Implementation of a Si/SiC Hybrid Optically Controlled High-Power Switching Device

The ever-increasing performance and economy of operation requirements placed on commercial and military transport aircraft are resulting in very complex systems. As a result, the use of fiber optic component technology has lead to high data throughput, immunity to EMI, reduced certification and maintenance costs and reduced weight features. In particular, in avionic systems, data integrity and high data rates are necessary for stable flight control. Fly-by-Light systems that use optical signals to actuate the flight control surfaces of an aircraft have been suggested as a solution to the EMI problem in avionic systems. Current fly-by-light systems are limited by the lack of optically activated high-power switching devices. The challenge has been the development of an optoelectronic switching technology that can withstand the high power and harsh environmental conditions common in a flight surface actuation system. Wide bandgap semiconductors such as Silicon Carbide offer the potential to overcome both the temperature and voltage blocking limitations that inhibit the use of Silicon. Unfortunately, SiC is not optically active at the near IR wavelengths where communications grade light sources are readily available. Thus, we have proposed a hybrid device that combines a silicon based photoreceiver model with a SiC power transistor. When illuminated with the 5mW optical control signal the silicon chip produces a 15mA drive current for a SiC Darlington pair. The SiC Darlington pair then produces a 150 A current that is suitable for driving an electric motor with sufficient horsepower to actuate the control surfaces on an aircraft. Further, when the optical signal is turned off, the SiC is capable of holding off a 270 V potential to insure that the motor drive current is completely off. We present in this paper the design and initial tests from a prototype device that has recently been fabricated

Best performance with fewest resources: Unveiling the most resource-efficient Convolutional Neural Network for P300 detection with the aid of Explainable AI

Convolutional Neural Networks (CNNs) have shown remarkable prowess in detecting P300, an Event-Related Potential (ERP) crucial in Brain–Computer Interfaces (BCIs). Researchers persistently seek simple and efficient CNNs for P300 detection, exemplified by models like DeepConvNet, EEGNet, and SepConv1D. Noteworthy progress has been made, manifesting in reducing parameters from millions to hundreds while sustaining state-of-the-art performance. However, achieving further simplification or performance improvement beyond SepConv1D appears challenging due to inherent oversimplification. This study explores landmark CNNs and P300 data with the aid of Explainable AI, proposing a simpler yet superior-performing CNN architecture which incorporates (1) precise separable convolution for feature extraction of P300 data, (2) adaptive activation function tailored for P300 data, and (3) customized large learning rate schedules for training P300 data. Termed the Minimalist CNN for P300 detection (P300MCNN), this novel model is characterized by its requirement of the fewest filters and epochs to date, concurrently achieving best performance in cross-subject P300 detection. P300MCNN not only introduces groundbreaking concepts for CNN architectures in P300 detection but also showcases the importance of Explainable AI in demystifying the “black box” design of CNNs