Compressive sensing (CS) theory states that sparse signals can be recovered from a small number of linear measurements y = Ax using l ₁ norm minimization techniques, provided that the sensing matrix satisfies a restricted isometry property (RIP). Unfortunately, the RIP is difficult to verify in electromagnetic imaging applications, where the sensing matrix is computed deterministically. Although it provides weaker reconstruction guarantees than the RIP, the mutual coherence is a more practical metric for assessing the CS recovery properties of deterministic matrices. In this paper, we describe a method for minimizing the mutual coherence of sensing matrices in electromagnetic imaging applications. Numerical results for the design method are presented for a simple multiple monostatic imaging application, in which the sensor positions for each measurement serve as the design variables. These results demonstrate the algorithm's ability to both decrease the coherence and to generate sensing matrices with improved CS recovery capabilities.

An approximation is presented for the calculation of the equivalent currents based on the oblique incidence of a plane wave on the interface between free space and lossy dielectric media. The Snell reflection coefficients establish the relation between incident and reflected waves, then, the boundary conditions establish the equivalent electric/magnetic currents in the free space region using the total magnetic/electric fields. The interface is discretized into planar triangular facets, on which currents are assumed to have constant amplitude and linear phase variation in order to analytically calculate the scattered fields. Our modified equivalent current approximation (MECA) reduces to the well-studied physical optics (PO) formulation in case of PEC surfaces. Simulations to validate the formulation are performed over electrically large canonical geometries - a lossy dielectric sphere, a rough plate and a right dihedral - to compare the calculated Radar cross section (RCS). Good agreement between MECA and analytical/method of moments (MoM) results is demonstrated.

The process of burn debridement is a challenging technique requiring significant skills to identify the regions that need excision and their appropriate excision depths. In order to assist surgeons, a machine learning tool is being developed to provide a quantitative assessment of burn-injured tissue. This paper presents three non-invasive optical imaging techniques capable of distinguishing four kinds of tissue—healthy skin, viable wound bed, shallow burn, and deep burn—during serial burn debridement in a porcine model. All combinations of these three techniques have been studied through a k-fold cross-validation method. In terms of global performance, the combination of all three techniques significantly improves the classification accuracy with respect to just one technique, from 0.42 up to more than 0.76. Furthermore, a non-linear spatial filtering based on the mode of a small neighborhood has been applied as a post-processing technique, in order to improve the performance of the classification. Using this technique, the global accuracy reaches a value close to 0.78 and, for some particular tissues and combination of techniques, the accuracy improves by 13%.

Conventional multistatic radar systems using microwave and millimeter-wave (mm-wave) frequencies seek to reconstruct the target in the imaging domain, employing many transmitting and receiving antenna elements. These systems are suboptimal, in that they do not take into consideration the large mutual information existing between the measurements. This work reports a new mm-wave radar system for high sensing capacity applications. The system is composed of a Compressive Reflector Antenna (CRA), whose surface is specially tailored by digitized Metamaterial Absorbers (MMAs). TheMMAelements are designed to have a highly frequency-dispersive response in the operating band of the radar. This enables the CRA to create highly uncorrelated spatial and spectral codes in the imaging region. A semi-analytic method based on Drude-Lorentz model is used to approximate the reflection response of the MMAs. The performance of the developed radar system is evaluated in active mm-wave sensing systems by imaging PEC scatterers and an extended human-size model in the near-field of the radar. A computational method based on physical optics is established for solving the numerical examples. For reconstructing the image using compressive sensing techniques, a norm-1 regularized iterative algorithm based on the Alternating Direction Method of Multipliers (ADMM) and a Nesterov-based algorithm (NESTA) were applied.

A linear polarized 118-GHz antenna is designed for a radiometer payload hosted aboard a 3U atmospheric CubeSat. The radiometer antenna is a horn-fed offset parabolic reflector. The antenna has a maximum +37 dBi realized gain, 2.4 ^° half-power beamwidth, and a minimum 95% beam efficiency within the operational bandwidth. The antenna is compact, meeting the MicroMAS mission requirement for a highly integrated and ultra-compact radiometer. CubeSats present stringent size/volume and weight constraints on overall component design. We present our design, along with simulated and measured results, that meet the size/volume, weight, and electrical requirements for the radiometer antenna and comply with CubeSat standards.