Reconfigurable Architecture for Spatial Sensing in Wideband Radio Front-End

The deployment of cellular spectrum in licensed, shared and unlicensed spectrum demands wideband sensing over non-contiguous sub-6 GHz spectrum. To improve the spectrum and energy efficiency, beamforming and massive multi-antenna systems are being explored which demand spatial sensing i.e. blind identification of vacant frequency bands and direction-of-arrival (DoA) of the occupied bands. We propose a reconfigurable architecture to perform spatial sensing of multi-band spectrum digitized via wideband radio front-end comprising of the sparse antenna array (SAA) and Sub-Nyquist Sampling (SNS). The proposed architecture comprises SAA pre-processing and algorithms to perform spatial sensing directly on SNS samples. The proposed architecture is realized on Zynq System on Chip (SoC), consisting of the ARM processor and FPGA, via hardware-software co-design (HSCD). Using the dynamic partial reconfiguration (DPR), on-the-fly switching between algorithms depending on the number of active signals in the sensed spectrum is enabled. The functionality, resource utilization, and execution time of the proposed architecture are analyzed for various HSCD configurations, word-length, number of digitized samples, signal-to-noise ratio (SNR), and antenna array (sparse/non-sparse).

Stacked Autoencoders for Medical Image Search

Medical images can be a valuable resource for reliable information to support medical diagnosis. However, the large volume of medical images makes it challenging to retrieve relevant information given a particular scenario. To solve this challenge, content-based image retrieval (CBIR) attempts to characterize images (or image regions) with invariant content information in order to facilitate image search. This work presents a feature extraction technique for medical images using stacked autoencoders, which encode images to binary vectors. The technique is applied to the IRMA dataset, a collection of 14,410 x-ray images in order to demonstrate the ability of autoencoders to retrieve similar x-rays given test queries. Using IRMA dataset as a benchmark, it was found that stacked autoencoders gave excellent results with a retrieval error of 376 for 1,733 test images with a compression of 74.61%.