PROJECT TITLE :

Microwave Stepped Frequency Head Imaging Using Compressive Sensing With Limited Number of Frequency Steps

ABSTRACT:

Compressive sensing (CS) can be used to recover sparse data (signal) from limited measurements by solving a constrained convex optimization drawback. If this approach is applied on microwave stepped frequency imaging technique, the required number of frequency steps to induce clear pictures will be significantly reduced resulting in straightforward systems with fast knowledge acquisition and real time results. To that end, three totally different CS techniques are applied on head imaging systems aiming at the detection of brain injuries by utilizing the sparse characteristic of the correlated time domain scattered signals. The presented measured results using a head imaging system indicate that the time domain correlation signals are indeed sparse and thus can be recovered employing a limited number of frequency steps. Those recovered signals are then used to successfully generate clear pictures that show brain injuries. A comparison between using the proposed and therefore the traditional approaches using two quality metrics indicates superiority of the presented CS-primarily based approach in not simply the restricted required frequency steps, but conjointly in the quality of the obtained pictures.


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