Missing traces in ground penetrating radar (GPR) B-scans (radargrams) may appear because of limited scanning resolution, failures during the acquisition process or the lack of accessibility to some areas under test. Four statistical interpolation methods for recovering these missing traces are compared in this paper: Kriging, Wiener structures, Splines and the expectation assuming an independent component analyzers mixture model (E-ICAMM). Kriging is an adaptation to the spatial context of the linear least mean squared error estimator. Wiener structures improve the linear estimator by including a nonlinear scalar function. Splines are a commonly used method to interpolate GPR traces. This consists of piecewise-defined polynomial curves that are smooth at the connections (or knots) between pieces. E-ICAMM is a new method proposed in this paper. E-ICAMM consists of computing the optimum nonlinear estimator (the conditional mean) assuming a non-Gaussian mixture model for the joint probability density in the observation space. The proposed methods were tested on a set of simulated data and a set of real data, and four performance indicators were computed. Real data were obtained by GPR inspection of two replicas of historical walls. Results show the superiority of E-ICAMM in comparison with the other three methods in the application of reconstructing incomplete B-scans.

Direct ink writing (DIW) of conductive ink is a printed electronics technology that allows a variety of electronic circuits to be produced in a simple way and with minimal waste of materials. In recent years it has been used for rapid prototyping of RF circuits typically working at S-band frequencies (2–4 GHz). In an attempt to extend this frequency range while maintaining cost-effective prototyping, this work has focused on proving the feasibility of DIW of silver-conductive (SC) ink for the fabrication of planar microwave circuits beyond 10 GHz, more specifically, ultra-wideband (UWB) antennas for medical applications. For this purpose, the DC and RF performance of the SC ink, as well as the FR4 substrate used, were first evaluated. Based on the comparison between experimental and simulated results, we have found that the effective RF conductivity of the SC ink is approximately 27.6% of its DC value and 3.4% of the copper conductivity. A few test microstrip circuits were fabricated by DIW, namely two S-band filters and one UWB antenna. The overall measured performance of all of them agreed well with simulations. In particular, the DIW antenna exhibited a bandwidth of 8.2 GHz (between 2.4 and 10.6 GHz), and was compared with an identical copper antenna showing that both have very similar characteristics. It was also found that the lower conductivity of SC ink as compared to copper led to a gain reduction of only 0.3 dB.

This work shows a proof of concept of a radar-based microwave imaging system for the detection of the brain-shift effect with minimum delay. The system is based on the estimation of the variations of both the position and the geometry of the brain in order to detect if remarkable variations have occurred. To that end, the system makes use of 12 twin antennas as sensing elements, which transmit broadband signals towards the patient’s head and receive the corresponding reflections. Printed monopole antennas with coplanar feed are considered, featuring a broad bandwidth and an omnidirectional radiation pattern. A signal processing strategy to estimate the detected distances to the target from the reflection seen by each antenna is also shown, as well as a calibration and fine-tuning procedure for accurate distance detection capabilities. To demonstrate the potential of the system, a cranium phantom and several brain phantoms, emulating different brain-shift cases, are fabricated and measured. The results show the effective detection of the brain-shift in all the cases under consideration, thereby demonstrating the potential of microwave imaging systems for effective intraoperative brain-shift detection.

A large number of the vocalization studies on mammals are based on time-frequency analysis of the produced sounds. The patterns, which are extracted from the time-frequency representations, determine the classification in the different sound categories. However, there are situations where this pattern related recognition does not allow a precise characterization of the vocalization to be obtained. In these situations, a feasible alternative, which can help by giving the dominant component of the sound, is to measure the strength of the tonal and pulsed constituent units. In this work, the use of a ratio of pulsed to tonal strength is proposed to objectively measure the distribution of energy between these two components. This pulsed to tonal ratio (PTR) can be computed with the aid of the discrete cosine transform. It is demonstrated that the PTR can be obtained with a relatively simple expression without having to go through the time- frequency representation. This work presents examples that show how the PTR can be used to distinguish between two very similar Beluga whale sounds and how to dynamically track the power distribution between the pulsed and tonal components in non-stationary signals.

It is demonstrated that data augmentation is a promising approach to reduce the size of the captured dataset required for training automatic road surface classifiers. The context is on-board systems for autonomous or semi-autonomous driving assistance: automatic power-assisted steering. Evidence is obtained by extensive experiments involving multiple captures from a 10-channel multisensor deployment: three channels from the accelerometer (acceleration in the X, Y, and Z axes); three microphone channels; two speed channels; and the torque and position of the handwheel. These captures were made under different settings: three worm-gear interface configurations; hands on or off the wheel; vehicle speed (constant speed of 10, 15, 20, 30 km/h, or accelerating from 0 to 30 km/h); and road surface (smooth flat asphalt, stripes, or cobblestones). It has been demonstrated in the experiments that data augmentation allows a reduction by an approximate factor of 1.5 in the size of the captured training dataset.