This thesis reports on the design, fabrication and testing of microstructured devices for the manipulation of terahertz radiation. In particular, there is an emphasis on the fabrication and test of diffractive optics type components; including a surface micromachined, multilevel SU-8 based Fresnel lens and a micromilled aluminium Fresnel Zone Plate Reflector (FZPR). For both of these devices, the focal spot is characterized by measuring the electric field intensity and phase as a function of distance along the optical axis. This is carried out using a THz Vector Network Analyzer with associated free space optics. The results are compared directly with Finite Difference Time Domain simulations. A commercial FDTD solver, Lumerical, is used throughout the thesis. FDTD is first introduced for the design of antireflective subwavelength surfaces. These surface structures are bulk micromachined in silicon and their performance experimentally validated using THz Time-Domain Spectroscopy and Durham's THz VNA. A compact THz VNA based S11 measurement configuration is presented which uses the FZPR and a single parabolic mirror. This reflection configuration is used for the characterization of liquid samples (e.g. water and Isopropyl Alcohol mixtures) in microfluidic channels. Two types of channels are presented; one is formed using bulk micromachined silicon whereas the other type uses acetate films to create low cost, disposable devices. The results from the compact measurement configuration are compared with those obtained using a more conventional four parabolic mirror transmission arrangement (as found in THz Time-Domain Spectroscopy systems). Even in the compact configuration, the alignment of the components is found to be a significant factor in determining the measurement performance. Consequently, a six-axis micropositioner (Hexapod), is used to automatically sweep the reflector with the aim of producing a self-aligning system.

Increasing attention has been given to reverse supply chains because of the increasing value of technology and products at the end of direct supply chains and the impact of new green legislation. Design strategies for reverse supply chains have remained relatively unexplored and underdeveloped. Meanwhile measuring performance has become important. The research described in this Dissertation investigated several industries with reverse supply chains: manufacture of aircraft, computers and carpets, and telecommunications, and retail. From that investigation, a new model was created that combined forward and reverse chains and then a general mathematical model was created to describe it. Specific models (including mathematical models) could then be created for specific companies. The new models allowed performance of both forward and reverse supply chains to be measured at the same time so that different modes of operation could be compared by testing with different data sets. From an initial investigation of two case studies about an aeroplane company dealing with returned machines and a telecommunications company dealing with end of life products, a first initial model to describe their forward and reverse supply chains was created. This was the first time that an attempt had been made to create a general model that could be used in more than one industry and general models that included both the forward and reverse supply chains did not exist. A general mathematical model was created to represent the new general model and from that two specific mathematical models were created to represent the computer manufacture and general retail companies. The model was modified to include new aspects found in the two new companies and then verified against another (fifth) industry, carpet manufacture. The models were tested with sets of data including a high number of returned products and a low number of returned products, and companies were categorised according to the results. Six types of company were identified and are presented.

Meteorological processes are highly non-linear and complicated to predict at high spatial resolutions. Weather forecasting provides critical information about future weather that is important for flooding disaster prediction system and disaster management. This information is also important to businesses, industry, agricultural sector, government and local authorities for a wide range of reasons. Processes leading to rainfall are non-linear with the relationships between meteorological parameters are dynamic and disproportionate. The uncertainty of future occurrence and rain intensity can have a negative impact on many sectors which depend on the weather condition. Therefore, having an accurate rainfall prediction is important in human decisions. Innovative computer technologies such as soft computing can be used to improve the accuracy of rainfall prediction. Soft computing approaches, such as neural network and fuzzy soft clustering are computational intelligent systems that are capable of integrating humanlike knowledge within a specific domain, adapt themselves and learn in changing environments. This study evaluates the performance of a rainfall forecasting model. The data pre-processing method of Principal Component Analysis (PCA) is combined with an Artificial Neural Network (ANN) and Fuzzy C-Means (FCM) clustering algorithm and used to forecast short-term localized rainfall in tropical climate. State forecast (raining or not raining) and value forecast (rain intensity) are tested using a number of trained networks. Different types of ANN structures were trained with a combination of multilayer perceptron with a back propagation network. Levenberg-Marquardt, Bayesian Regularization and a Scaled Conjugate Gradient training algorithm are used in the network training. Each neuron uses linear, logistic sigmoid and hyperbolic tangent sigmoid as a transfer function. Preliminary analysis of input parameter data pre-processing and FCM clustering were used to prepare input data for the ANN forecast model. Meteorological data such as atmospheric pressure, temperature, dew point, humidity and wind speedhave been used as input parameters. The magnitude of errors and correlation coefficient were used to evaluate the performance of trained neural networks. The predicted rainfall forecast for one to six hour ahead are compared and analysed. One hour ahead for state and value forecast yield more than 80% accuracy. The increasing hours of rain prediction will reduce the forecast accuracy because input-output mapping of the forecast model reached termination criterion early during validation test and no improvement of convergence in the consecutive number of epochs. Result shows that, the combination of PCA-FCM-ANN forecast model produces better accuracy compared to a basic ANN forecast model.

Traffic models are important for the evaluation of various Intelligent Transport Systems and the development of new traffic infrastructure. In order for this to be done accurately and with confidence the correct parameter values of the model must be identified. The focus of this thesis is the identification and confirmation of these parameters, which is model validation. Validation is performed on two different models; the first-order CTM and the second-order METANET model. The CTM is validated for two UK sites of 7.8 and 21.9 km and METANET for the same two sites using a variety of meta-heuristic algorithms. This is done using a newly developed method to allow for the optimisation method to determine the number of parameters to be used and the spatial extent of their application. This allows for the removal of expert engineering knowledge and ad-hoc decomposition of networks. This thesis also develops a methodology by use of Automatic Differentiation to allow gradient based optimisation to be used. This approach successfully validated the METANET model for the 21.9 km site and also a large network surrounding the city of Manchester of 186.9 km. This proves that gradient based optimisation can be used for the macroscopic traffic model validation problem. In fact the performance of the developed gradient method is superior to the meta-heuristics tested for the same sites. The methodology defined also allows for more data to be obtained from the model such as its Jacobian and the sensitivity of the objective function being used relative to the individual parameters. Space-Time contour plots of this newly acquired data show structures and shock waves that are not visible in the mean speed contour diagrams.

This research presents a comprehensive experimental investigation on the machinability effects of variable drilling parameters (feed rate, cutting speed and thrust force), drill diameters and chips formation mainly on delamination and surface roughness, in addition to other drilling-induced damage on both natural and synthetic fibre reinforced polymer (FRP) composites: hemp fibre reinforced polymer (HFRP) and carbon fibre reinforced polymer (CFRP) composite laminates respectively, using double-fluted coated high speed steel (HSS) drills under dry machining and compressed air cooling conditions. It also describes a thermo-mechanical models for predicting and analysing onset push-out delamination during FRP composite machining. After a broad and critical literature survey on FRP composites and their drilling has been carried out, three principal stages of experimental, and an analytical works were conducted to investigate and analyse the influence of both conventional drilling (CD) and ultrasonically-assisted drilling (UAD) techniques on different specimens of HFRP and CFRP composites. Stage 1 involved the CD of 5 specimens of 197 x 197 mm, 7.5 mm thickness HFRP composite laminates of aspect ratios (AR) of 00 (neat), 19, 23, 30 and 38, using diameter holes of 5.0 and 10.0 mm for delamination and surface roughness respectively, among other drilling-induced damage. Taguchi’s technique was used in the design of experiment. The results obtained show that increase in cutting speed reduced delamination factor and surface roughness of drilled holes. However, increase in feed rate caused an increase in both delamination factor and surface roughness. Feed rate and cutting speed had the greater influence on delamination and surface roughness respectively, when compared with aspect ratio, while an increase in fibre AR caused a significant increase in both delamination factor and surface roughness. The optimum results occurred at cutting speed and feed rate (drilling parameters) of 20 mm/min and 0.10 mm/rev, respectively, when drilling specimen of AR 19. The stage 2 experiment described a comprehensive investigation on the machinability effects of CD parameters, drill diameters and chips formation on the same drilling-induced damage on an optimal specimen of 19-HFRP and MTM 44-1/CFRP composite laminates, using the same specimen dimensions, drills, drilling parameters and condition. The results obtained depict that an increase in feed rate and thrust force caused an increase in delamination and surface roughness of both specimens, different from cutting speed. But HFRP and CFRP specimens have greater surface roughness and delamination-drilling damage respectively. Also, increased drill diameter and types of chips formation caused an increase in both delamination and surface roughness of both specimens as the material removal rate (MRR) increased. Evidently, the minimum surface roughness and delamination factor of the two specimens for an optimal drilling are associated with feed rates of 0.05-0.10mm/rev and cutting speed of 30m/min. Stage 3 of the research focused on the benefits of UAD technique compared with the CD, initially on the first 5 hemp fibre/thermoplastic polycaprolactone (HF/PCL) composite specimens under similar drills, drilling parameters and condition. The results obtained show that UAD technique further confirmed and validated the optimal performance of specimen with AR of 19 (19-HF/PCL) composites, because of the minimum value of thrust force and machining time recorded, when compared with other aspect ratios and CD technique. The 19-HF/PCL laminate has maximum thrust force of 90N and 75N during UAD and CD respectively, which were the lowest force reduction at minimum drilling-induced damage, with the lowest machining time of 30 seconds for both. But comparatively, an improved drilled holes, optimal drilling and nearly 40 % of an average drilling forces (thrust and toque) reduction were recorded with UAD of hemp fibre/thermoset vinyl ester (HF/VE) composite specimens, when compared with both CD and HF/PCL specimens, respectively. Conclusively, the stage 4 addressed the theoretical aspect of this research through application of analytical method. Hence, in this last stage, an analytical thermo-mechanical model is proposed to predict critical feed rate and critical thrust force at the onset of delamination crack on CFRP composite cross-ply laminates, using the principle of linear elastic fracture mechanics (LEFM), laminated classical plate theory (LCPT), cutting mechanics and energy conservation theory. The delamination zone (crack opening Mode I)is modelled as an elliptical plate. The advantages of this proposed model over the existing models in literature are that the influence of drill geometry (chisel edge and point angle) on push-out delamination are incorporated, and mixed loads condition are considered. The forces on chisel edges and cutting lips are modelled as a concentrated(point) and uniformly distributed loads, resulting into a better prediction. The model is validated with models in the literature and the results obtained show the flexibility of the proposed model to imitate the results of existing models. Evidently, it can be summarily concluded that the quality of the drilled holes and total machinability of the FRP composites depend on the nature and properties of the composite specimens, drill designed geometry, drilling parameters, conditions and techniques.

The transition in the energy market, from large fossil fuel consumption to the broad diffusion of renewable energies, involves an intermediate phase where more efficient techniques are developed for the existing power generation technologies. Among new combustion techniques, MILD (Moderate or Intense Low-oxygen Dilution) combustion is particularly attractive because of its potential characteristic to enhance thermal efficiency and reduce emissions like nitrogen oxides (NOx). The successful application of MILD combustion requires a significant entrainment of hot combustion products into the fuel and/or oxidizer stream(s), yielding an increase of the reacting mixture temperature over its auto-ignition value. Such intense dilution causes a reduction in peak temperature levels, with a consequent reduction of NOx emissions, and a homogeneous temperature field followed by enhanced flame stability. Also the overall thermal efficiency is improved because of the recuperated heat. The relative ease of obtaining reactant dilution in a full scale burner makes the MILD combustion regime interesting also from a technological point of view. Despite some interesting applications of MILD technique in industrial cases, its broad adoption is prevented by gaps in the knowledge of this combustion regime. Particularly, the development of simple and reliable numerical models is required to allow testing of full scale industrial burners with reasonable computational expense. The present dissertation is focused on how the oxidiser temperature, oxidiser concentration and fuel concentration affect the complex interaction among molecular transport, chemical kinetics and turbulence that leads to self-ignition in MILD combustion. The diffusion-chemistry contribution to ignition is investigated by means of a one dimensional (1D) zero velocity Direct Numerical Simulations (DNS) of two mixing layers representing a cold fuel mixture and a hot diluted oxidiser. Different oxidiser mixtures as well as different fuel blends are considered. Each case studied showed a different ignition behaviour. An in deep investigation of physical and chemical changes observed for each case along the ignition period is provided. A temporal and a spatial scaling methods are proposed to account for ignition behaviour differences and compare cases. The comparison revealed different aspects of the self-ignition process. The differential diffusion effect plays an important role in the early stages of ignition, for cases presenting methane/hydrogen fuel mixture. If high is the level of hydrogen in the fuel blend, major stages of methane (CH₄) consumption pathway,from the CH₄ dehydrogenation to the carbon dioxide (CO₂) release, are significantly affected by hydrogen (H₂) chemistry. In the latest stages of ignition, the methane pathway is also affected by the drop in oxygen level. The influence of turbulence on the diffusion-chemistry interaction is studied by means of three-dimensional (3D) Direct Numerical Simulations modelling a methane/hydrogen circular jet mixing with a diluted oxidiser co-flow. The effects of different fuel and oxidiser blends is also considered in the 3D study. In cases where large is the H2 presence in the fuel jet, the presence of turbulent mixing has a minimal effects on early stages of self-ignition, where instead differential diffusion still plays a major role. As turbulence develops, more marked difference between 1D and 3D studies are observed. The role of turbulent mixing dominates over chemistry where the fuel blend includes a low amount hydrogen. For this configuration the temperature increment is strongly limited compared to corresponding 1D study. The outcome of this study is expected to be of use to other researches in MILD combustion, particularly those adopting existing RANS and LES models to MILD combustion cases.

Different technologies are used to count people but people counting systems based on computer vision are good choices due to different priorities. These priorities may include accuracy, flexibility, cost and acquiring people distribution information. People counting systems based on computer vision can use closed circuit television (CCTV) cameras that have already become ubiquitous and their uses are increasing. This thesis aims to develop people counting systems that can be incorporated with existing CCTV cameras. People counting is a useful task for safety, security and operational purposes and can be important for improving awareness. This thesis presents two intelligent people counting systems; pixel-wise optimisation based and features regression based people counting systems. Each system works independently to count people and may be more appropriate for particular scenarios. The pixel-wise optimisation based people counting system based on two algorithms that estimate the density of each pixel in each frame and use it as a basis for counting people. One algorithm uses scale-invariant feature transform (SIFT) features and clustering to represent pixels of frames (SIFT algorithm) and the other uses features from accelerated segment test (FAST) corner points with SIFT features (SIFT-FAST algorithm). Both algorithms are designed using a novel combination of pixel-wise, motion edges, grid map, background subtraction using Gaussian mixture model (GMM). The features regression based people counting system is composed of a pair of collaborative Gaussian process regression (GPR) model with different kernels, which are designed to count people by taking the level of occlusion into account. The level of occlusion is measured and compared with a predefined threshold for regression model selection for each frame. In addition, this system dynamically identifies the best combination of features for people counting. The University of California (UCSD), Mall and New York Grand Central Station datasets have been used to test and evaluate the proposed systems. These datasets have been chosen because they cover a wide range of variation of characteristics. They cover a variation of frame rate (fps), resolution, colour, location, shadows, loitering, reflections, crowd size and frame type. By means of comparisons with state of the art methods, the results of the proposed systems outperform the others methods with respect to mean absolute error (MAE), mean squared error (MSE) and the mean deviation error (MDE) metrics. The MAE, MSE and MDE of the proposed systems are 2.83, 13.92 and 0.092, respectively, for the Mall dataset; 1.63, 4.32, and 0.066, respectively, for UCSD dataset; and 4.41, 25.62 and 0.029, respectively, for New York Grand Central dataset. The computational efficiency results of the proposed systems are 20.76 fps, 38.47fps and 19.23 fps for the Mall, UCSD and New York Grand Central datasets, respectively.

It is widely accepted that future generation warehouses require a real-time visibility and accuracy of inventory data in order to maintain efficient and effective warehousing operations, optimal SKU levels, and up-to-date inventory management and control of incoming and outgoing goods that often occur today at increasingly centralised distribution centres. This phenomenon is partially due to a sharp rise of online shopping activities in many countries where customers now prefer to purchase goods online and demand a fast delivery of ordered products to be dispatched directly at their door steps. Thus, there is a strong desire from supply chain and logistics sectors to seek even more efficient and cost-effective methods for sorting, storing, picking and dispatching goods at increasingly centralised distribution centres in which automation and integration of warehousing systems is inevitable. As part of a research programme for future generation warehouses, this thesis presents an investigation into some design theories and an integrated optimisation methodology for a future generation warehousing system in which an RFID-based inventory management system has the capability of interacting with a proposed RFID-enabled automated storage and retrieval mechanism without any human intervention. An efficient item selection algorithm based on pre-defined rules was developed and implemented within an RFID-based inventory management system, which also allows a manipulation of RFID-tracked items to seek an optimal solution by assigning a priority to one of selected items to travel in an order (if applicable) with both minimal travel time and waiting time to a specified collection point; this maximises efficiency in material-handling and minimises operational costs. A pilot test was established and carried out based on the proposed RFID-enabled inventory management system for examining the feasibility and applicability of its embedded RFID-enabled item-selection optimisation algorithm. Experimental results demonstrate that the developed methodology can be useful for determining an optimal solution (or route) for the RFID-enabled pusher to push a selected RFID-tagged item located randomly from a storage rack to an output conveyor system in a sequence that allows all the selected items traveling to a specified collection point with a minimal waiting time for packing. In theory, such a system can also be expanded by incorporating other pre-defined selection parameters as requested by users. Moreover, a multi-objective model using the multi-criterion fuzzy programming approach was developed and used for obtaining a trade-off decision based on conflicting objectives: minimisation of the total cost, maximisation of capacity utilisation, maximisation of service level and minimisation of travel distance within the proposed warehousing system. The developed model also supports design decisions in determining an optimum number of storage racks and collection points that need be established for the proposed warehouse. To reveal the alternative Pareto-optimal solutions, a decision-making algorithm namely TOPSIS was also employed to select the best Pareto-optimal solution obtained using the multi-criterion fuzzy programming approach. Case-studies were also conducted to demonstrate the feasibility and applicability of the developed multi-objective model and optimisation methods. The study concluded that the research work provided a useful basis by developing a framework as part of contributions in design theories and optimisation approaches for integration of future generation RFID-based warehousing systems and a practical means of exploring the further work in this field.

Disasters caused by floods are a major cause of losses of properties and lives. The unpredictability in weather conditions due to changing weather patterns do not only lead to flooding but also contribute to water resource management problems. Rapid development in many tropical countries, like in Malaysia, has resulted in the loss of natural floodplains leading to an increase in flooding and water shortage. Sufficient advanced flood warning system that can save lives and properties can be developed using accurate river model. The work reported in this thesis has made significant contributions in the prediction of river flow rate based on rainfall rate in the catchment area using Artificial Neural Network (ANN). The proposed approach models the non-linear process of the rainfall-runoff in a wide variety of catchment area conditions. This study demonstrates significant improvement in the accuracy and reliability of water resource management by using ANN modelling to predict river flow rate. It also shows ANN as a fast and adaptable approach that is suitable for river flow rate modelling that does not need detailed geographical information of the catchment area. Its attractiveness is in its ability to adapt to changing conditions and therefore does not become outdated like conventional hydrology models. The research shows that river flow rate is a better parameter to be used for an early flood warning system as it is more sensitive to rainfall rate compared to the river level which is used in conventional flood warning systems. The study has also shown that ANN with a feed-backward network with one hidden layer provides the best results and it is able to produce river flow rate prediction up to 132 hours with root mean square error of 0.02 m³/s . This is a significant contribution as the flood warning system currently used in Malaysia can only predict flooding within 8 to 24 hours. The work in this thesis can assist the authorities to manage water from dams thereby effectively managing floods and ensuring sufficient water for domestic and agricultural use. The findings of this research has already been presented to the Malaysian government agency responsible for managing waterways and dams.

Food supply chains (FSCs) are one of the major sectors in the global economy. Developing efficient and cost-effective food supply chains, provide an opportunity for supply chain and logistics companies to survive in the increasingly competitive market of today. In order to achieve this, one of the methods is to enhance the traceability of food during production, transportation and storage throughout the entire supply chain network in order to improve and maintain the quality and safety of the food provided to customers. Other methods include design and optimization of a supply chain network towards objectives such as the minimization of costs, transportation time and environmental pollution, and the maximization of service level and profits and so on. This study proposes a radio frequency identification (RFID)-enabled monitoring system for a meat production and supply chains network that ensures the integrity and quality of its meat products. The study also includes the development of three multi-objective optimization models as an aid to solving the facility location and allocation problem and the quantity flow of products travelling throughout the meat supply chain network with respect to trade-off solutions among a number of objectives. To deal with the uncertainty of the input data (e.g., costs, capacity and demands), stochastic programming and fuzzy programming models were also developed. Furthermore, by applying suitable solution approaches, Pareto solutions can be obtained based on the developed multi-objective models. For this a decision-making algorithm was used to select the best Pareto solution. In order to examine feasibility and applicability of the developed approaches, a proposed RFID-enabled automated warehousing system and a proposed RFID-enabled passport tracking system were also used as case studies by applying the developed approaches for the design and optimization of these two systems, respectively. Research findings demonstrate that the proposed RFID-enabled monitoring system for the meat supply chain is economically feasible as a relatively higher profit can be achieved. The study concludes that the developed mathematical models and optimization approaches can be a useful decision-maker for tackling a number of design and optimization problems for RFID-based supply chains and logistics systems and tracking systems.