This dissertation documents a study of very rare B-meson decays at the LHCb experiment, using data taken during the first experiment run of the Large Hadron Collider (LHC) and during the second experiment run until September 2016. The LHCb experiment was designed to test the Standard Model of particle physics and to search for New Physics effects that go beyond the scope of the Standard Model through the decay of b hadrons produced in high energy proton-proton collisions at the LHC. The measurements described in this dissertation are made using data samples of proton-proton collisions with integrated luminosities of 1.0, 2.0 and 1.4fb⁻¹, collected at centre-of-mass energies of 7, 8 and 13 TeV, respectively. All results are consistent with the predictions of the Standard Model.

Pattern detection is a well-studied area of computer vision, but still current methods are unstable in images of poor quality. This thesis describes improvements over contemporary methods in the fast detection of unseen patterns in a large corpus of videos that vary tremendously in colour and texture definition, captured "in the wild" by mobile devices and surveillance cameras. We focus on three key areas of this broad subject; First, we identify consistency weaknesses in existing techniques of processing an image and it's horizontally reflected (mirror) image. This is important in police investigations where subjects change their appearance to try to avoid recognition, and we propose that invariance to horizontal reflection should be more widely considered in image description and recognition tasks too. We observe online Deep Learning system behaviours in this respect, and provide a comprehensive assessment of 10 popular low level feature detectors. Second, we develop simple and fast algorithms that combine to provide memory- and processing-efficient feature matching. These involve static scene elimination in the presence of noise and on-screen time indicators, a blur-sensitive feature detection that finds a greater number of corresponding features in images of varying sharpness, and a combinatorial texture and colour feature matching algorithm that matches features when either attribute may be poorly defined. A comprehensive evaluation is given, showing some improvements over existing feature correspondence methods. Finally, we study random decision forests for pattern detection. A new method of indexing patterns in video sequences is devised and evaluated. We automatically label positive and negative image training data, reducing a task of unsupervised learning to one of supervised learning, and devise a node split function that is invariant to mirror reflection and rotation through 90 degree angles. A high dimensional vote accumulator encodes the hypothesis support, yielding implicit back-projection for pattern detection.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / by James Joseph Koschella. / B.S.

by Ronald Siu-kwong, Ip. / "June 1999." / Thesis (Ph.D.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (p. 89-94). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Two dimensional (2D) materials have been explosively researched since the discovery of graphene but the applications of 2D materials have been extremely constrained because of a variety of shortcomings in the materials such as zero bandgap in graphene or defective growth techniques for wide-bandgap materials. Nonetheless, such novel materials are very promising in the doomed situation which Moore’s law keeps slowing down. Graphene and αMoO3 have been particularly of interest because graphene has developed large-scale growth methods and αMoO3 has wide bandgap. In case of graphene, searching for the applications with zero bandgap has been important and in the other, αMoO3 has not been developed for large-scale growth techniques yet even though the applications are strongly expected to be developed. In this thesis, unconventional CVD graphene electronics and large scale αMoO3 synthesis have been studied for very large scale integration (VLSI). A 512 flexible graphene voltage amplifier array and the highest peak-to-valley current ratio NDR devices emitting green color in graphene nanogap are presented so that large-scale CMOS compatible circuit integration can be available for bio and RF (radio frequency) applications. Having 2.8eV bandgap, a large-scale growth method for αMoO3 is developed for the first time showing ambipolar and memristive behaviors.

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Large industrial accidents attract attention due to their catastrophic effects on human lives, economic growth, and the environment. Early studies on accident causations have concluded that 98% of all industrial accidents are preventable, and 88% can be prevented through the enforcement of a proper control system. There have been relentless efforts to develop models of accidents to understand this phenomenon and minimise the catastrophic outcomes of mishap events. These efforts have led to the development of systematic models of accident causations; in which accidents causations are viewed as dynamic processes that interact in a non-linear fashion. One of these systematic models is Resilience Engineering (RE), which takes a holistic view of the organisation and its natural abilities to maintain the system in a dynamically stable state under either stresses or normal operations. This research attempts to explore safety of industrial corporations by evaluating resilience in the Saudi Arabian process industry. The Saudi Arabia context is substantially different from the Western cultures where resilience concepts were developed and studied. The rapid expansion of the petroleum industries has had a major impact on the development of the Saudi socio-economic dynamics. The unique national culture dimensions of the Saudi Arabian society (power distance, uncertainty avoidance, and individualism) will help in understanding cultural influences on resilience and safety in general. Therefore, this research is conducted to identify the main contributing factors to resilience in the Saudi Arabian context. The outcomes of the research help engineers and administrators in industrial organisations to engineer resilient systems that minimize the risks of mishaps and recover quickly to a normal state of operations. The findings support the influence of the national culture in different countries on organisational safety culture, which is extended to individuals' behaviour towards safety. Evidence has shown that collectivism, on the contrary to the common belief, has a positive impact on both resilience potential and safety culture in the process industry. In addition, the process industry in Saudi Arabia is characterised by resilience of the second type, where there is good ability to respond and monitor but a low ability to learn or anticipate. The main contributing factors to this resilience are: effective communication, information availability, control over work tasks, and dealing with external pressure. Lastly, the findings suggest an association between management commitment to safety and both resilience optimisation and organisational safety culture.

by Ma Yiguang. / "April 2000." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (p. 117-[124]). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.