PPT-Reconstruction-free Inference on Compressive Measurements

Author : mitsue-stanley | Published Date : 2016-09-18

Suhas Lohit Kuldeep Kulkarni Pavan Turaga Jian Wang Aswin Sankaranarayanan Arizona State University Carnegie Mellon University

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Reconstruction-free Inference on Compressive Measurements: Transcript

Suhas Lohit Kuldeep Kulkarni Pavan Turaga Jian Wang Aswin Sankaranarayanan Arizona State University Carnegie Mellon University. An Introduction and Survey of Applications. Objectives. Description of theory. Discussion of important results. Study of relevant applications. Introduction to the Problem. CS is a new paradigm that makes possible fast acquisition of data using few number of samples. IT530, Lecture Notes. Outline of the Lectures. Review of Shannon’s sampling theorem. Compressive Sensing: Overview of theory and key results. Practical Compressive Sensing Systems. Proof of one of the key results. Stafford. Mentor: Alex . Cloninger. Directed Reading Project. May 3, 2013. Compressive Sensing & Applications. What is Compressive Sensing?. Signal Processing: . . Acquiring measurements of a . signal. By: . Motahareh. . Eslami. . Mehdiabadi. eslami@ce.sharif.edu. Sharif University of Technology. Authors: . Payam. . Siyari. , Hamid R. . Rabiee. . . Mostafa. . Salehi. , . Motahareh. . A Brief Overview. With slides contributed by. W.H.Chuang. and Dr. . . Avinash. L. Varna. Ravi . Garg. Sampling Theorem. Sampling: record a . signal. in the form of . samples. Nyquist. Sampling Theorem: . University . of Montenegro. Faculty of Electrical Engineering . Prof. . d. r Srdjan Stanković. MECO’2015. . Budva. , . . June . 14. th. – 18. th. , 2015, Montenegro. About CS group. Project CS-ICT supported by the Ministry of Science of Montenegro. Stafford. Mentor: Alex . Cloninger. Directed Reading Project. May 3, 2013. Compressive Sensing & Applications. What is Compressive Sensing?. Signal Processing: . . Acquiring measurements of a . signal. By: . Motahareh. . Eslami. . Mehdiabadi. eslami@ce.sharif.edu. Sharif University of Technology. Authors: . Payam. . Siyari. , Hamid R. . Rabiee. . . Mostafa. . Salehi. , . Motahareh. . Jiawen. Chen Dennis . Bautembach. . Shahram. Izadi. Microsoft Research, Cambridge, UK. Computer Graphics. Mian Athar Naqash. 21570550. 1. Introduction - Reconstruction. Takes . 2D image . as input. Rodrigo de Salvo Braz. Ciaran O’Reilly. Artificial Intelligence Center - SRI International. Vibhav Gogate. University of Texas at Dallas. Rina Dechter. University of California, Irvine. IJCAI-16. , . 1. University of Oklahoma -Tulsa. Aminmohammad Roozgard. , . Nafise Barzigar, . Dr. Pramode Verma, . Dr. Samuel Cheng. University of . O. klahoma - Tulsa. Today’s Presentation. Privacy concerns of releasing genomic data. 1865-1877. Bargain of 1877. Black Codes. Carpetbaggers. Civil Rights Act of 1875. . Civil . Rights Bill of 1866. Crop lien. Enforcement Acts. Fifteenth . Amendment. Fourteenth Amendment. Redeemers. Chapter 19 . Temporal models. 2. Goal. To track object state from frame to frame in a video. Difficulties:. Clutter (data association). One image may not be enough to fully define state. Relationship between frames may be complicated. Efficient Algorithms for Sparse . Recovery . Problems. Sidharth Jaggi. The Chinese University of Hong Kong. Sheng. . Cai. Mayank Bakshi. Minghua Chen. 1. Sparse Recovery. Compressive Sensing. Network Tomography.

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