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%.

This paper describes a new coded interferometric system for sensing the physical temperature radiated from the Earth's surface. The proposed system consists of a compressive reflector antenna (CRA) coated with metamaterial absorbers (MMAs). The CRA and the MMA are used to code the received electromagnetic (EM) field in space and in frequency, at the focal plane array. The MMA is modeled by an equivalent magnetodielectric medium having a definite thickness. A high-frequency method based on physical optics is used to build the sensing matrix of the system, and the inverse problem is solved using a Nesterov-based compressive sensing (CS) methodology. Numerical examples are carried out in order to reconstruct the physical temperature of the Earth's surface. The performance of the proposed system is compared to that of the conventional interferometric system, GeoSTAR. Preliminary results show that the metamaterial-based CRA provides comparable performance to the GeoSTAR configuration with only half of the feeding elements while keeping the same physical aperture size for the two configurations.

Conventional security screening systems cannot operate in real time and often suffer from false alarms due to the profile-only imaging without further analysis on the object material properties. Reflectarrays were recently proposed as devices capable of performing real-time security screening using a single-frequency radar signal. This article presents first a physical optics (PO)-based simulation method to facilitate the design of a multireflectarray system. Then, an object material characterization method using geometrical optics (GO) is derived for such a system. The characterization process not only retrieves the complex relative permittivity of the object but also improves its profile reconstruction in terms of a more accurate thickness. Both simulations and experiments are carried out to verify the effectiveness and efficiency of the proposed methods. Primary results show great potentials for security screening, especially in scenarios where the inspection of human bodies for threat materials, such as narcotics and explosives, is required.

This letter presents the first experimental results of our three-dimensional (3D) millimeter-wave (mm-wave) Compressive-Reflector-Antenna (CRA) imaging system. In this prototype, the CRA is 3D-printed and coated with a metallic spray to easily introduce pseudo-random scatterers on the surface of a traditional reflector antenna (TRA). The CRA performs a pseudo-random coding of the incident wavefront, thus adding spatial diversity in the imaging region and enabling the effective use of compressive sensing (CS) and imaging techniques. The CRA is fed with a multiple-input-multiple-output (MIMO) radar, which consists of four transmitting and four receiving ports. Consequently, the mechanical scanning parts and phase shifters, which are necessary in conventional physical or synthetic aperture arrays, are not needed in this system. Experimental results show the effectiveness of the prototype to perform a successful 3D detection of a T-shaped metallic target.

This paper presents three distributed techniques to find a sparse solution of the under-determined linear problem ${\bf g}={\bf Hu}$ with a norm-1 regularization, based on the Alternating Direction Method of Multipliers (ADMM). Each one of these techniques divide the matrix ${\bf H}$ into submatrices by rows, columns, or both rows and columns, leading to the so-called consensus -based ADMM, sectioning -based ADMM, and consensus and sectioning -based ADMM, respectively. They are validated for a particular millimeter-wave imaging problem based on the use of a Compressive Reflector Antenna (CRA). The CRA is a hardware designed to increase the sensing capacity of an imaging system and reduce the mutual information among measurements, allowing an effective imaging of sparse targets with the use of Compressive Sensing (CS) techniques. In previous works, the consensus -based ADMM has been proved to accelerate the imaging process, and the sectioning -based ADMM has shown the ability to potentially reduce the amount of information to be exchanged among the computational nodes, based on the system configuration. In this paper, the mathematical formulation and graphical interpretation of these two techniques, together with the consensus and sectioning -based ADMM approach, are presented. The imaging quality, the imaging time, the convergence, the communication efficiency among the computational nodes, and the computational complexity are analyzed and compared. The distributed capabilities of the ADMM-based approaches, together with the high sensing capacity of the CRA, allow the imaging of metallic targets in a 3D domain in quasi-real time with a reduced amount of information exchanged among the nodes.