Abstract-In this paper, we explore fundamental limits on the number of tests required to identify a given number of "healthy" items from a large population containing a small number of "defective" items, in a nonadaptive group testing framework. Specifically, we derive mutual information-based upper bounds on the number of tests required to identify the required number of healthy items. Our results show that an impressive reduction in the number of tests is achievable compared to the conventional approach of using classical group testing to first identify the defective items and then pick the required number of healthy items from the complement set. For example, to identify L healthy items out of a population of N items containing K defective items, when the tests are reliable, our results show that O(K(L − 1)/(N − K)) measurements are sufficient. In contrast, the conventional approach requires O(K log(N/K)) measurements. We derive our results in a general sparse signal setup, and hence, they are applicable to other sparse signal-based applications such as compressive sensing also.
This article focuses on Metamaterial inspired UWB-MIMO fractal antenna. Antenna loaded with the metamaterial structure achieved an impedance bandwidth of 5.8-15 GHz (98.63 %). Mutual coupling between the two radiating elements is obtained to be less than -25 dB. The patterns of radiation are stable over the operating range and a peak gain of 6.2 dBi is obtained at 14 GHz. Throughout the operating range, the radiation efficiency is > 88%. The obtained Envelope correlation coefficient-ECC (<0.07), Mean effective gain-MEG (< 3 dB), Total active reflection coefficient-TARC (< -10 dB) and Diversity gain-DG (>9.98) proved that the desired antenna has a good diversity performance. The measured and simulated results are well adjusted, so that the antenna can be used for ultra-wide band applications. Simulation is conducted using HFSS simulation tool and printed on a 30 x 30 x 1.6 mm 3 FR4 material.
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