PPT-Sensing via Dimensionality Reduction

Structured Sparsity Models Volkan Cevher volkanriceedu Sensors 160MP 200000fps 192000Hz 2009 Real time 1977 5hours Digital Data Acquisition Foundation ShannonNyquist sampling theorem. . local. image . descriptors. . into. . compact. . codes. Authors. :. Hervé. . Jegou. Florent. . Perroonnin. Matthijs. . Douze. Jorge. . Sánchez. Patrick . Pérez. Cordelia. Schmidt. Presented. 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. Dimensionality Reduction. Author: . Christoph. . Eick. The material is mostly based on the . Shlens. PCA. Tutorial . http://www2.cs.uh.edu/~. ceick/ML/pca.pdf. . and . to a lesser extend based on material . Kenneth D. Harris 24/6/15. Exploratory vs. confirmatory analysis. Exploratory analysis. Helps you formulate a hypothesis. End result is usually a nice-looking picture. Any method is equally valid – because it just helps you think of a hypothesis. Computer Graphics Course. June 2013. What is high dimensional data?. Images. Videos. Documents. Most data, actually!. What is high dimensional data?. Images – dimension 3·X·Y. Videos – dimension of image * number of frames. instrumentation examples. 1. A little example of noise measurement . . in time domain and frequency domain. I acquired noise after a CSP (sampling 10ns) :. s. = 0.170mV. RMS. Charges Sensing Preamplifier & noise. Regulatory Issues & Judicial Developments. SHAH MURAD. Assistant Professor - Law . Federal Urdu University of Arts, Sciences and Technology (FUUAST), Karachi, Pakistan. Email: . shahmurad@live.com. Principle Component Analysis. Why Dimensionality Reduction?. It becomes more difficult to extract meaningful conclusions from a data set as data dimensionality increases--------D. L. . Donoho. Curse of dimensionality. Bot/. Geog. 4111/5111. Ken Driese. Dept. of Botany . Group Activity: . Solving Remote Sensing Problems. How could you assess the effect of drought on plant biomass in California?. How could you map sage grouse habitat in Wyoming?. Kenneth D. Harris. April 29, 2015. Predictions in neurophysiology. Predict neuronal activity from sensory stimulus/behaviour. “encoding model”. Predict stimulus/behaviour from neuronal activity. “decoding model”. Devansh Arpit. Motivation. Abundance of data. Required storage space explodes!. Images. Documents. Videos. Motivation. Speedup Algorithms. Motivation. Dimensionality reduction for noise filtering. Vector Representation. k. Ramachandra . murthy. Why Dimensionality Reduction. ?. It . is so easy and convenient to collect . data. Data is not collected only for data mining. Data . accumulates in an unprecedented speed. Data pre-processing . Md. . . Sujan. . Ali. Associate Professor. Dept. of Computer Science and Engineering. Jatiya. . Kabi. . Kazi. . Nazrul. Islam University. Dimensionality Reduction and Classification. V. ariance.