Patrick Goh

TL;DR: A multi-sensor obstacle detection system for a smart cane is proposed via a model-based state-feedback control strategy to regulate the detection angle of the sensors and minimize the false alerts to the user.

TL;DR: A robust method is formulated that is able to perform transient simulations significantly faster than the conventional LIM method and is verified with existing commercial tools for circuit simulations.

TL;DR: A machine learning approach is presented to extract the random and deterministic jitter components from the timing histograms of the eye diagram using the dual-Dirac model and results show that the preprocessing step is able to accelerate the training process and improve the overall performance of the neural networks significantly.

TL;DR: In this paper, a fast convolution method using scattering parameters is presented for the macromodeling of blackbox multiport networks, which is compared to model-order reduction passive macrommodeling techniques in terms of robustness and computational efficiency.

TL;DR: This study presents a new technique to improve the indoor localization of a mobile node by utilizing a Zigbee-based received-signal-strength indicator (RSSI) and odometry, and contributes to a novel methodological framework in which coordinates of the mobile node can more accurately be predicted by improving the path-loss propagation model.