'Convolution' diaporamas de présentation

Edges

Edges. Edges = jumps in brightness/color. Brightness jumps marked in white. Edges. Edges = jumps in brightness/color Important! Give object outlines and shapes. Brightness jumps marked in white. Edges. Edges = jumps in brightness/color Important! Give object outlines and shapes

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Chap 4 Laplace Transform

Chap 4 Laplace Transform . ???? ????? Fall 2002. Outline. Basic Concepts Laplace Transform Definition, Theorems, Formula Inverse Laplace Transform Definition, Theorems, Formula Solving Differential Equation Solving Integral Equation. Basic Concepts. ?????. ?????. Laplace Transform.

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Sampling, Aliasing, & Mipmaps

Sampling, Aliasing, & Mipmaps. Last Time?. 2D Texture Mapping Perspective Correct Interpolation Common Texture Coordinate Projections Bump Mapping Displacement Mapping Environment Mapping. Texture Maps for Illumination. Also called "Light Maps". Quake. Today. What is a Pixel?

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Physics coordination meeting summary report

Physics coordination meeting summary report. (on behalf of Pasquale). Emulsion simulation: GenIma Update of brick finding analysis ?/e separation: test beam analysis HE cosmic ray muons, e/ p identification and ? ? ? ? e oscillations Multi-prong tau decay analysis

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Convolution and Deconvolution

Convolution and Deconvolution. Convolution means several things:. IS multiplication of a polynomial series IS a mathematical process IS filtering. Convolution means several things:. IS multiplication of a polynomial series. A * B = C. E.g., A= 0.25 + 0.5 -0.25 0.75]; B = [1 2 -0.5];.

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Computer Vision

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GG 450 March 18, 2008

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GG 313 Lecture 25 Transform Pairs and Convolution 11/22/05

GG 313 Lecture 25 Transform Pairs and Convolution 11/22/05. Today we continue our look at some important transform pairs and introduce convolution. Recall from last class:. Lets look at the sine wave transform.

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Signals and Systems (Lab) Resource Person : Hafiz Muhammad Ijaz

Signals and Systems (Lab) Resource Person : Hafiz Muhammad Ijaz

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Signal and systems

Signal and systems. LTI Systems. Linear Time-Invariant Systems. Linear. Memoryless. A system is memoryless if its output any time depends only on the value of input at that same time For LTI system If h(n)=0 for n 0. Invertiblity. x(t). y(t). h(t).

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Earth Science Applications of Space Based Geodesy DES-7355 Tu-Th 9:40-11:05

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FFT-based filtering and the Short-Time Fourier Transform (STFT)

FFT-based filtering and the Short-Time Fourier Transform (STFT). R.C. Maher ECEN4002/5002 DSP Laboratory Spring 2003. Using the FFT for DSP.

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CSC 578 Neural Networks and Deep Learning

CSC 578 Neural Networks and Deep Learning. 6-1. Convolutional Neural Networks (1) (Some figures adapted from NNDL book ). Convolution Neural Networks. Convolutional Neural Networks Convolution, pooling and fully-connected layers Convolution kernel/filter Local receptive field

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Center for Embedded Systems Research (CESR) Department of Computer Science

Timing Analysis: In Search of Multiple Paradigms. Frank Mueller. Center for Embedded Systems Research (CESR) Department of Computer Science North Carolina State University. WCET Dilemma. WCET of task needed for schedulability analysis WCET bounds should be safe and tight

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Lecture 18: Linear convolution of Sequences and Vectors Sections 2.2.3, 2.3

Lecture 18: Linear convolution of Sequences and Vectors Sections 2.2.3, 2.3.

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Blocked 2D Convolution

Blocked 2D Convolution. Ravi Sankar P Nair 010469036. Implement 2D Convolution. Source: http://www.songho.ca/dsp/convolution/convolution2d_example.html. Implement 2D Convolution.cpp in GPU Kernel. Implement 2D Convolution.cpp in GPU Kernel. Use Constant memory to store M matrix.

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LECTURE 27: PROPERTIES OF THE Z-TRANSFORM AND THE INVERSE Z-TRANSFORM. Objectives: Modulation Summation Convolution Initial Value and Final Value Theorems Inverse z-Transform by Long Division Inverse z-Transform by Partial Fractions

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>> x=stepseq(0,-5,49)-stepseq(10,-5,49);stem(-5:49,x); title('x(n)')

>> x=stepseq(0,-5,49)-stepseq(10,-5,49);stem(-5:49,x); title('x(n)'). >>h=[zeros(1,5),0.9.^(0:49)];stem(-5:49,h); title('h(n)'). >> y=[zeros(1,5),10*(1-0.9.^(1:9)),10*(1-0.9^10)*0.9.^(0:40)]; stem(-5:49,y);title('y(n)'). >> nx=-3:3;x=[3,11,7,0,-1,4,2];nh=-1:4;h=[2,3,0,-5,2,1];

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EE 345S Real-Time Digital Signal Processing Lab Spring 2009

EE 345S Real-Time Digital Signal Processing Lab Spring 2009. Lab #3.1 Digital Filters Akshaya Srivatsa. Outline. Discrete-Time Convolution FIR Filter Design Convolution Using Circular Buffer FIR Filter Implementation Task List for Lab #3.1. Discrete-Time Convolution.

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Standard

Standard. Type. Year of Introduction. Multiple Access. Frequency Band. Modulation. Channel Bandwidth. ETACS. Cellular. 1985. FDMA. 900 MHz. FM. 25 kHz. NMT-450. Cellular. 1981. FDMA. 450-470 MHz. FM. 25 kHz. NMT-900. Cellular. 1986. FDMA. 890-960 MHz.

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