2 publications

• Published Subscription 11 Jan 2021

  Application of Compressive Sensing for Sampling and Reconstruction of MRI Images

  By Mit Patel, Hardik Modi, Mayur Sevak, Himanshu Patel

  Abstract: In recent years, a new theory of compressive sensing has evolved which asserts that super resolved signals and images can be recovered with far fewer samples than that demanded by the Nyquist sampling theorem. It is required that the signal being sensed has a low information-rate meaning that it is sparse in original or some transform domain. Former approaches capture the complete signal and process it to extract the information. …

  Published in Current Trends in Signal Processing · Vol. 6, Issue 2, 2016 · pp. 42–48 Read article →

• Published Subscription 8 Jan 2021

  A Review on Fundamental Premises and Algorithmic Approaches in Compressive Sensing

  By Mit Patel, Hardik Modi, Himanshu Patel

  Abstract: This paper addresses the classical approach of acquiring signals by following the well celebrated Shannon sampling theorem that underlies the majority devices of current technology, viz. analog-to-digital conversion, medical imaging, or audio and video electronics. In medical imaging, there are problems related to acquisition time and compression. Compressive sensing (CS) paradigm addresses the shortcomings of traditional data acquisition by sampling signals much more efficiently. CS is a novel kind of …

  Published in Current Trends in Signal Processing · Vol. 6, Issue 1, 2016 · pp. 18–24 Read article →