PPT-Signal processing and Networking for Big Data Applications

Lecture 10 Sublinear Algorithm Zhu Han University of Houston Thanks for Professor Dan Wangs slides 1 outline Motivations Inequalities and classifications Examples Applications 2. Digital Filters In many applications of signal processing we want to change the relative ampli tudes and frequency contents of a signal This process is generally referred to as 64257ltering Since the Fourier tr Decimation or downsampling reduces the sampling rate whereas expansion or upsampling fol lowed by interpolation increases the sampling rate Some applications of multirate signal processing are Upsampling ie increasing the sampling frequency before D project Guitar Effects. Joshua “Rock Star” Jenkins . Jeff “Tremolo” Smith . Jairo. “the boss” Rojas. Table of contents. Typical Guitar Effects Pipeline.. Classifying Effects for guitar implementation.. . Martin Vetterli EPFL & UC Berkeley. BOAT WAKE © Pete Turner. 1. Acknowledgements. Sponsors and supporters. NSF Switzerland. : thank you!. Conference organizers, Akihiko Sugiyama in particular!. Jayanthi. . Sivaswamy. . and. . Suryakanth. V . Gangashetty. 2. Broad Areas of . Signal . Processing . Activities. Audio, speech and language . Medical image processing . Computer vision (image and video). Tata Institute of Fundamental Research, Mumbai. Contact: bsn@tifr.res.in. Signal Processing Electronics. B.Satyanarayana, TIFR, Mumbai Signal Processing Electronics SERCEHEP11, VECC, Kolkata. CSIRO Astronomy and space science. John Tuthill . | Digital Systems Engineer. 25 September 2012. Star-on Machine. Dr. Seuss - The . Sneetches. and Other Stories. Outline. What is “back-end signal processing”. Applications. Lectures 11-12: Deep Learning Basics. Zhu Han. University of Houston. Thanks for Dr. . Hien. Nguyen slides and help by . Xunshen. Du and Kevin Tsai. 1. outline. Motivation and overview. Lecture . 10: . Sublinear. Algorithm. Zhu Han. University of Houston. Thanks for Professor Dan Wang’s slides. 1. outline. Motivations. Inequalities and classifications . Examples. Applications. 2. Applications. Lecture . 21: . Tensor Basics. Zhu Han. University of Houston. Thanks for Dr. Hung Nguyen’s Slides. 1. Outline. 1. Basic concepts. 2. Tensor operations. 3. Tensor analysis. 4. Applications. Applications. Lecture . 2: . Preliminary Review. Zhu Han. University of Houston. 1. outline. Convex . optimization (thanks for Dr. . Mingyi. Hong’s slides). Convex Optimization. Gradient descent and Newton methods. University of Central Florida. July 20, 2012. Applications of Images and Signals in High Schools. Contributors. Dr. . . Veton. . Këpuska. , . Faculty Mentor, FIT. vkepuska@fit.edu. Jacob . Zurasky. The study and implementation of microphone arrays originated over 20 years ago. Thanks to the research and experimental developments pursued to the present day, the field has matured to the point that array-based technology now has immediate applicability to a number of current systems and a vast potential for the improvement of existing products and the creation of future devices. In putting this book together, our goal was to provide, for the first time, a single complete reference on microphone arrays. We invited the top re searchers in the field to contribute articles addressing their specific topic(s) of study. The reception we received from our colleagues was quite enthusi astic and very encouraging. There was the general consensus that a work of this kind was well overdue. The results provided in this collection cover the current state of the art in microphone array research, development, and technological application. This text is organized into four sections which roughly follow the major areas of microphone array research today. Parts I and II are primarily the oretical in nature and emphasize the use of microphone arrays for speech enhancement and source localization, respectively. Part III presents a num ber of specific applications of array-based technology. Part IV addresses some open questions and explores the future of the field. Jayanthi. . Sivaswamy. . and. . Suryakanth. V . Gangashetty. 2. Broad Areas of . Signal . Processing . Activities. Audio, speech and language . Medical image processing . Computer vision (image and video).