Open Access. Powered by Scholars. Published by Universities.®

Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Articles 1 - 3 of 3

Full-Text Articles in Engineering

Human-Robot Interaction For Assistive Robotics, Jiawei Li Dec 2020

Human-Robot Interaction For Assistive Robotics, Jiawei Li

Dissertations

This dissertation presents an in-depth study of human-robot interaction (HRI) withapplication to assistive robotics. In various studies, dexterous in-hand manipulation is included, assistive robots for Sit-To-stand (STS) assistance along with the human intention estimation. In Chapter 1, the background and issues of HRI are explicitly discussed. In Chapter 2, the literature review introduces the recent state-of-the-art research on HRI, such as physical Human-Robot Interaction (HRI), robot STS assistance, dexterous in hand manipulation and human intention estimation. In Chapter 3, various models and control algorithms are described in detail. Chapter 4 introduces the research equipment. Chapter 5 presents innovative theories and …


The Law Of Black Mirror - Syllabus, Yafit Lev-Aretz, Nizan Packin Aug 2020

The Law Of Black Mirror - Syllabus, Yafit Lev-Aretz, Nizan Packin

Open Educational Resources

Using episodes from the show Black Mirror as a study tool - a show that features tales that explore techno-paranoia - the course analyzes legal and policy considerations of futuristic or hypothetical case studies. The case studies tap into the collective unease about the modern world and bring up a variety of fascinating key philosophical, legal, and economic-based questions.


Application Of Advanced Algorithms And Statistical Techniques For Weed-Plant Discrimination, Saman Akbar Zadeh Jan 2020

Application Of Advanced Algorithms And Statistical Techniques For Weed-Plant Discrimination, Saman Akbar Zadeh

Theses: Doctorates and Masters

Precision agriculture requires automated systems for weed detection as weeds compete with the crop for water, nutrients, and light. The purpose of this study is to investigate the use of machine learning methods to classify weeds/crops in agriculture. Statistical methods, support vector machines, convolutional neural networks (CNNs) are introduced, investigated and optimized as classifiers to provide high accuracy at high vehicular speed for weed detection.

Initially, Support Vector Machine (SVM) algorithms are developed for weed-crop discrimination and their accuracies are compared with a conventional data-aggregation method based on the evaluation of discrete Normalised Difference Vegetation Indices (NDVIs) at two different …