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Synthetic data, complementary with real-world datasets, have shown their effectiveness for autonomous driving in urban scenarios. However, for specific and complex environments, such as surface mines, which often involve extreme weather and corner cases, synthetic datasets are notably scarce. In this article, PMScenes, the first synthetic dataset for autonomous driving in surface mines, is built upon the previously public AutoMine dataset. It extends the available data by constructing corner case scenarios, such as unusual intrusions; collisions; and extreme weather scenarios including rain, blizzards, fog, and dust storms—situations that would bring significant safety risks and are hard to capture in the real world. Our article explores the enhancement methods for synthetic to real for perception tasks of autonomous driving under different challenging scenarios. The outcomes demonstrate that PMScenes significantly enhances the performance across a variety of perception tasks compared with the results obtained when training only with real-world datasets. PMScenes is released at https://github.com/PMScenes/PMScenes. [ABSTRACT FROM AUTHOR]

Recently, a method was proposed to determine the parameters for each Class DE driver in high-intensity focused ultrasound arrays for efficient operation and to compensate for variations in the impedance of each array element. This work extends that method to consider the effect of switch resistance and to provide limited control on the power delivered to the transducers with a constant supply voltage while keeping a good efficiency. The method is experimentally validated using an integrated driver developed by the authors. This paper also shows that the frequency range for efficient electrical operation is close to the frequency where the transducer array presents a peak in the conversion efficiency. [ABSTRACT FROM AUTHOR]