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In a typical application scenario for electric aircraft, the emerging urban air mobility is faced with uncertain environmental conditions. To investigate the potential influence of uncertainties, this paper first develops comprehensive models of aircraft rigid body motion and electric propulsive performance. The urban environment model is built with emphasis on wind speed and the heat island effect. Thereafter, a flight guidance law augmented with nonlinear dynamic inversion is proposed to facilitate the performance evaluation of electric aircraft. Multiple simulations at various dates, times, and with different battery aging statuses are conducted. The results show that the battery aging effect and ambient temperature change are the most important factors that influence the aircraft performance. Suggestions to enhance the performance are given based on simulations. [ABSTRACT FROM AUTHOR]

Many human activities are tactile. Recognizing how a person touches an object or a surface surrounding them is an active area of research and it has generated keen interest within the interactive surface community. In this paper, we compare two machine learning techniques, namely Artificial Neural Network (ANN) and Hidden Markov Models (HMM), as they are some of the most common techniques with low computational cost used to classify an acoustic-based input. We employ a small and low-cost hardware design composed of a microphone, a stethoscope, a conditioning circuit, and a microcontroller. Together with an appropriate surface, we integrated these components into a passive gesture recognition input system for experimental evaluation. To perform the evaluation, we acquire the signals using a small microphone and send it through the microcontroller to MATLAB's toolboxes to implement and evaluate the ANN and HMM models. We also present the hardware and software implementation and discuss the advantages and limitations of these techniques in gesture recognition while using a simple alphabet of three geometrical figures: circle, square, and triangle. The results validate the robustness of the HMM technique that achieved a success rate of 90%, with a shorter training time than the ANN. [ABSTRACT FROM AUTHOR]