PROJECT TITLE:

A Case Study in Low-Complexity ECG Signal Encoding: How Compressing is Compressed Sensing?

ABSTRACT:

When transmission or storage costs are a difficulty, lossy data compression enters the processing chain of resource-constrained sensor nodes. However, their restricted computational power imposes the utilization of encoding methods primarily based on a small number of digital computations. During this case study, we tend to propose the use of an embodiment of compressed sensing as a lossy digital signal compression, whose encoding stage solely requires a number of mounted-point accumulations that's linear within the dimension of the encoded signal. We support this design with some evidence that for the task of compressing ECG signals, the simplicity of this scheme is well-balanced by its achieved code rates when its performances are compared against those of standard signal compression techniques.


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