PROJECT TITLE :

Improving M-SBL for Joint Sparse Recovery Using a Subspace Penalty

ABSTRACT:

A multiple measurement vector drawback (MMV) may be a generalization of the compressed sensing drawback that addresses the recovery of a set of jointly sparse signal vectors. One amongst the important contributions of this paper is to show that the seemingly least connected state-of-the-art MMV joint sparse recovery algorithms—the M-SBL (multiple sparse Bayesian learning) and subspace-based mostly hybrid greedy algorithms—have a terribly vital link. A lot of specifically, we tend to show that replacing the $logdet(,cdot,)$ term in the M-SBL by a rank surrogate that exploits the spark reduction property discovered within the subspace-based joint sparse recovery algorithms provides significant improvements. In specific, if we use the Schatten-$p$ quasi-norm as the corresponding rank surrogate, the world minimizer of the price operate in the proposed algorithm becomes a twin of the true resolution as $p rightarrow zero$. Furthermore, underneath regularity conditions, we show that convergence to a native minimizer is guaranteed using an alternating minimization algorithm that has closed kind expressions for each of the minimization steps, which are convex. Numerical simulations underneath a variety of eventualities in terms of SNR and also the condition number of the signal amplitude matrix show that the proposed algorithm consistently outperformed the M-SBL and other state-of-the art algorithms.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Improving I/O Complexity of Triangle Enumeration ABSTRACT: Many graph algorithms are now required to operate in external memory and deliver performance that does not significantly degrade with the scale of the
PROJECT TITLE : Improving Speech Emotion Recognition With Adversarial Data Augmentation Network ABSTRACT: When there aren't many training data to work with, it can be difficult to train a deep neural network without triggering
PROJECT TITLE : A Time-Series Feature-Based Recursive Classification Model to Optimize Treatment Strategies for Improving Outcomes and Resource Allocations of COVID-19 Patients ABSTRACT: This paper presents a novel Lasso Logistic
PROJECT TITLE : Recommender Systems and Scratch An integrated approach for enhancing computer programming learning ABSTRACT: Learning computer programming is a difficult task. Visual programming languages (VPLs), such as Scratch,
PROJECT TITLE : Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification ABSTRACT: The problem of unbalanced data is common in network intrusion detection, spam filtering,

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry