The process of converting natural language specifications into conceptual models requires detailed analysis of natural language text, and designers frequently make mistakes when undertaking this transformation manually. Although many approaches have been used to help designers translate natural language text into conceptual models, each approach has its limitations. One of the main limitations is the lack of a domain-independent ontology that can be used as a repository for entities and relationships, thus guiding the transition from natural language processing into a conceptual model. Such an ontology is not currently available because it would be very difficult and time consuming to produce. In this thesis, a semi-automated system for mapping natural language text into conceptual models is proposed. The model, which is called SACMES, combines a linguistic approach with an ontological approach and human intervention to achieve the task. The model learns from the natural language specifications that it processes, and stores the information that is learnt in a conceptual model ontology and a user history knowledge database. It then uses the stored information to improve performance and reduce the need for human intervention. The evaluation conducted on SACMES demonstrates that (1) designers’ creation of conceptual models is improved when using the system comparing with not using any system, and that (2) the performance of the system is improved by processing more natural language requirements, and thus, the need for human intervention has decreased. However, these advantages may be improved further through development of the learning and retrieval techniques used by the system.

In recent years, diesel engines with reduced emissions and low fuel consumption have been developed worldwide for the purpose of environmental protection and energy conservation. Turbochargers are playing an important role in these modern engines by providing power boost to the engine. A turbocharger comprises of three major parts i.e. the turbine stage, the bearing housing and the compressor stage. Turbocharger designers are continuously seeking for compact stage designs, while maintaining the stage performance. A turbocharger’s compressor stage comprises of various parts i.e. inlet, impeller, diffuser and volute. The diffuser is an important section of the turbocharger compressor stage that plays a key role in increasing the isentropic efficiency of the stage. The diffuser converts the kinetic energy imparted to the flow by the impeller, into static pressure rise, which inturn increases the isentropic efficiency of the stage. The shape of a diffuser is conventionally simple in design. Modifications to the diffuser geometry can lead to higher efficiencies and compact designs of the compressor stage. The present study focuses on the use of advanced computational techniques, such as Computational Fluid Dynamics (CFD), to analyse the effects of diffuser modifications on the local flow features, and the global performance parameters. A baseline diffuser configuration, consisting of a parallel wall diffuser, is numerically analysed to establish the accuracy of CFD based predictions. Various diffusers’ geometrical configurations have been analysed in the present study, both qualitatively and quantitatively. These geometrical configurations cover a wide range, such as diverging, tilting and curving of the diffuser walls. These parametric investigations aid to improve the compressor stage performance and make it more compact. The first aim of the study is to quantify the increase in the stage performance by diverging the straight wall vaneless diffuser passage. This is carried out by diverging the shroud wall (i.e. increasing the outlet-to-inlet width ratio) and varying the location of the divergence point on the shroud wall. The results obtained depict that the effect of increasing the diffuser’s outlet-to-inlet width ratio is dominant in comparison with the location of the wall divergence point. Moreover, increase in diffuser’s outlet-to-inlet width ratio increases the downstream area ratio of the diffuser, causing the flow to separate and creating flow recirculation near the hub wall. This creates restriction to the flow and causes air blockage. Furthermore, shifting the wall divergence point towards the outlet of the diffuser relocates the flow separation point closer to the diffuser exit. The second aim of this study is to analyse the effects of tilted diffuser walls on the flow variables within the compressor stage of the turbocharger. Tilting diffuser walls provides an increased streamwise length to the flow. Furthermore, divergence is applied to the diffuser hub wall in order to increase the outlet-to-inlet width ratio. This makes the turbocharger compressor stage compact in design, while maintaining the stage performance, which is the current requirement of the automotive sector. Design of Experiments, using Taguchi method, has been incorporated in this study to define the scope of the numerical work. The results obtained show that the diffuser with both titled and diverged walls together, performs optimally as compared to the other configurations considered. The third aim of this study is to use curved diffuser walls in order to make the design more compact. Divergence to the hub wall is also applied to enhance the performance of the compressor stage. Various configurations of curvilinear diffuser walls have been considered for numerical analysis. The local flow field analysis has been carried out, quantifying the effects of the geometrical parameters on the stage performance. The results depict that a curved diffuser model reduces the losses within the diffuser passage, but there is negligible effect on the stage efficiency. However, when the divergence is applied to the hub wall of the curved diffuser, there is significant increase in the stage efficiency. Based on these investigations, a turbocharger’s compressor stage can be designed for a compact design and optimal efficiency.

This thesis describes DesignTag, a system which can be used to identify electronic design intellectual property (IP) within operating integrated circuits (ICs). The ability to accurately identify IP within ICs has several important applications, including IP theft detection, identifying unmarked ICs and detecting counterfeit devices. Current and proposed methods for identifying IP within ICs are either inadequate for many applications, or are expensive, time consuming and difficult to use. In contrast, the DesignTag system is useful for a wide range of applications, is easy to use, and allows inexpensive and fast detection of IP. The DesignTag system consists of a small “tag” circuit which is added to a piece of IP and a tag detection sub-system which is used to detect signals transmitted by on-chip tags. These signals are transmitted using a thermal side channel, a novel approach in which information is communicated by varying the temperature of an IC package. In addition to discussing the DesignTag system as a whole, this thesis focuses on the development of the tag detection sub-system, adapting concepts from CDMA wireless communications to enable the detection of thermally transmitted tag signals.

As CMOS scales down to sub-50 nm, it faces critical dimensions of charge and matter granularities, leading to the drastic increase of device parameter dispersion, named statistical variability, which is one of the main contemporary challenges for further downscaling and makes each device atomistically different leading to broad dispersion of their electrical characteristics. In addition, device reliability concerns gain inertia; among them Bias Temperature Instability (BTI) shortens device lifetime by trapping charges in defect states of the insulator or at the interface. The interplay between statistical variability and BTI results in more variations on device performance and thus greatly affect circuit performance. In turn design methodologies must evolve towards variability and reliability aware design. To do so statistical compact models including both the effects of statistical variability and BTI-induced ageing are required for the large-scale statistical circuit simulation of variability and reliability. In this study, the application of accurate compact models, that describe performance variation in the presence of both statistical variability and reliability at arbitrary BTI-induced ageing levels, to SRAM circuit simulation is described. Both SRAM cell stability and write performance are evaluated and it is seen that, due to the accurate description of device performance distributions provided by the compact models and the sensitivity of these SRAM performance metrics on device performance, the approach presented here is better suited to high-sigma statistical circuit analysis than conventional approaches based upon assumed Gaussian distributions. The approach is demonstrated using a 25 nm gate length bulk MOSFET whose performance variation is obtained from statistical TCAD simulation using the GSS simulator GARAND. The simulated performance data is then used directly as the target for BSIM4 compact model extraction that ensures device figures of merit are well resolved for each device in a statistical ensemble. The distribution of compact model parameters is then generalised into an algebraic form using Generalized Lambda Distribution (GLD) methods, so that a sufficiently large number of compact models can later be generated and interpolated at arbitrary ageing levels. Finally compact models generated in this way are used to evaluate SRAM write performance and stability under the influence of statistical variability and BTI-induced ageing.

The large hadron collider (LHC) uses the most energetic and highest luminosity man made proton beams on Earth. The high luminosity (HL-LHC) [1] upgrade aims to increase the levelled luminosity of the LHC by a factor of five, to 5 · 1034cm−2 s −1 , by increasing the bunch population from 1 to 2.22·1011 protons, and decreasing emittance, and β ∗ . Thus the stored beam energy increases from ≈ 362 MJ to ≈ 675 MJ per beam. All synchrotrons encounter unavoidable proton losses. Protons that populate the beam halo pose a threat to the performance and lifetime of certain hardware, such as superconducting magnets, which in the LHC may be quenched by an impact of ≈ 1 · 106 protons [2]. A multi stage collimation system must operate at an efficiency such that no more than 2 · 10−5% of protons incident on collimators may escape and impact upon these magnets [3]. To predict and protect against proton losses in the HL-LHC, collimation simulations must be performed. MERLIN, a C++ accelerator physics library, has been updated to carry out such simulations for the HL-LHC. Novel materials such as molybdenum graphite have been investigated as collimator materials, and a novel technique - collimation enhancement via a hollow electron lens (HEL) - has been studied. Using detailed simulations the performance and operation of possible collimation upgrades are explored.

This research focuses on classification of categorical events using advanced statistical models. Primarily utilised to detect and identify individual component faults and deviations from normal healthy operation of reciprocating compressors. Effective monitoring of condition ensuring optimal efficiency and reliability whilst maintaining the highest possible safety standards and reducing costs and inconvenience due to impaired performance. Variability of operating conditions being revealed through examination of vibration signals recorded at strategic points of the process. Analysis of these signals informing expectations with respect to tolerable degrees of imperfection in specific components. Isolating inherent process variability from extraneous variability affords reliable means of ascertaining system health and functionality. Vibration envelope spectra offering highly responsive model parameters for diagnostic purposes. This thesis examines novel approaches to alleviating the computational burdens of large data analysis through investigation of the potential input variables. Three methods are investigated as follows: Method one employs multivariate variable clustering to ascertain homogeneity amongst input variables. A series of heterogeneous groups being formed from each of which explanatory input variables are selected. Data reduction techniques, method two, offer an alternative means of constructing predictive classifiers. A reduced number of reconstructed explanatory variables provide enhanced modelling capabilities ensuring algorithmic convergence. The final novel approach proposed combines both these methods alongside wavelet data compression techniques. Simplifying number of input parameters and individual signal volume whilst retaining crucial information for deterministic supremacy.

Ion irradiation modification of silicon nanowires has been explored in-situ using the Microscope and Ion Accelerator for Materials Investigations (MIAMI) facility at the University of Huddersfield. Ion irradiation experiments were designed using the Stopping and Range of Ions in Matter (SRIM) Monte Carlo computer code. A multislice SRIM method was developed to estimate the damage and ion implantation in a nanowires geometry and a code was developed to incorporate SRIM into MatLab. In-situ Transmission Electron Microscopy (TEM) has been used to explore the ion-beam-induced bending of silicon nanowires under different irradiation conditions and the underlying mechanisms which drive nanowire bending have been identified. Furthermore, a tipping point for direction reversal of bending under different irradiation conditions has been identified. Recrystallisation of silicon nanowires was also investigated using thermal annealing. The effects of 7 keV Xe+ ions on silicon nanowires have been investigated under industrial processing conditions. Structural and morphological changes of silicon nanowires have been observed. These include nanowire bending, amorphisation, bubble formation and sputtering. The depth of damage has also been measured experimentally and was compared with the predictive damage using SRIM. In order to calculate the temperature along a nanowire during an in-situ TEM heating experiment, the relevant parameters have been found from the literature which will be used to set up a finite element model. Atomic Force Microscopy (AFM) was used to measure the surface roughness of silicon nanowires and molybdenum grids. Other parameters of interest include the Hamaker’s constant for the Van der Waals forces as well as the Young’s modulus, thermal conductivity and specific heat capacity of silicon nanowires and the ultra-thin oxide layer on their surface.

This thesis forms a report of the progress made by the author in developing a rotating cylinder viscometer for measurement at elevated temperature and pressure. Initially, some proposals of Whitelaw (5) were tested in a preliminary design of viscometer. Experience gained with this instrument made possible the construction of a viscometer from which the viscosity of liquid water has been obtained in the range 12° to 116°C and at pressures up to 230 Kgf/cm2. Important features of this design, which have been successfully applied, include a null technique for measuring the angular deflection of the stationary cylinder and the use of a high pressure mechanical face seal for the shaft driving the rotating cylinder. A vacuum rig was constructed for measuring the torsional properties of suspension wires used in the viscometer. The temperature of this rig could be varied which allowed the torsional stiffness of tungsten wire to be determined experimentally up to 150°C. Viscosity measurements at 1 atm. pressure were fitted to a modified Arrhenius equation of the form η = AeB/T-T0 The standard deviation of experimental values from this equqtion was 7 x 10-3 cP which, at 20°C, amounts to approximately 0.7% of the measured viscosity. The absolute value of viscosity given by this equation was approximately higher than the presently accepted standard value at this temperature. At temperatures above 50°C the results obtained appear to be slightly low, the possible reasons for which have been explained. Results at elevated pressure were made over a similar temperature range although the majority of determinations were made at temperatures below 20°C. These low temperature measurements lend support to the findings of Bett and Cappi (48) rather than those of Home and Johnson (52) who have made the most recent experimental investigations in this field. The results obtained are regarded as preliminary in so far as a systematic coverage of the whole range has not yet been made. A second, partially completed, instrument is described which will extend the rsinge of measurement to 400°C and 1000 Kgf/cm2. A system of radial diffraction gratings producing moire fringes has been incorporated which, it is expected, will improve the precision with which the deflection of the inner cylinder may be obtained. Since the dynamic method of measuring the torsional stiffness of the wire has been used a number of tests were performed to investigate the effect of varying the mass, inertia and amplitude of a torsional pendulum. The results of these tests are described in a number of Appendices, the main conclusion being that the amplitude effect is significant and a correction factor may be derived which can be applied to the angular deflection of the wire when it is used statically.

The age of big digital data is emerged and the size of generating data is rapidly increasing in a millisecond through the Internet of Things (IoT) and Internet of Everything (IoE) objects. Specifically, most of today’s available data are generated in a form of streams through different applications including sensor networks, bioinformatics, smart airport, smart highway traffic, smart home applications, e-commerce online shopping, and social media streams. In this context, processing and mining such high volume of data stream becomes one of the research priority concern and challenging tasks. On the one hand, processing high volumes of streaming data with low-latency response is a critical concern in most of the real-time application before the important information can be missed or disregarded. On the other hand, detecting events from data stream is becoming a new research challenging task since the existing traditional anomaly detection method is mainly focusing on; a) limited size of data, b) centralised detection with limited computing resource, and c) specific anomaly detection types of either point or collective rather than the Contextual behaviour of the data. Thus, detecting Contextual events from high sequence volume of data stream is one of the research concerns to be addressed in this thesis. As the size of IoT data stream is scaled up to a high volume, it is impractical to propose existing processing data structure and anomaly detection method. This is due to the space, time and the complexity of the existing data processing model and learning algorithms. In this thesis, a novel distributed anomaly detection method and algorithm is proposed to detect Contextual behaviours from the sequence of bounded streams. Capturing event streams and partitioning them over several windows to control the high rate of event streams mainly base on, the proposed solution firstly. Secondly, by proposing a parallel and distributed algorithm to detect Contextual anomalous event. The experimental results are evaluated based on the algorithm’s performances, processing low-latency response, and detecting Contextual anomalous behaviour accuracy rate from the event streams. Finally, to address scalability concerned of the Contextual events, appropriate computational metrics are proposed to measure and evaluate the processing latency of distributed method. The achieved result is evidenced distributed detection is effective in terms of learning from high volumes of streams in real-time.

The topic of this study is self-tracking. To “self-track” is to quantify steps, calories, heart rate, social interactions, geo-location, posture, and so on. The few monographs, academic papers, and conferences, concerned with this topic often deliver similar philosophical representations of self-tracking based on shared material and socio-cultural assumptions. Generally, these assumptions are wedded to a series of essentialist, absolutist, and deterministic tropes, as well as to neo-humanist and more traditionally humanist forms of discourse. The present study argues that these established discourses play against the predominant tendencies of much contemporary philosophical thought. Contemporary thought is more post humanist than humanist, draws attention to mediation more than media, qualities of indeterminacy more than determinacy, and dynamism more than stasis. The lack of imagination in current discourses of self-tracking, as well as their disengagement with contemporary thought, has resulted in the most pertinent philosophical insight of the phenomena of self-tracking being overlooked: namely the permanence of change. Unique to discourses of self-tracking, this study foregrounds the permanence of change as an already kinetic and transformative aspect of the natural world. As a result of this unique contribution, this study backgrounds the monotony of substance-metaphysical thought as a resistance to the established trends of self-tracking discourses. There are four parts to this process-oriented world view that make up the main features of this research. Firstly, the missing dynamism of self-tracking is rendered actual by its reframing in an event-based ontology, therefore emphasising the emergence, mutation, and inevitable excesses of self-tracking phenomena. Secondly, self-tracking is critically situated in anti-representational terms by focusing on the recent processual-turn in new media theory. Thirdly, via a genealogical method, self-tracking is rearticulated as something dynamic, with a long and plural history of transformative relations. Finally, by emphasising the political, relational, and fictional, a re-imagined speculative alternative to self-tracking is brought to focus through the optic of contemporary design theory.