在上述背景下,2018年,一种称为稀疏矢量码(Sparse Vector Coding,SVC)的短包传输技术被提出[1]。与传统传输方式不同的是,SVC将发送的信息比特映射到一个稀疏向量的少量非零索引上,并通过一个非正交码本将该稀疏向量随机扩展到一个低维的序列,最后将该序列映射到时频资源进行传输。在接收端,接收机只需检测接收信号的
在上述背景下,2018年,一种称为稀疏矢量码(Sparse Vector Coding,SVC)的短包传输技术被提出[1]。与传统传输方式不同的是,SVC将发送的信息比特映射到一个稀疏向量的少量非零索引上,并通过一个非正交码本将该稀疏向量随机扩展到一个低维的序列,最后将该序列映射到时频资源进行传输。在接收端,接收机只需检测接收信...
Yang, X., Tian, Y.L.: Action Recognition using super sparse coding vector with spatio-temporal awareness. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part II. LNCS, vol. 8690, pp. 727–741. Springer, Heidelberg (2014)X. Yang, Y. Tian, ...
Short A short packet transmission scheme, such as Sparse Vector Coding (SVC), is a primary candidate for achieving ultra-low latency and high-reliability communication (URLLC). This paper proposes a spectral-efficient multi-user SVC (MU-SVC) scheme for achieving next-generation URLLC or eXtreme ...
by the input vector . Therefore, in sparse coding, we introduce the additional criterion of sparsity t o resolve the degeneracy introduced by over-completeness. 过完备的基能发掘出数据的内在结构和模型,但是其系数表示将不唯一 Here, we define sparsity as having few non-zero components or having few...
2.1.1 Sparse coding Given the data matrix X=[x1,x2,…,xm]∈Rk×m, let D=[d1,d2,…,ds]∈Rk×s be the dictionary matrix, where each column represents a basis vector of the dictionary, and s is the dictionary size. Let α=[α1,α2,…,αm]∈Rs×m be the coefficient matrix,...
Trained on the raw pixels, a linear Support Vector Machine (SVM) classifier performs poorly on MNIST, with an error rate of 8.2%. But the same linear classifier reaches a much better performance if we train it on the output of the sparse coding network instead of the raw pixels. With N...
pythonimage-processingpursuitsparse-codingdictionary-learningimage-denoisingsparse-representationsk-svddct-dictionaryhaar-dictionary UpdatedJul 9, 2024 Python hiroyuki-kasai/ClassifierToolbox Star86 A MATLAB toolbox for classifier: Version 1.0.7 linear-regressionpcaclassificationsrcface-recognitionsupport-vector-ma...
基于互补学习系统(Complementary Learning Systems,简称CLS)理论,提出了在DNNs中模仿大脑中不同记忆系统之间相互作用的想法。 本文提出了SCoMMER(Sparse Coding in a Multi-Memory Experience Replay mechanism)方法,其结合了稀疏编码和多记忆系统体验回放机制的方法,用于提高DNNs在持续学习任务中的表现。下图给出了SCoMMER...
sparse-coding/sparse-nonneg.cc Go to file Copy path 252 lines (225 sloc)7.58 KB RawBlame #include<iostream> #include<vector> #include<fstream> #include<functional> #include<numeric> #include<cmath> #include<cstdlib> #include #include<string...