Дисертації з теми "Applied computing not elsewhere classified"
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Jones, Christopher Charles Rawlinson. "A study of novel computing methods for solving large electromagnetic hazards problems." Thesis, University of Central Lancashire, 2002. http://clok.uclan.ac.uk/18842/.
Повний текст джерелаBratton, Daniel. "Simple and adaptive particle swarms." Thesis, Goldsmiths College (University of London), 2010. http://research.gold.ac.uk/4752/.
Повний текст джерелаJenkins, David William. "Risk assessment applied to consumer products with reference to CE marking machines for use at work." Thesis, Aston University, 2004. http://publications.aston.ac.uk/12230/.
Повний текст джерелаTimperley, Matthew. "The integration of explanation-based learning and fuzzy control in the context of software assurance as applied to modular avionics." Thesis, University of Central Lancashire, 2015. http://clok.uclan.ac.uk/16726/.
Повний текст джерелаZhu, Huaiyu. "Neural networks and adaptive computers : theory and methods of stochastic adaptive computation." Thesis, University of Liverpool, 1993. http://eprints.aston.ac.uk/365/.
Повний текст джерелаRattray, Magnus. "Modelling the dynamics of genetic algorithms using statistical mechanics." Thesis, University of Manchester, 1996. http://publications.aston.ac.uk/598/.
Повний текст джерелаSvénsen, Johan F. M. "GTM: the generative topographic mapping." Thesis, Aston University, 1998. http://publications.aston.ac.uk/1245/.
Повний текст джерелаCsató, Lehel. "Gaussian processes : iterative sparse approximations." Thesis, Aston University, 2002. http://publications.aston.ac.uk/1327/.
Повний текст джерелаGoldingay, Harry J. "Agent Based Models of Competition and Collaboration." Thesis, Aston University, 2010. http://publications.aston.ac.uk/15212/.
Повний текст джерелаDas, Gupta Jishu. "Performance issues for VOIP in Access Networks." Thesis, University of Southern Queensland, 2005. https://eprints.qut.edu.au/12724/1/Das_Gupta_MComputing_Dissertation.pdf.
Повний текст джерелаCaon, Maurizio. "Context-aware gestural interaction in the smart environments of the ubiquitous computing era." Thesis, University of Bedfordshire, 2014. http://hdl.handle.net/10547/344619.
Повний текст джерелаAlmalki, Obaid. "A framework for e-government success from the user's perspective." Thesis, University of Bedfordshire, 2014. http://hdl.handle.net/10547/344620.
Повний текст джерела(9189365), Anthony A. Lowe. "The Theory of Applied Mind of Programming." Thesis, 2020.
Знайти повний текст джерелаThe Theory of Applied Mind of Programming (TAMP) provides a new model for describing how programmers think and learn. Historically, many students have struggled when learning to program. Programming as a discipline lives in logic and reason, but theory and science tell us that people do not always think rationally. TAMP builds upon the groundbreaking work of dual process theory and classical educational theorists (Piaget, Vygotsky, and Bruner) to rethink our assumptions about cognition and learning. Theory guides educators and researchers to improve their practice, not just their work but also their thinking. TAMP provides new theoretical constructs for describing the mental activities of programming, the challenges in learning to program, as well as a guidebook for creating and recognizing the value of theory.
This dissertation is highly nontraditional. It does not include a typical empirical study using a familiar research methodology to guide data collection and analysis. Instead, it leverages existing data, as accumulated over a half-century of computing education research and a century of research into cognition and learning. Since an applicable methodology of theory-building did not exist, this work also defines a new methodology for theory building. The methodology of this dissertation borrows notation from philosophy and methods from grounded theory to define a transparent and rigorous approach to creating applied theories. By revisiting past studies through the lens of new theoretical propositions, theorists can conceive, refine, and internally validate new constructs and propositions to revolutionize how we view technical education.
The takeaway from this dissertation is a set of new theoretical constructs and promising research and pedagogical approaches. TAMP proposes an applied model of Jerome Bruner's mental representations that describe the knowledge and cognitive processes of an experienced programmer. TAMP highlights implicit learning and the role of intuition in decision making across many aspects of programming. This work includes numerous examples of how to apply TAMP and its supporting theories in re-imagining teaching and research to offer alternative explanations for previously puzzling findings on student learning. TAMP may challenge conventional beliefs about applied reasoning and the extent of traditional pedagogy, but it also offers insights on how to promote creative problem-solving in students.
(9751070), Vaibhav R. Ostwal. "SPINTRONIC DEVICES FROM CONVENTIONAL AND EMERGING 2D MATERIALS FOR PROBABILISTIC COMPUTING." Thesis, 2020.
Знайти повний текст джерелаNovel computational paradigms based on non-von Neumann architectures are being extensively explored for modern data-intensive applications and big-data problems. One direction in this context is to harness the intrinsic physics of spintronics devices for the implementation of nanoscale and low-power building blocks of such emerging computational systems. For example, a Probabilistic Spin Logic (PSL) that consists of networks of p-bits has been proposed for neuromorphic computing, Bayesian networks, and for solving optimization problems. In my work, I will discuss two types of device-components required for PSL: (i) p-bits mimicking binary stochastic neurons (BSN) and (ii) compound synapses for implementing weighted interconnects between p-bits. Furthermore, I will also show how the integration of recently discovered van der Waals ferromagnets in spintronics devices can reduce the current densities required by orders of magnitude, paving the way for future low-power spintronics devices.
First, a spin-device with input-output isolation and stable magnets capable of generating tunable random numbers, similar to a BSN, was demonstrated. In this device, spin-orbit torque pulses are used to initialize a nano-magnet with perpendicular magnetic anisotropy (PMA) along its hard axis. After removal of each pulse, the nano-magnet can relax back to either of its two stable states, generating a stream of binary random numbers. By applying a small Oersted field using the input terminal of the device, the probability of obtaining 0 or 1 in binary random numbers (P) can be tuned electrically. Furthermore, our work shows that in the case when two stochastic devices are connected in series, “P” of the second device is a function of “P” of the first p-bit and the weight of the interconnection between them. Such control over correlated probabilities of stochastic devices using interconnecting weights is the working principle of PSL.
Next my work focused on compact and energy efficient implementations of p-bits and interconnecting weights using modified spin-devices. It was shown that unstable in-plane magnetic tunneling junctions (MTJs), i.e. MTJs with a low energy barrier, naturally fluctuate between two states (parallel and anti-parallel) without any external excitation, in this way generating binary random numbers. Furthermore, spin-orbit torque of tantalum is used to control the time spent by the in-plane MTJ in either of its two states i.e. “P” of the device. In this device, the READ and WRITE paths are separated since the MTJ state is read by passing a current through the MTJ (READ path) while “P” is controlled by passing a current through the tantalum bar (WRITE path). Hence, a BSN/p-bit is implemented without energy-consuming hard axis initialization of the magnet and Oersted fields. Next, probabilistic switching of stable magnets was utilized to implement a novel compound synapse, which can be used for weighted interconnects between p-bits. In this experiment, an ensemble of nano-magnets was subjected to spin-orbit torque pulses such that each nano-magnet has a finite probability of switching. Hence, when a series of pulses are applied, the total magnetization of the ensemble gradually increases with the number of pulses
applied similar to the potentiation and depression curves of synapses. Furthermore, it was shown that a modified pulse scheme can improve the linearity of the synaptic behavior, which is desired for neuromorphic computing. By implementing both neuronal and synaptic devices using simple nano-magnets, we have shown that PSL can be realized using a modified Magnetic Random Access Memory (MRAM) technology. Note that MRAM technology exists in many current foundries.
To further reduce the current densities required for spin-torque devices, we have fabricated heterostructures consisting of a 2-dimensional semiconducting ferromagnet (Cr2Ge2Te6) and a metal with spin-orbit coupling metal (tantalum). Because of properties such as clean interfaces, perfect crystalline nanomagnet structure and sustained magnetic moments down to the mono-layer limit and low current shunting, 2D ferromagnets require orders of magnitude lower current densities for spin-orbit torque switching than conventional metallic ferromagnets such as CoFeB.
(10184063), Younghoon Kim. "Approximate Computing: From Circuits to Software." Thesis, 2021.
Знайти повний текст джерела(10994988), Minglu Li. "ENVIRONMENTAL FACTORS AFFECT SOCIAL ENGINEERING ATTACKS." Thesis, 2021.
Знайти повний текст джерелаSocial engineering attacks can have serious consequences when it comes to information security. A social engineering attack aims at sensitive personal information by using personality weaknesses and using manipulation techniques. Because the user is often seen as the weakest link, techniques like phishing, baiting, and vishing, and deception are used to glean important personal information successfully. This article will analyze the relationship between the environment and social engineering attacks. This data consists of 516 people taking a survey. When it comes to discovering the relationship, there are two parts of the analysis. One is a high-dimensional analysis using multiple algorithms to find a connection between the environment and people’s behavior. The other uses a text analysis algorithm to study the pattern of survey questions, which can help discover why certain people have the same tendency in the same scenario. After combining these two, we might show how people have different reactions when dealing with social engineering attacks due to environmental factors.
(10711986), Michelle E. Coverdale. "The Effect of Choice on Memory and Value for Consumer Products." Thesis, 2021.
Знайти повний текст джерела(8088431), Gopalakrishnan Srinivasan. "Training Spiking Neural Networks for Energy-Efficient Neuromorphic Computing." Thesis, 2019.
Знайти повний текст джерелаSpiking Neural Networks (SNNs), widely known as the third
generation of artificial neural networks, offer a promising solution to
approaching the brains' processing capability for cognitive tasks. With more
biologically realistic perspective on input processing, SNN performs neural
computations using spikes in an event-driven manner. The asynchronous
spike-based computing capability can be exploited to achieve improved energy
efficiency in neuromorphic hardware. Furthermore, SNN, on account of
spike-based processing, can be trained in an unsupervised manner using Spike
Timing Dependent Plasticity (STDP). STDP-based learning rules modulate the strength
of a multi-bit synapse based on the correlation between the spike times of the
input and output neurons. In order to achieve plasticity with compressed
synaptic memory, stochastic binary synapse is proposed where spike timing
information is embedded in the synaptic switching probability. A bio-plausible
probabilistic-STDP learning rule consistent with Hebbian learning theory is
proposed to train a network of binary as well as quaternary synapses. In
addition, hybrid probabilistic-STDP learning rule incorporating Hebbian and
anti-Hebbian mechanisms is proposed to enhance the learnt representations of
the stochastic SNN. The efficacy of the presented learning rules are
demonstrated for feed-forward fully-connected and residual convolutional SNNs
on the MNIST and the CIFAR-10 datasets.
STDP-based learning is limited to shallow SNNs (<5
layers) yielding lower than acceptable accuracy on complex datasets. This
thesis proposes block-wise complexity-aware training algorithm, referred to as
BlocTrain, for incrementally training deep SNNs with reduced memory
requirements using spike-based backpropagation through time. The deep network
is divided into blocks, where each block consists of few convolutional layers
followed by an auxiliary classifier. The blocks are trained sequentially using
local errors from the respective auxiliary classifiers. Also, the deeper blocks
are trained only on the hard classes determined using the class-wise accuracy
obtained from the classifier of previously trained blocks. Thus, BlocTrain
improves the training time and computational efficiency with increasing block
depth. In addition, higher computational efficiency is obtained during
inference by exiting early for easy class instances and activating the deeper
blocks only for hard class instances. The ability of BlocTrain to provide
improved accuracy as well as higher training and inference efficiency compared
to end-to-end approaches is demonstrated for deep SNNs (up to 11 layers) on the
CIFAR-10 and the CIFAR-100 datasets.
Feed-forward SNNs are typically used for static image recognition while recurrent Liquid State Machines (LSMs) have been shown to encode time-varying speech data. Liquid-SNN, consisting of input neurons sparsely connected by plastic synapses to randomly interlinked reservoir of spiking neurons (or liquid), is proposed for unsupervised speech and image recognition. The strength of the synapses interconnecting the input and liquid are trained using STDP, which makes it possible to infer the class of a test pattern without a readout layer typical in standard LSMs. The Liquid-SNN suffers from scalability challenges due to the need to primarily increase the number of neurons to enhance the accuracy. SpiLinC, composed of an ensemble of multiple liquids, where each liquid is trained on a unique input segment, is proposed as a scalable model to achieve improved accuracy. SpiLinC recognizes a test pattern by combining the spiking activity of the individual liquids, each of which identifies unique input features. As a result, SpiLinC offers comparable accuracy to Liquid-SNN with added synaptic sparsity and faster training convergence, which is validated on the digit subset of TI46 speech corpus and the MNIST dataset.
(11008509), Nathanael D. Cox. "Two Problems in Applied Topology." Thesis, 2021.
Знайти повний текст джерелаNguyen, Van-Tuong. "An implementation of the parallelism, distribution and nondeterminism of membrane computing models on reconfigurable hardware." 2010. http://arrow.unisa.edu.au:8081/1959.8/100802.
Повний текст джерелаThesis (PhDInformationTechnology)--University of South Australia, 2010
(8815964), Minsuk Koo. "Energy Efficient Neuromorphic Computing: Circuits, Interconnects and Architecture." Thesis, 2020.
Знайти повний текст джерела(10506350), Amogh Agrawal. "Compute-in-Memory Primitives for Energy-Efficient Machine Learning." Thesis, 2021.
Знайти повний текст джерела(11218029), Herschel R. Bowling. "A Forensic Analysis of Microsoft Teams." Thesis, 2021.
Знайти повний текст джерела(9868160), Wan-Eih Huang. "Image Processing, Image Analysis, and Data Science Applied to Problems in Printing and Semantic Understanding of Images Containing Fashion Items." Thesis, 2020.
Знайти повний текст джерела(11161374), Emma J. Reid. "Multi-Resolution Data Fusion for Super Resolution of Microscopy Images." Thesis, 2021.
Знайти повний текст джерелаApplications in materials and biological imaging are currently limited by the ability to collect high-resolution data over large areas in practical amounts of time. One possible solution to this problem is to collect low-resolution data and apply a super-resolution interpolation algorithm to produce a high-resolution image. However, state-of-the-art super-resolution algorithms are typically designed for natural images, require aligned pairing of high and low-resolution training data for optimal performance, and do not directly incorporate a data-fidelity mechanism.
We present a Multi-Resolution Data Fusion (MDF) algorithm for accurate interpolation of low-resolution SEM and TEM data by factors of 4x and 8x. This MDF interpolation algorithm achieves these high rates of interpolation by first learning an accurate prior model denoiser for the TEM sample from small quantities of unpaired high-resolution data and then balancing this learned denoiser with a novel mismatched proximal map that maintains fidelity to measured data. The method is based on Multi-Agent Consensus Equilibrium (MACE), a generalization of the Plug-and-Play method, and allows for interpolation at arbitrary resolutions without retraining. We present electron microscopy results at 4x and 8x super resolution that exhibit reduced artifacts relative to existing methods while maintaining fidelity to acquired data and accurately resolving sub-pixel-scale features.
(8772923), Chinyi Chen. "Quantum phenomena for next generation computing." Thesis, 2020.
Знайти повний текст джерела(7464389), Shubham Jain. "IN-MEMORY COMPUTING WITH CMOS AND EMERGING MEMORY TECHNOLOGIES." Thesis, 2019.
Знайти повний текст джерела(5930528), Joseph W. Balazs. "A Forensic Examination of Database Slack." Thesis, 2021.
Знайти повний текст джерелаDatabase forensics is an underexplored subfield of Digital Forensics, and the lack of research is
becoming more important with every breach and theft of data. A small amount of research exists
in the literature regarding database slack. This exploratory work examined what partial records of
forensic significance can be found in database slack. A series of experiments performed update
and delete transactions upon data in a PostgreSQL database, which created database slack.
Patterns of hexadecimal indicators for database slack in the file system were found and analyzed.
Despite limitations in the experiments, the results indicated that partial records of forensic
significance are found in database slack. Significantly, partial records found in database slack
may aid a forensic investigation of a database breach. The details of the hexadecimal patterns of
the database slack fill in gaps in the literature, the impact of log findings on an investigation was
shown, and complexity aspects back up existing parts of database forensics research. This
research helped to lessen the dearth of work in the area of database forensics as well as database slack.