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GlobalSIP 2017

The fifth IEEE Global Conference on Signal and Information Processing (GlobalSIP) will be held in Montreal, Quebec, Canada on November 14-16, 2017. GlobalSIP is a flagship IEEE Signal Processing Society conference. It focuses on signal and information processing with an emphasis on up-and-coming signal processing themes. The conference features world-class plenary speeches, distinguished Symposium talks, tutorials, exhibits, oral and poster sessions, and panels. Visit website.

ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING


In this paper we examine a technique for developing prognostic image characteristics, termed radiomics, for non-small cell lung cancer based on a tumour edge region-based analysis. Texture features were extracted from the rind of the tumour in a publicly available 3D CT data set to predict two-year survival. The derived models were compared against the previous methods of training radiomic signatures that are descriptive of the whole tumour volume. Radiomic features derived solely from regions external, but neighbouring, the tumour were shown to also have prognostic value.

Paper Details

Authors:
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller
Submitted On:
11 December 2017 - 5:01pm
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GlobalSIP - Conference Presentation v2.pdf

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[1] Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller, "ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2371. Accessed: Jan. 22, 2018.
@article{2371-17,
url = {http://sigport.org/2371},
author = {Alanna Vial; David Stirling; Matthew Field; Montserrat Ros; Christian Ritz; Martin Carolan; Lois Hollowayn; Alexis A. Miller },
publisher = {IEEE SigPort},
title = {ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING},
year = {2017} }
TY - EJOUR
T1 - ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING
AU - Alanna Vial; David Stirling; Matthew Field; Montserrat Ros; Christian Ritz; Martin Carolan; Lois Hollowayn; Alexis A. Miller
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2371
ER -
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller. (2017). ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING. IEEE SigPort. http://sigport.org/2371
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller, 2017. ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING. Available at: http://sigport.org/2371.
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller. (2017). "ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING." Web.
1. Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller. ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2371

Scattering Features for Multimodal Gait Recognition


Gait.pdf

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Authors:
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton
Submitted On:
25 November 2017 - 8:19pm
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[1] Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton, "Scattering Features for Multimodal Gait Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2369. Accessed: Jan. 22, 2018.
@article{2369-17,
url = {http://sigport.org/2369},
author = {Srdan Kitic;Gilles Puy;Patrick Perez;Philippe Gilberton },
publisher = {IEEE SigPort},
title = {Scattering Features for Multimodal Gait Recognition},
year = {2017} }
TY - EJOUR
T1 - Scattering Features for Multimodal Gait Recognition
AU - Srdan Kitic;Gilles Puy;Patrick Perez;Philippe Gilberton
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2369
ER -
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. (2017). Scattering Features for Multimodal Gait Recognition. IEEE SigPort. http://sigport.org/2369
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton, 2017. Scattering Features for Multimodal Gait Recognition. Available at: http://sigport.org/2369.
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. (2017). "Scattering Features for Multimodal Gait Recognition." Web.
1. Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. Scattering Features for Multimodal Gait Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2369

Scattering Features for Multimodal Gait Recognition


Gait.pdf

PDF icon Gait.pdf (21 downloads)

Paper Details

Authors:
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton
Submitted On:
25 November 2017 - 8:19pm
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Gait.pdf

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[1] Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton, "Scattering Features for Multimodal Gait Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2368. Accessed: Jan. 22, 2018.
@article{2368-17,
url = {http://sigport.org/2368},
author = {Srdan Kitic;Gilles Puy;Patrick Perez;Philippe Gilberton },
publisher = {IEEE SigPort},
title = {Scattering Features for Multimodal Gait Recognition},
year = {2017} }
TY - EJOUR
T1 - Scattering Features for Multimodal Gait Recognition
AU - Srdan Kitic;Gilles Puy;Patrick Perez;Philippe Gilberton
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2368
ER -
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. (2017). Scattering Features for Multimodal Gait Recognition. IEEE SigPort. http://sigport.org/2368
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton, 2017. Scattering Features for Multimodal Gait Recognition. Available at: http://sigport.org/2368.
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. (2017). "Scattering Features for Multimodal Gait Recognition." Web.
1. Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. Scattering Features for Multimodal Gait Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2368

Chance Constrained Optimization of Distributed Energy Resources via Affine Policies

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Authors:
Krishna Sandeep Ayyagari, Nikolaos Gatsis, and Ahmad Taha
Submitted On:
21 November 2017 - 1:47pm
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talk_GLOBALSIP_2017.pdf

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[1] Krishna Sandeep Ayyagari, Nikolaos Gatsis, and Ahmad Taha, "Chance Constrained Optimization of Distributed Energy Resources via Affine Policies", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2367. Accessed: Jan. 22, 2018.
@article{2367-17,
url = {http://sigport.org/2367},
author = {Krishna Sandeep Ayyagari; Nikolaos Gatsis; and Ahmad Taha },
publisher = {IEEE SigPort},
title = {Chance Constrained Optimization of Distributed Energy Resources via Affine Policies},
year = {2017} }
TY - EJOUR
T1 - Chance Constrained Optimization of Distributed Energy Resources via Affine Policies
AU - Krishna Sandeep Ayyagari; Nikolaos Gatsis; and Ahmad Taha
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2367
ER -
Krishna Sandeep Ayyagari, Nikolaos Gatsis, and Ahmad Taha. (2017). Chance Constrained Optimization of Distributed Energy Resources via Affine Policies. IEEE SigPort. http://sigport.org/2367
Krishna Sandeep Ayyagari, Nikolaos Gatsis, and Ahmad Taha, 2017. Chance Constrained Optimization of Distributed Energy Resources via Affine Policies. Available at: http://sigport.org/2367.
Krishna Sandeep Ayyagari, Nikolaos Gatsis, and Ahmad Taha. (2017). "Chance Constrained Optimization of Distributed Energy Resources via Affine Policies." Web.
1. Krishna Sandeep Ayyagari, Nikolaos Gatsis, and Ahmad Taha. Chance Constrained Optimization of Distributed Energy Resources via Affine Policies [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2367

Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes

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Authors:
Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo, Reiner Thomä
Submitted On:
20 November 2017 - 9:21am
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GlobalSIP_2017_presentation.pdf

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[1] Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo, Reiner Thomä, "Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2366. Accessed: Jan. 22, 2018.
@article{2366-17,
url = {http://sigport.org/2366},
author = {Anastasia Lavrenko; Florian Roemer; Giovanni Del Galdo; Reiner Thomä },
publisher = {IEEE SigPort},
title = {Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes},
year = {2017} }
TY - EJOUR
T1 - Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes
AU - Anastasia Lavrenko; Florian Roemer; Giovanni Del Galdo; Reiner Thomä
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2366
ER -
Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo, Reiner Thomä. (2017). Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes. IEEE SigPort. http://sigport.org/2366
Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo, Reiner Thomä, 2017. Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes. Available at: http://sigport.org/2366.
Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo, Reiner Thomä. (2017). "Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes." Web.
1. Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo, Reiner Thomä. Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2366

Counting Plants Using Deep Learning


In this paper we address the task of counting crop plants in a field using CNNs. The number of plants in an Unmanned Aerial Vehicle (UAV) image of the field is estimated using regression instead of classification. This avoids to need to know (or guess) the maximum expected number of plants. We also describe a method to extract images of sections or "plots" from an orthorectified image of the entire crop field. These images will be used for training and evaluation of the CNN.

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Authors:
Yuhao Chen, Christopher Boomsma, Edward Delp
Submitted On:
19 November 2017 - 12:46pm
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counting_plants_using_deep_learning_globalsip2017

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[1] Yuhao Chen, Christopher Boomsma, Edward Delp, "Counting Plants Using Deep Learning", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2364. Accessed: Jan. 22, 2018.
@article{2364-17,
url = {http://sigport.org/2364},
author = {Yuhao Chen; Christopher Boomsma; Edward Delp },
publisher = {IEEE SigPort},
title = {Counting Plants Using Deep Learning},
year = {2017} }
TY - EJOUR
T1 - Counting Plants Using Deep Learning
AU - Yuhao Chen; Christopher Boomsma; Edward Delp
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2364
ER -
Yuhao Chen, Christopher Boomsma, Edward Delp. (2017). Counting Plants Using Deep Learning. IEEE SigPort. http://sigport.org/2364
Yuhao Chen, Christopher Boomsma, Edward Delp, 2017. Counting Plants Using Deep Learning. Available at: http://sigport.org/2364.
Yuhao Chen, Christopher Boomsma, Edward Delp. (2017). "Counting Plants Using Deep Learning." Web.
1. Yuhao Chen, Christopher Boomsma, Edward Delp. Counting Plants Using Deep Learning [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2364

Using Smart Meter and PMU Data for Load Inference


Power distribution system operators require knowledge of power injections for accomplishing various grid dispatch tasks. Monitoring, collecting, and processing smart meter data across all grid nodes, however, may not be affordable given the communication and storage resources. In this context,

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Authors:
Siddharth Bhela, Vassilis Kekatos, Sriharsha Veeramachaneni
Submitted On:
18 November 2017 - 2:34pm
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GlobalSIP2017presentation.pdf

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[1] Siddharth Bhela, Vassilis Kekatos, Sriharsha Veeramachaneni, "Using Smart Meter and PMU Data for Load Inference", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2363. Accessed: Jan. 22, 2018.
@article{2363-17,
url = {http://sigport.org/2363},
author = {Siddharth Bhela; Vassilis Kekatos; Sriharsha Veeramachaneni },
publisher = {IEEE SigPort},
title = {Using Smart Meter and PMU Data for Load Inference},
year = {2017} }
TY - EJOUR
T1 - Using Smart Meter and PMU Data for Load Inference
AU - Siddharth Bhela; Vassilis Kekatos; Sriharsha Veeramachaneni
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2363
ER -
Siddharth Bhela, Vassilis Kekatos, Sriharsha Veeramachaneni. (2017). Using Smart Meter and PMU Data for Load Inference. IEEE SigPort. http://sigport.org/2363
Siddharth Bhela, Vassilis Kekatos, Sriharsha Veeramachaneni, 2017. Using Smart Meter and PMU Data for Load Inference. Available at: http://sigport.org/2363.
Siddharth Bhela, Vassilis Kekatos, Sriharsha Veeramachaneni. (2017). "Using Smart Meter and PMU Data for Load Inference." Web.
1. Siddharth Bhela, Vassilis Kekatos, Sriharsha Veeramachaneni. Using Smart Meter and PMU Data for Load Inference [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2363

Distinguished Lecture: IOT, Data and Healthcare: How do we get it right


Abstract

DL_Wendy.pdf

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Authors:
Wendy Nilsen
Submitted On:
16 November 2017 - 11:01am
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[1] Wendy Nilsen, "Distinguished Lecture: IOT, Data and Healthcare: How do we get it right", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2362. Accessed: Jan. 22, 2018.
@article{2362-17,
url = {http://sigport.org/2362},
author = {Wendy Nilsen },
publisher = {IEEE SigPort},
title = {Distinguished Lecture: IOT, Data and Healthcare: How do we get it right},
year = {2017} }
TY - EJOUR
T1 - Distinguished Lecture: IOT, Data and Healthcare: How do we get it right
AU - Wendy Nilsen
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2362
ER -
Wendy Nilsen. (2017). Distinguished Lecture: IOT, Data and Healthcare: How do we get it right. IEEE SigPort. http://sigport.org/2362
Wendy Nilsen, 2017. Distinguished Lecture: IOT, Data and Healthcare: How do we get it right. Available at: http://sigport.org/2362.
Wendy Nilsen. (2017). "Distinguished Lecture: IOT, Data and Healthcare: How do we get it right." Web.
1. Wendy Nilsen. Distinguished Lecture: IOT, Data and Healthcare: How do we get it right [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2362

On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices

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16 November 2017 - 8:34am
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On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices.pdf

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[1] , "On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2361. Accessed: Jan. 22, 2018.
@article{2361-17,
url = {http://sigport.org/2361},
author = { },
publisher = {IEEE SigPort},
title = {On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices},
year = {2017} }
TY - EJOUR
T1 - On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2361
ER -
. (2017). On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices. IEEE SigPort. http://sigport.org/2361
, 2017. On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices. Available at: http://sigport.org/2361.
. (2017). "On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices." Web.
1. . On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2361

Keynote: Physical Security is from Mars, Cybersecurity is from Venus


Corporations are dealing with the traditions of managing security in silos, leading to an increased number of attacks on things like critical infrastructure. Corporate (Physical) Security deals with facilities and building access, whereas IT Security is dealing with cybersecurity as well as system and network access. It’s as if they are living on different planets!

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Authors:
Brian Harrell
Submitted On:
15 November 2017 - 8:38pm
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KeyNote Talk.pdf

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[1] Brian Harrell, "Keynote: Physical Security is from Mars, Cybersecurity is from Venus ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2359. Accessed: Jan. 22, 2018.
@article{2359-17,
url = {http://sigport.org/2359},
author = {Brian Harrell },
publisher = {IEEE SigPort},
title = {Keynote: Physical Security is from Mars, Cybersecurity is from Venus },
year = {2017} }
TY - EJOUR
T1 - Keynote: Physical Security is from Mars, Cybersecurity is from Venus
AU - Brian Harrell
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2359
ER -
Brian Harrell. (2017). Keynote: Physical Security is from Mars, Cybersecurity is from Venus . IEEE SigPort. http://sigport.org/2359
Brian Harrell, 2017. Keynote: Physical Security is from Mars, Cybersecurity is from Venus . Available at: http://sigport.org/2359.
Brian Harrell. (2017). "Keynote: Physical Security is from Mars, Cybersecurity is from Venus ." Web.
1. Brian Harrell. Keynote: Physical Security is from Mars, Cybersecurity is from Venus [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2359

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