The Viterbi algorithm is renowned as a maximum likelihood (ML) decoding technique for convolutional codes. The path memory unit in an (n,k,m) Viterbi Decoder is responsible for keeping track of the information bits associated with the surviving paths designated by the path metric unit.
![]() ![]()
A binary convolutional code is denoted by a three-tuple (n, k, m), where:
Viterbi decoders are typically ASIC based and therefore have a upper bound on the size of the path memory. A novel approach to achieving path memory savings is proposed in Viterbi Decoders. A number of traceback Viterbi decoders using this path memory were successfully developed It is shown that Viterbi decoders using this storage efficient path memory unit require a smaller chip area and achieves a faster decoding time without loss of decoding performance.
A Viterbi decoder utilizing this novel path memory achieves savings of 20% in storage for (n,1,m) codes, and <=20% for general (n,k,m) codes without loss of decoding performance. There is also a similar increase decoding performance with the novel path memory.
Software - CCStudio 3 & MATLAB 7.5. THEORY: Convolution is a formal mathematical operation, just as multiplication, addition, and integration. Addition takes two numbers and produces a third number, while convolution takes two signals and produces a third signal.
SRCNNCppC Implementation of Image Super-Resolution using Convolutional Neural Network IntroductionSRCNNCpp is a C Implementation of Image Super-Resolution using SRCNN which is proposed by Chao Dong in 2014.If you want to find the details of SRCNN algorithm, please read the paper:Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. Learning a Deep Convolutional Network for Image Super-Resolution, in Proceedings of European Conference on Computer Vision (ECCV), 2014.If you want to download the training code(caffe) or test code(Matlab) for SRCNN, please open your browse and visit for more details.And thank you very much for Chao's work in SRCNN.LicenseSRCNNCpp is released under the GPL v2 License (refer to the LICENSE file for details). Contents.Requirements.You need to install OpenCV2+ or OpenCV3+ in your computer.OpenCV download site:.You also need to install pkg-config.And we really need g which is already installed in almost all Linux systems.Note: we do not need Caffe in your system!Our SRCNNCpp is developed in fc22 x6486 system with g-5.3 and OpenCV 3.0.0. CompileYou can compile the C/C files on the command line in your Linux system.
![]() Comments are closed.
|
AuthorWrite something about yourself. No need to be fancy, just an overview. Archives
March 2023
Categories |