CS: Adaptive feature-specific imaging for recognition of non-Gaussian classes.

Pawan Baheti now at Qualcomm jusst mentioned to me his latest paper on the Feature specific imager we covered before (see the compressive sensing hardware 1.1.3 item). The paper is behind a paywall but you can ask Pawan directly for a preprint. Here it is:

Adaptive feature-specific imaging for recognition of non-Gaussian classes by Pawan K. Baheti, Jun Ke, and Mark A. Neifeld. The abstract reads:

We present an adaptive feature-specific imaging (AFSI) system for application to an M-class recognition task. The proposed system uses nearest-neighbor-based density estimation to compute the (non-Gaussian) class-conditional densities. We refine the density estimates based on the training data and the knowledge from previous measurements at each step. The projection basis for the AFSI system is also adapted based on the previous measurements at each step. The decision-making process is based on sequential hypothesis testing. We quantify the number of measurements required to achieve a specified probability of error (Pe) and we compare the AFSI system with an adaptive-conventional (ACONV) system. The AFSI system exhibits significant improvement compared to the ACONV system at low signal-to-noise ratio (SNR), and it is shown that, for an M=4 hypotheses, SNR=−10 dB, and a desired Pe=10−2, the AFSI system requires 30 times fewer measurements than the ACONV system. Experimental results validating the AFSI system are presented.

They use an SLM to provide differet random measurements and use their ability to change the SLM to devise an adaptive strategy. Nice. Thanks Pawan!