Moixa Technology (United Kingdom)

As autonomous vehicles develop, driver distraction becomes even more crucial as it affects both safety and operational efficiency. In this work, we investigate the gamut of new AI tools for combating and processing visual distraction scenarios within autonomous vehicles. This includes AI-based driver monitoring systems to determine the level of attention, visual distraction classification with deep learning models, augmented reality head-up displays for focal projection of critical information and gesture/voice-controlled interfaces are used in order to reduce visual interactions. This also includes how predictive analytics; adaptive user interfaces and personalized distraction mitigation programs will see AI improve driver focus and thus safety. These advanced systems are designed to provide a safer and more efficient driving experience in the emerging era of autonomous capabilities by leveraging the scalability of advanced driver-assistance technologies.

In this paper, a study of surface-wave (SW) excitation by means of a simple parallel-plate waveguide (PPW) for substrate integrated image guides (SIIGs) is proposed. As a proof of concept, a wideband, low profile and fully planar feeding network is designed. It consists of a surface-wave launcher (SWL) based on a truncated PPW with the addition of a sub-wavelength matching section. This method for SW excitation is intended to overcome the feeding limitations encountered when using SIIG-based antennas or other non-radiating structures at microwave frequencies. A comparison between the performance of the proposed excitation method and structures that can be found in the literature are reported showing an improved bandwidth of more than 50% with respect to previous studies. In addition, this simple feed system has a reduced volume of more than 95% when compared with other alternatives, demonstrating its compactness. Applications include new SW driven feeds, leaky-wave antennas, sensors, or novel passive structures.

We present a cyber-physical control system for deployment on a smart pedelec (Ebike). The goal of the control is to manage the interaction between a human and a motor intervention, for applications in which we wish to control physical aspects of the cycling behaviour (e.g. heart rate and breathing rate). The basis of the control is a pitchfork bifurcation system, augmented using ideas from gain-scheduling. Experiments have been conducted, showing the validity of the proposed control strategy. A use case dealing with the regulation of human ventilation characteristics in response to exogenous pollution measurements is presented.

Abstract Many scanning electron microscope (SEM) laboratories use double sticky carbon tape applied directly to SEM stubs as samplers for small particles. The SEM samplers used by law enforcement to test the hands of a suspect for gunshot residue (GSR) are constructed in this manner. A few years ago, during a presentation at a professional meeting, I presented spectra of particles that were mounted on carbon double sticky tape that was applied directly to the aluminum- alloy SEM stubs. After the presentation a colleague posited that small peaks of copper, which appeared in some of my spectra, were due to a copper support frame for the backscatter detector at the top of the SEM chamber and not the particles. Indeed, I have frequently noticed small peaks of copper in spectra of particles where these should not be present.