In our research project we have been developing new methods for super-resolution imaging, in both fluorescence microscopy and contrast enhanced ultrasound imaging. This dramatic improvement in the temporal resolution paves the way to both sub-diffraction live-cell imaging in microscopy and clinical sub-diffraction diagnosis in ultrasound. Another area we are working on is low-rate acquisition and processing framework based on frequency domain processing for 3D ultrasound imaging. The method allows obtaining optimal image quality with improved SNR and higher penetration depth, while keeping computational load low enough to enable real time implementation. We also considered a generalization of low-rate frequency domain processing for ultrafast ultrasound imaging based on coherent plane-wave compounding. Another aspect we are working on in the area of ultrasound is improving quality and performance of medical ultrasound systems using a small number of antenna elements.
Moving on to more basic sampling paradigms, we have explored-Sub-Nyquist sampling of correlated signals: A variety of interesting theories have been developed for sampling and reconstruction of individual signals with certain structure but very few results have been reported on sampling a set of signals with a certain underlying correlation. In the area of radar we have considered various methods to achieve high resolution radar using a small number of resources in time, space and frequency.
In the interface of information theory and sampling theory we have recently begun exploring Task-based quantization. Massive multiple-input multiple-output (MIMO) systems have been drawing considerable. We quantify the gain in spectral efficiency of joint-decoding compared to the standard approach of separate linear decoding, and show that the standard approach fails to capture the actual achievable rates of massive MIMO systems. In our work we address the challenges of hardware complexity and pilot contamination.
One of the research areas is Deep Cognitive Sparse Arrays for Automotive Radars. In this case the high-resolution direction of arrival (DOA) estimation requires a large number of antenna elements. In order to balance between hardware complexity and resolution, we proposed a cognitive, scalable, sparse array selection technique based deep neural networks. In this demo, we present a design and implementation of a hardware prototype that demonstrate the proposed sparse antenna selection strategy. Through real-time experiments, we show that the proposed sparse selection method results in a 2-3 dB lower error compared to a typically employed random selection method.
In the area of autonomous vehicles, we came with a Joint Radar-Communications System demo. Our solution implements both radar and communications within the same domain and the same resources. Our alternative strategy, which is the focus of growing research attention, is to handle them as a dual function radar-communications (DFRC) system. Such joint designs improve performance by facilitating coexistence, as well as contributes to reducing the number of antennas, system size, weight and power consumption.
In the area of radars, we came with the Sub-Nyquist Radar with Distorted Pulse Shape demo. Sub-Nyquist systems uses the knowledge of the transmit pulse and the receive signal model to estimate the targets from low-rate samples. In this demo, we build a hardware prototype to demonstrate the proposed sub-Nyquist radar. We show that while operating at 10 times below the Nyquist rate the proposed two-receiver system with unknown pulse has comparable performance to a single-receiver sub-Nyquist system with a known pulse.
