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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 paper presents three distributed techniques to find a sparse solution of the underdetermined linear problem $\textbf{g}=\textbf{Hu}$ with a norm-1 regularization, based on the Alternating Direction Method of Multipliers (ADMM). These techniques divide the matrix $\textbf{H}$ in 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. These techniques are applied particularly for millimeter-wave imaging through 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. Consensus-based ADMM has been proved to accelerate the imaging process and sectioning-based ADMM has shown to highly reduce the amount of information to be exchange among the computational nodes. 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, and the communication efficiency among the nodes are analyzed and compared. The distributed capabitities 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.

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 (CRAs) are a class of antennas that have been shown to provide enhanced image reconstruction performance over traditional reflector antennas (TRAs) when CS techniques are employed. In this article, 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.

Contrary to the active detection methods, the passive sound source localization technologies only rely on the differences among the signals recorded by different receivers to localize the object generating the sound wave, without the knowledge of the sound source profile and excitation time. Time domain investigation methods have been widely applied in the analysis of sound source tracking, which greatly suffers from the high ambient noise problems. Instead, this paper applies an imaging method based on frequency domain cross-coherence (FDCC) operation to reconstruct the source to alleviate the effect of noise. The experimental results show the reliability of this method to recover the source in 3D. Moreover, a parabolic reflector is proposed to further increase the measurement amplitude and signal to noise ratio. The FDCC method successfully localizes the source even if the propagation path of the sound wave is disturbed by the reflector.

Ground penetrating radar (GPR) is a viable tool for fast and high fidelity detection of concealed explosive threats. The radar effectiveness is limited by scattering from rough terrain which considerably obscures the buried target response. To calculate the rough ground scattering, a 3-D full-wave algorithm such as finite-difference frequency-domain (FDFD) method is required but is often prohibitive for multiple frames when the GPR antennas are distant from the target region. This paper presents a real-time 3-D modeling of a moving platform forward-looking GPR scattering from rough terrain located at great electrical distances from the GPR antenna. For a synthetic aperture, the computational domain of the focal region is reduced to a very small subset of the entire observed volume, and the surface clutter is computed via a mere multiplication of a precomputed impulse response matrix of the rough ground with the matrix characterizing the GPR transmitting signal. For a vehicle-mounted GPR detection system, this results in a significant reduction of complexity and saving of computation resources. The effectiveness of the algorithm is evaluated through an implementation of 3-D Monte Carlo simulation for various rough surface parameters. Our developed model compares well with the direct FDFD results, and can be used for lossy and frequency-dispersive soils.

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 randomize the measurements to some degree, these sensing matrices do not achieve the same reconstruction performance as the aforementioned truly random sensing matrices. This paper presents a novel method, based upon capacity maximization, for designing sensing matrices with enhanced block-sparse signal reconstruction capabilities. Additionally, several numerical examples are also included to show how the proposed method enhances reconstruction performance.

In this letter, a novel norm-1 regularized imaging technique, based on the alternating direction method of multipliers (ADMM) algorithm, is presented. This technique divides the sensing matrix of the imaging system in submatrices by columns. This method is capable of imaging metallic targets with a reduced amount of measurements, by sectioning the imaging domain into several regions and optimizing them individually in different nodes. This technique reduces the amount of information to be shared among the nodes, compared to the consensus ADMM that divides the sensing matrix into submatrices by rows. This makes the communication among the nodes faster for large imaging domains that need to be distributed into several computational nodes of a cluster. A compressive reflector antenna (CRA) has been recently proposed as a low cost hardware to provide high sensing capacity. The use of the columnwise division ADMM algorithm in combination with the imaging capabilities of the CRA allows a distributed and quasi-real-time imaging, while reducing the communications among the computational nodes.

The accurate and real-time monitoring of fluid flow in porous media can boost the prediction of mass transport and chemical reactions, which profoundly impacts the subsurface exploration and hydrocarbon extractions. Our preliminary effort has shown the efficacy of employing a thermoacoustic (TA) technology for imaging an immobile rock sample. The results support the applicability of making this methodology to move forward for imaging a dynamical process. But the real-time monitoring of fluid flow requires the target under test to excite TA signals with a higher signal-to-noise ratio (SNR), which will promise a sufficient image resolution with fewer necessary measurements or less averaged times, and then lead to a faster scan. It is proved that the excitation pulse is directly proportional to the microwave absorption rate, and thus determines the observability of the corresponding TA signals. Unfortunately, due to the thermal and stress confinements, a microsecond-width pulse envelope is greatly limited and is not sufficient for achieving a high SNR. Although a recently proposed Frequency Modulation Continuous Wave (FMCW) showed an improvement on SNR, it signifies a deficiency of the long-time irradiation and additional electronic disturbance especially at a high peak power. To address this issue, we propose a new excitation envelope with multi-pulses, to favor the coherent frequency-domain signaling method for optimizing the image reconstruction while shortening the total envelope duration than that of the FMCW. In the present paper, the TA sensing of a dry sandstone sample is presented, which efficiently enhances the SNR of TA signals and the image resolution, thus validating the appropriateness of the proposed multi-pulse envelope. The current study also promises a future possibility towards its application for dynamically exploring the underground flow transport.

The microwave-induced thermoacoustic (TA) sensing has been proven to promise a great potential in clinical and biomedical applications. This novel technology has also been explored to boost its use in subsurface geophysical applications. A conventional TA sensing system, however, is greatly limited by either a slow scan or a dramatical cost, and making infeasible the real-time monitoring when covering large domains. One remarkable solution of such issues is to design a compressive sensing (CS)-based TA system, with the aim of reducing the sampling intensity necessary to high resolution imaging. Specifically, to favor the demand for CS, this work studies the appropriateness of metamaterial (MM) resonators and proposes a MM linear array-coded TA system to randomize the transmitted acoustics waves. To prove the efficacy, images obtained from both of the proposed TA system and the conventional TA system without coding are assessed and compared. Under the same scenario, the MM-coded TA system shows higher imaging capabilities and a better performance when using a much-limited amount of measurements or in noisy enviornments. This method opens the door for a fast scan of the geological imaging and the real-time monitoring of the underground flow.

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). The MMA elements 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.

Renewable energy plants can participate in the energy pool market including day-ahead, intraday and balancing markets. The aim of this work is to develop a decision-making framework for a Wind and Storage Power Plant participating in the pool market to handle the uncertainty associated with the parameters of energy price and available wind energy, which are not known when decisions are to be made. Thus, the problem of maximizing the net income of such a plant participating in the pool market is formulated as a two-stage convex stochastic program. A novel hybrid approach using multivariate clustering technique and recurrent neural network is used to derive scenarios to handle the uncertainty associated with the energy price. Lastly, simulation experiments are carried out to show the effectiveness of the proposed methods using a real-world case study. Operators of variable renewable resource generators could use the proposed framework to make robust decisions and better manage their operations to gain competitive advantage.

Spectral coding is of special interest in high-sensing-capacity imaging applications. This letter presents the design, fabrication, and experimental validation of a lightweight and robust 3-D-printed compressive horn antenna, capable of performing spectral coding for such applications. First, a pyramidal horn antenna is designed to operate within the $E$ -band. Then, a dielectric piece with a pseudorandom geometric pattern is inserted inside the horn to distort the radiation pattern of the antenna in both near- and far-field regions. The performance of both antennas is evaluated by their return loss and radiation patterns.

A more comprehensive participation of renewable generators in the power market is being practiced in many countries. To add storage capability to these generators is also a major trend nowadays. Decisions concerning the participation in the power market have to be made several hours in advance, which is a key challenge for the renewable energy-based generators. In this work, a decision making framework under uncertainty for a wind and storage power plant participating in day-ahead and reserve markets is developed. Available wind energy and regulation requirements by the system operator are considered as uncertain parameters. To maximize the net income of this system under uncertainty, a two-stage convex stochastic model is developed. In order to create meaningful scenarios to be used in our proposed stochastic model, at first, a Long Short-Term Memory Recurrent Neural Network is designed to generate forecasts for regulation requirements. Univariate and multivariate clustering based on k-means algorithms are also used to generate influential scenarios from historical data. Several simulation experiments are carried out to evaluate the quality of the proposed stochastic approach using real-world wind farm data. Simulation result shows the validity and usefulness of the proposed data-driven approaches to handle the uncertainty in regulation requirements.

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.

In the design of human-in-the-loop robotic systems , it is of importance to evaluate human mental workload changes as this could inform the robots the degree to which a human is overwhelmed and error-prone in decision making. With an accurate assessment of mental workload, one can modulate the robots accordingly with the aim to assist the human in a task toward reduced mental workload. One key metric reliably utilized in the literature to assess human mental workload changes is called the pNN50—a metric associated with variations in the instantaneous frequency of human heart rate (HR). pNN50 can be extracted, for instance, from photoplethysmography (PPG) sensor measurements by implementing a well-known time-domain technique that is based on inter-beat intervals (IBI) of HR. When PPG measurements are contaminated with noise, the traditional time-domain approach may lead to inaccurate estimations of the pNN50 metric, as it heavily depends on precise calculation of the IBI from time series. In this manuscript, we present a combined time-frequency technique to remedy this problem. This new approach does not rely on time-domain based IBI data; and, hence, it can effectively avoid problems associated with noisy signals, rendering reliable computation of the pNN50. Examples using noisy synthetic signals and experimentally measured PPG data with and without noise contamination confirm the benefits of the proposed technique over the traditional time-domain based approach.

Traditional breast cancer imaging methods using microwave imaging (MWI) 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. The authors describe a novel unmixing algorithm for detecting breast cancer. In this approach, the breast tissue is separated into three components, low water content, high water content, and cancerous tissues, and the goal of the optimisation procedure is to recover the mixture proportions of each component. By utilising this approach in a hybrid digital breast tomosynthesis/MWI system, the unmixing reconstruction process can be posed as a sparse recovery problem such that compressive sensing 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.

Compressive sensing (CS) theory states that sparse signals can be recovered from a small number of linear measurements $y=Ax$ using $\ell _1\text{-}$ 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.

Acoustic metamaterials have been proposed for numerous applications including subwavelength imaging, impedance matching, and lensing. Yet, their application in compressive sensing and imaging has not been fully investigated. When metamaterials are used as resonators at certain frequencies, they can generate random radiation patterns in the transmitted waves from the transducers and received waves from a target. Compressive sensing favors such randomness inasmuch as it can increase incoherence by decreasing the amount of mutual information between any two different measurements. This study aims at assessing whether the use of resonating metamaterial unit cells in a single-layered non-optimized array between a number of ultrasound transceivers and targets can improve the sensing capacity, point-spread function of the sensing array (their beam focusing ability), and imaging performance in point-like target detection. The theoretical results are promising and can open the way for more efficient metamaterial designs with the aim of enhancing ultrasound imaging with lower number of transceivers compared to the regular systems.

Seismic and electromagnetic imaging modalities are conventionally used in subsurface situational awareness applications. These modalities have been very effective at characterizing the geological media in terms of its constitutive mechanical properties such as density and compressibility, as well as electromagnetic properties such as electric conductivity, permeability, and permittivity. In order to enhance these imaging capabilities, a Thermoacoustic (TA) imaging system is used in this work. TA imaging relies on the coupling of mechanical and electromagnetic waves through a thermodynamic process, and it has the potential to reconstruct thermodynamic constitutive properties such as volumetric expansion coefficient and heat capacity. TA imaging has been mostly used in biological applications; this is due to the low signal-to-noise ratio that can be created with this physical mechanism. This work is aimed at addressing such limitation and exploring the use of TA imaging in geophysical applications. Conventionally, a short microwave pulse excitation is used to create the TA wave; so that the stress confinement condition is met while providing high resolution images. This approach requires the use of expensive high power amplifiers to create a detectable TA signal. This limitation can be addressed by using a frequency-modulated continuous wave (FMCW) excitation, which has been recently proposed as a suitable mechanism to enhance the signal-to-noise ratio of the TA signal generated for a given peak power constrain. This paper discusses and compares both pulsed and FMCW TA imaging in geological media. Preliminary experimental results show the efficacy of this approach to image a rock immersed in an oil bath; thus paving the way towards its future use for subsurface sensing and imaging of fluid flow and transport in porous media.

Optical and thermal sensors mounted on Unmanned Aerial Vehicles (UAVs) have been successfully used to image difficult-to-access regions. Nevertheless, none of these sensors provide range information about the scene; and, therefore, their fusion with high-resolution mm-wave radars has the potential to improve the performance of the imaging system. This paper presents our preliminary experimental results of a downward-looking UAV system equipped with a passive optical video camera and an active Multiple-Input-Multiple-Output (MIMO) mm-wave radar sensor. The 3D imaging of the mm-wave radar is enabled by collecting data through the line of motion, thus producing a synthetic aperture, and by using a co-linear MIMO array perpendicular to the motion trajectory. Our preliminary results shows that the fused optical and mm-wave image provides shape and range information, that ultimately results in an enhanced imaging capability of the UAV system.

Within the compressive sensing (CS) framework, one effective way to increase the likelihood of successful signal reconstruction is to employ random processes in the construction of the sensing matrix. This study presents a 3D holey cavity, with diverse frequency modes, to spectrally code, that is, randomize, the ultrasound wave fields. The simulated results show that the use of such a cavity enables imaging simple or complex targets, such as spheres or the letter E, by only a single transceiver—something that is not possible without the use of a coding structure like the cavity. The effect of noise on imaging results and the size of the targets on the first-order Born approximation (BA) are also investigated. Moreover, this study attempts to optimize the cavity, based on a single numerical metric, such as the sum of singular values (SSV) or mutual coherence (MC). Yet, it will be shown that neither of these metrics can consistently compare the norm-one imaging performance between two cavities of different materials or hole sizes. This leaves finding a quantitative metric for these kinds of optimizations an open problem.

This letter 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 capacity of the compressive antenna by varying the constitutive properties of scatterers distributed along the antenna. Preliminary two-dimensional design results demonstrate the new method's ability to produce antenna configurations with enhanced imaging capabilities.

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 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.

This paper presents a new method, based on the well-known method of moments (MoM), for the numerical electromagnetic analysis of scattering and radiation from metallic or dielectric structures, or both structure types in the same simulation, that are in contact with other metallic or dielectric structures. The proposed method for solving the MoM junction problem consists of two separate algorithms, one of which comprises a generalization for bodies in contact of the surface integral equation (SIE) formulations. Unlike some other published SIE generalizations in the field of computational electromagnetics, this generalization does not require duplicating unknowns on the dielectric separation surfaces. Additionally, this generalization is applicable to any ordinary single-scatterer SIE formulations employed as baseline. The other algorithm deals with enforcing boundary conditions and Kirchhoff's Law, relating the surface current flow across a junction edge. Two important features inherent to this latter algorithm consist of a mathematically compact description in matrix form, and, importantly from a software engineering point of view, an easy implementation in existing MoM codes which makes the debugging process more comprehensible. A practical example involving a real grounded monopole antenna for airplane-satellite communication is analyzed for validation purposes by comparing with precise measurements covering different electrical sizes.

Non-invasive optical imaging techniques have been recently proposed for distinguishing between different types of tissue in burns generated in porcine models. These techniques are designed to assist surgeons during the process of burn debridement, to identify regions requiring excision and their appropriate excision depth. This paper presents a machine learning tool for discriminating between Viable and Non- Viable tissues in human injuries. This tool merges a supervised (QDA) with an unsupervised (k-means clustering) classification algorithms. This combination improves the Non-Viable tissue detection in 23.7% with respect to a simple QDA classifier.

Acoustic metamaterials have been proposed for numerous applications including subwavelength imaging, impedance matching, and lensing. Yet, their application in compressive sensing and imaging has not been fully investigated. When metamaterials are used as resonators at certain frequencies, they can generate random radiation patterns in the transmitted and received waves to and from a target. Compressive sensing favors such randomness inasmuch as it can increase incoherence by decreasing the amount of mutual information between any two different measurements. This study aims at assessing whether the use of resonating metamaterial unit cells in a single-layered array between a number of ultrasound transceivers and targets can improve the sensing capacity, point-spread function of the sensing array (their beam focusing ability), and imaging performance in pointlike target detection. The theoretical results are promising and can open the way for more efficient metamaterial designs with the aim of enhancing ultrasound imaging with lower number of transceivers compared to the regular systems.

Microwave-induced Thermoacoustics (TA) sensing has the potential to be a breakthrough in subsurface imaging applications. This is because it combines the advantages of high contrast of microwave imaging and high resolution of ultrasound imaging. However, state-of-the-art TA hardware requires that the receiving transducer is scanned in a linear or rotational fashion in order to be able to collect enough orthogonal data needed to produce a TA image possessing high-spatial resolution both in range and cross-range. This process is slow, increases the detection time, and adds an extra complexity to the system. In order to address these problems, a Compressive Sensing (CS) methodology is presented in this paper as a mechanism to reduce the minimum number of data samples required to reconstruct a sparse signal. Furthermore, in order to reduce the mutual information shared by different measurements, a holey cavity structure is proposed to be used to perform 4D coding. In this work, the TA imaging theory is introduced; and the impact that the holey cavity parameters have in the imaging performance is studied. The imaging results in this work are carried out using a distributed Alternating Direction Method of Multipliers (ADMM) algorithm, capable of using norm-1 and norm-2 regularizers; and they reveal the effectiveness of the proposed holey-cavity and CS TA imaging approach.

Compressive sensing (CS) theory states that, if certain conditions are met, a signal can be retrieved at a sampling rate that is lower than what Nyquist theorem requires. Among these conditions are the sparsity of the signal and the incoherence of the sensing matrix, which is constructed based on how the sensing system is designed. One effective method to render the sensing matrix incoherent is to use random processes in its construction. Diverse approaches have been proposed to randomize the sensing matrix including transmission at random transmitter positions and spectral coding with the use of a physical structure that responds very differently at disparate frequencies. In this work, a holey cavity with various frequency modes is used to spectrally code the ultrasound wave fields. Then, with the use of CS theory and simulations, it is shown that the sensing system that is equipped with such a cavity performs meaningfully better than a regular system in terms of sensing capacity, beam focusing, and imaging. What is more, the validity of Born approximation is investigated in this work to show its extent of applicability in imaging relatively small targets. Due to computational limitations, the simulation domain has been selected to be comparatively small; yet, the achieved results evidently show the concept and warrant further studies on holey cavities in ultrasound imaging, including their fabrication and experimental corroboration. The decrease in the number of measurements necessary for correct image reconstruction can make ultrasound sensing systems more efficient in size and scan time in a variety of applications including medical diagnosis, non-destructive testing, and monitoring.

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 reconstruction of a T-shaped metallic target., 5 pages, 6 figures

Norm-1 regularized optimization algorithms are commonly used for Compressive Sensing applications. In this paper, an optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) together with the Elastic Net regularization is presented. This type of regularization is a linear combination of the norm-1 and norm-2 regularizations, allowing a solution between the sparsest and the minimum energy solutions, but still enforcing some sparsivity. The combination of these two regularizations and the distributive capabilities of the ADMM algorithm enables a fast sparse signal recovering with minimum error.

Machine learning techniques have been recently applied for discriminating between Viable and Non-Viable tissues in animal wounds, to help surgeons to identify areas that need to be excised in the process of burn debridement. However, the presence of outliers in the training data set can degrade the performance of that discrimination. This paper presents an outlier removal technique based on the Mahalanobis distance to improve the accuracy detection of Non-Viable skin in human injuries. The iteratively application of this technique improves the accuracy results of the Non-Viable skin in a 13.6% when applying K-fold cross-validation.

In this paper, a new waveguide-fed Antipodal Vi-valdi Antenna (AVA) for mm-wave imaging applications is presented. A waveguide-to-broadside coupled Antipodal finline transition is designed to couple the dominant mode of a WR-12 waveguide into the AVA. The transition provides a wideband and low insertion loss in the entire E - band. A dielectric lens is added in front of the AVA to increase the directivity in the endfire direction of the antenna. The performance of the designed antenna is evaluated in the E - band in terms of its return loss, gain, and radiation pattern.

This paper explores the use of compressive sensing (CS) methods to microwave-induced thermoacoustics (TA) imaging. Moreover, it proposes the use of a holey cavity as a mechanism to enhance the reconstruction properties of the sensing matrix. The CS imaging and the holey cavity reduce the number of measurements needed to perform the imaging, thus reducing the overall complexity of the imaging system.

A low cost millimeter-wave (mm-wave) electronically reconfigurable Reflect Array (RA) has been presented in this paper. Aperture-Coupled Patch (ACP) elements are used to form a 40×40 element reconfigurable RA operating in the 71–74 GHz range of the mm-wave band. A feeding network, integrated with Single-Pole, Single-Throw (SPST) switches is designed to adjust the phase of the reflected field for the ON/OFF state of the switch. The performance of the reconfigurable RA is evaluated by performing beam focusing at the near-field of the array.

This paper presents our preliminary results for a three-dimensional (3D) imaging of an on-the-move target using a MIMO millimeter-wave (mm-wave) radar, which uses 9 transmitters and 12 receivers. The operating frequency of the mm-wave radar is from 70 GHz to 77 GHz. Experimental results show that the images can be created as the target under detection moves in front of the radar system, which is combined with 3D video to show the continuous movement of the target. This preliminary work paves the way towards a mm-wave imaging system that can be used at checkpoints; thus enabling on-the-move detection of potential threats, enhancing passenger's overall experience, and achieving a high scanning throughput.