The propagation of ultrawideband (UWB) signals in indoor environments is an important issue with significant impacts on the future direction and scope of UWB technology. The propagation of UWB signals is governed, among other things, by the properties of materials in the propagation medium. The information on electromagnetic properties of construction materials in the UWB frequency range would provide valuable insights into the appreciation of the capabilities and limitations of UWB technology. Although electromagnetic properties of certain construction materials over relatively narrow bandwidths in GHz frequency ranges are available, ultrawideband characterisation of most typical construction materials for UWB communication purposes has not been reported. In narrowband wireless communications, only the magnitude of insertion loss has been the quantity of interest. But for UWB signals, in addition to the magnitude, the phase information is an equally important factor that needs to be accounted for. In fact, UWB signals not only suffer attenuation when propagating through walls, but also suffer distortion due to the dispersive properties of the walls. This research examines propagation through typical construction materials and their ultrawideband characterisation. Ten commonly used construction materials are chosen for this investigation. Results for the dielectric constant and loss tangent of the materials over the UWB frequency range are presented. Accuracy of the measured results is discussed and distortions of UWB signals due to the dispersive properties of wall materials are addressed.

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Sensor array configurations such as coprime and nested arrays have attracted many researchers because they increase the degree of freedom (DOF). For example, in the direction of arrival estimation, the number of sources that can be estimated is greater than the total number of sensors. This study proposes a multi-level prime array (MLPA) configuration for sparse sampling that can further increase the DOF. The proposed array uses multiple uniform linear subarrays where the number of sensors in the subarrays is pairwise coprime integers. The inter-element spacing between the sensors is formulated as a scaled multiple of half-wavelength where the subarrays share only their first element. For a fixed number of sensors, multiple MLPA configurations can be constructed by controlling the number of sensors in the subarrays or by adjusting the interelement spacing. For a given number of sensors, the proposed array has a smaller aperture and achieves more number of unique and consecutive lags compared with coprime arrays. The proposed configuration has limited holes in the difference coarray. The analytical expressions of both the difference coarray and the aperture size are derived. Simulation results confirm the advantage of the proposed configurations compared with the two level coprime arrays.

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One of the most challenging problems in condensed matter physics is to predict crystal structure just from the chemical formula of the material. In this work, we present a robust machine learning (ML) predictor for the crystal point group of ternary materials (A$$_l$$ l B$$_m$$ m C$$_n$$ n ) - as first step to predict the structure - with very small set of ionic and positional fundamental features. From ML perspective, the problem is strenuous due to multi-labelity, multi-class, and data imbalance. The resulted prediction is very reliable as high balanced accuracies are obtained by different ML methods. Many similarity-based approaches resulted in a balanced accuracy above 95% indicating that the physics is well captured by the reduced set of features; namely, stoichiometry, ionic radii, ionization energies, and oxidation states for each of the three elements in the ternary compound. The accuracy is not limited by the approach; but rather by the limited data points and we should expect higher accuracy prediction by having more reliable data.