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This article proposes a predictive modulation scheme for a differential mode resonant switched capacitor rectifier (DMRSCR) to achieve high efficiency and power factor correction (PFC) for wide voltage gain. The modulation scheme ensures extensive zero-voltage switching (ZVS) turn-ON on all the switches under varying sinusoidal input voltage without requiring additional circuits or sensors. Four key control parameters, namely, phase shift ratio, duty cycle ratios, and switching frequency, are controlled for the converter to maintain ZVS turn-ON, PFC, output voltage regulation, and reduced resonant inductor current ripple. The article outlines a detailed DMRSCR model to deduce the dependency of the four control and converter design parameters on the converter operation. Based on the model, a complete converter design process is provided. A DMRSCR prototype rated at 1.1 kW was built using the underscored design methodology to validate the proposed modulation scheme, reaching a peak efficiency of 98.27% 

In highly and fully automated vehicles (AV), drivers could divert their attention to non-driving-related activities. Drivers may also take over AVs if they do not trust the way AVs drive in specific driving scenarios. Existing models have been developed to predict drivers’ takeover performance in responding to takeover requests initiated by AVs in semi-AVs. However, few models predicted driver-initiated takeover behavior in highly and fully AVs. The present study develops an attention-based multiple-input Convolutional Neural Network (CNN) to predict drivers’ takeover intention in fully AVs. The results indicated that the developed model successfully predicted takeover intentions of drivers with a precision of 0.982 and an F1-Score of.989, which were found to be substantially higher than other machine learning algorithms. The developed CNN model could be applied in improving the driving algorithms of the AV by considering drivers’ driving styles to reduce drivers’ unnecessary takeover behaviors. 

Digital camera pixels measure image intensities by converting incident light energy into an analog electrical current, and then digitizing it into a fixed-width binary representation. This direct measurement method, while conceptually simple, suffers from limited dynamic range and poor performance under extreme illumination — electronic noise dominates under low illumination, and pixel full-well capacity results in saturation under bright illumination. We propose a novel intensity cue based on measuring inter-photon timing, defined as the time delay between detection of successive photons. Based on the statistics of inter-photon times measured by a time-resolved single-photon sensor, we develop theory and algorithms for a scene brightness estimator which works over extreme dynamic range; we experimentally demonstrate imaging scenes with a dynamic range of over ten million to one. The proposed techniques, aided by the emergence of single-photon sensors such as single-photon avalanche diodes (SPADs) with picosecond timing resolution, will have implications for a wide range of imaging applications: robotics, consumer photography, astronomy, microscopy and biomedical imaging.