Workshops

(Deep Learning for Computer Vision with Keras)

Presenter:

Dr. Mohammad Reza Mohammadi— Assistant professor, Iran University of Science and Technology 

Faculty Members - School of Computer Engineering - Iran University of  Science & Technology (IUST)

Abstract 

Deep learning is the state of the art in most computer vision tasks. In this workshop, we first present the basics of deep learning for computer vision. Then, we introduce the Keras framework and write a simple deep neural network code for image classification in the Google Colab environment. We then discuss some modern deep convolutional neural networks and implement one of them for image classification. It also discusses how to convert these networks into fully convolutional networks for object detection and semantic segmentation. Finally, we introduce AutoDL to reduce the need for highly-educated data scientists to build, train and maintain deep learning algorithms.

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(The new generations of the Mobile Networks: Challenges and opportunities)

Presenter:

Dr. Abolfazl Diyanat— Assistant professor, Iran University of Science and Technology 

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Abstract

Today, Cellular Mobile Networks are one of the most lucrative and engaging industries in most of the countries in the world. It can be discerned that design, implementation, or any advancement in the new generations of mobile networks is considered as a major criterion in the development of the countries. In this workshop, the importance of this industry, research perspective, and the main features in different generations of Mobile Networks will be discussed along with a look at the features of the next generation of mobile networks. The growth and advancement of mobile networks from 1G to the fifth generation, and now, the sixth generation, is due to the development of technologies like Machine Learning, MIMO, IoT, SDN, NFV, SDR, Cloud Computing, and so on. This is especially true in the fifth and sixth-generation of Mobile Networks.

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(Signal and Image Decomposition and its applications: From Fourier transform to sparse and Deep models)

Presenter:

Dr. Aboozar Ghaffari— Assistant professor, Iran University of Science and Technology 

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Abstract

Signal and image representation has a critical role in signal and image processing, such as denoising, classification, compression, and source separation.  In this workshop, we present a review of signal decomposition approaches based on different viewpoints. At first, the linear decomposition based on Fourier and wavelet as deterministic transforms is illustrated. Then, Statistical decomposition based on multi-channel data such as principal component analysis PCA, Singular Spectrum Analysis (SSA), and independent component analysis ICA and their relations with deterministic transforms are investigated.   Recently, two models based on sparse presentation and Deep neural networks have been used in many applications. In this workshop, these models and their relationships are described. Finally, some applications of these approaches have been described in many applications, such as classification, denoising, and vital sign estimation based on face video. 

Conference Countdown
2 month(s), and 6 day(s)
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Poster

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Important Dates

Submission Deadline: Oct 7,  2021  Oct 22, 2021 (Extended)  Oct 27, 2021 (Final Extension)

Workshop Proposal deadline: Nov 6, 2021

Workshop Admission Announcement: Nov 20, 2021

Notification of Acceptance: Nov 22, 2021

Registration and Submission for Final Version: Dec 6, 2021

Conference Date: Dec 29 and 30, 2021

Contact Us

Postal Address: School of Computer Engineering, Iran University of Science and Technology, University Road, Hengam Street, Resalat Square, Narmak, Tehran, Iran
Zip Code: 
16846-13114
Tel: +98 (21) 73225316 
Fax: +98 (21) 73225322
Email: ICSPIS2021@gmail.com
Conference Correspondence
: Dr. Marzieh Malekimajd


Last Update

 October 5, 2021

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