PPT-Convolutional Codes COS 463 : Wireless Networks Lecture
Author : celsa-spraggs | Published Date : 2019-11-02
Convolutional Codes COS 463 Wireless Networks Lecture 9 Kyle Jamieson Parts adapted from H Balakrishnan So far weve seen block codes Convolutional Codes Simple
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Convolutional Codes COS 463 : Wireless Networks Lecture: Transcript
Convolutional Codes COS 463 Wireless Networks Lecture 9 Kyle Jamieson Parts adapted from H Balakrishnan So far weve seen block codes Convolutional Codes Simple design especially at the transmitter. 4KEITHCONRADGeometrically,thee ectofthesematricesispicturedbelow.Ontheleft,(cos sinsincos)isacounterclockwiserotationbyanglearoundtheorigin.Ontheright,(cossinsin cos)isare ectionacrosstheline RECOGNITION. does size matter?. Karen . Simonyan. Andrew . Zisserman. Contents. Why I Care. Introduction. Convolutional Configuration . Classification. Experiments. Conclusion. Big Picture. Why I . care. Theabovecalculationalsoallowsustoobtain~J2,~J2=J2z+1 2(J+J +J J+)=(jhcos)2+1 2q jh(1+cos)(jh(1 cos)+h)q jh(1+cos) q jh(1 cos)(jh(1+cos)+h)q jh(1 cos)=j(j+1)h2:(25)3.3WaveFunctionsWe 20.sin( + )=sin cos +sin cos 21.sin( )=sin cos sin cos 22.cos( + )=cos cos sin cos 23.cos( )=cos cos +sin cos 24.tan( + )=tan +tan 1 tan tan 25.tan( )=tan tan 1+tan tan DoubleAngl 1+x=1+x=2+O(x2):E(x;l)=E0"cos 2l + lx d 22!+cos 2l + lx+d 22!#=2E0cos2l +2 lx2+d2 4cos2d lxwherewealsousedcos +cos =2cos + 2cos 2.Theoscillatorydependenceonxofthemeasur M(i)sin1002+sin(i) N(i)cos(i)sin1002A12=NPi=1sin2(i)sin(i)cos(i) (M(i)sin100)2 sin(i)cos(i) (N(i)cos(i)sin100)2A13=NPi=1 sin(i)cos(i)cos(i) (M(i)sin100)2A22=NPi=1sin(i)sin(i) M( (dx0)2+(dy0)2=Z0d0q (1 cos0)2+sin20=21 2Z0d0p 1 cos0= 23 2hp 1+cos0i0=4241 s 1+cos 235:Thetotallengthofthependulumis`,thepartthatisstillstraightattimethaslength` ,andsothecoordinatesoft Neural . Network Architectures:. f. rom . LeNet. to ResNet. Lana Lazebnik. Figure source: A. . Karpathy. What happened to my field?. . Classification:. . ImageNet. Challenge top-5 error. Figure source: . Sergey Zagoruyko & Nikos Komodakis. Introduction. Comparing Patches across images is one of the most fundamental tasks in computer vision. Applications include structure from motion, wide baseline matching and building panorama. Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . Sergey Zagoruyko & Nikos Komodakis. Introduction. Comparing Patches across images is one of the most fundamental tasks in computer vision. Applications include structure from motion, wide baseline matching and building panorama. ECE 463/563 Fall `18 RISC-V instruction f ormats Design RISC-V unpipelined datapath Introduce RISC-V pipelined datapath Prof. Eric Rotenberg 1 Fall 2018 ECE 463/563, Microprocessor Architecture, Prof. Eric Rotenberg .. Wireless is Global. The standard radio frequencies run in the range of 3Hz to 300 GHz. There is other frequencies used for example the 222 MHz through 225 MHz is for amateur radio. If your interested in what frequencies your country is using for what? Take a look at the frequencies. person. grass. trees. motorbike. road. Evaluation metric. Pixel classification!. Accuracy?. Heavily unbalanced. Common classes are over-emphasized. Intersection over Union. Average across classes and images.
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