Dissertations / Theses on the topic '080399 Computer Software not elsewhere classified'
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Soltani, Hamidreza. "Development and application of real-time and interactive software for complex system." Thesis, University of Central Lancashire, 2016. http://clok.uclan.ac.uk/20443/.
Full textTimperley, 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/.
Full textJiang, Feng. "Capturing event metadata in the sky : a Java-based application for receiving astronomical internet feeds : a thesis presented in partial fulfilment of the requirements for the degree of Master of Computer Science in Computer Science at Massey University, Auckland, New Zealand." Massey University, 2008. http://hdl.handle.net/10179/897.
Full textBlakey, Jeremy Peter. "Database training for novice end users : a design research approach : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems at Massey University, Albany, New Zealand." Massey University, 2008. http://hdl.handle.net/10179/880.
Full textHumphreys, Alison M. (Sal). "Massively Multiplayer Online Games. Productive players and their disruptions to conventional media practices." Thesis, QUT, 2005.
Find full text(8071232), Patrick Raymond Glass. "THE EFFECTS OF COMPUTER SIMULATION ON REDUCING THE INCIDENCE OF MEDICAL ERRORS ASSOCIATED WITH MASS DISTRIBUTION OF CHEMOPROPHYLAXIS AS A RESULT OF A BIOTERRORISM EVENT." Thesis, 2019.
Find full text(10184063), Younghoon Kim. "Approximate Computing: From Circuits to Software." Thesis, 2021.
Find full text(11173323), Hanlin Chen. "Adaptive Safety and Cyber Security for Connected and Automated Vehicle System." Thesis, 2021.
Find full textThis dissertation discussed the potential benefits that CAV systems can bring to the
general well-being, and how the threat lies within the CAV system can affect its performance and
functionality.
Particularly, this dissertation discovered how CAV technology can benefit homeland security and crime investigations involving child abduction crimes. By proposing the initial design network, this dissertation proposed a solution that enhances the current AMBER Alert system using CAV technology. This dissertation also discussed how CAV technology can help perception in corner-case driving scenarios and reduce the risk of traffic accidents, by proposing a dataset that covers various corner cases including different weather and lighting conditions targeting the work zone. Evaluation is made on the collected data and several impact factors have been figured out.
This dissertation also discussed an attack scenario that a ROS-based CAV platform was attacked by DoS attacks. We analized the system response after we attacked the system. Discussion and analysis was made on the functionality and stability of the system.
Overall, we determined that CAV technology can greatly benefit in general well-being, and threats within the CAV system can cast potential negative benefits once the CAV system is being attacked.
(8781095), Alexa G. Sears. "MANIPULATION OF THREE-DIMENSIONAL SCENES AND ANIMATION USING IMMERSIVE TECHNOLOGY." Thesis, 2020.
Find full textMastilovich, Nikola. "Automatisation of programming of a PLC code : a thesis presented in partial fulfilment of the requirements of the degree of Masters of Engineering in Mechatronics." 2010. http://hdl.handle.net/10179/1681.
Full textA competitive edge is one of the requirements of a successful business. Tools, which increase an engineer s productivity and minimize cost, can be considered as a competitive edge. The objective of this thesis was to design, create, and implement Automatic PLC Code Generator (APCG) software. A secondary objective was to demonstrate that the use of the APCG software will lead to improved project efficiency and enhanced profit margin. To create the APCG software, the MS Excel and Visual Basic for Applications (VBA) programs were used as the platform. MS Excel sheets were used as a user interface, while VBA creates the PLC code from the information entered by the engineer. The PLC code, created by the APCG software, follows the PLC structure of the Realcold Milmech Pty. Ltd, as well as the research Automatic generation of PLC code beyond the nominal sequence written by Guttel et al [1]. The APCG software was used to design and create a PLC code for one of the projects undertaken by Realcold Milmech Pty. Ltd. By using APCG software, time to design, create, and test the PLC code was improved when compared to the budgeted time. In addition, the project's profit margin was increased. Based on the results of this thesis it is expected that the APCG software will be useful for programmers that tend to handle a variety of projects on a regular basis, where programming in a modular way is not appropriate.
(10063480), Monil Vallabhbh Chheta. "DESIGN AND IMPLEMENTATION OF ENERGY USAGE MONITORING AND CONTROL SYSTEMS USING MODULAR IIOT FRAMEWORK." Thesis, 2021.
Find full textThis project aims to develop a cloud-based platform that integrates sensors with business intelligence for real-time energy management at the plant level. It provides facility managers, an energy management platform that allows them to monitor equipment and plant-level energy consumption remotely, receive a warning, identify energy loss due to malfunction, present options with quantifiable effects for decision-making, and take actions, and assess the outcomes. The objectives consist of:
Developing a generic platform for the monitoring energy consumption of industrial equipment using sensors
Control the connected equipment using an actuator
Integrating hardware, cloud, and application algorithms into the platform
Validating the system using an Energy Consumption Forecast scenario
A Demo station was created for testing the system. The demo station consists of equipment such as air compressor, motor and light bulb. The current usage of these equipment is measured using current sensors. Apart from current sensors, temperature sensor, pres- sure sensor and CO2 sensor were also used. Current consumption of these equipment was measured over a couple of days. The control system was tested randomly by turning on equipment at random times. Turning on the equipment resulted in current consumption which ensured that the system is running. Thus, the system worked as expected and user could monitor and control the connected equipment remotely.
(8072417), Braiden M. Frantz. "Active Shooter Mitigation for Open-Air Venues." Thesis, 2021.
Find full textThis dissertation examines the impact of active shooters upon patrons attending large outdoor events. There has been a spike in shooters targeting densely populated spaces in recent years, to include open-air venues. The 2019 Gilroy Garlic Festival was selected for modeling replication using AnyLogic software to test various experiments designed to reduce casualties in the event of an active shooter situation. Through achievement of validation to produce identical outcomes of the real-world Gilroy Garlic Festival shooting, the researcher established a reliable foundational model for experimental purposes. This active shooter research project identifies the need for rapid response efforts to neutralize the shooter(s) as quickly as possible to minimize casualties. Key findings include the importance of armed officers patrolling event grounds to reduce response time, the need for adequate exits during emergency evacuations, incorporation of modern technology to identify the shooter’s location, and applicability of a 1:548 police to patron ratio.
(6838184), Parami Wijesinghe. "Neuro-inspired computing enhanced by scalable algorithms and physics of emerging nanoscale resistive devices." 2019.
Find full textDeep ‘Analog Artificial Neural Networks’ (AANNs) perform complex classification problems with high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain on the other hand is significantly more powerful than such networks and consumes orders of magnitude less power, indicating some conceptual mismatch. Given that the biological neurons are locally connected, communicate using energy efficient trains of spikes, and the behavior is non-deterministic, incorporating these effects in Artificial Neural Networks (ANNs) may drive us few steps towards a more realistic neural networks.
Emerging devices can offer a plethora of benefits including power efficiency, faster operation, low area in a vast array of applications. For example, memristors and Magnetic Tunnel Junctions (MTJs) are suitable for high density, non-volatile Random Access Memories when compared with CMOS implementations. In this work, we analyze the possibility of harnessing the characteristics of such emerging devices, to achieve neuro-inspired solutions to intricate problems.
We propose how the inherent stochasticity of nano-scale resistive devices can be utilized to realize the functionality of spiking neurons and synapses that can be incorporated in deep stochastic Spiking Neural Networks (SNN) for image classification problems. While ANNs mainly dwell in the aforementioned classification problem solving domain, they can be adapted for a variety of other applications. One such neuro-inspired solution is the Cellular Neural Network (CNN) based Boolean satisfiability solver. Boolean satisfiability (k-SAT) is an NP-complete (k≥3) problem that constitute one of the hardest classes of constraint satisfaction problems. We provide a proof of concept hardware based analog k-SAT solver that is built using MTJs. The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog, CNN based, satisfiability (SAT) solver.
Furthermore, in the effort of reaching human level performance in terms of accuracy, increasing the complexity and size of ANNs is crucial. Efficient algorithms for evaluating neural network performance is of significant importance to improve the scalability of networks, in addition to designing hardware accelerators. We propose a scalable approach for evaluating Liquid State Machines: a bio-inspired computing model where the inputs are sparsely connected to a randomly interlinked reservoir (or liquid). It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to improved accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lower number of connections and the freedom to parallelize the liquid evaluation process.
(8771429), Ashley S. Dale. "3D OBJECT DETECTION USING VIRTUAL ENVIRONMENT ASSISTED DEEP NETWORK TRAINING." Thesis, 2021.
Find full textAn RGBZ synthetic dataset consisting of five object classes in a variety of virtual environments and orientations was combined with a small sample of real-world image data and used to train the Mask R-CNN (MR-CNN) architecture in a variety of configurations. When the MR-CNN architecture was initialized with MS COCO weights and the heads were trained with a mix of synthetic data and real world data, F1 scores improved in four of the five classes: The average maximum F1-score of all classes and all epochs for the networks trained with synthetic data is F1∗ = 0.91, compared to F1 = 0.89 for the networks trained exclusively with real data, and the standard deviation of the maximum mean F1-score for synthetically trained networks is σ∗ F1 = 0.015, compared to σF 1 = 0.020 for the networks trained exclusively with real data. Various backgrounds in synthetic data were shown to have negligible impact on F1 scores, opening the door to abstract backgrounds and minimizing the need for intensive synthetic data fabrication. When the MR-CNN architecture was initialized with MS COCO weights and depth data was included in the training data, the net- work was shown to rely heavily on the initial convolutional input to feed features into the network, the image depth channel was shown to influence mask generation, and the image color channels were shown to influence object classification. A set of latent variables for a subset of the synthetic datatset was generated with a Variational Autoencoder then analyzed using Principle Component Analysis and Uniform Manifold Projection and Approximation (UMAP). The UMAP analysis showed no meaningful distinction between real-world and synthetic data, and a small bias towards clustering based on image background.
(8803076), Jordan M. McGraw. "Implementation and Analysis of Co-Located Virtual Reality for Scientific Data Visualization." Thesis, 2020.
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