'Auto correlation' diaporamas de présentation

Local Invariant Features

Local Invariant Features. This is a compilation of slides by: Darya Frolova, Denis Simakov,The Weizmann Institute of Science Jiri Matas, Martin Urban Center for Machine Percpetion Prague Matthew Brown,David Lowe, University of British Columbia. Building a Panorama.

1.23k views • 109 slides

The Divergence of High- and Low-Frequency Estimation: Causes and Consequences

The Divergence of High- and Low-Frequency Estimation: Causes and Consequences. Will Kinlaw Mark Kritzman David Turkington. Motivation. The divergence of high- and low-frequency estimation. The divergence of high- and low-frequency estimation.

762 views • 66 slides

Seismology of Stellar Coronal Flares , 21-24 May 2013, Leiden

Seismology of Stellar Coronal Flares , 21-24 May 2013, Leiden. Quasi-Periodic Pulsations as a Feature of the Microwave Emission Generated by Solar Single-Loop Flares. 1. Introduction.

679 views • 53 slides

Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs)

Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs) Submission Title: [ System Simulation ] Date Submitted: [ Dec.16, 2004 ] Source: [Liang Li, Liang Zhang, Yafei Tian, Chenyang Yang, Zhijian Hu, HongYu Gu ] Company: [.WXZJ Inc.]

645 views • 51 slides

Environmental Data Analysis with MatLab

Environmental Data Analysis with MatLab. Lecture 18: Cross-correlation. SYLLABUS.

755 views • 46 slides

Introduction to Speech Signal Processing

Introduction to Speech Signal Processing. Dr. Zhang Sen zhangsen@gscas.ac.cn Chinese Academy of Sciences Beijing, China 2014/9/10. Introduction Sampling and quantization Speech coding Features and Analysis Main features Some transformations Text-to-Speech State of the art

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A Novel Approach to Speech Cod ing After Time Scale Modification

A Novel Approach to Speech Cod ing After Time Scale Modification. Presented by, H. Gokhan Ilk, Ph.D. Something about the presenter. B.Sc , Ankara University Electronics Eng. Dept. M.Sc.

494 views • 42 slides

correlation

correlation

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Recognition and Matching based on local invariant features

Recognition and Matching based on local invariant features. Cordelia Schmid INRIA, Grenoble. David Lowe Univ. of British Columbia. ( ). Introduction. Local invariant photometric descriptors. local descriptor. Local : robust to occlusion/clutter + no segmentation

594 views • 37 slides

Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs)

Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs) Submission Title: [The ParthusCeva Ultra Wideband PHY proposal ] Date Submitted: [03 Mar, 2003] Source: [ Michael Mc Laughlin, Vincent Ashe ] Company [ ParthusCeva Inc. ]

337 views • 28 slides

Chapter 8

Chapter 8. Correlation and Auto-Correlation of Signals. Objectives. Develop an intuitive understanding of the cross-correlation of two signals. Define the meaning of the auto-correlation of a signal. Develop a method to calculate the cross-correlation and auto-correlation of signals.

376 views • 24 slides

Chapter 8

Chapter 8. Correlation and Auto-Correlation of Signals. Objectives. Develop an intuitive understanding of the cross-correlation of two signals. Define the meaning of the auto-correlation of a signal. Develop a method to calculate the cross-correlation and auto-correlation of signals.

413 views • 24 slides

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Solar-Terrestrial Influences Laboratory, BAS, Department Stara Zagora, Stara Zagora, Bulgaria

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Instructor: Lichuan Gui lichuan-gui@uiowa lcgui

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Pseudo Random Numbers

Pseudo Random Numbers. Random numbers generated by a computer are not really random They just behave like random numbers For a large enough sample, the generated values will pass all tests for a uniform distribution If you look at a histogram of a large number, it will look uniform

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Stat 131 Time Series Analysis: Introductory Examples

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Correlation of Discrete-Time Signals

Correlation of Discrete-Time Signals. Transmitted Signal, x(n). Reflected Signal, y(n) = x(n-D) + w(n). 0. T. Cross-Correlation. Cross-correlation of x(n) and y(n) is a sequence, r xy (l) Reversing the order, r yx (l) =>. Similarity to Convolution. No folding (time-reversal)

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Analysis Tools

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Time series Analysis

(a) Measuring the memory in a time series with auto-correlation function (ACF). Time series Analysis. r = 0.45. Shifting the time series by one time step gives pairs of observations We calculate the (auto-)correlation at lag 1. Time series Analysis. r = 0.13.

329 views • 10 slides