Compressive Sensing (CS) is a novel signal sampling theory under the condition that the signal is sparse or compressible. It has the ability of compressing a signal during the process of sampling. Reconstruction algorithm is one of the key parts in compressive sensing. We propose a novel iterative greedy algorithm for reconstructing sparse signals, called Modified Regularized Adaptive Matching ...

Representing data as a linear combination of a set of selected known samples is of interest for various machine learning applications such as dimensionality reduction or classification. k-Nearest Neighbors (kNN) and its variants are still among the best-known and most often used techniques. Some popular richer representations are Sparse Representation (SR) based on solving an l1-regularized lea...

A technique for designing frames to use with vector selection algorithms, for example Matching Pursuits (MP), is presented. The design algorithm is iterative and requires a training set of signal vectors. An MP algorithm chooses frame vectors to approximate each training vector. Each vector in the frame is then adjusted by using the residuals for the training vectors which used that particular ...

A frame design technique for use with vector selection algorithms, for example Matching Pursuits (MP), is presented. The design algorithm is iterative and requires a training set of signal vectors. The algorithm, called Method of Optimal Directions (MOD), is an improvement of the algorithm presented in [1]. The MOD is applied to speech and electrocardiogram (ECG) signals, and the designed frame...

Background: With increasing age, some changes appeared in specifications of vessels which including dimensions and elasticity in their. The changes in parameters such as resistance, inertance and compliance vessels appear and eventually changes in the environmental pulse releases are in circulation. These changes clearly appear in specification of photoplethysmogram particularly in the size and...