PDF-COMPRESSIVE SENSING BY RANDOM CONVOLUTION JUSTIN ROMBERG Abstract

This paper outlines a new framework for compressive sensing convolution with a random waveform followed by random time domain subsampling We show that sensing by random convolution is a universally e64259cient data acquisition strategy in that an di. 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. . LTI: . h(t). g(t). g(t) . . h(t). Example: g[n] = u[n] – u[3-n]. h[n] = . . [n] + . . [n-1]. LTI: . h[n]. g[n]. g[n] . . h[n]. Convolution methods:. Method 1: “running sum”. Plot . Suhas Lohit, . Kuldeep. Kulkarni, . Pavan. . Turaga. ,. . Jian Wang, . Aswin. . Sankaranarayanan. Arizona . State . University. . Carnegie Mellon University. Compressed Sensing. Mobashir. . Mohammad. Aditya Kulkarni. Tobias Bertelsen. Malay Singh. Hirak. . Sarkar. Nirandika. . Wanigasekara. Yamilet Serrano . Llerena. Parvathy. . Sudhir. Introduction. Mobashir. Dawei Fan. Contents. Introduction. 1. Methodology. 2. RTL Design and Optimization. 3. Physical Layout Design. 4. Conclusion. 5. Introduction. What is convolution?. Convolution . is defined as the . CNN. KH Wong. CNN. V7b. 1. Introduction. Very Popular: . Toolboxes: . tensorflow. , . cuda-convnet. and . caffe. (user friendlier). A high performance Classifier (multi-class). Successful in object recognition, handwritten optical character OCR recognition, image noise removal etc.. 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. Review the case history and lab findings presented to answer the diagnosis questions. Provide all responses to your diagnosis questions in a word document or on a sheet of paper. Be sure to justify your responses to the diagnosis questions.. Georgia Tech. Topics. : . Announcements. Transposed . convolutions. Administrativia. HW2 PS2 out. No class on Tuesday 10/03. Guest Lecture by Dr. Stefan Lee on 10/05. No papers to read. No student presentations. . Cross correlation. Convolution. Last time: Convolution and cross-correlation. Properties. Shift-invariant: a sensible thing to require. Linearity: convenient. Can be used for smoothing, sharpening. Also main component of CNNs. Ge Wang, PhD. Biomedical . Imaging . Center. CBIS/BME. , . RPI. wangg6@rpi.edu. January 26, 2018. Tue. Topic. Fri. Topic. 1/16. I. ntro. d. u. ction. 1/19. MatLab I (Basics). 1/23. System. 1/26. Convolution.