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Audio and Acoustic Signal Processing

Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)


With the strong growth of assistive and personal listening devices, natural sound rendering over headphones is becoming a necessity for prolonged listening in multimedia and virtual reality applications. The aim of natural sound rendering is to naturally recreate the sound scenes with the spatial and timbral quality as natural as possible, so as to achieve a truly immersive listening experience. However, rendering natural sound over headphones encounters many challenges. This tutorial article presents signal processing techniques to tackle these challenges to assist human listening.

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Authors:
Kaushik Sunder, Ee-Leng Tan
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23 February 2016 - 1:43pm
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SPM15slides_Natural Sound Rendering for Headphones.pdf

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[1] Kaushik Sunder, Ee-Leng Tan, "Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/167. Accessed: Nov. 17, 2018.
@article{167-15,
url = {http://sigport.org/167},
author = {Kaushik Sunder; Ee-Leng Tan },
publisher = {IEEE SigPort},
title = {Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)},
year = {2015} }
TY - EJOUR
T1 - Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)
AU - Kaushik Sunder; Ee-Leng Tan
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/167
ER -
Kaushik Sunder, Ee-Leng Tan. (2015). Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides). IEEE SigPort. http://sigport.org/167
Kaushik Sunder, Ee-Leng Tan, 2015. Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides). Available at: http://sigport.org/167.
Kaushik Sunder, Ee-Leng Tan. (2015). "Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)." Web.
1. Kaushik Sunder, Ee-Leng Tan. Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides) [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/167

Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition


Fine-grained recognition focuses on the challenging task of automatically identifying the subtle differences between similar categories. Current state-of-the-art approaches require elaborated feature learning procedures, involving tuning several hyper-parameters, or rely on expensive human annotations such as objects or parts location. In this paper we propose a simple method for fine-grained recognition that exploits a nearly cost-free attention-based focus operation to construct an ensemble of increasingly specialized Convolutional Neural Networks.

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Authors:
Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo'
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8 October 2018 - 9:25am
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[1] Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo', "Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3632. Accessed: Nov. 17, 2018.
@article{3632-18,
url = {http://sigport.org/3632},
author = {Andrea Simonelli; Stefano Messelodi; Francesco De Natale; Samuel Rota Bulo' },
publisher = {IEEE SigPort},
title = {Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition},
year = {2018} }
TY - EJOUR
T1 - Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition
AU - Andrea Simonelli; Stefano Messelodi; Francesco De Natale; Samuel Rota Bulo'
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3632
ER -
Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo'. (2018). Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition. IEEE SigPort. http://sigport.org/3632
Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo', 2018. Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition. Available at: http://sigport.org/3632.
Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo'. (2018). "Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition." Web.
1. Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo'. Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3632

An Interior Point Method for Nonnegative Sparse Signal Reconstruction


We present a primal-dual interior point method (IPM) with a novel preconditioner to solve the ℓ1-norm regularized least square problem for nonnegative sparse signal reconstruction. IPM is a second-order method that uses both gradient and Hessian information to compute effective search directions and achieve super-linear convergence rates. It therefore requires many fewer iterations than first-order methods such as iterative shrinkage/thresholding algorithms (ISTA) that only achieve sub-linear convergence rates.

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Authors:
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld
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7 October 2018 - 5:05pm
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2018_Huang_IPAlgorithm_ICIP_Poster.pdf

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[1] Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld, "An Interior Point Method for Nonnegative Sparse Signal Reconstruction", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3603. Accessed: Nov. 17, 2018.
@article{3603-18,
url = {http://sigport.org/3603},
author = {Xiang Huang; Kuan He; Seunghwan Yoo; Oliver Cossairt; Aggelos Katsaggelos; Nicola Ferrier; and Mark Hereld },
publisher = {IEEE SigPort},
title = {An Interior Point Method for Nonnegative Sparse Signal Reconstruction},
year = {2018} }
TY - EJOUR
T1 - An Interior Point Method for Nonnegative Sparse Signal Reconstruction
AU - Xiang Huang; Kuan He; Seunghwan Yoo; Oliver Cossairt; Aggelos Katsaggelos; Nicola Ferrier; and Mark Hereld
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3603
ER -
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. (2018). An Interior Point Method for Nonnegative Sparse Signal Reconstruction. IEEE SigPort. http://sigport.org/3603
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld, 2018. An Interior Point Method for Nonnegative Sparse Signal Reconstruction. Available at: http://sigport.org/3603.
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. (2018). "An Interior Point Method for Nonnegative Sparse Signal Reconstruction." Web.
1. Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. An Interior Point Method for Nonnegative Sparse Signal Reconstruction [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3603

An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging


There exist various types of information in retail food packages, including food product name, ingredients list and use by date. The correct recognition and coding of use by dates is especially critical in ensuring proper distribution of the product to the market and eliminating potential health risks caused by erroneous mislabelling. The latter can have a major negative effect on the health of consumers and consequently raise legal issues for suppliers.

ICIP_poster (1).pdf

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Authors:
Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva', Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias
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4 October 2018 - 12:42pm
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Poster

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[1] Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva', Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias, "An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3454. Accessed: Nov. 17, 2018.
@article{3454-18,
url = {http://sigport.org/3454},
author = {Fabio De Sousa Ribeiro; Liyun Gong; Francesco Caliva'; Mark Swainson; Kjartan Gudmundsson; Miao Yu; Georgios Leontidis; Xujiong Ye; Stefanos Kollias },
publisher = {IEEE SigPort},
title = {An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging},
year = {2018} }
TY - EJOUR
T1 - An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging
AU - Fabio De Sousa Ribeiro; Liyun Gong; Francesco Caliva'; Mark Swainson; Kjartan Gudmundsson; Miao Yu; Georgios Leontidis; Xujiong Ye; Stefanos Kollias
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3454
ER -
Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva', Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias. (2018). An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging. IEEE SigPort. http://sigport.org/3454
Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva', Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias, 2018. An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging. Available at: http://sigport.org/3454.
Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva', Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias. (2018). "An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging." Web.
1. Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva', Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias. An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3454

Sufficiency quantification for seamless text-independent speaker enrollment


Text-independent speaker recognition (TI-SR) requires a lengthy enrollment process that involves asking dedicated time from the user to create a reliable model of their voice. Seamless enrollment is a highly attractive feature which refers to the enrollment process that happens in the background and asks for no dedicated time from the user. One of the key problems in a fully automated seamless enrollment process is to determine the sufficiency of a given utterance collection for the purpose of TI-SR. No known metric exists in the literature to quantify sufficiency.

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Authors:
Gokcen Cilingir, Jonathan Huang, Mandar S Joshi, Narayan Biswal
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13 July 2018 - 3:38pm
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Poster presented at ICASSP 2018

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Paper for ICASSP 2018

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[1] Gokcen Cilingir, Jonathan Huang, Mandar S Joshi, Narayan Biswal, "Sufficiency quantification for seamless text-independent speaker enrollment", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3379. Accessed: Nov. 17, 2018.
@article{3379-18,
url = {http://sigport.org/3379},
author = {Gokcen Cilingir; Jonathan Huang; Mandar S Joshi; Narayan Biswal },
publisher = {IEEE SigPort},
title = {Sufficiency quantification for seamless text-independent speaker enrollment},
year = {2018} }
TY - EJOUR
T1 - Sufficiency quantification for seamless text-independent speaker enrollment
AU - Gokcen Cilingir; Jonathan Huang; Mandar S Joshi; Narayan Biswal
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3379
ER -
Gokcen Cilingir, Jonathan Huang, Mandar S Joshi, Narayan Biswal. (2018). Sufficiency quantification for seamless text-independent speaker enrollment. IEEE SigPort. http://sigport.org/3379
Gokcen Cilingir, Jonathan Huang, Mandar S Joshi, Narayan Biswal, 2018. Sufficiency quantification for seamless text-independent speaker enrollment. Available at: http://sigport.org/3379.
Gokcen Cilingir, Jonathan Huang, Mandar S Joshi, Narayan Biswal. (2018). "Sufficiency quantification for seamless text-independent speaker enrollment." Web.
1. Gokcen Cilingir, Jonathan Huang, Mandar S Joshi, Narayan Biswal. Sufficiency quantification for seamless text-independent speaker enrollment [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3379

Coalition Game for Emergency Vehicles Re-routing in Smart Cities


Traffic jam is considered as a difficult problem to deal with in many cities around the world due to the continuously

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Authors:
Hayder M. Amer, Hayder A. A. Al-Kashoash, Andrew Kemp, Lyudmila Mihaylova and Martin Mayfield
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5 July 2018 - 3:19pm
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IEEE SAM conference presentation LSM1.pdf

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[1] Hayder M. Amer, Hayder A. A. Al-Kashoash, Andrew Kemp, Lyudmila Mihaylova and Martin Mayfield, "Coalition Game for Emergency Vehicles Re-routing in Smart Cities", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3362. Accessed: Nov. 17, 2018.
@article{3362-18,
url = {http://sigport.org/3362},
author = {Hayder M. Amer; Hayder A. A. Al-Kashoash; Andrew Kemp; Lyudmila Mihaylova and Martin Mayfield },
publisher = {IEEE SigPort},
title = {Coalition Game for Emergency Vehicles Re-routing in Smart Cities},
year = {2018} }
TY - EJOUR
T1 - Coalition Game for Emergency Vehicles Re-routing in Smart Cities
AU - Hayder M. Amer; Hayder A. A. Al-Kashoash; Andrew Kemp; Lyudmila Mihaylova and Martin Mayfield
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3362
ER -
Hayder M. Amer, Hayder A. A. Al-Kashoash, Andrew Kemp, Lyudmila Mihaylova and Martin Mayfield. (2018). Coalition Game for Emergency Vehicles Re-routing in Smart Cities. IEEE SigPort. http://sigport.org/3362
Hayder M. Amer, Hayder A. A. Al-Kashoash, Andrew Kemp, Lyudmila Mihaylova and Martin Mayfield, 2018. Coalition Game for Emergency Vehicles Re-routing in Smart Cities. Available at: http://sigport.org/3362.
Hayder M. Amer, Hayder A. A. Al-Kashoash, Andrew Kemp, Lyudmila Mihaylova and Martin Mayfield. (2018). "Coalition Game for Emergency Vehicles Re-routing in Smart Cities." Web.
1. Hayder M. Amer, Hayder A. A. Al-Kashoash, Andrew Kemp, Lyudmila Mihaylova and Martin Mayfield. Coalition Game for Emergency Vehicles Re-routing in Smart Cities [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3362

Impact of Space-Time Covariance Estimation Errors on a Parahermitian Matrix EVD


This paper studies the impact of estimation errors in the sample space-time covariance matrix on its parahermitian matrix eigenvalue decomposition. We provide theoretical bounds for the perturbation of the ground-truth eigenvalues and of the subspaces of their corresponding eigenvectors. We show that for the eigenvalues, the perturbation depends on the norm of the estimation error in the space-time covariance matrix, while the perturbation of eigenvector subspaces can additionally be influenced by the distance between the eigenvalues. We confirm these theoretical results by simulations.

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Authors:
Connor Delaosa, Fraser K. Coutts, Jennifer Pestana, Stephan Weiss
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4 July 2018 - 5:17am
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sam_p18a_a0.pdf

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[1] Connor Delaosa, Fraser K. Coutts, Jennifer Pestana, Stephan Weiss, "Impact of Space-Time Covariance Estimation Errors on a Parahermitian Matrix EVD", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3351. Accessed: Nov. 17, 2018.
@article{3351-18,
url = {http://sigport.org/3351},
author = {Connor Delaosa; Fraser K. Coutts; Jennifer Pestana; Stephan Weiss },
publisher = {IEEE SigPort},
title = {Impact of Space-Time Covariance Estimation Errors on a Parahermitian Matrix EVD},
year = {2018} }
TY - EJOUR
T1 - Impact of Space-Time Covariance Estimation Errors on a Parahermitian Matrix EVD
AU - Connor Delaosa; Fraser K. Coutts; Jennifer Pestana; Stephan Weiss
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3351
ER -
Connor Delaosa, Fraser K. Coutts, Jennifer Pestana, Stephan Weiss. (2018). Impact of Space-Time Covariance Estimation Errors on a Parahermitian Matrix EVD. IEEE SigPort. http://sigport.org/3351
Connor Delaosa, Fraser K. Coutts, Jennifer Pestana, Stephan Weiss, 2018. Impact of Space-Time Covariance Estimation Errors on a Parahermitian Matrix EVD. Available at: http://sigport.org/3351.
Connor Delaosa, Fraser K. Coutts, Jennifer Pestana, Stephan Weiss. (2018). "Impact of Space-Time Covariance Estimation Errors on a Parahermitian Matrix EVD." Web.
1. Connor Delaosa, Fraser K. Coutts, Jennifer Pestana, Stephan Weiss. Impact of Space-Time Covariance Estimation Errors on a Parahermitian Matrix EVD [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3351

On Transmitter Polarization Optimization for Target Scattering Matrix Estimation

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Authors:
Shuli Shi;Zhiwen Liu;Yougen Xu
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2 July 2018 - 3:49am
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poster_20180622.pdf

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[1] Shuli Shi;Zhiwen Liu;Yougen Xu, "On Transmitter Polarization Optimization for Target Scattering Matrix Estimation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3346. Accessed: Nov. 17, 2018.
@article{3346-18,
url = {http://sigport.org/3346},
author = {Shuli Shi;Zhiwen Liu;Yougen Xu },
publisher = {IEEE SigPort},
title = {On Transmitter Polarization Optimization for Target Scattering Matrix Estimation},
year = {2018} }
TY - EJOUR
T1 - On Transmitter Polarization Optimization for Target Scattering Matrix Estimation
AU - Shuli Shi;Zhiwen Liu;Yougen Xu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3346
ER -
Shuli Shi;Zhiwen Liu;Yougen Xu. (2018). On Transmitter Polarization Optimization for Target Scattering Matrix Estimation. IEEE SigPort. http://sigport.org/3346
Shuli Shi;Zhiwen Liu;Yougen Xu, 2018. On Transmitter Polarization Optimization for Target Scattering Matrix Estimation. Available at: http://sigport.org/3346.
Shuli Shi;Zhiwen Liu;Yougen Xu. (2018). "On Transmitter Polarization Optimization for Target Scattering Matrix Estimation." Web.
1. Shuli Shi;Zhiwen Liu;Yougen Xu. On Transmitter Polarization Optimization for Target Scattering Matrix Estimation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3346

A Convolutionally Encoded OSTBC System with SNR-Adaptive Constellations for Low-Latency and Low-Complexity Communications

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Authors:
Mehmet Cagri Ilter, Risto Wichman, Jyri Hamalainen, and Halim Yanikomeroglu
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29 June 2018 - 4:28pm
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SPAWC2018posterEdited.pdf

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[1] Mehmet Cagri Ilter, Risto Wichman, Jyri Hamalainen, and Halim Yanikomeroglu, "A Convolutionally Encoded OSTBC System with SNR-Adaptive Constellations for Low-Latency and Low-Complexity Communications", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3342. Accessed: Nov. 17, 2018.
@article{3342-18,
url = {http://sigport.org/3342},
author = {Mehmet Cagri Ilter; Risto Wichman; Jyri Hamalainen; and Halim Yanikomeroglu },
publisher = {IEEE SigPort},
title = {A Convolutionally Encoded OSTBC System with SNR-Adaptive Constellations for Low-Latency and Low-Complexity Communications},
year = {2018} }
TY - EJOUR
T1 - A Convolutionally Encoded OSTBC System with SNR-Adaptive Constellations for Low-Latency and Low-Complexity Communications
AU - Mehmet Cagri Ilter; Risto Wichman; Jyri Hamalainen; and Halim Yanikomeroglu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3342
ER -
Mehmet Cagri Ilter, Risto Wichman, Jyri Hamalainen, and Halim Yanikomeroglu. (2018). A Convolutionally Encoded OSTBC System with SNR-Adaptive Constellations for Low-Latency and Low-Complexity Communications. IEEE SigPort. http://sigport.org/3342
Mehmet Cagri Ilter, Risto Wichman, Jyri Hamalainen, and Halim Yanikomeroglu, 2018. A Convolutionally Encoded OSTBC System with SNR-Adaptive Constellations for Low-Latency and Low-Complexity Communications. Available at: http://sigport.org/3342.
Mehmet Cagri Ilter, Risto Wichman, Jyri Hamalainen, and Halim Yanikomeroglu. (2018). "A Convolutionally Encoded OSTBC System with SNR-Adaptive Constellations for Low-Latency and Low-Complexity Communications." Web.
1. Mehmet Cagri Ilter, Risto Wichman, Jyri Hamalainen, and Halim Yanikomeroglu. A Convolutionally Encoded OSTBC System with SNR-Adaptive Constellations for Low-Latency and Low-Complexity Communications [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3342

A Surveillance System for Drone Localization and Tracking Using Acoustic Arrays

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29 June 2018 - 1:35am
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[1] , "A Surveillance System for Drone Localization and Tracking Using Acoustic Arrays", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3341. Accessed: Nov. 17, 2018.
@article{3341-18,
url = {http://sigport.org/3341},
author = { },
publisher = {IEEE SigPort},
title = {A Surveillance System for Drone Localization and Tracking Using Acoustic Arrays},
year = {2018} }
TY - EJOUR
T1 - A Surveillance System for Drone Localization and Tracking Using Acoustic Arrays
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3341
ER -
. (2018). A Surveillance System for Drone Localization and Tracking Using Acoustic Arrays. IEEE SigPort. http://sigport.org/3341
, 2018. A Surveillance System for Drone Localization and Tracking Using Acoustic Arrays. Available at: http://sigport.org/3341.
. (2018). "A Surveillance System for Drone Localization and Tracking Using Acoustic Arrays." Web.
1. . A Surveillance System for Drone Localization and Tracking Using Acoustic Arrays [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3341

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