Topic : Principal Component Analysis and its Application in face recognition.
Date: 3rd October 2019 (Thursday)
Speaker: Deepak Raya (M.Tech in Digital Signal Processing,IIST)
Affiliation: MTech in DSP, IIST.
This Presentation discusses about PCA method in general and its application to face recognition using Eigen faces concept.
Slides: PCA_MLDL.pdf
Title: “Hiding Data using deep learning”
Speaker Name: Rohit Gandikota
Affiliation: Alumni IIST & Scientist, NRSC.
Date: 1st November 2019
Abstract: Explore how one can hide any form of data inside an image in the easiest way possible.
Watermarking, secret messaging and a lot of privacy based applications make this work interesting for all demography.
Harnessing deep learning models for this data hiding is an interesting and novel idea that has a lot of scope for further research.
Slides:Data_Hiding_using_deep_learning.pdf
Resources:
Title: “Architectures and Protocols for Internet of Things- Ambient Assisted Living”
Speaker: Prescilla Koshy
Affiliation: Postdoctoral researcher, Systems and Networks lab, IIST.
Date: 24th January 2020
The lecture discusses about Ambient Assisted living - IOT architectures and addresses security issues and limitations in AAL and IOT.
It also gives an introduction to Block chain technologies and sliding window block chain technology and its suitablity for IOT.
Further discusses the scope for ML and DL algorithms for IOT.
Slides:presentation24-01-2020.pdf
Title: “Design and Development of OWL-DL Ontologies for Satellite Launch Vehicle Mission”
Speaker: S. S. Uma Sankari
Affiliation: Section Head, QAMD, QRSG/SR, VSSC, ISRO
Date: 26th February 2020
Abstract:Ontology_Abstract.pdf
Slides:Uma_IIST_presented_26feb20.pdf
Title: “Graph Convolution Neural Network”
Speaker: Asif Salim
Affiliation: PhD research scholar, Mathematics Department, IIST
Date: 11 March 2020
About: Graph convolution neural networks, have wide applications in the domains that can harness graph structures out of data.
The lecture discusses mainly about the Spectral approaches graph neural nets, Filtering graph signals, Importance of smoothness functionals.
Introduces about few application of graph neural networks in various fields.
Slides: talk.pdf
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