Thesis (M.S. in computer science)--Washington State University, May 2009. / Title from PDF title page (viewed on May 26, 2009). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 41-44).

Thesis (M. Sc.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on Oct. 19, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta." Includes bibliographical references.

Thesis (M.S.)--Washington State University, August 2007. / Includes bibliographical references (p. 49-51).

Artificial neural networks are increasing in power, with attendant increases in demand for efficient processing. Performance is limited by clock speed and degree of parallelization available through multi-core processors and GPUs. With a design tailored to a specific network, a field-programmable gate array (FPGA) can be used to minimize latency without the need for geographically distributed computing. However, the task of programming an FPGA is outside the realm of most data scientists. There are tools to program FPGAs from a high level description of a network, but there is no unified interface for programmers across these tools. In this thesis, I present the design and implementation of NeuralSynth, a prototype Python framework which aims to bridge the gap between data scientists and FPGA programming for neural networks. My method relies on creating an extensible Python framework that is used to automate programming and interaction with an FPGA. The implementation includes a digital design for the FPGA that is completed by a Python framework. Programming and interacting with the FPGA does not require leaving the Python environment. The extensible approach allows multiple implementations, resulting in a similar workflow for each implementation. For evaluation, I compare the results of my implementation with a known neural network framework. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

The work presented here develops a gamma-ray spectral construction and analysis software tool that was used to analyze multi-detector data collected using a digital spectrometer with list mode capabilities. The tool was used to parse the output from three detectors and generate new spectra that the user chooses from post-processing suppression routines, such as simulated anticoincidence and coincidence spectra. Part of this research was also to characterize the improvements in the detection limits and the various detector efficiencies from this method as opposed to creating these spectra using traditional electronic gating systems. A focus is placed on the detection capability improvements for nuclear forensics purposes, particularly the identification and quantification of fission product samples, and structuring the code framework for handling these types of time-dependent samples while increasing the versatility of the detector system. Improvements to the minimum detectable activity for a series of fission products was accomplished through post-processing suppression methods and multi-dimensional spectral data structures are now achievable. / text

Batteries play a pivotal role in the modern world, powering everything from portable electronics to electric vehicles, and are critical in the shift towards renewable energy sources by enabling efficient energy storage. This thesis presents new computational strategies to understand and predict surface degradation and stacking faults in battery cathodes, phenomena that have crucial impact on the battery lifetime. The starting point is a detailed first-principles analysis of LiNiO₂ surface degradation, assessing the thermodynamics of oxygen release and its impact on the surface integrity of this prospective cathode material. This research led to the development of a method for the automated enumeration of surface reconstructions and the development of a Python software package implementing the methodology, thereby greatly accelerating the computational surface characterization of electrode materials. The methodology made it feasible to extend the investigation to LiCoO₂ surfaces, comparing their oxygen retention and surface stability with LiNiO₂ and identifying the unique properties of the two transition metals that control their behavior during battery operation. In addition to surface phase changes, stacking faults are another important class of two-dimensional defects that can affect the properties of cathode materials. Combining information from first principles calculations with 17O nuclear magnetic resonance (NMR) spectroscopy provided by collaborators, we uncovered how stacking faults affect the capacity and cyclability of Li₂MnO₃ cathodes, a prototypical lithium-rich material with oxygen redox activity. Although automated first-principles calculations are, in principle, an ideal tool for understanding atomic-scale degradation phenomena in batteries, they are computationally demanding and, therefore, limited to materials with simple compositions. In the final chapter, we explore the application of machine learning for further accelerating computational battery degradation simulations by leveraging existing data first-principles calculations for predicting the stability of new surface reconstructions. This chapter points toward a new direction that should be further explored in the future. The research presented in this thesis not only advances the understanding of lithium-ion battery cathode materials but also introduces more-widely applicable computational methodologies that lay a foundation for the development of advanced materials for energy storage applications. This work demonstrates the benefits of integrating traditional computational methods with machine learning, contributing to ongoing progress in materials science and opening up new possibilities for advancements in energy technology and material engineering.

Thesis submitted in fulfilment of the requirements for the degree Master of Technology: Electrical Engineering in the Faculty of Engineering at the Cape Peninsula University of Technology 2014 / The IEC 61850 standard, “Communication networks and systems in substations” was promulgated to accommodate the need for a common communication platform within substations for devices from different vendors. The IEC 61850 standard proposes a substation automation architecture that is Ethernet-based, with a “station-bus” for protection devices within the substation and a “process bus” where raw data from the voltage and current transformers are published onto the data network using a device known as a Merging Unit. To date, most of the standardization efforts were focused at the station bus level where event-triggered messages are exchanged between the substation automation devices, commonly referred to as Intelligent Electronic Devices (IEDs). These messages are known as Generic Object Oriented Substation Event messages. Equipment from vendors to accommodate the “process bus” paradigm, however is still limited at present. The Centre for Substation Automation and Energy Management Systems was established within the Electrical Engineering Department at the Cape Peninsula University of Technology with one of its objectives being the development of equipment either for simulation or real-time purposes in compliance with the IEC 61850 standard. In order to fulfil this long-term objective of the Centre, an in-depth understanding of the IEC 61850 standard is required. This document details the efforts at acquiring the requisite knowledge base in support of the educational objectives of the Centre and the research project implements a simulation of a merging unit which is compliant with the functional behavior as stipulated by the standard. This limited functional implementation (i.e. non-real-time) of the merging unit, is achieved through the development of a virtualized data acquisition node capable of synthetic generation of waveforms, encoding of the data and publishing the data in a format compliant with the IEC 61850-9-2 sampled value message structure. This functional behavior of the virtual sensor node which was implemented has been validated against the behavior of a commercial device and the sampled value message structure is validated against the standard. The temporal behavior of the proposed device is commented upon. This research project forms the basis for future real-time implementation of a merging unit.

Investigating the solid-state properties of the Earth’s core and mantle presents a formidable challenge due to the extreme conditions that prevail in these areas. Although we can achieve high pressures using a variety of static and dynamic compression techniques, it is still unfeasible to comprehensively sample the entire pressure-temperature (𝑃-𝑇) domain for materials. Therefore, computational methodologies have evolved as a crucial instrument for examining material properties under increased pressures and temperatures. These techniques have demonstrated their efficacy in navigating the phase space, thereby contributing significantly to the understanding of the intrinsic behavior of materials within the Earth’s interior. In this work, we present 𝚎𝚡𝚙𝚛𝚎𝚜𝚜, a comprehensive suite of simulation tools designed for conducting 𝘢𝘣 𝘪𝘯𝘪𝘵𝘪𝘰 calculations within the realm of the physical sciences. These tools are specifically engineered to streamline the associated data processing tasks, and they leverage the capabilities of the Julia programming language. At the core of this toolset lies a versatile, high-throughput, and user-friendly workflow framework. This framework is capable of automating a wide range of 𝘢𝘣 𝘪𝘯𝘪𝘵𝘪𝘰 calculations. By addressing the limitations encountered with existing libraries, 𝚎𝚡𝚙𝚛𝚎𝚜𝚜 simplifies intricate workflows, offers a software-agnostic interface, and ensures modularity—all of which are pivotal features within this domain. In addition to the workflow, we have developed a diverse set of software packages tailored to tackle the challenges inherent in data manipulation for 𝘢𝘣 𝘪𝘯𝘪𝘵𝘪𝘰 calculations. These packages encompass a wide spectrum of functionalities, including crystal symmetry search, conversion of units and reference frames, data visualization, parsing and generation of files, estimation of computing resources, and database storage, among other capabilities. We proceed to showcase the effectiveness of express across a diverse spectrum of mineral materials. For each substance, we conducted calculations of their thermodynamic properties using the quasi-harmonic approximation (QHA). This method was executed with the assistance of a Python package called 𝚚𝚑𝚊, which we developed specifically for multi-configuration quasi-harmonic approximation computations. In pursuit of our objective, we employ three distinct sets of exchange-correlation functionals: the local-density approximation (LDA), the Perdew–Burke–Ernzerhof generalized gradient approximation (PBE-GGA), and the PBE functional revised for solids (PBEsol). Subsequently, we compared these results with other calculations and experimental data, thereby elucidating the varying suitability of these functionals. Notably, the LDA functional, when integrated with thermal effects, exhibited exceptional overall performance. This observation implies that numerous studies that favored GGA functionals but solely relied on static DFT outcomes may have inadvertently incorporated erroneous material characteristics into their research.

In this project, two tests were performed. On the first test, Mininet-WiFi was used to simulate a Software Defined Network to demonstrate Mininet-WiFi’ s ability to be used as the Software Defined Network emulator which can also be integrated to the existing network using a Network Virtualized Function (NVF). A typical organization’s computer network was simulated which consisted of a website hosted on the LAMP (Linux, Apache, MySQL, PHP) virtual machine, and an F5 application delivery controller (ADC) which provided load balancing of requests sent to the web applications. A website page request was sent from the virtual stations inside Mininet-WiFi. The request was received by the application delivery controller, which then used round robin technique to send the request to one of the web servers on the LAMP virtual machine. The web server then returned the requested website to the requesting virtual stations using the simulated virtual network. The significance of these results is that it presents Mininet-WiFi as an emulator, which can be integrated into a real programmable networking environment offering a portable, cost effective and easily deployable testing network, which can be run on a single computer. These results are also beneficial to modern network deployments as the live network devices can also communicate with the testing environment for the data center, cloud and mobile provides. On the second test, a Software Defined Network was created in Mininet using python script. An external interface was added to enable communication with the network outside of Mininet. The amazon web services elastic computing cloud was used to host an OpenDaylight controller. This controller is used as a control plane device for the virtual switch within Mininet. In order to test the network, a webserver hosted on the Emulated Virtual Environment – Next Generation (EVENG) software is connected to Mininet. EVE-NG is the Emulated Virtual Environment for networking. It provides tools to be able to model virtual devices and interconnect them with other virtual or physical devices. The OpenDaylight controller was able to create the flows to facilitate communication between the hosts in Mininet and the webserver in the real-life network. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)