Real-time localisation system for GPS denied open areas using smart street furniture

Real-time measurement of crowd dynamics has been attracting significant interest, as it has many applications including real-time monitoring of emergencies and evacuation plans. To effectively measure crowd behaviour, an accurate estimate for pedestrians’ locations is required. However, estimating pedestrians’ locations is a great challenge especially for open areas with poor Global Positioning System (GPS) signal reception and/or lack of infrastructure to install expensive solutions such as video-based systems. Street furniture assets such as rubbish bins have become smart, as they have been equipped with low-power sensors. Currently, their role is limited to certain applications such as waste management. We believe that the role of street furniture can be extended to include building real-time localisation systems as street furniture provides excellent coverage across different areas such as parks, streets, homes, universities. In this thesis, we propose a novel wireless sensor network architecture designed for smart street furniture. We extend the functionality of sensor nodes to act as soft Access Point (AP), sensing Wifi signals received from surrounding Wifi-enabled devices. Our proposed architecture includes a real-time and low-power design for sensor nodes. We attached sensor nodes to rubbish bins located in a busy GPS denied open area at Murdoch University (Perth, Western Australia), known as Bush Court. This enabled us to introduce two unique Wifi-based localisation datasets: the first is the Fingerprint dataset called MurdochBushCourtLoC-FP (MBCLFP) in which four users generated Wifi fingerprints for all available cells in the gridded Bush Court, called Reference Points (RPs), using their smartphones, and the second is the APs dataset called MurdochBushCourtLoC-AP (MBCLAP) that includes auto-generated records received from over 1000 users’ devices. Finally, we developed a real-time localisation approach based on the two datasets using a four-layer deep learning classifier. The approach includes a light-weight algorithm to label the MBCLAP dataset using the MBCLFP dataset and convert the MBCLAP dataset to be synchronous. With the use of our proposed approach, up to 19% improvement in location prediction is achieved

Система автоматичного керування асинхронним двигуном безпілотного трактора

Магістерська дисертація містить: сторінок – 100, рисунків – 32, таблиць – 29, графічна частина на 6 листах А1. Метою даної роботи побудова та дослідження системи автоматичного керування асинхронним двигуном безпілотного трактора з оптимізацією втрат потужності. В роботі проведено аналітичний огляд безпілотних тракторів, на базі якого поставлені вимоги до електроприводу. Надано математичний опис системи векторного керування. Розраховано тяговий двигун, до якого підібрано елементи живлення та перетворюючий пристрій. Було побудовано функціональну схему, яка йшла в основі моделі для системи в середовищі MATLAB/SIMULINK. Проведена симуляція роботи моделі в двох режимах трактора: оранки та фрезерування ґрунту. В результаті отримано працездатну модель САК АД безпілотним трактором з мінімізатором втрат потужності.The master's dissertation contains: pages - 100, figures - 32, tables - 29, graphic part on 6 sheets of A1. The purpose of this work is to minimize power losses and study the system of automatic control of an asynchronous motor of an driverless tractor. In the work, an analytical review of unmanned tractors was carried out, on the basis of which the requirements for the electric drive were set. A mathematical description of the vector control system is provided. A traction motor is calculated, to which power elements and a converting device are selected. A functional diagram was built, which was the basis of the model for the system in the MATLAB/SIMULINK environment. Simulation of model operation in two tractor modes: plowing and rototilling operation was carried out. As a result, a working model of SAC AM driverless tractor with a power loss minimizer was obtained