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Current Projects

Understanding unpaved road condition for asset management by Earth Observation in Low Income Countries

Supervisors: Dr. Alex Wright (TRL Ltd), Dr. Patrick Wong

PhD Student: Mr. Robin Workman

This research is focused on unpaved road condition assessment using remote sensing, which is used extensively for many aspects of our lives today and is increasingly playing a prominent role in identifying and assessing road networks worldwide. For example, online maps are largely devised from remote sensing such as optical satellite imagery and LIDAR, photographs taken from aeroplanes have been used for decades to assess road alignments and Unmanned Aerial Vehicles have become a common platform for asset inspections. There is potential for Low Income Countries to benefit from remote sensing, potentially to enhance the knowledge of rural unpaved roads, which are usually the most neglected part of a network, but are nevertheless one of the most important forms of infrastructure to influence rural poverty.

A comprehensive literature review has revealed that optical satellite imagery is a less researched area for unpaved roads, but that it does have the possibility to be feasible for the assessment of road condition, especially where there are problems with undertaking traditional driven type surveys in remote and conflict affected areas where access is difficult. There is also potential for it to provide information for international development indicators, for example the Sustainable Development Goals.

This research therefore decided to focus on optical satellite imagery as a source of data for the assessment of unpaved road condition. Some initial investigations have been carried out into interpreting the imagery to monitor road condition using variation in road width and pixel brightness of the road surface, as these have been identified in previous research as the features most likely to provide relevant information. Assuming that this research confirms its potential, it will be expanded on in the next phase of the research.

A methodology has been proposed that will inform the process for the next phase of this research, which includes considering asset management of unpaved roads and how the information gained from optical satellite imagery can be useful for assessing and prioritising maintenance. This will require investigation into how road condition is used in an asset management system, and what quality and frequency is required to make the key decisions in that system. Key questions will be asked, such as ‘is the satellite based system able to capture enough information to make those decisions?’ and ‘what quality of data is required to prioritise the maintenance of unpaved roads in LICs?’ Finally the research will consider the potential for using the knowledge learned from this research to contribute towards a computer based, automatically generated system of asset management.

Completed projects

Interference Aware Cognitive Femtocell Networks

Supervisors: Prof. Laurence S. Dooley, Dr. Adrian Poulton

PhD Student: Mr. Faisal Tariq

Femtocells Access Points (FAP) are low power, plug and play home base stations which are designed to extend the cellular radio range in indoor environments where macrocell coverage is generally poor. They offer significant increases in data rates over a short range, enabling high speed wireless and mobile broadband services, with the femtocell network overlaid onto the macrocell in a dual-tier arrangement. In contrast to conventional cellular systems which are well planned, FAP are arbitrarily installed by the end users and this can create harmful interference to both collocated femtocell and macrocell users. The interference becomes particularly serious in high FAP density scenarios and compromises the ensuing data rate. Consequently, effective management of both cross and co-tier interference is a major design challenge in dual-tier networks.

Since traditional radio resource management techniques and architectures for single-tier systems are either not applicable or operate inefficiently, innovative dual-tier approaches to intelligently manage interference are required. This thesis presents a number of original contributions to fulfill this objective including, a new hybrid cross-tier spectrum sharing model which builds upon an existing fractional frequency reuse technique to ensure minimal impact on the macro-tier resource allocation. A new flexible and adaptive virtual clustering framework is then formulated to alleviate co-tier interference in high FAP densities situations and finally, an intelligent coverage extension algorithm is developed to mitigate excessive femto-macrocell handovers, while upholding the required quality of service provision.

This thesis contends that to exploit the undoubted potential of dual-tier, macro-femtocell architectures an interference awareness solution is necessary. Rigorous evidence confirms that noteworthy performance improvements can be achieved in the quality of the received signal and throughput by applying cognitive methods to manage interference.

A Cognitive TV White Space Access Framework

Supervisors: Prof. Laurence S. Dooley, Dr. Patrick Wong

PhD Student: Mr. John Martin (Alcatel-Lucent Telecoms, Chepstow)

Given the current boom in applications and services for mobile devices, data traffic is rapidly expanding, with the consequence that increasing spectrum capacity is being mandated. Following the switchover from analogue to digital platforms, Television White Space (TVWS) affords a fertile opportunity to supplement existing licensed spectrum to ease this scarcity. There are however, a number of obstacles to wide-scale TVWS adoption, including the accurate detection of primary users (PU), the hidden node problem and bandwidth availability for unlicensed secondary users (SU). Regulatory and industry bodies have sought to address some of these issues using a static database for spectrum access decisions, though this involves manual maintenance and accuracy can be compromised due to a lack of real-time information. While the new IEEE802.11af wireless local area network (WLAN) standard attempts to resolve some SU access issues, there remain many challenges, such as the critical asymmetry between mobile and base station power resources.

This thesis presents a new cognitive TVWS access framework encompassing a real-time sensing paradigm for TVWS deployment that uses a spectrum-efficient scheme to uphold quality-of-service (QoS) for both PU and SU. A novel dynamic spectrum allocation (DSA) model has been formulated allied with a resilient interference management system which exploits the unique way digital terrestrial TV channels are allocated in different geographical areas. A margin strategy has been framed to support efficient TVWS channel reuse, with an exclusion zone established to overcome the hidden node problem, while an innovative routing algorithm using cross-layer information, both extends coverage capacity and maximises QoS provision by ensuring a more balanced resource allocation.

Critical evaluation of the new access framework confirms that significant QoS improvements for SU are achieved compared to existing TVWS techniques. It importantly embodies a generic, practical, resource-efficient solution for TVWS deployment, which is compliant with current PU regulatory requirements.

Intelligent Side Information Generation in Distributed Video Coding

Supervisors: Prof. Laurence Dooley, Dr. Patrick Wong

PhD Student: Mr. Mobolaji Akinola

Distributed video coding (DVC) reverses the traditional coding paradigm of complex encoders allied with basic decoding to one where the computational cost is largely incurred by the decoder. This is attractive as the proven theoretical work of Wyner-Ziv (WZ) and Slepian-Wolf (SW) shows that the performance by such a system should be exactly the same as a conventional coder. Despite the solid theoretical foundations, current DVC qualitative and quantitative performance falls short of existing conventional coders and there remain crucial limitations. A key constraint governing DVC performance is the quality of side information (SI), a coarse representation of original video frames which are not available at the decoder. Techniques to generate SI have usually been based on linear motion compensated temporal interpolation (LMCTI), though these do not always produce satisfactory SI quality, especially in sequences exhibiting non-linear motion.

This thesis presents an intelligent higher order piecewise trajectory temporal interpolation (HOPTTI) framework for SI generation with original contributions that afford better SI quality in comparison to existing LMCTI-based approaches. The major elements in this framework are: (i) a cubic trajectory interpolation algorithm model that significantly improves the accuracy of motion vector estimations; (ii) an adaptive overlapped block motion compensation (AOBMC) model which reduces both blocking and overlapping artefacts in the SI emanating from the block matching algorithm; (iii) the development of an empirical mode switching algorithm; and (iv) an intelligent switching mechanism to construct SI by automatically selecting the best macroblock from the intermediate SI generated by HOPTTI and AOBMC algorithms. Rigorous analysis and evaluation confirms that significant quantitative and perceptual improvements in SI quality are achieved with the new framework.

A Unified Wormhole Attack Detection Framework for Mobile Ad hoc Networks

Supervisors: Prof. Laurence S. Dooley, Dr. David Chapman, Dr. Goran Pulkkiss (Arcada University of Applied Sciences, Helsinki)

PhD Student: Mr. Jonny Karlsson

The Internet is experiencing an evolution towards a ubiquitous network paradigm, via the so-called internet-of-things (IoT), where small wireless computing devices like sensors and actuators are integrated into daily activities. Simultaneously, infrastructure-less systems such as mobile ad hoc networks (MANET) are gaining popularity since they provide the possibility for devices in wireless sensor networks or vehicular ad hoc networks to share measured and monitored information without having to be connected to a base station. While MANETs offer many advantages, including self-configurability and application in rural areas which lack network infrastructure, they also present major challenges especially in regard to routing security. In a highly dynamic MANET, where nodes arbitrarily join and leave the network, it is difficult to ensure that nodes are trustworthy for multi-hop routing. Wormhole attacks belong to most severe routing threats because they are able to disrupt a major part of the network traffic, while concomitantly being extremely difficult to detect.

This thesis presents a new unified wormhole attack detection framework which is effective for all known wormhole types, alongside incurring low false positive rates, network loads and computational time, for a variety of diverse MANET scenarios. The framework makes three original technical contributions: i) a new accurate wormhole detection algorithm based on packet traversal time and hop count analysis (TTHCA) which identifies infected routes, ii) an enhanced, dynamic traversal time per hop analysis (TTpHA) detection model which is adaptable to node radio range fluctuations, and iii) a method for automatically detecting time measurement tampering in both TTHCA and TTpHA.

The thesis findings indicate that this new wormhole detection framework provides significant performance improvements compared to other existing solutions by accurately, efficiently and robustly detecting all wormhole variants under a wide range of network conditions.

Scalable base station switching framework for green cellular networks

Supervisors: Prof. Laurence S. Dooley, Dr. Adrian Poulton

PhD Student: Mr. Alam Atm Shafiul

With the recent unprecedented growth in the wireless market, network operators are obliged not only to find new techniques including dense deployment of base stations (BSs) in order to support high data rate services and high user density, but also to reduce the operating costs and energy consumption of various network elements. To solve these challenges, powering down certain BSs during low-traffic periods, so-called BS sleeping, has emerged as an effective green communications paradigm. While BS sleeping offers the potential to significantly lower energy consumption, it also raises many challenges, since when a BS is switched off, this can lead to, for example, coverage holes, sudden degradation in quality of service (QoS), higher transmit power dissipation in off-cell mobile stations (MSs), an inability to rapidly power up/down equipment and finally, a failure to uphold regulatory requirements. In order to realise greener network designs which both maximise energy savings whilst guaranteeing QoS, innovative BS switching mechanisms need to be developed.

 This thesis presents a novel BS switching framework which improves energy efficiency (EE) in comparison with existing approaches, while guaranteeing the minimum QoS and seamless services. The major technical contributions in this framework are: i) a new BS to relay station (RS) switching model where certain BSs are switched to RS mode rather than being turned off, firstly using a fixed threshold based switching algorithm utilizing temporal traffic diversity, and ii) then subsequently by means of an adaptive threshold by exploiting the inherently asymmetric traffic profile between cells, i.e., by exploiting both the temporal and spatial traffic diversity; iii) a traffic-and-interference-aware BS switching strategy that considers the impact of inter-cell interference in the decision making process to dynamically determine the best BS set to be kept active for improved EE; and finally iv) a novel scalable multimode BS switching model which enables each BS to operate in different power modes i.e., macro/micro/sleep to explore energy savings potential even at higher traffic conditions.

The thesis findings conclusively confirm this new BS switching framework provides significant EE improvements from both BS and MS perspectives, under diverse network conditions and represents a notable step towards greener communications.

A Knowledge Integration Framework for 3D Shape Reconstruction

Supervisors: Prof. Laurence S. Dooley, Dr. Adrian Poulton and Dr.-Ing. Anko Börner (German Aerospace Center (DLR), Germany).

PhD Student: Mr. Eugen Funk

The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points in sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment in which the robots operate with. This means unstructured 3D samples must be processed by application-specific models to enable a robot, for instance, to detect and identify objects and infer the scene geometry for path-planning more efficiently than by using raw 3D data. This thesis specifically focuses on the fundamental task of 3D shape reconstruction and modelling by presenting a new knowledge integration framework for unstructured 3D samples. The novelty lies in the representation of surfaces by algebraic functions with limited support, which enables the extraction of smooth consistent shapes from noisy samples with a heterogeneous density. Moreover, many surfaces in urban environments can reasonably be assumed to be planar, and the framework exploits this knowledge to enable effective noise suppression without loss of detail. This is achieved by using a convex optimization technique which has linear computational complexity. Thus is much more efficient than existing solutions. The new framework has been validated by critical experimental analysis and evaluation and has been shown to increase the accuracy of the reconstructed shape significantly compared to state-of-the-art methods. Applying this new knowledge integration framework means that less accurate, low-cost 3D sensors can be employed without sacrificing the high demands that 3D perception must achieve. This links well into the area of robotic inspection, as for example regarding small drones that use inaccurate and lightweight image sensors.

A Hybrid Similarity Measure Framework for Multimodal Medical Image Registration

Supervisors: Prof. Laurence S. Dooley, Dr. Patrick Wong and Dr.-Ing. Anko Börner (German Aerospace Center (DLR), Germany).

PhD Student: Mr. Parminder Singh Reel

Medical imaging is widely used today to facilitate both disease diagnosis and treatment planning practice, with a key prerequisite being the systematic process of medical image registration (MIR) to align either mono or multimodal images of different anatomical parts of the human body. MIR utilises a similarity measure (SM) to quantify the level of spatial alignment and is particularly demanding due to the presence of inherent modality characteristics like intensity non-uniformities (INU) in magnetic resonance images and large homogeneous non-vascular regions in retinal images. While various intensity and feature-based SMs exist for MIR, mutual information (MI) has become established because of its computational efficiency and ability to register multimodal images. It is however, very sensitive to interpolation artefacts in the presence of INU with noise and can be compromised when overlapping areas are small. Recently MI-based hybrid variants which combine regional features with intensity have emerged, though these incur high dimensionality and large computational overheads.

To address these challenges and secure accurate, efficient and robust registration of images containing high INU, noise and large homogeneous regions, this thesis presents a new hybrid SM framework for 2D multimodal rigid MIR. The framework consistently provides superior quantitative and qualitative performance, while offering a uniquely flexible design trade-off between registration accuracy and computational time. It makes three significant technical contributions to the field: i) An expectation maximisation-based principal component analysis with mutual information (EMPCA-MI) framework incorporating neighbourhood feature information; ii) Two innovative enhancements to reduce information redundancy and improve MI computational efficiency; and iii) an adaptive algorithm to select the most significant principal components for feature selection.

The thesis findings conclusively confirm the hybrid SM framework offers an accurate and robust 2D registration solution for challenging multimodal medical imaging datasets, while its inherent flexibility means it can also be extended to the 3D registration domain.

A Novel Inpainting Framework for Virtual View Synthesis

Supervisors: Prof. Laurence S. Dooley, Dr. Patrick Wong and Dr. Gene Cheung (National Institute of Informatics, Tokyo, Japan).

PhD Student: Mrs. Smarti Reel

Multi-view imaging has stimulated significant research to enhance the user experience of free viewpoint video, allowing interactive navigation between views and the freedom to select a desired view to watch. This usually involves transmitting both textural and depth information captured from different viewpoints to the receiver, to enable the synthesis of an arbitrary view. In rendering these virtual views, perceptual holes can appear due to certain regions, hidden in the original view by a closer object, becoming visible in the virtual view. To provide a high quality experience these holes must be filled in a visually plausible way, in a process known as inpainting. This is challenging because the missing information is generally unknown and the hole-regions can be large. Recently depth-based inpainting techniques have been proposed to address this challenge and while these generally perform better than non-depth assisted methods, they are not very robust and can produce perceptual artefacts.

This thesis presents a new inpainting framework that innovatively exploits depth and textural self-similarity characteristics to construct subjectively enhanced virtual viewpoints. The framework makes three significant contributions to the field: i) the exploitation of view information to jointly inpaint textural and depth hole regions; ii) the introduction of the novel concept of self-similarity characterisation which is combined with relevant depth information; and iii) an advanced self-similarity characterising scheme that automatically determines key spatial transform parameters for effective and flexible inpainting.

The presented inpainting framework has been critically analysed and shown to provide superior performance both perceptually and numerically compared to existing techniques, especially in terms of lower visual artefacts. It provides a flexible robust framework to develop new inpainting strategies for the next generation of interactive multi-view technologies.

A Cognitive Radio Compressive Sensing Framework

Supervisors: Prof. Laurence S. Dooley and Dr. Soraya Kouadri Mostéfaoui

PhD Student:  Mr. Dimitrios Karampoulas

With the proliferation of wireless devices and services, allied with further significant predicted growth, there is an ever increasing demand for higher transmission rates. This is especially challenging given the limited availability of radio spectrum, and is further exacerbated by a rigid licensing regulatory regime. Spectrum however, is largely underutilized and this has prompted regulators to promote the concept of opportunistic spectrum access. This allows unlicensed secondary users to use bands which are licensed to primary users, but are currently unoccupied, so leading to more efficient spectrum utilization.

A potentially attractive solution to this spectrum underutilisation problem is cognitive radio (CR) technology, which enables the identification and usage of vacant bands by continuously sensing the radio environment, though CR enforces stringent timing requirements and high sampling rates. Compressive sensing (CS) has emerged as a novel sampling paradigm, which provides the theoretical basis to resolve some of these issues, especially for signals exhibiting sparsity in some domain. For CR-related signals however, existing CS architectures such as the random demodulator and compressive multiplexer have limitations in regard to the signal types used, spectrum estimation methods applied, spectral band classification and a dependence on Fourier domain based sparsity.

This thesis presents a new generic CS framework which addresses these issues by specifically embracing three original scientific contributions: i) seamless embedding of the concept of precolouring into existing CS architectures to enhance signal sparsity for CR-related digital modulation schemes; ii) integration of the multitaper spectral estimator to improve sparsity in CR narrowband modulation schemes; and iii) exploiting sparsity in an alternative, non-Fourier (Walsh-Hadamard) domain to expand the applicable CR-related modulation schemes.

Critical analysis reveals the new CS framework provides a consistently superior and robust solution for the recovery of an extensive set of currently employed CR-type signals encountered in wireless communication standards. Significantly, the generic and portable nature of the framework affords the opportunity for further extensions into other CS architectures and sparsity domains.

Sustainable Low-Carbon Isolated Island Electricity Systems - Policy and Investment Impacts Assessed Using System Dynamics

Supervisors: Prof. William Nuttall, Dr. Ben Mestel and Prof. Laurence S. Dooley

PhD Student: Mr. George Jr. S Matthew

This thesis presents a novel System Dynamics (SD) policy and investment analysis framework for future low-carbon electricity systems, using an electrically isolated island system as its case study.

Current electricity systems are undergoing a long-term transition towards reduced fossil fuel use, primarily driven by high fuel costs, environmental concerns and the desire for energy security. These systems are facing a number of evolving policy drivers: most notably, current attempts to pursue higher levels of renewable energy sources, greater energy efficiency and other supporting technologies. Emerging challenges are shaping the low-carbon objectives of future electricity systems and the ensuing implications for future policy and investment decisions. This thesis presents a number of critical policy recommendations allied with longer-term investment observations, evolving from the nexus between the environmental and energy security concerns of an island-based electricity system.

Island systems such as São Miguel, are small enough to be understood while being large enough to reveal highly complex structures and inherent time and spatial interactions within and between social, economic and technical factors. It is argued that a systematic SD-based approach can reveal possible system structure trajectories, with such insights assisting the understanding of overall sustainability while recognising emergent challenges and behaviours.

The thesis shows that learning-by-doing renewables cost reductions exists but are not very significant in island electricity systems. Additionally, it shows that setting low-carbon policy targets is beneficial for emissions reductions, but meeting these targets too early is either inefficient or impractical if targets are unrealistic. Critical evaluations of endogenous electricity demand growth and the system capacity margin are provided, which highlights consequential policy challenges for island-based systems. The most important and influential low-carbon agendas giving endogenous impacts on electricity demand are elaborated. The thesis also confirms that more effective policies, for sustained renewables uptake and improved investor decision-making for the generation mix, can be achieved. Insights distilled from smaller electricity systems can help frame the outlook of larger systems.

A Novel Multi-View Tennis Table Umpiring Framework

Supervisors: Dr. Patrick Wong, Prof. Laurence S. Dooley and Prof. Adrian Hopgood (University of Portsmouth)

PhD Student:  Mrs. Hnin Myint

This research investigates the development of a low-cost multi-view umpiring framework, as an alternative to the current expensive systems that are almost exclusively restricted to elite professional sports. Table tennis has been selected as the testbed because, while automating the process is challenging, it has many different complex match elements including the service, return and rallies, which are governed by a strict set of regulations. The focus is mainly on the rally element rather than the whole match. Ball detection and tracking in video frames are undertaken to determine reliably the ball position relative to key reference objects like the table surface and net, and the ball’s flight path is used to determine the rally’s status.

While a low-cost option has benefits, it is technically challenging due to the limited number of cameras and generally low video resolution used. This thesis presents a portable multi-view umpiring framework that identifies each state change in a rally. It makes three significant contributions to knowledge: i) a reliable ball detection strategy that accurately detects the location of the ball in low-resolution sequences; ii) a novel framework for ball tracking using a multi-view system, and iii) a new state-machine based evaluation system for analysing table tennis rallies.

In a series of ten different test scenarios, the system achieved an average of 94% system detection rate and 100% accurate decisions. A test sequence of duration 1 s can be processed in 8 s, leading to a delay of only 7 s, which is considered acceptable for practical purposes. This solution has the potential to reform the way matches are umpired, providing objectivity in resolving disputed decisions. It affords an economic technology for amateur players, while the multi-view facility is extendible to other relevant ball-based sports. Finally, the ball flight path analysis mechanism can be a valuable training tool for skills development.

A Distributed Video Coding Framework for Higher Resolution Sequences

Supervisors: Prof. Laurence S. Dooley and Dr. Patrick Wong

PhD Student:  Mr. Asif Mahmood

Video coding enables affordable video transmission by compressing a video to reduce its colossal bandwidth requirement. Distributed video coding (DVC) in particular is attractive for low power video applications due to its simple encoder structure. Contrary to theoretical expectancy, DVC architectures available in the literature exhibit lower rate-distortion (RD) performance to traditional complex inter-frame codecs. They do however perform superior to traditional low-complexity intra-frame codecs and thus advantageous in certain scenarios. Nevertheless, this advantage is pertinent only for very low spatial resolution videos, specifically, quarter common interchange format (QCIF) sequences. If the spatial resolution is increased, for instance, for common interchange format (CIF) sequences, the RD performance of a DVC codec dwindles, and the advantage diminishes. Moreover, inadequate and inflexible rate-control options offered by existing DVC codecs makes it difficult to achieve desired quality of service (QoS), which is further stressed at higher spatial resolutions. Additionally, though complexity of the decoder is not critical in DVC context and expected to be much more than the complexity of its encoder counterpart, the disparity between them dilates at higher spatial resolution and the decoder complexity becomes a bottleneck of a practical DVC codec implementation.

This thesis presents a novel DVC architecture based on the DISCOVER DVC architecture to overcome the aforementioned complications that emerge and escalate at higher spatial resolutions. The new architecture features content-aware quantisation (CAQ) – a dynamic and robust rate-control mechanism to provide efficient and flexible rate control. It aims to reduce perceptible distortion as well as to utilise available bandwidth in order to produce maximum output quality. The CAQ supports various transform block sizes, which enable greater exploitation of spatial redundancy at higher spatial resolutions. Furthermore, this thesis discusses the finding of a pilot study, which addresses the DVC complexity bottleneck by employing modern dynamic channel decoding algorithms to reduce maximal decoding time and computational complexity.

The new DVC architecture has been simulated and tested with several standard test sequences of different spatial resolutions. The rigorous analysis of empirical results show that it consistently outperforms the origin DISCOVER DVC architecture in terms of RD performance, rate-control flexibility, bandwidth usage and overall decoding complexity at higher spatial resolutions. The benefits can be transferred to recent DVC architectures to complement their advanced side information (SI) generation methods for further performance improvements.