Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems

Compressed Sensing & Wireless Communications

Compressed sensing MRI exploiting complementary dual decomposition

Compressed sensing (CS) MRI exploits the sparsity of an image in a transform domain to reconstruct the image from incoherently under-sampled k-space data. However, it has been shown that CS suffers particularly from loss of low-contrast image features with increasing reduction factors. To retain image details in such degraded experimental conditions, in this work we introduce a novel CS reconst...

Flexibly Exploiting Prior Knowledge in Empirical Learning

This paper presents a method to incorporate knowledge from possibly imperfect models and domain theories into inductive learning of decision trees for classification The approach assumes that a model or domain theory reflects useful prior knowledge of th< task Thus the default bias should accept the model s predictions as accurate even in the face of somewhat contradictory data which may be unr...

Compressed Sensing in Wireless Sensor Networks: Survey

Wireless Sensor Networks (WSNs) are adopted in many applications such as, industrial automation, military, transportation, environmental monitoring, web controlling, biomedical and energy management. As WSNs continue to grow, so does the need for new mechanisms to reduce parameters such as power consumption, cost, delay and traffic. The Compressed Sensing (CS) theory holds promising improvement...

A Brief Review: Compressed Sensing of ECG Signal For Wireless System

CS, as a new compression paradigm, relies on three main requirements: sparsity representation, incoherence measurement, and nonlinear reconstruction, which pertain to the signals of interest, the encoding modality, and the decoding method, respectively. The main goal of the CS is to accurately reconstruct a high dimensional sparse vector using a small number of linear measurements. As in wirele...