Detection of Obstacles in Monocular Image Sequences

The ability to detect and locate runways/taxiways and obstacles in images captured using on-board sensors is an essential first step in the automation of low-altitude flight, landing, takeoff, and taxiing phase of aircraft navigation. Automation of these functions under different weather and lighting situations, can be facilitated by using sensors of different modalities. An aircraft-based Synthetic Vision System (SVS), with sensors of different modalities mounted on-board, complements the current ground-based systems in functions such as detection and prevention of potential runway collisions, airport surface navigation, and landing and takeoff in all weather conditions. In this report, we address the problem of detection of objects in monocular image sequences obtained from two types of sensors, a Passive Millimeter Wave (PMMW) sensor and a video camera mounted on-board a landing aircraft. Since the sensors differ in their spatial resolution, and the quality of the images obtained using these sensors is not the same, different approaches are used for detecting obstacles depending on the sensor type. These approaches are described separately in two parts of this report. The goal of the first part of the report is to develop a method for detecting runways/taxiways and objects on the runway in a sequence of images obtained from a moving PMMW sensor. Since the sensor resolution is low and the image quality is very poor, we propose a model-based approach for detecting runways/taxiways. We use the approximate runway model and the position information of the camera provided by the Global Positioning System (GPS) to define regions of interest in the image plane to search for the image features corresponding to the runway markers. Once the runway region is identified, we use histogram-based thresholding to detect obstacles on the runway and regions outside the runway. This algorithm is tested using image sequences simulated from a single real PMMW image

Learning spatiotemporal patterns for monitoring smart cities and infrastructure

Recent advances in the Internet of Things (IoT) have changed the way we interact with the world. The ability to monitor and manage objects in the physical world electronically makes it possible to bring data-driven decision making to new realms of city infrastructure and management. Large volumes of spatiotemporal data have been collected from pervasive sensors in both indoor and outdoor environments, and this data reveals dynamic patterns in cities, infrastructure, and public property. In light of the need for new approaches to analysing such data, in this thesis, we propose present relevant data mining techniques and machine learning approaches to extract knowledge from spatiotemporal data to solve real-world problems. Many challenges and problems are under-addressed in smart cities and infrastructure monitoring systems such as indoor person identification, evaluation of city regions segmentation with parking events, fine collection from cars in violations, parking occupancy prediction and airport aircraft path map reconstruction. All the above problems are associated with both spatial and temporal information and the accurate pattern recognition of these spatiotemporal data are essential for determining problem solutions. Therefore, how to incorporate spatiotemporal data mining techniques, artificial intelligence approaches and expert knowledge in each specific domain is a common challenge. In the indoor person identification area, identifying the person accessing a secured room without vision-based or device-based systems is very challenging. In particular, to distinguish time-series patterns on high-dimensional wireless signal channels caused by different activities and people, requires novel time-series data mining approaches. To solve this important problem, we established a device-free system and proposed a two-step solution to identify a person who has accessed a secure area such as an office. Establishing smart parking systems in cities is a key component of smart cities and infrastructure construction. Many sub-problems such as parking space arrangements, fine collection and parking occupancy prediction are urgent and important for city managers. Arranging parking spaces based on historical data can improve the utilisation rate of parking spaces. To arrange parking spaces based on collected spatiotemporal data requires reasonable region segmentation approaches. Moreover, evaluating parking space grouping results needs to consider the correlation between the spatial and temporal domains since these are heterogeneous. Therefore, we have designed a spatiotemporal data clustering evaluation approach, which exploits the correlation between the spatial domain and the temporal domain. It can evaluate the segmentation results of parking spaces in cities using historical data and similar clustering results that group data consisting of both spatial and temporal domains. For fine collection problem, using the sensor instrumentation installed in parking spaces to detect cars in violation and issue infringement notices in a short time-window to catch these cars in time is significantly difficult. This is because most cars in violation leave within a short period and multiple cars are in violation at the same time. Parking officers need to choose the best route to collect fines from these drivers in the shortest time. Therefore, we proposed a new optimisation problem called the Travelling Officer Problem and a general probability-based model. We succeeded in integrating temporal information and the traditional optimisation algorithm. This model can suggest to parking officers an optimised path that maximise the probability to catch the cars in violation in time. To solve this problem in real-time, we incorporated the model with deep learning methods. We proposed a theoretical approach to solving the traditional orienteering problem with deep learning networks. This approach could improve the efficiency of similar urban computing problems as well. For parking occupancy prediction, a key problem in parking space management is with providing a car parking availability prediction service that can inform car drivers of vacant parking lots before they start their journeys using prediction approaches. We proposed a deep learning-based model to solve this parking occupancy prediction problem using spatiotemporal data analysis techniques. This model can be generalised to other spatiotemporal data prediction problems also. In the airport aircraft management area, grouping similar spatiotemporal data is widely used in the real world. Determining key features and combining similar data are two key problems in this area. We presented a new framework to group similar spatiotemporal data and construct a road graph with GPS data. We evaluated our framework experimentally using a state-of-the-art test-bed technique and found that it could effectively and efficiently construct and update airport aircraft route map. In conclusion, the studies in this thesis aimed to discover intrinsic and dynamic patterns from spatiotemporal data and proposed corresponding solutions for real-world smart cities and infrastructures monitoring problems via spatiotemporal pattern analysis and machine learning approaches. We hope this research will inspire the research community to develop more robust and effective approaches to solve existing problems in this area in the future

Modelling the dynamic flight behaviour of birds in different frames of reference

In this thesis I consider two aspects – energetics and guidance – of two dynamic flight behaviours performed by birds – dynamic soaring and prey pursuit. Uniting the thesis is the collection and modelling of bird trajectory data in different frames of reference to make inferences on dynamic flight behaviour. In particular, I collect data in a camera-fixed reference frame to model the dynamic soaring flight trajectories of Manx shearwater in an aerodynamic reference frame, whereas I model the attack trajectories of Harris’ hawks in both an inertial and a background frame of reference using data collected in an Earth-fixed frame of reference. The output of my investigation into the energetics of dynamic soaring is the first empirical demonstration of dynamic soaring outside the albatrosses, the formulation of a new metric for identifying and quantifying dynamic soaring, and the demonstration that the large-scale distribution of the Manx shearwater is affected by their dynamic soaring behaviour. The output of my investigation into the guidance of prey pursuit is the finding that Harris’ hawk attack trajectories are well modelled by the proportional navigation (PN) guidance law commonly used by homing missiles. However, I also show that a guidance law that can be mechanised using only visual information – rather than the inertial and visual information required by PN – also successfully fits attack trajectory data. Finally, I propose a method for analysing eye-in-head movements during dynamic flight in birds, and I find that Harris’ hawks limit eye-in-head movement during terminal pursuit, a necessary condition to implement PN guidance. In being reflective about the reference frames in which I model bird behaviour and, in cases, by modelling the same data in different reference frames, I expose the utility of the reference frame concept in analysing biological systems

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others

Biologically Inspired Vision and Control for an Autonomous Flying Vehicle

This thesis makes a number of new contributions to control and sensing for unmanned vehicles. I begin by developing a non-linear simulation of a small unmanned helicopter and then proceed to develop new algorithms for control and sensing using the simulation. The work is field-tested in successful flight trials of biologically inspired vision and neural network control for an unstable rotorcraft. The techniques are more robust and more easily implemented on a small flying vehicle than previously attempted methods. ¶ ..

Reversible silencing of spinal neurons unmasks a left-right coordination continuum.

This dissertation is focused on dissecting the functional role of two anatomically-defined pathways in the adult rat spinal cord. A TetOn dual virus system was used to selectively and reversibly induce enhanced tetanus neurotoxin expression in L2 neurons that project to L5 (L2-L5) or C6 (long ascending propriospinal neurons, LAPNs). Results focus on the changes observed during overground locomotion. The dissertation is divided into four chapters. Chapter One is a focused introduction to locomotion, including its broad description, the central mechanisms of its expression, how genetic-based approaches defined these mechanisms, and the limitations in these approaches. It concludes with details of the silencing paradigm used here and a summary of the main findings. Chapter Two describes the functional consequences of silencing L2-L5 interneurons. The focus is on selective disruption of hindlimb coordination during overground locomotion, revealing a continuum from walk to hop. These changes are independent of speed, step frequency, and other spatiotemporal features of gait. Left-right alternation was restored during swimming and stereotypic exploration, suggesting a task-specific role. Silencing L2-L5 interneurons partially uncoupled the hindlimbs, allowing spontaneous shifts in coordination on a step-by-step basis. It is proposed this pathway distributes temporal information for left-right hindlimb alternation, securing effective coordination in a context-dependent manner. Chapter Three focuses on the consequences of silencing LAPNs.Three patterns of interlimb coupling are disrupted: left-right forelimb, left-right hindlimb, and contralateral hindlimb-forelimb coordination. Observed again was a context-dependent continuum from walk-to-hop, irrespective of step frequency, speed, and the salient features that define locomotion. However, instead of spontaneous shifts in coordination as observed from L2-L5 interneuron silencing, the breadth of coupling patterns expressed were maintained on a step-by-step basis. It is proposed that this ascending, inter-enlargement pathway distributes temporal information required for left-right alternation at the shoulder and pelvic girdles in a context-dependent manner. Collectively, these data suggest that L2-L5 interneurons and LAPNs are key pathways that distribute left-right patterning information throughout the neuraxis. The functional role(s) of these pathways are exquisitely gated to the context at hand, suggesting that the locomotor circuitry undergoes functional reorganization thereby endowing or masking the silencing-induced disruptions to interlimb coordination