Automated system design for the efficient processing of solar satellite images. Developing novel techniques and software platform for the robust feature detection and the creation of 3D anaglyphs and super-resolution images for solar satellite images.

The Sun is of fundamental importance to life on earth and is studied by scientists from many disciplines. It exhibits phenomena on a wide range of observable scales, timescales and wavelengths and due to technological developments there is a continuing increase in the rate at which solar data is becoming available for study which presents both opportunities and challenges. Two satellites recently launched to observe the sun are STEREO (Solar TErrestrial RElations Observatory), providing simultaneous views of the SUN from two different viewpoints and SDO (Solar Dynamics Observatory) which aims to study the solar atmosphere on small scales and times and in many wavelengths. The STEREO and SDO missions are providing huge volumes of data at rates of about 15 GB per day (initially it was 30 GB per day) and 1.5 terabytes per day respectively. Accessing these huge data volumes efficiently at both high spatial and high time resolutions is important to support scientific discovery but requires increasingly efficient tools to browse, locate and process specific data sets. This thesis investigates the development of new technologies for processing information contained in multiple and overlapping images of the same scene to produce images of improved quality. This area in general is titled Super Resolution (SR), and offers a technique for reducing artefacts and increasing the spatial resolution. Another challenge is to generate 3D images such as Anaglyphs from uncalibrated pairs of SR images. An automated method to generate SR images is presented here. The SR technique consists of three stages: image registration, interpolation and filtration. Then a method to produce enhanced, near real-time, 3D solar images from uncalibrated pairs of images is introduced. Image registration is an essential enabling step in SR and Anaglyph processing. An accurate point-to-point mapping between views is estimated, with multiple images registered using only information contained within the images themselves. The performances of the proposed methods are evaluated using benchmark evaluation techniques. A software application called the SOLARSTUDIO has been developed to integrate and run all the methods introduced in this thesis. SOLARSTUDIO offers a number of useful image processing tools associated with activities highly focused on solar images including: Active Region (AR) segmentation, anaglyph creation, solar limb extraction, solar events tracking and video creation

Adoption of Virtual Reality Technology in Learning Elementary of Music Theory to Enhance the Learning Outcomes of Students with Disabilities

Advancements in technology have led to the widespread use of moderntechnologies in education, including the integration of augmented reality (AR) andvirtual reality (VR) technologies. During the COVID-19 era, Music learning likeother applied educational disciplines, has faced many technical and applicationproblems, especially in finding alternative educational tools that fulfill the learningpurpose and preserve the expected learning outcomes. This study came to explorethe effectiveness of using Virtual Reality technology in music education and tofigure out the students' acceptance and their intention to use the technology. Forthis purpose, an interactive virtual learning environment was developed to aidstudents in learning the “principle of music theory”, a sample group of 20 studentsincluding students with Motor Disabilities from Luminus Technical UniversityCollege (LTUC) participated in the study experiment. The results of the studyshow that the use of virtual reality technology is effective in learning music, andalso there is an acceptable degree of intention to use the technology but at a highrate among students with motor disabilities. In addition, the analysis of the resultshighlights the essential factors that should be considered to improve the VRlearning environment and the expected learning outcomes

Automated system design for the efficient processing of solar satellite images : developing novel techniques and software platform for the robust feature detection and the creation of 3D anaglyphs and super-resolution images for solar satellite images

The Sun is of fundamental importance to life on earth and is studied by scientists from many disciplines. It exhibits phenomena on a wide range of observable scales, timescales and wavelengths and due to technological developments there is a continuing increase in the rate at which solar data is becoming available for study which presents both opportunities and challenges. Two satellites recently launched to observe the sun are STEREO (Solar TErrestrial RElations Observatory), providing simultaneous views of the SUN from two different viewpoints and SDO (Solar Dynamics Observatory) which aims to study the solar atmosphere on small scales and times and in many wavelengths. The STEREO and SDO missions are providing huge volumes of data at rates of about 15 GB per day (initially it was 30 GB per day) and 1.5 terabytes per day respectively. Accessing these huge data volumes efficiently at both high spatial and high time resolutions is important to support scientific discovery but requires increasingly efficient tools to browse, locate and process specific data sets. This thesis investigates the development of new technologies for processing information contained in multiple and overlapping images of the same scene to produce images of improved quality. This area in general is titled Super Resolution (SR), and offers a technique for reducing artefacts and increasing the spatial resolution. Another challenge is to generate 3D images such as Anaglyphs from uncalibrated pairs of SR images. An automated method to generate SR images is presented here. The SR technique consists of three stages: image registration, interpolation and filtration. Then a method to produce enhanced, near real-time, 3D solar images from uncalibrated pairs of images is introduced. Image registration is an essential enabling step in SR and Anaglyph processing. An accurate point-to-point mapping between views is estimated, with multiple images registered using only information contained within the images themselves. The performances of the proposed methods are evaluated using benchmark evaluation techniques. A software application called the SOLARSTUDIO has been developed to integrate and run all the methods introduced in this thesis. SOLARSTUDIO offers a number of useful image processing tools associated with activities highly focused on solar images including: Active Region (AR) segmentation, anaglyph creation, solar limb extraction, solar events tracking and video creation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A cognitive deep learning approach for medical image processing

In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation

Trojan Horse Infection Detection in Cloud Based Environment Using Machine Learning

Cloud computing technology is known as a distributed computing network, which consists of a large number of servers connected via the internet. This technology involves many worthwhile resources, such as applications, services, and large database storage. Users have the ability to access cloud services and resources through web services. Cloud computing provides a considerable number of benefits, such as effective virtualized resources, cost efficiency, self-service access, flexibility, and scalability. However, many security issues are present in cloud computing environment. One of the most common security challenges in the cloud computing environment is the trojan horses. Trojan horses can disrupt cloud computing services and damage the resources, applications, or virtual machines in the cloud structure. Trojan horse attacks are dangerous, complicated and very difficult to be detected. In this research, eight machine learning classifiers for trojan horse detection in a cloud-based environment have been investigated. The accuracy of the cloud trojan horses detection rate has been investigated using dynamic analysis, Cukoo sandbox, and the Weka data mining tool. Based on the conducted experiments, the SMO and Multilayer Perceptron have been found to be the best classifiers for trojan horse detection in a cloud-based environment. Although SMO and Multilayer Perceptron have achieved the highest accuracy rate of 95.86%, Multilayer Perceptron has outperformed SMO in term of Receiver Operating Characteristic (ROC) area

Improved Path Testing Using Multi-Verse Optimization Algorithm and the Integration of Test Path Distance

Emerging technologies in artificial intelligence (AI) and advanced optimization methodologies have opened up a new frontier in the field of software engineering. Among these methodologies, optimization algorithms such as the multi-verse optimizer (MVO) provide a compelling and structured technique for identifying software vulnerabilities, thereby enhancing software robustness and reliability. This research investigates the feasibility and efficacy of applying an augmented version of this technique, known as the test path distance multiverse optimization (TPDMVO) algorithm, for comprehensive path coverage testing, which is an indispensable aspect of software validation. The algorithm’s versatility and robustness are examined through its application to a wide range of case studies with varying degrees of complexity. These case studies include rudimentary functions like maximum and middle value extraction, as well as more sophisticated data structures such as binary search trees and AVL trees. A notable innovation in this research is the introduction of a customized fitness function, carefully designed to guide TPDMVO towards traversing all possible execution paths in a program, thereby ensuring comprehensive coverage. To further enhance the comprehensiveness of the test, a metric called ‘test path distance’ (TPD) is utilized. This metric is designed to guide TPDMVO towards paths that have not been explored before. The findings confirm the superior performance of the TPDMVO algorithm, which achieves complete path coverage in all test scenarios. This demonstrates its robustness and adaptability in handling different program complexities