Since the birth of information theory, to understand, study, and optimize optical imaging
systems from the information–theoretic viewpoint has been an important research subfield of …

With the adoption of arbitrary and increasingly wideband signals, the design of modern
radar systems continues to be limited by analog-to-digital converter technology and data …

Classifying raw, unpainted materials--metal, plastic, ceramic, fabric, and so on--is an
important yet challenging task for computer vision. Previous works measure subsets of …

In this paper, methods for detection of frequency-hop** spread spectrum (FHSS) signals
from compressive measurements are proposed. Rapid switching of the carrier frequency in a …

The design of wideband radar systems is often limited by existing analog-to-digital (A/D)
converter technology. State-of-the-art A/D rates and high effective number of bits result in …

We present a task-specific information (TSI) based framework for designing compressive
imaging (CI) systems. The task of target detection is chosen to demonstrate the performance …

We present a pipeline that rapidly simulates X-ray transmission imaging for arbitrary system
architectures using GPU-based ray-tracing techniques. The purpose of the pipeline is to …

When using the image mutual information to assess the quality of reconstructed image in
pseudo-thermal light ghost imaging, a negative exponential behavior with respect to the …

In this paper, we propose a pathological image compression framework to address the
needs of Big Data image analysis in digital pathology. Big Data image analytics require …

This paper studies the classification of high-dimensional Gaussian signals from low-
dimensional noisy, linear measurements. In particular, it provides upper bounds (sufficient …