ICASSP 2020

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.

Experiments in Creating Online Course Content for Signal Processing Education


A brief introduction to the NPTEL.ac.in platform of India which
provides free access to quality online educational content for
Signal Processing. Experiences of creating courses related to Signal Processing,
supported by the European Union-funded project, MIELES.

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Authors:
C.-G. Jansson, R. Thottappillil, S. Hillmann, S. Möller, K V S Hari, R. Sundaresan
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16 May 2020 - 10:14pm
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TH3.PD-2914-ICASSP2020-KVS-HARI-Presentation.pdf

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[1] C.-G. Jansson, R. Thottappillil, S. Hillmann, S. Möller, K V S Hari, R. Sundaresan, "Experiments in Creating Online Course Content for Signal Processing Education", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5381. Accessed: Aug. 05, 2020.
@article{5381-20,
url = {http://sigport.org/5381},
author = {C.-G. Jansson; R. Thottappillil; S. Hillmann; S. Möller; K V S Hari; R. Sundaresan },
publisher = {IEEE SigPort},
title = {Experiments in Creating Online Course Content for Signal Processing Education},
year = {2020} }
TY - EJOUR
T1 - Experiments in Creating Online Course Content for Signal Processing Education
AU - C.-G. Jansson; R. Thottappillil; S. Hillmann; S. Möller; K V S Hari; R. Sundaresan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5381
ER -
C.-G. Jansson, R. Thottappillil, S. Hillmann, S. Möller, K V S Hari, R. Sundaresan. (2020). Experiments in Creating Online Course Content for Signal Processing Education. IEEE SigPort. http://sigport.org/5381
C.-G. Jansson, R. Thottappillil, S. Hillmann, S. Möller, K V S Hari, R. Sundaresan, 2020. Experiments in Creating Online Course Content for Signal Processing Education. Available at: http://sigport.org/5381.
C.-G. Jansson, R. Thottappillil, S. Hillmann, S. Möller, K V S Hari, R. Sundaresan. (2020). "Experiments in Creating Online Course Content for Signal Processing Education." Web.
1. C.-G. Jansson, R. Thottappillil, S. Hillmann, S. Möller, K V S Hari, R. Sundaresan. Experiments in Creating Online Course Content for Signal Processing Education [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5381

Adversarial Networks for Secure Wireless Communications - Slides


We propose a data-driven secure wireless communication scheme, in which the goal is to transmit a signal to a legitimate receiver with minimal distortion, while keeping some information about the signal private from an eavesdropping adversary. When the data distribution is known, the optimal trade-off between the reconstruction quality at the legitimate receiver and the leakage to the adversary can be characterised in the information theoretic asymptotic limit.

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Authors:
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz
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16 May 2020 - 4:33pm
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[1] Thomas Marchioro, Nicola Laurenti, Deniz Gunduz, "Adversarial Networks for Secure Wireless Communications - Slides", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5380. Accessed: Aug. 05, 2020.
@article{5380-20,
url = {http://sigport.org/5380},
author = {Thomas Marchioro; Nicola Laurenti; Deniz Gunduz },
publisher = {IEEE SigPort},
title = {Adversarial Networks for Secure Wireless Communications - Slides},
year = {2020} }
TY - EJOUR
T1 - Adversarial Networks for Secure Wireless Communications - Slides
AU - Thomas Marchioro; Nicola Laurenti; Deniz Gunduz
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5380
ER -
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz. (2020). Adversarial Networks for Secure Wireless Communications - Slides. IEEE SigPort. http://sigport.org/5380
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz, 2020. Adversarial Networks for Secure Wireless Communications - Slides. Available at: http://sigport.org/5380.
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz. (2020). "Adversarial Networks for Secure Wireless Communications - Slides." Web.
1. Thomas Marchioro, Nicola Laurenti, Deniz Gunduz. Adversarial Networks for Secure Wireless Communications - Slides [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5380

UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS


We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals. The compressed signal is represented by a latent vector fed into a generator network which is trained to produce high-quality signals that minimize a target objective function. To efficiently quantize the compressed signal, non-uniformly quantized optimal latent vectors are identified by iterative back-propagation with ADMM optimization performed for each iteration.

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16 May 2020 - 4:30pm
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[1] , "UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5379. Accessed: Aug. 05, 2020.
@article{5379-20,
url = {http://sigport.org/5379},
author = { },
publisher = {IEEE SigPort},
title = {UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS},
year = {2020} }
TY - EJOUR
T1 - UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5379
ER -
. (2020). UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS. IEEE SigPort. http://sigport.org/5379
, 2020. UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS. Available at: http://sigport.org/5379.
. (2020). "UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS." Web.
1. . UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5379

EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK


In speech production, epochs are glottal closure instants where significant energy is released from the lungs. Extracting an epoch accurately is important in speech synthesis, analysis, and pitch oriented studies. The time-varying characteristics of the source and the system, and channel attenuation of low-frequency components by telephone channels make estimation of epoch from a speech signal a challenging task.

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Authors:
Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula
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16 May 2020 - 2:22pm
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Epoch Extraction using gammatone wavelets

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[1] Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula, "EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5378. Accessed: Aug. 05, 2020.
@article{5378-20,
url = {http://sigport.org/5378},
author = {Pavan Kulkarni; Jishnu Sadasivan; Aniruddha Adiga; Chandra Sekhar Seelamantula },
publisher = {IEEE SigPort},
title = {EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK},
year = {2020} }
TY - EJOUR
T1 - EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK
AU - Pavan Kulkarni; Jishnu Sadasivan; Aniruddha Adiga; Chandra Sekhar Seelamantula
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5378
ER -
Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula. (2020). EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK. IEEE SigPort. http://sigport.org/5378
Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula, 2020. EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK. Available at: http://sigport.org/5378.
Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula. (2020). "EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK." Web.
1. Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula. EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5378

Embedded Large–Scale Handwritten Chinese Character Recognition


As handwriting input becomes more prevalent, the large symbol inventory required to support Chinese handwriting recognition poses unique challenges. This paper describes how the Apple deep learning recognition system can accurately handle up to 30,000 Chinese characters while running in real-time across a range of mobile devices.

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Authors:
Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda
Submitted On:
16 May 2020 - 11:46am
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_Embedded Large Scale Handwritten Chinese Character.pdf

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[1] Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda, "Embedded Large–Scale Handwritten Chinese Character Recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5377. Accessed: Aug. 05, 2020.
@article{5377-20,
url = {http://sigport.org/5377},
author = {Hans J. G. A. Dolfing; Ryan S. Dixon; Jerome R. Bellegarda },
publisher = {IEEE SigPort},
title = {Embedded Large–Scale Handwritten Chinese Character Recognition},
year = {2020} }
TY - EJOUR
T1 - Embedded Large–Scale Handwritten Chinese Character Recognition
AU - Hans J. G. A. Dolfing; Ryan S. Dixon; Jerome R. Bellegarda
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5377
ER -
Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda. (2020). Embedded Large–Scale Handwritten Chinese Character Recognition. IEEE SigPort. http://sigport.org/5377
Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda, 2020. Embedded Large–Scale Handwritten Chinese Character Recognition. Available at: http://sigport.org/5377.
Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda. (2020). "Embedded Large–Scale Handwritten Chinese Character Recognition." Web.
1. Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda. Embedded Large–Scale Handwritten Chinese Character Recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5377

MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING

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Authors:
Shikha Singh, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia
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16 May 2020 - 11:32am
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[1] Shikha Singh, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia, "MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5376. Accessed: Aug. 05, 2020.
@article{5376-20,
url = {http://sigport.org/5376},
author = {Shikha Singh; Jyoti Maggu; Angshul Majumdar; Emilie Chouzenoux; Giovanni Chierchia },
publisher = {IEEE SigPort},
title = {MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING},
year = {2020} }
TY - EJOUR
T1 - MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING
AU - Shikha Singh; Jyoti Maggu; Angshul Majumdar; Emilie Chouzenoux; Giovanni Chierchia
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5376
ER -
Shikha Singh, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia. (2020). MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING. IEEE SigPort. http://sigport.org/5376
Shikha Singh, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia, 2020. MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING. Available at: http://sigport.org/5376.
Shikha Singh, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia. (2020). "MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING." Web.
1. Shikha Singh, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia. MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5376

Automatic identification of speakers from head gestures in a narration


In this work, we focus on quantifying speaker identity information encoded in the head gestures of speakers, while they narrate a story. We hypothesize that the head gestures over a long duration have speaker-specific patterns. To establish this, we consider a classification problem to identify speakers from head gestures. We represent every head orientation as a triplet of Euler angles and a sequence of head orientations as head gestures.

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Authors:
Sanjeev Kadagathur Vadiraj, Achuth Rao M V, Prasanta Kumar Ghosh
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16 May 2020 - 10:52am
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[1] Sanjeev Kadagathur Vadiraj, Achuth Rao M V, Prasanta Kumar Ghosh, "Automatic identification of speakers from head gestures in a narration", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5375. Accessed: Aug. 05, 2020.
@article{5375-20,
url = {http://sigport.org/5375},
author = {Sanjeev Kadagathur Vadiraj; Achuth Rao M V; Prasanta Kumar Ghosh },
publisher = {IEEE SigPort},
title = {Automatic identification of speakers from head gestures in a narration},
year = {2020} }
TY - EJOUR
T1 - Automatic identification of speakers from head gestures in a narration
AU - Sanjeev Kadagathur Vadiraj; Achuth Rao M V; Prasanta Kumar Ghosh
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5375
ER -
Sanjeev Kadagathur Vadiraj, Achuth Rao M V, Prasanta Kumar Ghosh. (2020). Automatic identification of speakers from head gestures in a narration. IEEE SigPort. http://sigport.org/5375
Sanjeev Kadagathur Vadiraj, Achuth Rao M V, Prasanta Kumar Ghosh, 2020. Automatic identification of speakers from head gestures in a narration. Available at: http://sigport.org/5375.
Sanjeev Kadagathur Vadiraj, Achuth Rao M V, Prasanta Kumar Ghosh. (2020). "Automatic identification of speakers from head gestures in a narration." Web.
1. Sanjeev Kadagathur Vadiraj, Achuth Rao M V, Prasanta Kumar Ghosh. Automatic identification of speakers from head gestures in a narration [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5375

Multi-scale Octave Convolutions for Robust Speech Recognition


We propose a multi-scale octave convolution layer to learn robust speech representations efficiently. Octave convolutions were introduced by Chen et al [1] in the computer vision field to reduce the spatial redundancy of the feature maps by decomposing the output of a convolutional layer into feature maps at two different spatial resolutions, one octave apart. This approach improved the efficiency as well as the accuracy of the CNN models. The accuracy gain was attributed to the enlargement of the receptive field in the original input space.

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Authors:
Joanna Rownicka, Peter Bell, Steve Renals
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16 May 2020 - 8:58am
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[1] Joanna Rownicka, Peter Bell, Steve Renals, "Multi-scale Octave Convolutions for Robust Speech Recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5374. Accessed: Aug. 05, 2020.
@article{5374-20,
url = {http://sigport.org/5374},
author = {Joanna Rownicka; Peter Bell; Steve Renals },
publisher = {IEEE SigPort},
title = {Multi-scale Octave Convolutions for Robust Speech Recognition},
year = {2020} }
TY - EJOUR
T1 - Multi-scale Octave Convolutions for Robust Speech Recognition
AU - Joanna Rownicka; Peter Bell; Steve Renals
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5374
ER -
Joanna Rownicka, Peter Bell, Steve Renals. (2020). Multi-scale Octave Convolutions for Robust Speech Recognition. IEEE SigPort. http://sigport.org/5374
Joanna Rownicka, Peter Bell, Steve Renals, 2020. Multi-scale Octave Convolutions for Robust Speech Recognition. Available at: http://sigport.org/5374.
Joanna Rownicka, Peter Bell, Steve Renals. (2020). "Multi-scale Octave Convolutions for Robust Speech Recognition." Web.
1. Joanna Rownicka, Peter Bell, Steve Renals. Multi-scale Octave Convolutions for Robust Speech Recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5374

PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION

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Authors:
Bruno Scalzo, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic
Submitted On:
16 May 2020 - 8:06am
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[1] Bruno Scalzo, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic, "PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5373. Accessed: Aug. 05, 2020.
@article{5373-20,
url = {http://sigport.org/5373},
author = {Bruno Scalzo; Ljubisa Stankovic; Anthony G. Constantinides; Danilo P. Mandic },
publisher = {IEEE SigPort},
title = {PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION},
year = {2020} }
TY - EJOUR
T1 - PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION
AU - Bruno Scalzo; Ljubisa Stankovic; Anthony G. Constantinides; Danilo P. Mandic
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5373
ER -
Bruno Scalzo, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic. (2020). PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION. IEEE SigPort. http://sigport.org/5373
Bruno Scalzo, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic, 2020. PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION. Available at: http://sigport.org/5373.
Bruno Scalzo, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic. (2020). "PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION." Web.
1. Bruno Scalzo, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic. PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5373

Teaching Signals and Systems - A First Course in Signal Processing


Signals and systems is a well known fundamental course in signal processing. How this course is taught to a student can spell the difference between whether s/he pursues a career in this field or not. Giving due consideration to this matter, this paper reflects on the experiences in teaching this course. In addition, the authors share the experiences of creating and conducting a Massive Open Online Course (MOOC) on this subject under edX and subsequently following it up with deliberation among some students who did this course through the platform.

Paper Details

Authors:
Nikhar P. Rakhashia, Ankit A. Bhurane, Vikram M. Gadre
Submitted On:
16 May 2020 - 8:02am
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[1] Nikhar P. Rakhashia, Ankit A. Bhurane, Vikram M. Gadre, "Teaching Signals and Systems - A First Course in Signal Processing", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5372. Accessed: Aug. 05, 2020.
@article{5372-20,
url = {http://sigport.org/5372},
author = {Nikhar P. Rakhashia; Ankit A. Bhurane; Vikram M. Gadre },
publisher = {IEEE SigPort},
title = {Teaching Signals and Systems - A First Course in Signal Processing},
year = {2020} }
TY - EJOUR
T1 - Teaching Signals and Systems - A First Course in Signal Processing
AU - Nikhar P. Rakhashia; Ankit A. Bhurane; Vikram M. Gadre
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5372
ER -
Nikhar P. Rakhashia, Ankit A. Bhurane, Vikram M. Gadre. (2020). Teaching Signals and Systems - A First Course in Signal Processing. IEEE SigPort. http://sigport.org/5372
Nikhar P. Rakhashia, Ankit A. Bhurane, Vikram M. Gadre, 2020. Teaching Signals and Systems - A First Course in Signal Processing. Available at: http://sigport.org/5372.
Nikhar P. Rakhashia, Ankit A. Bhurane, Vikram M. Gadre. (2020). "Teaching Signals and Systems - A First Course in Signal Processing." Web.
1. Nikhar P. Rakhashia, Ankit A. Bhurane, Vikram M. Gadre. Teaching Signals and Systems - A First Course in Signal Processing [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5372

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