arXiv subjects

Richard Obermeier

Publications and source records attributed to Richard Obermeier.

5 recordsLinked to original sources

arXiv2018

Generalized Optimization of High Capacity Compressive Imaging Systems

One of the greatest challenges in applying compressive sensing (CS) signal processing techniques to electromagnetic imaging applications is designing a sensing matrix that has good reconstruction capabilities. Compressive reflector antennas (CRA) are a class of antennas that have been shown to provide enhanced image reconstruction performance over traditional reflector antennas (TRA) when CS techniques are employed. In this paper, we present a unified CRA design method, which considers both the sensing capacity and efficiency of the antenna, and can be used for both compressive imaging and multiple-input multiple-output (MIMO) communication applications. The unified design method is assessed for a CRA configuration in which dielectric scatterers are added to the surface of a TRA. The design results demonstrate the ability of the unified design method to enhance the CS reconstruction capabilities of the CRA.

eess.IV↗

arXiv2018

Sensing Matrix Design via Capacity Maximization for Block Compressive Sensing Applications

It is well established in the compressive sensing (CS) literature that sensing matrices whose elements are drawn from independent random distributions exhibit enhanced reconstruction capabilities. In many CS applications, such as electromagnetic imaging, practical limitations on the measurement system prevent one from generating sensing matrices in this fashion. Although one can usually randomized the measurements to some degree, these sensing matrices do not achieve the same reconstruction performance as the truly randomized sensing matrices. In this paper, we present a novel method, based upon capacity maximization, for designing sensing matrices with enhanced block-sparse signal reconstruction capabilities. Through several numerical examples, we demonstrate how our method significantly enhances reconstruction performance.

math.OC↗

arXiv2016

Detecting Breast Cancer using a Compressive Sensing Unmixing Algorithm

Traditional breast cancer imaging methods using microwave Nearfield Radar Imaging (NRI) seek to recover the complex permittivity of the tissues at each voxel in the imaging region. This approach is suboptimal, in that it does not directly consider the permittivity values that healthy and cancerous breast tissues typically have. In this paper, we describe a novel unmixing algorithm for detecting breast cancer. In this approach, the breast tissue is separated into three components, low water content (LWC), high water content (HWC), and cancerous tissues, and the goal of the optimization procedure is to recover the mixture proportions for each component. By utilizing this approach in a hybrid DBT / NRI system, the unmixing reconstruction process can be posed as a sparse recovery problem, such that compressive sensing (CS) techniques can be employed. A numerical analysis is performed, which demonstrates that cancerous lesions can be detected from their mixture proportion under the appropriate conditions.

cs.CV↗

arXiv2016

A compressive sensing approach for enhancing breast cancer detection using a hybrid DBT / NRI configuration

This work presents a novel breast cancer imaging approach that uses compressive sensing in a hybrid Digital Breast Tomosynthesis (DBT) / Nearfield Radar Imaging (NRI) system configuration. The non-homogeneous tissue distribution of the breast, described in terms of dielectric constant and conductivity, is extracted from the DBT image, and it is used by a full-wave Finite Difference in the Frequency Domain (FDFD) method to build a linearized model of the non-linear NRI imaging problem. The inversion of the linear problem is solved using compressive sensing imaging techniques, which lead to a reduction on the required number of sensing antennas and operational bandwidth without loss of performance.

math.OC↗

arXiv2015

Model-based Optimization of Compressive Antennas for High-Sensing-Capacity Applications

This paper presents a novel, model-based compressive antenna design method for high sensing capacity imaging applications. Given a set of design constraints, the method maximizes the sensing capacity of the compressive antenna by varying the constitutive properties of scatterers distributed along the antenna. Preliminary 2D design results demonstrate the new method's ability to produce antenna configurations with enhanced imaging capabilities.

math.OC↗