A real-time multi-sensor 3D surface shape measurement system using fringe analysis

This thesis presents a state-of-the-art multi-sensor, 3D surface shape measurement system that is based upon fringe projection/analysis and which operates at speeds approaching real-time. The research programme was carried out as part of MEGURATH (www.megurath.org), a collaborative research project with the aim of improving the treatment of cancer by radiotherapy. The aim of this research programme was to develop a real-time, multi-sensor 3D surface shape measurement system that is based on fringe analysis, which provides the flexibility to choose from amongst several different fringe profilometry methods and to manipulate their settings interactively. The system has been designed specifically to measure dynamic 3D human body surface shape and to act as an enabling technology for the purpose of performing Metrology Guided Radiotherapy (MGRT). However, the system has a wide variety of other potential applications, including 3D modelling and visualisation, verbatim replication, reverse engineering and industrial inspection. It can also be used as a rapid prototyping tool for algorithm development and testing, within the field of fringe pattern profilometry. The system that has been developed provides single, or multi-sensor, measurement modes that are adaptable to the specific requirements of a desired application. The multi-sensor mode can be useful for covering a larger measurement area, by providing a multi-viewpoint measurement. The overall measurement accuracy of the system is better than O.5mm, with measurement speeds of up to 3 million XYZ points/second using the single-sensor mode and rising to up to 4.6 million XYZ points/second when measuring in parallel using the three sensor multi-sensor mode. In addition the system provides a wide-ranging catalogue of fringe profilometry methods and techniques, that enables the reconstruction of 3D information through an interactive user selection of 183 possible different paths of main combinations. The research aspects behind the development of the system are presented in this thesis, along with the author's contribution to this field of research, which has included the provision of a comprehensive framework for producing such a novel optical profilometry system, and the specific techniques that were developed to fulfil the aims of this research programme. This mainly included the following advanced methods: a transversal calibration method for the optical system, an adaptive filtering technique for the Fourier Transform Profilometry (FTP) method, and a method to synthetically restore the locations of the triangulation spots. Similarly, potential applications for the system have been presented and feasibility and accuracy analyses have been conducted, presenting both qualitative and quantitative measurement results. To this end, the high robustness levels exhibited by the system have been demonstrated (in terms of adaptability, accuracy and measurement capability) by performing extensive real experiments and laboratory testing. Finally, a number of potential future system developments are described, with the intention of further extending the system capabilities

DroneRF dataset: A dataset of drones for RF-based detection, classification, and identification

DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone’s communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. The dataset can be used in drone detection, drone identification and drone tracking

A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system?s ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.Scopu

Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case.

Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developments in computer vision are creating a paradigm shift in the analysis of radiological images, where AI tools are already capable of automatically detecting and precisely delineating tumours. However, such tools are generally developed in technical departments that continue to be siloed from where the real benefit would be achieved with their usage. Significant effort still needs to be made to make these advancements available, first in academic clinical research and ultimately in the clinical setting. In this paper, we demonstrate a prototype pipeline based entirely on open-source software and free of cost to bridge this gap, simplifying the integration of tools and models developed within the AI community into the clinical research setting, ensuring an accessible platform with visualisation applications that allow end-users such as radiologists to view and interact with the outcome of these AI tools