Concurrency is a programming tool that is widely used in applications. Concurrent user-level threads can be used to structure the execution of a program in a uniprocessor environment and/or speed up its execution in a multiprocessor setting. Unfortunately, threads may interact with each other in unpredictable ways, often leading to performance problems that are nonexistent in the sequential domain. <br /><br /> A profiler can be used to help locate performance problems in sequential and concurrent programs. A profiler is a tool that monitors, analyzes, and visualizes the execution performance of a program to help users verify its expected behaviour, and locate its bottlenecks and hotspots. One of the important tools a profiler has at its disposal is a set of hardware counters, which are specialized CPU registers that count the occurrences of hardware events as a program executes. Hardware-event counts provide extremely precise insight into the execution behaviour of a program, and can be used to pinpoint portions of code where performance is suboptimal. <br /><br /> This thesis describes the design and implementation of <em>&micro;</em>Profiler, which is a profiler for sequential and concurrent programs written in a concurrent dialect of the C++ programming language called <em>??</em>C++. <em>??</em>C++ offers user-level concurrency in a uniprocessor or multiprocessor shared-memory environment. A new architecture-abstraction layer is developed, which allows <em>??</em>Profiler to access hardware counters on multiple CPU types. As well, two new profiling metrics are presented, which use the architecture-abstraction layer to gather hardware-event counts for <em>??</em>C++ programs. These metrics offer performance information about <em>??</em>C++ programs that is unavailable by any other means.

In the last 20 years mobile devices gained an important role in daily life and became must have items for everyone. As mobile devices give us the much needed flexibility and mobility, they also represent one major concern; security. As the information is transmitted from node to node via radio frequencies, an imposter can gain access into a mobile network without the need to gain physical access to firewalls and gateways. Also, as they are light and small, mobile devices are easily lost and often used without any PIN or password protection enabled. Hence, it is not difficult for someone even without any technical knowledge to gain access to such devices if they have been left behind or stolen. As traditional intrusion detection systems are not very effective against this kind of attack, there is a need of a different approach that can assist in the identification of a potential imposter. This thesis begins by assessing the security needs of the mobile devices, and establishes the perceived inadequacy of existing safeguards in this respect. Therefore this research considers using Behaviour-Based Mobile Intrusion Detection System (BeMIDS) that aims to assist the identification of anomalous user activity. This in return presents the two main characteristics needed to classify a legitimate user inside a mobile network: first with whom, when and what type of connection is established and then at where the mobile device is left open. After this the research proposes a novel approach that investigates the application of three machine learning algorithms to profile user behaviour in mobile networks. In BeMIDS, historical user profiles are created and then compared with the real-time ones in order to detect unusual activity in mobile networks. If a user’s behaviour changes, this results in alerting the system as an anomalous activity. Specific examples of behaviours that BeMIDS appears to be particularly sensitive to include duration (of calls and of connection with cell towers), time of day (calls are made and cell towers are connected), and frequency of caller usage. In order to classify a legitimate user over a mobile network the thesis then validates this approach by implementing C4.5, RIPPER and SOM algorithms over MIT’s Reality Mining Dataset. The results support the proposed architecture and present accuracy rate as high as 96% for call logs and 94% for tower logs under training conditions.

Context In the present world, there is an increase in the usage of the telecommunication networking services, as there is a need of efficient networking services in various fields which can be obtained by using the efficient networking components. For that purpose we have to know about the components parameters. One of the most important parameter is the energy usage of networking components. Therefore, there is a need in power profiling of the network switches. Objectives The objective of this research is to profile the power usage of different network components(Switches) for various load scenarios. Power measurements are done by using the open energy monitoring tool called emonpi. Methods The research method has been carried out by using an experimental test bed. In this research, we are going to conduct the experiments with different configurations to obtain different load conditions for sources and destinations which will be passed through DUT(Device Under Test). For that DUT’s we will measure power usage by monitoring tool called emonpi. Then the experiments are conducted for different load scenarios for different switches and results are discussed. Conclusion From the results obtained, the Power profiles of different DUT’s are tabulated and analyzed. These were done under different ports and load scenarios for Cisco2950, Cisco3560 and Netgear GS-724T. From the results and analysis it can be stated that the power usage of Cisco 2950 is having the maximum power usage in all the considered scenarios with respect to packet rate and also number of active ports. The Netgear-GS724T is having the minimum power usage from the three switches as it having the green switch characteristics in all scenarios. And the Cisco 3560 is in between the above two switches as it is having energy efficient management from Cisco. From this we have proposed a simple model for energy/power measurement.

Doctor of Philosophy / Department of Sociology, Anthropology, and Social Work / Chardie Baird / Dana Britton / Racial profiling by the police on the nation’s streets and highways has attracted much attention over the past two decades from scholars, media figures, politicians and police administrators. Several highly publicized cases propelled the issue into national consciousness in the early and mid 1990s, bringing a new public awareness to an undoubtedly old problem. Despite the proliferation of research and political attention, many questions remain unanswered. Among the most common criticisms facing racial profiling research today is the literature's lack of theoretical development. Grounded in focal concerns theory and the concept of symbolic assailants, the present research draws upon both crime control and discriminatory frameworks of racial disparity in traffic stop outcomes. The findings suggest that, while police concerns of crime and safety diminish the effect of race/ethnicity and gender on stop outcomes, race and gender remain important predictors of police decisions. The implications are discussed.

In Chapter one I introduce the subject of geographic profiling, its use in criminology and its previous application to biology. I go on in Chapter two to examine the original model and develop a likelihood-based approach to fit the parameters to data from 53 UK invasive species. GP performs well on this novel problem, and outperforms other simple spatial modelling techniques. Using simulations I show that GP is particularly efficient at locating sources when there is more than a single source. Chapter three develops a Bayesian approach using Dirichlet Processes to account for the problem of multiple sources. This model was developed in collaboration with Robert Verity. This new Bayesian model outperforms the original model used in criminology and offers a range of additional information from the data. The Bayesian GP model is then used to determine the sources of malaria outbreaks in Cairo. These developments significantly improve and extend the theory and application of GP. In Chapter four I discuss the possible shapes of dispersal functions. I conduct a review of the literature and find a geometric mistake in the way linear distributions have been extracted from two-dimensional data. The correct back-transformation allows these dispersal distributions to be properly generated. Using this information; ecologists, conservationists and resources managers can now apply GP to real world problems and effectively allocate limited resources to locate sources of species invasions and disease outbreaks. I go on in Chapter five to develop a method for fitting the primary parameter sigma from the point pattern data and run simulations to show the effectiveness of this new approach. In Chapter six I illustrate the application of GP to three problems, one in criminology, one in ecology and one in epidemiology. I finish by summarising the work in this thesis and discussing the potential future developments and applications of GP.

Olfactory ensheathing cells (OECs), the glia of the olfactory nerve, are promising candidates for patient-specific cell-mediated repair of both peripheral nerves and the spinal cord. The recent discovery that OECs originate from the neural crest, rather than the olfactory epithelium as previously thought, potentially means that homogeneous populations of OECs for repair could be expanded in culture from neural crest stem cells persisting in the patient's own skin and hair follicles. The first step towards this long-term goal is to understand the molecular mechanisms underlying neural crest differentiation into OECs, as opposed to Schwann cells (the glia of all other peripheral nerves), which are less effective in spinal cord repair. To identify transcription factors and signalling pathways that might be involved in OEC versus Schwann cell differentiation, I took an unbiased transcriptome profiling approach. Taking advantage of Sox10 expression throughout both OEC and Schwann cell development, I used laser-capture microdissection on cryosections of mouse embryos carrying a Sox10:H2BVenus transgene, to isolate OEC subpopulations (olfactory mucosal OECs, from the olfactory nerve, and olfactory nerve layer OECs, from the olfactory nerve layer surrounding the olfactory bulb) at different stages of development, and Schwann cells from trigeminal nerve branches on the same sections, for RNA-seq and cross-wise comparison of transcriptomes. Validation of candidate genes by in situ hybridisation revealed some contamination with adjacent cells from mesenchyme, olfactory epithelium or olfactory bulb, but also identified the expression in developing OECs of various genes previously reported to be expressed in adult OECs, and of over 20 genes previously unknown in OECs. Some of these genes are expressed by OECs but not Schwann cells; some are expressed by olfactory nerve layer OECs but not olfactory mucosal OECs, while some are expressed by olfactory mucosal OECs and Schwann cells but not olfactory nerve layer OECs. For a subset of the genes, I was also able to analyse OEC differentiation in mouse mutants. I also collected transcriptome data from neural crest-derived cells that persist on the olfactory nerve in Sox10-null embryos (in which neural crest-derived cells colonise the olfactory nerve, but normal OEC differentiation is disrupted). Comparison with wild-type OEC transcriptome data from the same embryonic stage identified genes whose expression is likely either downregulated or up-regulated in the absence of Sox10, supporting a role in normal OEC differentiation. Overall, these various transcriptomic comparisons (between OECs at different developmental stages, different OEC subpopulations, OECs versus Schwann cells, and OECs versus Sox10-null neural crest-derived cells on the olfactory nerve) have identified multiple transcription factor and signalling pathway genes, amongst others, that are expressed during OEC development in vivo (including some specific to different OEC subpopulations) and that may be important for OEC differentiation. Furthermore, some of these genes are not expressed by embryonic Schwann cells. This work provides a foundation for understanding how to promote OEC rather than Schwann cell differentiation from neural crest stem cells in culture, with the potential for clinical application in the future.

There is significant variation between individual patients in the magnitude and pattern of their systemic response to cardiac surgery. Poor outcomes in these patients have been associated with a dysfunctional host response. This thesis seeks to define such variability at the level of gene expression by sequential analysis of transcription before and after surgery for a low risk group of patients undergoing elective cardiac surgery and cardiopulmonary bypass (CPB) patients using expression microarray profiling. To that aim, we analysed sequential global gene expression patterns in circulating peripheral blood leukocytes. We also investigated the role of DNA sequence variation in modulating the observed changes in gene expression. This approach allowed us to identify important genetic modulators and novel biological pathways and gain new insights into the mechanisms that regulate the host response to surgery.

Gene expression analysis has evolved as an extensive toolfor elucidation of various biological and molecular eventsoccurring in different organisms. A variety of techniques andsoftware tools have been developed to enable easier and morerapid means of exploring the genetic information. A moreeffective approach than exploring the whole content of genesexpressed under certain conditions is to study fingerprintassays or to use subtracted cDNA libraries to identify onlydifferentially expressed genes. The objective for the work in this thesis has been toexplore differentially expressed genes in atherosclerosis. Thiswas done by applying and modifying a protocol for thesubtractive approach RDA (Representational Difference Analysis)in different model systems. Initially, the molecular effects of an anti-atheroscleroticdrug candidate were elucidated. In addition, two alternativeapproaches to identify differentially expressed genes obtainedafter iterative rounds of RDA subtraction cycles wereevaluated. This revealed that in most cases, the shotgunapproach in which the obtained gene fragments are clonedwithout any prior selection has clear advantages compared tothe more commonly used selection strategy, whereby distinctbands are excised after gel electrophoresis. A key process in the atherosclerotic plaque initiation isthe phenotypic change of macrophages into foam cells, which canbe triggered in a model system by using macrophages exposed tooxidised LDL. To investigate the genes expressed in thisprocess, the RDA technique was combined with microarrayanalysis, which allows for selectivity and sensitivity throughRDA, as well as rapid high-throughput analysis usingmicroarrays. The combination of these techniques enablessignificant differences in gene expression to be detected, evenfor weakly expressed genes and the results to be reliablyvalidated in a high throughput manner. Finally, investigation of the focal nature ofatherosclerotic lesions and gene expression profiling werestudied using in vivo aortic tissues from ApoE-/- and LDLR -/-mice. The study was based on a comparison between localisationsthat are likely, and others that are unlikely, to developatherosclerotic plaques, and the RDA technique was employed toexplore differential gene expression. <b>Keywords:</b>Representational Difference Analysis,atherosclerosis, gene expression profiling

Concurrency is a programming tool that is widely used in applications. Concurrent user-level threads can be used to structure the execution of a program in a uniprocessor environment and/or speed up its execution in a multiprocessor setting. Unfortunately, threads may interact with each other in unpredictable ways, often leading to performance problems that are nonexistent in the sequential domain. <br /><br /> A profiler can be used to help locate performance problems in sequential and concurrent programs. A profiler is a tool that monitors, analyzes, and visualizes the execution performance of a program to help users verify its expected behaviour, and locate its bottlenecks and hotspots. One of the important tools a profiler has at its disposal is a set of hardware counters, which are specialized CPU registers that count the occurrences of hardware events as a program executes. Hardware-event counts provide extremely precise insight into the execution behaviour of a program, and can be used to pinpoint portions of code where performance is suboptimal. <br /><br /> This thesis describes the design and implementation of <em>&micro;</em>Profiler, which is a profiler for sequential and concurrent programs written in a concurrent dialect of the C++ programming language called <em>µ</em>C++. <em>µ</em>C++ offers user-level concurrency in a uniprocessor or multiprocessor shared-memory environment. A new architecture-abstraction layer is developed, which allows <em>µ</em>Profiler to access hardware counters on multiple CPU types. As well, two new profiling metrics are presented, which use the architecture-abstraction layer to gather hardware-event counts for <em>µ</em>C++ programs. These metrics offer performance information about <em>µ</em>C++ programs that is unavailable by any other means.

Long-period fiber gratings (LPFGs) are typically fabricated by exposing photosensitive optical fiber to ultraviolet light. However, LPFGs can be fabricated by a variety of other techniques, including exposure to carbon-dioxide (CO2) laser light. The physical process by which the refractive-index change is induced in an optical fiber during exposure to CO2 laser light gives CO2-laser-induced LPFGs unique properties when compared to more traditional LPFGs fabricated by exposure to UV light. As such, CO2-laser-induced LPFGs respond differently to external perturbations and useful behavior has been observed, including variable attenuation tuning at a constant wavelength and wavelength tuning at constant amplitude with applied flexure. In order to manipulate, harness, and enhance the unique features of CO2-laser-induced LPFGs, it is necessary to understand their physical properties and optical characteristics. The main objectives of the research presented in this thesis are to quantify experimentally the optical performance of CO2-laser-induced LPFGs with respect to flexure, torsion, and variable incident polarization, to characterize grating cross-sectional refractive-index profiles, and to demonstrate applications of CO2-laser-induced LPFGs that exploit their unique properties. As part of the investigation of the effects of asymmetry, the fabrication and basic transmission characteristics of CO2-laser-induced LPFGs were examined. The polarization-dependent transmission characteristics, specifically polarization-dependent loss and polarization mode dispersion, of CO2-laser-induced LPFGs were investigated. The unique behavior of the gratings in response to applied flexure and applied torsion was also explored. Example variable optical attenuator, optical tunable filter, and fiber-to-waveguide coupler devices illustrate the potential advantages of the asymmetric index profile present in CO2-laser-induced LPFGs for certain applications. A new cross-sectional refractive-index profiling technique was presented that enables measurement of profiles containing small and irregular index variations. The profiling technique was used to measure the cross-sectional refractive-index profiles of optical fiber exposed to CO2 laser light. Future areas of research concerning CO2-laser-induced LPFGs were identified and discussed.