The performance of deep learning (DL) algorithms for radar-based human motion recognition (HMR) is hindered by the diversity and volume of the available training data. In this article, to tackle the issue of insufficient training data for HMR, we propose an instance-based transfer learning (ITL) method with limited radar micro-Doppler (MD) signatures, alleviating the burden of collecting and annotating a large number of radar samples. ITL is a unique algorithm that consists of three interconnected parts, including DL model pretraining, correlated source data selection, and adaptive collaborative fine-tuning (FT). Any of the three components cannot be excluded; otherwise, the performance of the entire algorithm decreases. The experiments with a radar data set of six human motions show that ITL achieves state-of-the-art performance for HMR with limited training samples, outperforming several existing transfer learning approaches. Especially, when there are only 100 samples per person per class, ITL yields an F1 score of 96.7%. Last but not least, ITL is more generalized to human motion differences. Though adapted to recognize the persons' motions in a small-scale target data set, ITL can also classify the persons' motion data used for pretraining, achieving up to 11.0% F1 score enhancement over the conventional FT method.@en

As web applications become more popular, 3D city models would greatly benefit from a proper web-based solution to visualise and manage them. CityJSON was introduced as a JSON encoding of the CityGML data model and promises, among several benefits, the ability to be integrated with modern web technologies. In order to provide an implementation of a web application for CityJSON data, that can be used as a reference for other applications, we developed <code>ninja</code>. It is a web application that allows the user to easily load and investigate a CityJSON model through a web browser. In addition, it offers support for a complex feature of CityJSON: the experimental versioning mechanism. In this paper, we describe the motivation, requirements, technical aspects and achieved functionality of <code>ninja</code>. We believe that such a web application can facilitate the adoption of 3D city models by more practitioners and decision makers.

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Eddy current pulsed thermography (ECPT) is well established for nondestructive
testing of electrical conductive materials, featuring the advantages of contactless, intuitive detecting and efficient heating. The concept of divergence characterization of the damage rate of carbon fibre-reinforced plastic (CFRP)-steel structures can be extended to ECPT thermal pattern characterization. It was found in this study that the use of ECPT technology on CFRP-steel structures generated a sizeable amount of valuable information for comprehensive
material diagnostics. The relationship between divergence and transient thermal patterns can be identified and analysed by deploying mathematical models to analyse the information about fibre texture-like orientations, gaps and undulations in these multilayered materials. The developed algorithm enabled the removal of information about fibre texture and the extraction of damage features.
The model of the CFRP-glue-steel structures with damage was established using COMSOL Multiphysics® software, and quantitative non-destructive damage evaluation from the ECPT image areas was derived. The results of this proposed method illustrate that damaged areas are highly affected by available information about fibre texture.
This proposed work can be applied for detection of impact induced damage and quantitative evaluation of CFRP structures.@en

A shape memory composite (SMC) coating with a self-healing ability was prepared by a facile method based on a thermoresponsive shape memory polymer (SMP) that utilized carnauba wax microparticles as the healing agent. Damages to the SMC coating was healed via heating, which triggered a two-step healing mechanism consisting of defect closure through a shape memory effect at 65 °C and then defect sealing by molten wax at 90 °C. The surface morphologies of the scratched and healed coatings as well as a wax-free SMP coating were first studied by optical stereomicroscopy and scanning electron microscopy (SEM). To assess the recovery of the coating’s barrier properties, macroscopic and localized information was obtained by electrochemical impedance spectroscopy (EIS) and scanning electrochemical microscopy (SECM), respectively. The healing performance was also evaluated by comparing the macroscopic morphologies of the intact, damaged and healed coatings after long-term immersion. The results from both tests were in agreement and confirmed the key roles of carnauba wax microparticles in the complete recovery of the barrier properties of initially damaged coatings upon thermally assisted self-healing.@en

Injectable neurostimulators are currently applied in clinical trials to minimize the side effects such as discomfort, risk of infection, and post-surgery trauma, which can be pronounced with conventional, bulky implantable neurostimulators. Owing to its smaller
size, wireless-updatable software, and wireless power supply, the injectable neurostimulator is presumably less invasive, ‘smarter’, and has a longer lifetime. We discuss the concept and development of the injectable neurostimulator, persistent implementation challenges, and obstacles to be overcome in its evolution. We survey the use of new materials, technologies, and design methods for injectable electrodes, batteries, antennas, and packaging to enhance reliability and other features. These advances in the field are accompanied by progress in electrophysiology, neuroscience, neurology, clinical trials, and treatments.@en