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Abstract
The problem of gathering sufficiently representative data, such as those about human actions, shapes, and facial expressions, is costly and time-consuming and also requires training robust models. This has led to the creation of techniques such as transfer learning or data augmentation. However, these are often insufficient. To address this, we propose a semi-automated mechanism that allows the generation and editing of visual scenes with synthetic humans performing various actions, with features such as background modification and manual adjustments of the 3D avatars to allow users to create data with greater variability. We also propose an evaluation methodology for assessing the results obtained using our method, which is two-fold: (i) the usage of an action classifier on the output data resulting from the mechanism and (ii) the generation of masks of the avatars and the actors to compare them through segmentation. The avatars were robust to occlusion, and their actions were recognizable and accurate to their respective input actors. The results also showed that even though the action classifier concentrates on the pose and movement of the synthetic humans, it strongly depends on contextual information to precisely recognize the actions. Generating the avatars for complex activities also proved problematic for action recognition and the clean and precise formation of the masks.

Abstract
Performing experiments with Internet-of-Things edge devices is not always a trivial task, as large physical testbeds or complex simulators are often needed, leading to low reproducibility and several difficulties in crafting complex scenarios and tweaking parameters. Most available simulators try to simulate as close to reality as possible. While we agree that this kind of high-fidelity simulation might be necessary for some scenarios, we argue that a low-fidelity easy-to-change simulator may be a good solution when rapid prototyping orchestration strategies and algorithms. In this work, we introduce SIMoT, a low-fidelity orchestrator simulator created to achieve shorter feedback loops when testing different orchestration strategies for task allocation in edge devices. We then transferred the simulator-validated algorithms to both physical and virtual testbeds, where it was possible to assert that the simulator results correlate strongly with the observations on those testbeds.

Abstract
Silicon carbide (SiC) switching devices have an enormous influence on power electronic systems, entitled to extraordinary outcomes attained in low switching and conduction losses. The research work exploration is to develop and analyze the interleaved SiC full-bridge converter. It is subjected to analyze the performance of silicon carbide (SiC) module-based converter design which can offer a power of 42kW. Power conversion is done between DC/AC by using a standard 1200V single-phase SiC module from Semikron [1]. The SiC-MOSFETs are controlled by an adequate galvanically isolated gate driver circuit. Several gate drivers' functionalities are added in the converter design for optimized performance and safe operation. The features include split turn-on/turn-off outputs, desaturation and active miller-clamp. The DC-link capacitors are designed to cancel the input ripple current and stabilizing the source voltage. The interleaving (180° phase-shift between the legs) helps to reduce ripple currents both, in the input capacitor as well as at the output. At the output coupled inductors are providing suppression of transverse currents between the interleaved legs. The coupled inductors help to reduce the size of the filter in the case of DC-DC or DC-AC grid-tie inverters. © 2023 IEEE.

Abstract
Road transportation emissions have increased in the last few decades and have been the primary source of pollutants in urban areas with ever-growing populations. In this context, it is important to have effective measures to monitor road emissions in regions. Creating an emissions inventory over a region that can map road emissions based on vehicle trips can be helpful. In this work, we show that it is possible to use raw GPS data to estimate vehicle-related levels of pollution in a region. By transforming the data using feature engineering and calculating the vehicle-specific power (VSP) as well as various specific pollutants by using a microscopic emissions model, we show the areas with higher emissions levels made by a fleet of taxis in Porto, Portugal.