Responses to Questions Asked by BIS Ahead of Challenger Business Programme – UAV Workshop, 23/11/15

This document is the University of Central Lancashire’s (UCLan) Civic Drone Centre’s responses to the questions asked by the Department for Business, Innovation and Skills (BIS) prior to Challenger Business Programme – Unmanned Aerial Vehicles (UAV) Workshop event to be held on 23 November 2015, 10am-2pm at the BIS Conference Centre1. As a university based research centre we are providing our responses based upon the university’s research, engagement with industry, and through the industrial experience of our staff members

TCitySmartF: A comprehensive systematic framework for transforming cities into smart cities

A shared agreed-upon definition of "smart city" (SC) is not available and there is no "best formula" to follow in transforming each and every city into SC. In a broader inclusive definition, it can be described as an opportunistic concept that enhances harmony between the lives and the environment around those lives perpetually in a city by harnessing the smart technology enabling a comfortable and convenient living ecosystem paving the way towards smarter countries and the smarter planet. SCs are being implemented to combine governors, organisations, institutions, citizens, environment, and emerging technologies in a highly synergistic synchronised ecosystem in order to increase the quality of life (QoL) and enable a more sustainable future for urban life with increasing natural resource constraints. In this study, we analyse how to develop citizen- and resource-centric smarter cities based on the recent SC development initiatives with the successful use cases, future SC development plans, and many other particular SC development solutions. The main features of SC are presented in a framework fuelled by recent technological advancement, particular city requirements and dynamics. This framework - TCitySmartF 1) aims to aspire a platform that seamlessly forges engineering and technology solutions with social dynamics in a new philosophical city automation concept - socio-technical transitions, 2) incorporates many smart evolving components, best practices, and contemporary solutions into a coherent synergistic SC topology, 3) unfolds current and future opportunities in order to adopt smarter, safer and more sustainable urban environments, and 4) demonstrates a variety of insights and orchestrational directions for local governors and private sector about how to transform cities into smarter cities from the technological, social, economic and environmental point of view, particularly by both putting residents and urban dynamics at the forefront of the development with participatory planning and interaction for the robust community- and citizen-tailored services. The framework developed in this paper is aimed to be incorporated into the real-world SC development projects in Lancashire, UK

Flight Guardian: Autonomous Flight Safety Improvement by Monitoring Aircraft Cockpit Instruments

During in-flight emergencies, a pilot’s workload increases significantly and it is often during this period of increased stress that human errors occur that consequently diminish the flight safety. Research studies indicate that many plane crashes can be attributed to ineffective cockpit instrument monitoring by the pilot. This manuscript entails the development of Flight Guardian(FG) system being first of its kind that aims to provide efficient flight-deck awareness to improve flight safety while assisting the pilot in abnormal situations. The system is intended to be used in older aircrafts that cannot easily or cost effectively be modified with modern digital avionic systems. One of the important feature of FG system being not physically connected to the aircraft, avoids any impact on airworthiness or the need for re-certification. For the first time, a composite of techniques including video analysis, knowledge representation, and machine belief representations are combined to build a novel flight-deck warning system. The prototype system is tested in both; simulation based Lab and real flight environments under the guidance of expert pilots. The overall system performance is evaluated using statistical analysis of experimental results that proved the robustness of proposed methodology in terms of automated warning generation in hazardous situations

DEMO: UAVs in crowd tagged mountain rescue

This project explores the potential for users to interact with live events in a new and dynamic way. It draws on fixed wing unmanned aircraft and associated sensor systems to provide realtime video and image data. It uses a web based software package as a crowd sourced imagery analysis tool allowing user involvement in the tagging and sorting of images. This technology allows a simulation of how the power of crowds[1] could be combined with Unmanned Aerial Vehicle (UAV) to monitor video footage and identify areas of particular interest by interacting with live video. A test flight in collaboration with Patterdale Mountain Rescue is used. The system fosters active citizenship by connecting communities to real life, live events in open-source creative communities. It explores the barriers and potential for an entirely new capacity for users to choose the perspective and proximity of their view by interacting with images from a UAV through ambient media. Copyright © 2013 ACM

Vision-Based Remote Sensing Imagery Datasets From Benkovac Landmine Test Site Using An Autonomous Drone For Detecting Landmine Locations

Mapping millions of buried landmines rapidly and removing them cost-effectively is supremely important to avoid their potential risks and ease this labour-intensive task. Deploying uninhabited vehicles equipped with multiple remote sensing modalities seems to be an ideal option for performing this task in a non-invasive fashion. This report provides researchers with vision-based remote sensing imagery datasets obtained from a real landmine field in Croatia that incorporated an autonomous uninhabited aerial vehicle (UAV), the so-called LMUAV. Additionally, the related knowledge regarding the literature survey is presented to guide the researchers properly. More explicitly, two remote sensing modalities, namely, multispectral and long-wave infrared (LWIR) cameras were mounted on an advanced autonomous UAV and datasets were collected from a well-designed field containing various types of landmines. In this report, multispectral imagery and LWIR imagery datasets are presented for researchers who can fuse these datasets using their bespoke applications to increase the probability of detection, decrease the false alarm rate, and most importantly, improve their techniques based on the features of vision-based imagery datasets

Automated Aircraft Instrument Reading Using Real Time Video Analysis

Automated Dial Reading (ADR) using image processing is a challenging task that has to deal with the dynamics of real time environment. Literature contains limited research work for ADR that is based on background subtraction, object tracking, and pattern recognition. These methods suffer from dynamic environment such as: varying light intensity, poor resolution, and vibrations in capturing device. A valuable contribution to the existing dial reading approaches is made in this paper by deploying convolution method which plays a significant role in needle/hand recognition within a dial. Proposed dial reading approach is successfully used and tested reading analogue aircraft instruments facilitated by the Flight Guardian (FG) project for automated reading of the cockpit devices in dynamic environments. Performance is evaluated by statistical analysis of the experimental results that proved the robustness of the proposed method

Airborne Vision-Based Remote Sensing Imagery Datasets From Large Farms Using Autonomous Drones For Monitoring Livestock

Livestock have high economic value and monitoring of them in large farms regularly is a labour-intensive task and costly. The emergence of smart data on individual animals and their surroundings opens up new opportunities for early detection and disease prevention, better animal care and traceability, better sustainability and farm economics. Precision Livestock Farming (PLF) relies on the constant and automated gathering of livestock data to support the expertise and management decisions made by farmers, vets, and authorities. The high mobility of UAVs combined with a high level of autonomy, sensor-driven technologies and AI decision-making abilities can provide many advantages to farmers in exploiting instant information from every corner of a large farm. The key objectives of this research are to i) explore various drone-mounted vision-based remote sensing modalities, particularly, visual band sensing and a thermal imager, ii) develop UAV-assisted autonomous PLF technologies and ii) collect data with various parameters for the researchers to establish further advanced AI-based approaches for monitoring livestock in large farms effectively by fusing a rich set of features acquired using vision-based multi-sensor modalities. The collected data suggest that the fuse of distinctive features of livestock obtained from multiple sensor modalities can be exploited to help farmers experience better livestock management in large farms through PLF

Intelligent Airborne Monitoring of Livestock Using Autonomous Uninhabited Aerial Vehicles

Precision Livestock Farming (PLF) is one of the most promising applications showing the benefits of using drones where a lack of human element in the farming industry is becoming evident. UAV-assisted smart farming within large farms has gained momentum in managing large farms effectively by avoiding high costs and increasing the quality of monitoring. To this end, the high mobility of UAVs combined with a high level of autonomy, sensor-driven technologies and AI decision-making abilities can provide many advantages to farmers in exploiting instant information from every corner of a large farm. The key objective of this research is to develop user-friendly AI-based software that can combine the sensor data sets and accurately detect animals and health anomalies, so the information can be presented in an easy-to-understand on-demand format for livestock farmers to take targeted or preemptive action, and improve the health, welfare, and productivity of their livestock. In this research, an automated drone solution with a cross-discipline approach has been developed to periodically survey livestock in an automated manner using vision-based sensor modalities involving both standard visual band sensing and a thermal imager. The experimental results suggest that the accuracy rates of detecting livestock are very high with very high sensitivity (Se) and specificity (Sp) values. Additionally, the results regarding the animal body heat signatures obtained from the thermal imagery show promising results in detecting disease-related cases. This research is a productivity and sustainability-focused pilot to investigate and demonstrate how drones and artificial intelligence software can provide a better way to regularly inspect animals on a large farm to avoid high costs and to increase the quality of monitoring. The research demonstrates how highly integrated technologies with drones can help the farming industry to overcome the challenging issues in the management of livestock, particularly, health monitoring of livestock in very large farms in an eco-friendly and sustainable way

Analysis and optimisation of unmanned aerial vehicle swarms in logistics: An intelligent delivery platform

Deploying Unmanned Aerial Vehicle (UAV) swarms in delivery systems is still in its infancy with regards to the technology, safety, aviation rules and regulations. Optimal use of UAVs in dynamic environments is important in many aspects- e.g., increasing efficacy, reducing the air traffic resulting in safer environment, and it requires new techniques and robust approaches based on the capabilities of UAVs and constraints. This manuscript analyses several delivery schemes within a platform, such as delivery with and without using air highways, and delivery using a hybrid scheme along with several delivery methods (i.e., optimal, premium and FIFO) to explore the use of UAV swarms as part of the logistics operations. In this platform, a dimension reduction technique, “dynamic multiple assignments in multi-dimensional space” (dMAiMD) and several other new techniques along with Hungarian and Cross-entropy Monte Carlo techniques are forged together to assign tasks and plan 3D routes dynamically. This particular approach is performed in such a way that UAV swarms in several warehouses are deployed optimally given the delivery scheme, method and constraints. Several scenarios are tested on the platform using small and big data sets. The results show that the distribution and the characteristics of data sets and constraints affect the decision on choosing the optimal delivery scheme and method. The findings are expected to guide the aviation authorities in their decisions before dictating rules and regulations regarding effective, efficient and safe use of UAVs. Furthermore, the companies that produce UAVs are going to take the demonstrated results into account for their functional design of UAVs along with other companies that aim to deliver their products using UAVs

MyPad – Intelligent Bladder Pre-void Alerting System: A project collaborated with NHS to treat Nocturnal Enureses (NE)

Unsatisfactory cure rates with currently available treatments of NE have led to the need to explore alternative modalities. New treatment methods that focus on preventing enuretic episodes by means of a pre-void alerting system could improve outcomes for children with NE in many aspects such as voiding in a dignified manner, reducing cost, reducing time to tackle the problem, improving the psychology of the sufferers. The aim of this project is to build, refine and evaluate a new safe, comfortable and non-invasive wearable intelligent electronic device to monitor the bladder and to treat NE by warning the patient at the pre- void stage, enhancing quality of life for these sufferers starting from the first use. No such technology exists currently to monitor bladder to alarm before bedwetting. Beyond this study, there are numerous other areas of application i.e. elder care (geriatric) settings, stroke patients and veterinary science in which My-PAD can be of potential benefit