<p dir="ltr">Functional connectomes (FCs) contain all pairwise estimations of functional couplings between brain regions. Neural activity of brain regions is estimated for subjects, sessions and tasks based on fMRI BOLD data. FCs are commonly represented as correlation matrices that are symmetric positive definite (SPD) matrices lying on or inside the SPD manifold. Since the geometry on the SPD manifold is non-Euclidean, the inter-related entries of FCs undermine the use of Euclidean-based distances and its stability when using them as features in machine learning algorithms. By projecting FCs into a tangent space, we can obtain tangent functional connectomes (tangent-FCs), whose entries would not be inter-related, and thus, allow the use of Euclidean-based methods. Tangent-FCs have shown a higher predictive power of behavior and cognition, but no studies have evaluated the effect of such projections with respect to fingerprinting.</p><p dir="ltr">To some extent, FCs possess a recurrent and reproducible individual fingerprint that can identify if two FCs belong to the same participant. This process is referred to as fingerprinting or subject-identification. As research objects, FCs are expected to be reliable, which means FCs of the same person doing the same thing are expected to be more similar to each other compared to FCs of other individuals/conditions. The level of fingerprint, usually estimated by identification rate, tries to capture this expectation of reliability. When focusing on the dynamic functional connectivity (dFC) of a single fMRI scan, we proposed the concept of cognitive fingerprinting where the timing of functional reconfiguration is identified. This suggests that the changes of cognitive states can be reflected by the similarities/dissimilarities among dFCs.</p><p dir="ltr">In this dissertation, we hypothesize that comparing FCs in tangent space by using Euclidean algebra should result in higher subject fingerprinting for static FCs and higher cognitive fingerprinting for dynamic FCs. This hypothesis is evaluated by addressing three research questions. The first question investigates the impact of tangent space projection on subject identification rates for static FCs. The second and third questions focus on dFCs, examining their performance in uncovering cognitive fingerprinting on the manifold and in tangent space, respectively. The timing of functional reconfiguration is identified by performing recurrence quantification analysis on dFCs on the manifold. And then, dFCs are projected onto tangent space to assess the influence of this projection on cognitive fingerprinting. Results reveal that identification rates improve systematically with tangent-FCs. Additionally, critical timepoints of functional reconfigurations align closely with ground truth for both manifold-based and tangent-space dFCs.</p><p dir="ltr">Lastly, we tested those research questions together with data-driven mapping methods, connectome-based predictive modeling (CPM) and partial least squares (PLS), on a dataset of FCs that includes healthy controls and HIV patients as a case study. </p><p dir="ltr">In conclusion, our findings support the proposed hypothesis, demonstrating that tangent space projection enhances comparisons and offers strong advantages as a transformation for FCs before their use in other analysis/applications.</p>

The focus of this study was to investigate an alternative and more cost effective solution for occupancy sensing in commercial office buildings. The intended purpose of this solution is to aid in efficient energy management. The main requirements were that the proposed solution made use of existing infrastructure only, and provided a means to focus on occupant location. This research was undertaken due to current solutions making use of custom occupancy sensors that are relatively costly and troublesome to implement. These solutions focus mainly on monitoring environmental changes, and not the physical locations of the occupants themselves. Furthermore, current occupancy sensing solutions are unable to provide proximity and timing information that indicate how far an occupant is located from a specific area, or how long the occupant resided there. The research question was answered by conducting a proof of concept study with data simulated in the OMNeT++ environment in conjunction with the MiXiM framework for wireless networks. The proposed solution investigated the fidelity of existing WiFi infrastructure for occupancy sensing, this entailed the creation of a Virtual Occupancy Sensor (VOS) that implemented RSS-based localisation for an occupant’s WiFi devices. Localisation was implemented with three different location estimation techniques; these were trilateration, constrained nearest neighbour RF mapping and unconstrained nearest neighbour RF mapping. The obtained positioning data was interpreted by a developed intelligent agent that was able to transform this regular position data into relevant occupancy information. This information included a distance from office measurement and an occupancy result that can be interpreted by existing energy management systems. The accuracy and operational behaviour of the developed VOS were tested with various scenarios. Sensitivity analysis and extreme condition testing were also conducted. Results showed that the constrained nearest neighbour RF mapping approach is the most accurate, and is best suited for occupancy determination. The created VOS system can function correctly with various tested sensitivities and device loads. Furthermore results indicated that the VOS is very accurate in determining room level occupancy although the accuracy of the position coordinate estimations fluctuated considerably. The operational behaviour of the VOS could be validated for all investigated scenarios. It was determined that the developed VOS can be deemed fit for its intended purpose, and is able to give indication to occupant proximity and movement timing. The conducted research confirmed the fidelity of WiFi infrastructure for occupancy sensing, and that the developed VOS can be considered a viable and cost effective alternative to current occupancy sensing solutions. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2014

The focus of this study was to investigate an alternative and more cost effective solution for occupancy sensing in commercial office buildings. The intended purpose of this solution is to aid in efficient energy management. The main requirements were that the proposed solution made use of existing infrastructure only, and provided a means to focus on occupant location. This research was undertaken due to current solutions making use of custom occupancy sensors that are relatively costly and troublesome to implement. These solutions focus mainly on monitoring environmental changes, and not the physical locations of the occupants themselves. Furthermore, current occupancy sensing solutions are unable to provide proximity and timing information that indicate how far an occupant is located from a specific area, or how long the occupant resided there. The research question was answered by conducting a proof of concept study with data simulated in the OMNeT++ environment in conjunction with the MiXiM framework for wireless networks. The proposed solution investigated the fidelity of existing WiFi infrastructure for occupancy sensing, this entailed the creation of a Virtual Occupancy Sensor (VOS) that implemented RSS-based localisation for an occupant’s WiFi devices. Localisation was implemented with three different location estimation techniques; these were trilateration, constrained nearest neighbour RF mapping and unconstrained nearest neighbour RF mapping. The obtained positioning data was interpreted by a developed intelligent agent that was able to transform this regular position data into relevant occupancy information. This information included a distance from office measurement and an occupancy result that can be interpreted by existing energy management systems. The accuracy and operational behaviour of the developed VOS were tested with various scenarios. Sensitivity analysis and extreme condition testing were also conducted. Results showed that the constrained nearest neighbour RF mapping approach is the most accurate, and is best suited for occupancy determination. The created VOS system can function correctly with various tested sensitivities and device loads. Furthermore results indicated that the VOS is very accurate in determining room level occupancy although the accuracy of the position coordinate estimations fluctuated considerably. The operational behaviour of the VOS could be validated for all investigated scenarios. It was determined that the developed VOS can be deemed fit for its intended purpose, and is able to give indication to occupant proximity and movement timing. The conducted research confirmed the fidelity of WiFi infrastructure for occupancy sensing, and that the developed VOS can be considered a viable and cost effective alternative to current occupancy sensing solutions. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2014

published_or_final_version / Pathology / Doctoral / Doctor of Philosophy

No description available.

In recent years, advances in signal processing have led to small, low power, inexpensive Wireless Sensor Network (WSN). The signal processing in WSN is different from the traditional wireless networks in two critical aspects: firstly, the signal processing in WSN is performed in a fully distributed manner, unlike in traditional wireless networks; secondly, due to the limited computation capabilities of sensor networks, it is essential to develop an energy and bandwidth efficient signal processing algorithms. Target localisation and tracking problems in WSNs have received considerable attention recently, driven by the necessity to achieve higher localisation accuracy, lower cost, and the smallest form factor. Received Signal Strength (RSS) based localisation techniques are at the forefront of tracking research applications. Since tracking algorithms have been attracting research and development attention recently, prolific literature and a wide range of proposed approaches regarding the topic have emerged. This thesis is devoted to discussing the existing WSN-based localisation and tracking approaches. This thesis includes five studies. The first study leads to the design and implementation of a triangulation-based localisation approach using RSS technique for indoor tracking applications. The presented work achieves low localisation error in complex environments by predicting the environmental characteristics among beacon nodes. The second study concentrates on investigating a fingerprinting localisation method for indoor tracking applications. The proposed approach offers reasonable localisation accuracy while requiring a short period of offline computation time. The third study focuses on designing and implementing a decentralised tracking approach for tracking multiple mobile targets with low resource requirements. Despite the interest in target tracking and localisation issues, there are few systems deployed using ZigBee network standard, and no tracking system has used the full features of the ZigBee network standard. Tracking through the ZigBee is a challenging task when the density of router and end-device nodes is low, due to the limited communication capabilities of end-device nodes. The fourth study focuses on developing and designing a practical ZigBee-based tracking approach. To save energy, different strategies were adopted. The fifth study outlines designing and implementing an energy-efficient approach for tracking applications. This study consists of two main approaches: a data aggregation approach, proposed and implemented in order to reduce the total number of messages transmitted over the network; and a prediction approach, deployed to increase the lifetime of the WSN. For evaluation purposes, two environmental models were used in this thesis: firstly, real experiments, in which the proposed approaches were implemented on real sensor nodes, to test the validity for the proposed approaches; secondly, simulation experiments, in which NS-2 was used to evaluate the power-consumption issues of the two approaches proposed in this thesis.

The thesis examines two related combinatorial objects, namely fingerprinting codes and separating hash families. Fingerprinting codes are combinatorial objects that have been studied for more than 15 years due to their applications in digital data copyright protection and their combinatorial interest. Four well-known types of fingerprinting codes are studied in this thesis; traceability, identifiable parent property, secure frameproof and frameproof. Each type of code is named after the security properties it guarantees. However, the power of these four types of fingerprinting codes is limited by a certain condition. The first known attempt to go beyond that came out in the concept of two-level traceability codes, introduced by Anthapadmanabhan and Barg (2009). This thesis extends their work to the other three types of fingerprinting codes, so in this thesis four types of two-level fingerprinting codes are defined. In addition, the relationships between the different types of codes are studied. We propose some first explicit non-trivial con- structions for two-level fingerprinting codes and provide some bounds on the size of these codes. Separating hash families were introduced by Stinson, van Trung, and Wei as a tool for creating an explicit construction for frameproof codes in 1998. In this thesis, we state a new definition of separating hash families, and mainly focus on improving previously known bounds for separating hash families in some special cases that related to fingerprinting codes. We improve upper bounds on the size of frameproof and secure frameproof codes under the language of separating hash families.

The rapid increase in mobile malware and deployment of over-privileged applications over the years has been of great concern to the security community. Encroaching on user’s privacy, mobile applications (apps) increasingly exploit various sensitive data on mobile devices. The information gathered by these applications is sufficient to uniquely and accurately profile users and can cause tremendous personal and financial damage. On Android specifically, the security and privacy holes in the operating system and framework code has created a whole new dynamic for malware and privacy exploitation. This research work seeks to develop novel analysis techniques that monitor Android applications for possible unwanted behaviors and then suggest various ways to deal with the privacy leaks associated with them. Current state-of-the-art static malware analysis techniques on Android-focused mainly on detecting known variants without factoring any kind of software obfuscation. The dynamic analysis systems, on the other hand, are heavily dependent on extending the Android OS and/or runtime virtual machine. These methodologies often tied the system to a single Android version and/or kernel making it very difficult to port to a new device. In privacy, accesses to the database system’s objects are not controlled by any security check beyond overly-broad read/write permissions. This flawed model exposes the database contents to abuse by privacy-agnostic apps and malware. This research addresses the problems above in three ways. First, we developed a novel static analysis technique that fingerprints known malware based on three-level similarity matching. It scores similarity as a function of normalized opcode sequences found in sensitive functional modules and application permission requests. Our system has an improved detection ratio over current research tools and top COTS anti-virus products while maintaining a high level of resiliency to both simple and complex obfuscation. Next, we augment the signature-related weaknesses of our static classifier with a hybrid analysis system which incorporates bytecode instrumentation and dynamic runtime monitoring to examine unknown malware samples. Using the concept of Aspect-oriented programming, this technique involves recompiling security checking code into an unknown binary for data flow analysis, resource abuse tracing, and analytics of other suspicious behaviors. Our system logs all the intercepted activities dynamically at runtime without the need for building custom kernels. Finally, we designed a user-level privacy policy enforcement system that gives users more control over their personal data saved in the SQLite database. Using bytecode weaving for query re-writing and enforcing access control, our system forces new policies at the schema, column, and entity levels of databases without rooting or voiding device warranty.

In accordance with the 2010 Dodd-Frank Act, conflict minerals refer to gold, tantalum, tin, and tungsten bearing minerals sourced from the Democratic Republic of Congo (DRC) that have been mined illegally and used to funnel funds to rebel forces. In response to an increasing demand for these metals used in cellphones, computers, and other popular technologies, Dodd-Frank mandates that industrial consumers demonstrate due diligence and assure that the materials they use have been extracted legally. Because current chain-of-custody methods have not been effective in sourcing ores, a study was undertaken whereby the range of mineralogical characteristics of 15 samples along the wolframite solid solution series were determined in order to ascertain if differences in these characteristics would permit fingerprinting of the source deposit of wolframite, of which the DRC is the world's fifth largest producer. For these 15 samples, single-crystal X-ray structure and powder X-ray diffraction studies have been conducted; major, minor and trace element chemistry has been determined using ICP-MS and ICP-OES; and Raman spectroscopy has been carried out. Finally, statistical methods were used to determine relationships between samples, and the results of that mathematical work show that there is no firm method at the present time of determining the provenance of a sample based on the information of the crystal structure, diffraction patterns, vibrational frequencies/scattering, or major and trace elemental chemistry. This study elucidates the range of mineralogical properties along the hübnerite-ferberite solid solution series while working towards to development of an analytical technique that is affordable, practical, accessible and effective for industrial consumers seeking product certification and compliance with the 2010 Dodd-Frank Act.

M.Sc. (Botany) / Global efforts to protect the world’s forests from unsustainable and inequitable exploitation have been undermined in recent years by rampant illegal logging in many timber-producing countries. A prerequisite for efficient control and seizure of illegally harvested forest product is a rapid, accurate and tamper proof method of species identification. DNA barcoding is one such a tool, relatively simple to apply. It is acknowledged to bring about accuracy and efficiency in species identification. In this study a DNA barcode reference library for traded and protected tree species of southern Africa was developed comprising of 81 species and 48 genera. Four primary analyses were conducted to assess the suitability of the core barcodes as a species identification tool using the R package Spider 1.2-0. Lastly, to evaluate this identification tool, query specimens independently sampled at a Mozambican logging concession were identified using DNA barcoding techniques. The nearest neighbour (k-NN) and best close match (BCM) distance based parameter yielded 90% and 85% identification success rate using the core plant barcodes respectively. DNA barcoding identification of query specimens maintained a constant 83% accuracy over the single marker dataset and the combined dataset. This database can serve as a backbone to a control mechanism based on DNA techniques for species identification and also advance the ability of relevant authorities to rapidly identify species of timber at entry and exit points between countries with simple, fast, and accurate DNA techniques.