'Spectral representation' diaporamas de présentation

Research Activities at Florida State Vision Group

Research Activities at Florida State Vision Group. Xiuwen Liu Florida State Vision Group Department of Computer Science Florida State University http://fsvision.cs.fsu.edu. Group members: Lei Cheng, Donghu Sun, Yunxun Wang, Chris Waring, Qiang Zhang,. Outline.

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CS 224S / LINGUIST 285 Spoken Language Processing

CS 224S / LINGUIST 285 Spoken Language Processing. Dan Jurafsky Stanford University Spring 2014 Lecture 6: Feature Extraction. Outline. Feature extraction How to compute MFCCs Dealing with variation Adaptation MLLR MAP Lombard speech Foreign accent Pronunciation variation.

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NMR Spectroscopy:

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Sampling and Reconstruction

Sampling and Reconstruction. The sampling and reconstruction process Real world: continuous Digital world: discrete Basic signal processing Fourier transforms The convolution theorem The sampling theorem Aliasing and antialiasing Uniform supersampling Nonuniform supersampling.

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Vector and axial-vector current correlators within the instanton model of QCD vacuum.

Vector and axial-vector current correlators within the instanton model of QCD vacuum. V and A current-current corelators OPE vs c QCD V-A and V correlator and ALEPH data Hadronic contribution to muon AMM in Instanton model Topological susceptibility Conclusions.

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Time Series Spectral Representation

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Time Series Spectral Representation

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A Spectral Clustering Approach to Optimally Combining Numerical Vectors with a Modular Network

A Spectral Clustering Approach to Optimally Combining Numerical Vectors with a Modular Network. Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka Bioinformatics Center, ICR, Kyoto University, Japan. KDD 2007, San Jose, California, USA, August 12-15 2007. 1. Table of Contents.

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Lecture 1 Signals in the Time and Frequency Domains

Lecture 1 Signals in the Time and Frequency Domains. 1. Introduction 2. Periodic Signals 3. Fourier Series Expansion of Periodic Signals 4. Spectral Representation of Periodic Signals 5. Duty Cycle of a Rectangular Wave 6. RMS Voltage and Power Spectrum of Aperiodic Signals

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Mixing . Stationary case unless otherwise indicated cov{dN(t+u),dN(t)} small for large |u|

Mixing . Stationary case unless otherwise indicated cov{dN(t+u),dN(t)} small for large |u| |p NN (u) - p N p N | small for large |u| h NN (u) = p NN (u)/p N ~ p N for large |u| q NN (u) = p NN (u) - p N p N u ? 0 ? |q NN (u)|du < ?

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Electrical Communications Systems ECE.09.331 Spring 2007

Electrical Communications Systems ECE.09.331 Spring 2007. Lecture 2a January 23, 2007. Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/spring07/ecomms/. Plan. Recall: Intoduction to Information Theory Properties of Signals and Noise Terminology

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Power spectral density . frequency-side, ? , vs. time-side, t

Power spectral density . frequency-side, ? , vs. time-side, t ?/2? : frequency (cycles/unit time). Non-negative Unifies analyses of processes of widely varying types. Examples. Spectral representation . stationary increments - Kolmogorov.

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Nicholas Sirica Quantum Field Theory II May 6, 2013

The Theory of Photoemission: From the Many-Body Propagator to the Breakdown of the Sudden Approximation. Nicholas Sirica Quantum Field Theory II May 6, 2013. The Propagator. Why so important?.

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