Developing integrated data fusion algorithms for a portable cargo screening detection system

Towards having a one size fits all solution to cocaine detection at borders; this thesis proposes a systematic cocaine detection methodology that can use raw data output from a fibre optic sensor to produce a set of unique features whose decisions can be combined to lead to reliable output. This multidisciplinary research makes use of real data sourced from cocaine analyte detecting fibre optic sensor developed by one of the collaborators - City University, London. This research advocates a two-step approach: For the first step, the raw sensor data are collected and stored. Level one fusion i.e. analyses, pre-processing and feature extraction is performed at this stage. In step two, using experimentally pre-determined thresholds, each feature decides on detection of cocaine or otherwise with a corresponding posterior probability. High level sensor fusion is then performed on this output locally to combine these decisions and their probabilities at time intervals. Output from every time interval is stored in the database and used as prior data for the next time interval. The final output is a decision on detection of cocaine. The key contributions of this thesis includes investigating the use of data fusion techniques as a solution for overcoming challenges in the real time detection of cocaine using fibre optic sensor technology together with an innovative user interface design. A generalizable sensor fusion architecture is suggested and implemented using the Bayesian and Dempster-Shafer techniques. The results from implemented experiments show great promise with this architecture especially in overcoming sensor limitations. A 5-fold cross validation system using a 12 13 - 1 Neural Network was used in validating the feature selection process. This validation step yielded 89.5% and 10.5% true positive and false alarm rates with 0.8 correlation coefficient. Using the Bayesian Technique, it is possible to achieve 100% detection whilst the Dempster Shafer technique achieves a 95% detection using the same features as inputs to the DF system

Human Shape-Motion Analysis In Athletics Videos for Coarse To Fine Action/Activity Recognition Using Transferable BeliefModel

We present an automatic human shape-motion analysis method based on a fusion architecture for human action and activity recognition in athletic videos. Robust shape and motion features are extracted from human detection and tracking. The features are combined within the Transferable Belief Model (TBM framework for two levels of recognition. The TBM-based modelling of the fusion process allows to take into account imprecision, uncertainty and conflict inherent to the features. First, in a coarse step, actions are roughly recognized. Then, in a fine step, an action sequence recognition method is used to discriminate activities. Belief on actions are made smooth by a Temporal Credal Filter and action sequences, i.e. activities, are recognized using a state machine, called belief scheduler, based on TBM. The belief scheduler is also exploited for feedback information extraction in order to improve tracking results. The system is tested on real videos of athletics meetings to recognize four types of actions (running, jumping, falling and standing) and four types of activities (high jump, pole vault, triple jump and long jump). Results on actions, activities and feedback demonstrate the relevance of the proposed features and as well the efficiency of the proposed recognition approach based on TBM

Reliability assessment of rock slopes by evidence theory

El objetivo de este proyecto de investigación es desarrollar una metodología para efectuar análisis de confiabilidad de la estabilidad de taludes rocosos, teniendo en cuenta la incertidumbre cuando la información sobre los parámetros geomecánicos de entrada es limitada. En mecánica de rocas, los métodos determinísticos y probabilísticos son ampliamente utilizados en el proceso de toma decisiones. No obstante, el primero no considera la incertidumbre y el segundo tiene limitaciones para representar la incertidumbre epistémica y tiene que asumir la distribución de probabilidad de las variables de entrada. Por lo tanto, se recurre a la Teoría de la Evidencia como una herramienta para describir la incertidumbre aleatoria y epistémica de los parámetros geomecánicos y propagarla a través de modelos de equilibrio límite, en los que la geometría es controlada por la orientación de las discontinuidades. Para llevar a cabo una mejor descripción de la variabilidad en el macizo, el proyecto utilizó fotogrametría de corto alcance, lo que permitió obtener series de datos robustas y confiables de la geometría de las discontinuidades, que fue modelada como una variable aleatoria con distribución Kent. Además, se desarrolló un procedimiento para actualizar los análisis de confiabilidad teniendo en cuenta la distribución de probabilidad de la orientación de las discontinuidades. La aplicación de la metodología en un talud rocoso de una mina de arenisca mostró su aplicabilidad a proyectos reales. Consecuentemente, la principal contribución de este trabajo es la generación de un marco de referencia para efectuar la evolución de confiabilidad de taludes rocoso basado en la teoría de la evidencia que permite combinar las series robustas de la orientación de los planos de discontinuidad, con información limitada de sus parámetros de resistencia, que puede ser actualizada a medida que se genera nueva información.This research project aims to develop a methodology to perform rock slope stability analysis considering the aleatory and epistemic uncertainty when the information on geomechanical parameters is limited. In rock mechanics, deterministic and probabilistic approaches are widely used in the decision-making process. However, the earlier does not consider the uncertainty, and the latter has limitations to account for the epistemic uncertainty and requires assumptions on probability distributions when robust data sets are not available. Therefore, we resorted to the Evidence Theory as a tool to describe the epistemic and aleatory uncertainty of input geomechanical variables and propagate them trough limit equilibrium models, in which the geometry is controlled by the joints orientation. To perform a better description of the variability of the rock mas properties, the project utilized a short-range photogrammetry system, which allowed us to have robust and reliable data sets on joints geometry to be modeled as Kent distributed variables. Besides, we suggested a procedure to update the reliability analysis acknowledging that orientations follow a Kent distribution. The application of the methodology to a rock slope in a sandstone mine showed its suitability to be applied in actual engineering projects. Consequently, the main contribution of this project is an rock slope evidence theory reliability-based framework for combining robust data sets on joints orientation, with limited information on geomechanical parameters, that can be updated as new information is available.ColcienciasAnalisis Cuantitativo de Riesgo en Taludes MinerosLínea de Investigación: Geotecnia y Riesgos Geo ambientalesDoctorad

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Enriching remote labs with computer vision and drones

165 p.With the technological advance, new learning technologies are being developed in order to contribute to better learning experience. In particular, remote labs constitute an interesting and a practical way that can motivate nowadays students to learn. The studen can at anytime, and from anywhere, access the remote lab and do his lab-work. Despite many advantages, remote tecnologies in education create a distance between the student and the teacher. Without the presence of a teacher, students can have difficulties, if no appropriate interventions can be taken to help them. In this thesis, we aim to enrich an existing remote electronic lab made for engineering students called "LaboREM" (for remote Laboratory) in two ways: first we enable the student to send high level commands to a mini-drone available in the remote lab facility. The objective is to examine the front panels of electronic measurement instruments, by the camera embedded on the drone. Furthermore, we allow remote student-teacher communication using the drone, in case there is a teacher present in the remote lab facility. Finally, the drone has to go back home when the mission is over to land on a platform for automatic recharge of the batteries. Second, we propose an automatic system that estimates the affective state of the student (frustrated/confused/flow) in order to take appropriate interventions to ensure good learning outcomes. For example, if the studen is having major difficulties we can try to give him hints or to reduce the difficulty level of the lab experiment. We propose to do this by using visual cues (head pose estimation and facil expression analysis). Many evidences on the state of the student can be acquired, however these evidences are incomplete, sometims inaccurate, and do not cover all the aspects of the state of the student alone. This is why we propose to fuse evidences using the theory of Dempster-Shafer that allows the fusion of incomplete evidence