Contributions to Improve Cognitive Strategies with Respect to Wireless Coexistence

Cognitive radio (CR) can identify temporarily available opportunities in a shared radio environment to improve spectral efficiency and coexistence behavior of radio systems. It operates as a secondary user (SU) and accommodates itself in detected opportunities with an intention to avoid harmful collisions with coexisting primary user (PU) systems. Such opportunistic operation of a CR system requires efficient situational awareness and reliable decision making for radio resource allocation. Situational awareness includes sensing the environment followed by a hypothesis testing for detection of available opportunities in the coexisting environment. This process is often known as spectral hole detection. Situational knowledge can be further enriched by forecasting the primary activities in the radio environment using predictive modeling based approaches. Improved knowledge about the coexisting environment essentially means better decision making for secondary resource allocation. This dissertation identifies limitations of existing predictive modeling and spectral hole detection based resource allocation strategies and suggest improvements. Firstly, accurate and efficient estimation of statistical parameters of the radio environment is identified as a fundamental challenge to realize predictive modeling based cognitive approaches. Lots of useful training data which are essential to learn the system parameters are not available either because of environmental effects such as noise, interference and fading or because of limited system resources particularly sensor bandwidth. While handling environmental effects to improve signal reception in radio systems has already gained much attention, this dissertation addresses the problem of data losses caused by limited sensor bandwidth as it is totally ignored so far and presents bandwidth independent parameter estimation methods. Where, bandwidth independent means achieving the same level of estimation accuracy for any sensor bandwidth. Secondly, this dissertation argues that the existing hole detection strategies are dumb because they provide very little information about the coexisting environment. Decision making for resource allocation based on this dumb hole detection approach cannot optimally exploit the opportunities available in the coexisting environment. As a solution, an intelligent hole detection scheme is proposed which suggests classifying the primary systems and using the documented knowledge of identified radio technologies to fully understand their coexistence behavior. Finally, this dissertation presents a neuro-fuzzy signal classifier (NFSC) that uses bandwidth, operating frequency, pulse shape, hopping behavior and time behavior of signals as distinct features in order to xii identify the PU signals in coexisting environments. This classifier provides the foundation for bandwidth independent parameter estimation and intelligent hole detection. MATLAB/Simulink based simulations are used to support the arguments throughout in this dissertation. A proof-of-concept demonstrator using microcontroller and hardware defined radio (HDR) based transceiver is also presented at the end.</p

Rehabilitation of Existing Building Stock: A System Dynamics Model to Support Policy Development

The development of policies to support an agenda of urban rehabilitation as a part of a city’s approach to engender sustainability is a difficult process owing to the need to consider a large number of stakeholder needs and demands. For example, Hong Kong has accumulated a considerable amount of building stock due to its rapid economic development over the last 50 years. However, despite Hong Kong’s economic growth, it has overlooked the need to rehabilitate its existing building stock (i.e. to restore the condition, operation and capacity of buildings) As such, a significant proportion of Hong Kong’s aging building stock is in need of rehabilitation. Further, the absence of a systemic maintenance policy, has stymied the Hong Kong’s government’s ability to ensure that its private building stock are resilient and adaptable for the future. Responding to the resulting need to develop urban rehabilitation policies for Hong Kong, this paper develops a system dynamics (SD) model to: (1) understand the future trend of the aged private buildings, particularly their sizes and age distribution; and (2) examine the complex relationships between allocated resources such as building professionals, number of aged private buildings and the strength of rehabilitation policies. The SD model developed can be used as a planning tool to simulate the effects of regulatory changes on aged private building stock management in consideration of available resources. The model development relies on a real-life database that can be applied to any city that faces the need to rehabilitate its ageing building stock as a part of an urban rehabilitation strategy

Gamma-ray spectroscopy: The diffuse galactic glow

The goal of this project is the development of a numerical code that provides statistical models of the sky distribution of gamma-ray lines due to the production of radioactive isotopes by ongoing Galactic nucleosynthesis. We are particularly interested in quasi-steady emission from novae, supernovae, and stellar winds, but continuum radiation and transient sources must also be considered. We have made significant progress during the first half period of this project and expect the timely completion of a code that can be applied to Oriented Scintillation Spectrometer Experiment (OSSE) Galactic plane survey data

3PL Implementing Corporate Social Responsibility in A Closed-loop Supply Chain : A Conceptual Approach

The responsibility of todays's reputable company are not only financial, but also corporate social responsibility(CSR). With regards to the environmental aspect, the public expects green logistics solutions. This paper focuses on how a third party logistics provider(3PL) might provide green logisticsservices through actions that foster sustainability. The methodology used is an in-depth interviewing approach, which described green logistics through the key drivers using depth interviews. An existing conceptual model is used to support our concepts and ideas. Finally, we discuss out the key factors from respondent regarding how to maintain sustainability through action, from 3PL's perspective. We also discuss the benefits and challenges to green logistics in future

Railway track asset management modelling

Railways are an important type of transport infrastructure but can be expensive to run with the UK railway costing \pounds 1.525~billion to maintain in 2018/19. To reduce the cost without reducing the quality of the infrastructure improved asset management is required. To enable the impact of possible decisions to be understood, and the optimum ones chosen, a railway track asset management model, such as the one developed in this thesis, is required. This thesis first studies the degradation and maintenance of railway track. The UK railways network actual geometry and maintenance data was used to understand track degradation. This data covered 8 years of the whole UK rail network, a much greater length of time and track than previous research has considered. Many aspects were shown to have a significant impact on the rate such as track speed, sleeper type and maintenance history. The methodology of singling out factors showed that rail types have less of an impact on track geometry than previous research had shown. Weibull distributions were then used to characterise the rates of degradation of separate combinations of these significant aspects. The improvement in geometry from maintenance is also explored, with the effectiveness reducing with each further maintenance action. The improvement in geometry from maintenance is modelled using a linear fit with a stochastic element added to model the effectiveness variability. Maintenance output rate, which has previously not been considered in literature, has also been analysed and modelled, utilising Weibull distributions, allowing working window lengths for maintenance to be incorporated within models. The likelihood of different rail faults occurring was also explored using data from the UK railway. The analysis showed that the rail type, joint and age were not only linked to the rate of faults but also the track geometry. This link has been mentioned in literature but has previously not been proved or quantified. The link between rail faults and track geometry shows how the railway tracks assets are interlinked and hence need to be modelled as such. Saving money by reducing the amount of track geometry maintenance will increase the quantity and hence the cost of rail faults. Rail faults have been modelled using probabilities within this thesis, with the fault rates of each fault type related to the track geometry. The second part of this thesis develops a Colour Petri-Net (CPN) model which incorporates the analysis and models developed. A CPN has been used in a novel way of acting as a decision framework, joining the separate degradation and maintenance models together and allowing them to interact. This removes some shortcomings of state based modelling techniques such as requiring discrete states of degradation. The model predicts, over any given line of track and time period, the number of inspections, maintenance actions, track quality and number of speed restrictions. Utilising the model the user can assess the impact of decisions such as maintenance thresholds, asset upgrades and traffic changes. Additional aspects such as opportunistic maintenance, as well as maintenance productivity and work windows lengths are considered. This allows aspects like opportunistic maintenance thresholds and varying the maintenance window length to be analysed, which previous models in literature have not. Different scenarios can be run through the model and the outputs compared to enable evidence based asset management decisions to be made