Thesis (MSc)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: With a view to future ship deck landings, a moving platform landing algorithm for an unmanned autonomous helicopter was successfully designed and a number of systems were developed in order to implement the landing algorithm. Through a combination of an MCA-based ship motion prediction algorithm and the appropriate analysis of platform motion criteria, a system was developed which can identify valid landing opportunities in real ship motion data recorded at sea state 4 for up to 5 s into the future with a 75% success rate. The bandwidth of the heave motion estimator and controller of the helicopter were increased by the implementation of GPS latency compensation, and velocity and acceleration feed forward terms respectively. The resulting bandwidth of at least 0.2 Hz is sufficient to track the heave motion of a platform which is simulating the motion of a ship at sea state 4 or lower. After the various systems were integrated they were coordinated in a landing state machine. A stationary platform landing was demonstrated successfully during flight tests, verifying the functionality of the landing state machine and the integration of the system. Landings on a platform simulating the motion of a ship at sea state 4 were demonstrated successfully in hardware-in-the-loop simulations. / AFRIKAANSE OPSOMMING: Met die oog op toekomstige skip dek landings, is 'n bewegende platformlandingsalgoritme vir 'n onbemande outonome helikopter suksesvol ontwerp en 'n aantal stelsels ontwikkel om die landingsalgoritme te implementeer. Deur 'n kombinasie van 'n MCA-gebaseerde skipbewegingvoorspellingsalgoritme en die toepaslike ontleding van platformbewegingkriteria, is 'n stelsel ontwikkel wat geldige landingsgeleenthede in realeskipbewegingsdata kan identifiseer. Vir skipbewegingsdata wat by seetoestand 4 opgeneem is kan landingsgeleenthede 5 s in die toekoms met ‘n 75% sekerheid identifiseer word. Die bandwydte van die afgeebewegingafskatter en beheerder van die helikopter is deur die implementering van GPS vertragingkompensasie, en snelheid en versnelling vorentoe-voer terme onderskeidelik verhoog. Die gevolglike bandwydte van minstens 0.2 Hz is voldoende om die afgeebeweging van 'n platform te volg wat die beweging van 'n skip by seetoestand 4 of laer simuleer. Nadat die stelsels geïntegreer is is hulle gekoördineer in 'n landingtoestandsmasjien. 'n Stilstaande platform landing is suksesvol gedemonstreer tydens vlugtoetse, wat die funksionaliteit van die landingtoestandsmasjien en die integrasie van die stelsel bewys. Landings op 'n platform wat die beweging van 'n skip by seetoestand 4 simuleer is suksesvol in hardeware-in-die-lus simulasies gedemonstreer.

Instruction systolic arrays (ISAs) provide a programmable high performance hardware for specific computationally intensive applications. Typically, such an array is connected to a sequential host, thus operating like a coprocessor which solves only the computationally intensive tasks within a global application. The ISA model is a mesh connected processor grid, which combines the advantages of special purpose systolic arrays with the flexible programmability of general purpose machines. The subject of this thesis is the analysis, design, and implementation of several special purpose algorithms and subroutines on the ISA that take advantage of the special features of the systolic information flow. The ability of ISAs to perform parallel prefix computations in an extremely efficient way is exploited as a key-operation to derive efficiency as well as local operations within each processor. Therefore, given sequential algorithms has to be decomposed in simple building blocks of parallel prefix computations and parallel local operations. To modify sequential algorithms for a parallelisation several techniques are introduced in this thesis, e. g. swapping of loops in the sequential algorithm, shearing of data, and appropriate mapping of input data onto the processor array It is demonstrated how these techniques can be exploited to derive efficient ISA algorithms for several computationally intensive applications. These include cryptographic applications (e. g. arithmetic operations on long operands, RSA encryption, RSA key generation) and image processing applications (e. g. convolution, Wavelet Transform, morphological operators, median filter, Fourier Transform, Hough Transform, Morphological Hough Transform, and tomographic image reconstruction). Their implementation on Systola 1024 - the first commercial parallel computer with the ISA architecture - shows that the concept of the ISA is very suitable for these applications and results in significant run time savings. The results of this thesis emphases the suitability of the ISA concept as an accelerator for computationally intensive applications in the areas of cryptography and image processing. This might lead research towards further high-speed low cost systems based on ISA hardware.

Human health is monitored through several physiological measurements such as heart rate, blood pressure, brain activity, etc. These measurements are taken at predefined points in the body and recorded as temporal signals or colorful images for diagnosis purposes. During the diagnosis, physicians analyze these recordings, sometimes visually, to make treatment decisions. These recordings are usually contaminated with noise caused by different factors such as physiological artifacts or electronic noises of the used electrodes/instruments. Therefore, the pre-processing of these signals and images becomes a crucial need to provide clinicians with useful information to make the right decisions. This Ph.D. work proposes and discusses different biomedical signal processing algorithms and their applications. It develops novel signal/image pre-processing algorithms, based on the Semi-Classical Signal Analysis method (SCSA), to enhance the quality of biomedical signals and images. The SCSA method is based on the decomposition of the input signal or image, using the squared eigenfunctions of a Semi-Classical Schrodinger operator. This approach shows great potential in denoising, and residual water-peak suppression for Magnetic Resonance Spectroscopy (MRS) signals compared to the existing methods. In addition, it shows very promising noise removal, particularly from pulse-shaped signals and from Magnetic Resonance (MR) images. In clinical practice, extracting informative characteristics or features from these pre-processed recordings is very important for advanced analysis and diagnosis. Therefore, new features and proposed are extracted based on the SCSA and fed to machine learning models for smart biomedical diagnosis such as predicting epileptic spikes in Magnetoencephalography (MEG). Moreover, a new Quantization-based Position Weight Matrix (QuPWM) feature extraction method is proposed for other biomedical classifications, such as predicting true Poly(A) regions in a DNA sequence, multiple hand gesture prediction. These features can be used to understand different complex systems, such as hand gesture/motion mechanism and help in the smart decision-making process. Finally, combining such features with reinforcement learning models will undoubtedly help automate the diagnoses and enhance the decision-making, which will accelerate the digitization of different industrial sectors. For instance, these features can help to study and understand fish growth in an End-To-End system for aquaculture environments. Precisely, this application’s preliminary results show very encouraging insights in optimally controlling the feeding while preserving the desired growth profile.

Multi -omics data analysis and integration facilitates hypothesis building toward an understanding of genes and pathway responses driven by environments. Methods designed to estimate and analyze gene expression, with regard to treatments or conditions, can be leveraged to understand gene-level responses in the cell. However, genes often interact and signal within larger structures such as pathways and networks. Complex studies guided toward describing dynamic genetic pathways and networks require algorithms or methods designed for inference based on gene interactions and related topologies. Classes of algorithms and methods may be integrated into generalized workflows for comparative genomics studies, as multi -omics data can be standardized between contact points in various software applications. Further, network inference or network comparison algorithmic designs may involve interchangeable operations given the structure of their implementations. Network comparison and inference methods can also guide transfer-of-knowledge between model organisms and those with less knowledge base.

This paper presents an investigation into the impact of algorithm design for real-time active control systems. An active vibration control (AVC) algorithm for flexible beam systems is employed to demonstrate the critical design impact for real-time control applications. The AVC algorithm is analyzed, designed in various forms and implemented to explore the impact. Finally, a comparative real-time computing performance of the algorithms is presented and discussed to demonstrate the merits of different design mechanisms through a set of experiments.

Age of Information (AoI) is a performance metric that can be used to measure the freshness of information. Since its inception, it has captured the attention of the research community and is now an area of active research. By its definition, AoI measures the elapsed time period between the present time and the generation time of the information. AoI is fundamentally different from traditional metrics such as delay or latency as the latter only considers the transit time for a packet to traverse the network. Among the state-of-the-art in the literature, we identify two limitations that deserve further investigation. First, many existing efforts on AoI have been limited to information-theoretic exploration by considering extremely simple models and unrealistic assumptions, which are far from real-world communication systems. Second, among most existing work on scheduling algorithms to optimize AoI, there is a lack of research on guaranteeing AoI deadlines. The goal of this dissertation is to address these two limitations in the state-of-the-art. First, we design schedulers to minimize AoI under more practical settings, including varying sampling periods, varying sample sizes, cellular transmission models, dynamic channel conditions, etc. Second, we design schedulers to guarantee hard or soft AoI deadlines for each information source. More important, inspired by our results from guaranteeing AoI deadlines, we develop a general design framework that can be applied to construct high-performance schedulers for AoI-related problems. This dissertation is organized into three parts. In the first part, we study two problems on AoI minimization under general settings. (i) We consider general and heterogeneous sampling behaviors among source nodes, varying sample size, and a cellular-based transmission model. We develop a near-optimal low-complexity scheduler---code-named Juventas---to minimize AoI. (ii) We study the AoI minimization problem under a 5G network with dynamic channels. To meet the stringent real-time requirement for 5G, we develop a GPU-based near-optimal algorithm---code-named Kronos---and implement it on commercial off-the-shelf (COTS) GPUs. In the second part, we investigate three problems on guaranteeing AoI deadlines. (i) We study the problem to guarantee a hard AoI deadline for information from each source. We present a novel low-complexity procedure, called Fictitious Polynomial Mapping (FPM), and prove that FPM can find a feasible scheduler for any hard deadline vector when the system load is under ln 2. (ii) For soft AoI deadlines, i.e., occasional violations can be tolerated, we present a novel procedure called Unstable Tolerant Scheduler (UTS). UTS hinges upon the notions of Almost Uniform Schedulers (AUSs) and step-down rate vectors. We show that UTS has strong performance guarantees under different settings. (iii) We investigate a 5G scheduling problem to minimize the proportion of time when the AoI exceeds a soft deadline. We derive a property called uniform fairness and use it as a guideline to develop a 5G scheduler---Aequitas. To meet the real-time requirement in 5G, we implement Aequitas on a COTS GPU. In the third part, we present Eywa---a general design framework that can be applied to construct high-performance schedulers for AoI-related optimization and decision problems. The design of Eywa is inspired by the notions of AUS schedulers and step-down rate vectors when we develop UTS in the second part. To validate the efficacy of the proposed Eywa framework, we apply it to solve a number of problems, such as minimizing the sum of AoIs, minimizing bandwidth requirement under AoI constraints, and determining the existence of feasible schedulers to satisfy AoI constraints. We find that for each problem, Eywa can either offer a stronger performance guarantee than the state-of-the-art algorithms, or provide new/general results that are not available in the literature. / Doctor of Philosophy / Age of Information (AoI) is a performance metric that can be used to measure the freshness of information. It measures the elapsed time period between the present time and the generation time of the information. Through a literature review, we have identified two limitations: (i) many existing efforts on AoI have employed extremely simple models and unrealistic assumptions, and (ii) most existing work focuses on optimizing AoI, while overlooking AoI deadline requirements in some applications. The goal of this dissertation is to address these two limitations. For the first limitation, we study the problem to minimize the average AoI in general and practical settings, such as dynamic channels and 5G NR networks. For the second limitation, we design schedulers to guarantee hard or soft AoI deadlines for information from each source. Finally, we develop a general design framework that can be applied to construct high-performance schedulers for AoI-related problems.

The radio spectrum that can be used for wireless communications is a finite but extremely valuable resource. During the past two decades, with the proliferation of new wireless applications, the use of the radio spectrum has intensified to the point that improved spectrum sharing policies and new mechanisms are needed to enhance its utilization efficiency. This dissertation studies spectrum sharing and coexistence on both licensed and unlicensed bands for wireless networks. For licensed bands, we study two coexistence paradigms: transparent coexistence (a.k.a., underlay) and policy-based network cooperation (a.k.a., overlay). These two paradigms can offer significant improvement in spectrum utilization and throughput performance than the interweave paradigm. For unlicensed band, we study coexistence of Wi-Fi and LTE, the two most poplar wireless networks. / Ph. D.

Interference is a fundamental problem in wireless networks. An effective solution to this problem usually calls for a cross-layer approach. Although there exist a large volume of works on interference management techniques in the literature, most of them are limited to signal processing at the physical (PHY) layer or information-theoretic exploitation. Studies of advanced interference techniques from a cross-layer optimization perspective remain limited, especially involving multi-hop wireless networks. This dissertation aims at filling this gap by offering a comprehensive investigation of three interference techniques: interference cancellation (IC), interference alignment (IA), and interference neutralization (IN). This dissertation consists of three parts: the first part studies IC in distributed multi-hop multiple-input multiple-output (MIMO) networks; the second part studies IA in multi-hop networks, cellular networks, and underwater acoustic (UWA) networks; and the third part focuses on IN in multi-hop single-antenna networks. While each part makes a step towards advancing an interference technique, they collectively constitute a body of work on interference management in the networking research community. Results in this dissertation not only advance network-level understanding of the three interference management techniques, but also offer insights and guidance on how these techniques may be incorporated in upper-layer protocol design. In the first part, we study IC in multi-hop MIMO networks where resource allocation is achieved through neighboring node coordination and local information exchange. Based on a well-established degree-of-freedom (DoF) MIMO model, we develop a distributed DoF scheduling algorithm with the objective of maximizing network-level throughput while guaranteeing solution feasibility at the PHY layer. The proposed algorithm accomplishes a number of beneficial features, including polynomial-time complexity, amenability to local implementation, a guarantee of feasibility at the PHY layer, and competitive throughput performance. Our results offer a definitive ``yes'' answer to the question --- Can the node-ordering DoF model be deployed in a distributed multi-hop MIMO network? In particular, we show that the essence of the DoF model --- a global node ordering, can be implicitly achieved via local operations, albeit it is invisible to individual node. In the second part, we investigate IA in various complex wireless networks from a networking perspective. Specifically, we study IA in three different domains: spatial domain, spectral domain, and temporal domain. In the spatial domain, we study IA for multi-hop MIMO networks. We derive a set of simple constraints to characterize the IA capability at the PHY layer. We prove that as long as the set of simple constraints are satisfied, there exists a feasible IA scheme (i.e., precoding and decoding vectors) at the PHY layer so that the data streams on each link can be transported free of interference. Therefore, instead of dealing with the complex design of precoding and decoding vectors, our IA constraints only require simple algebraic addition/subtraction operations. Such simplicity allows us to study network-level IA problems without being distracted by the tedious details in signal design at the PHY layer. Based on these IA constraints, we develop an optimization framework for unicast and multicast communications. In the spectral domain, we study IA in OFDM-based cellular networks. Different from spatial IA, spectral IA is achieved by mapping data streams onto a set of frequency bands/subcarriers (rather than a set of antenna elements). For the uplink, we derive a set of simple IA constraints to characterize a feasible DoF region for a cellular network. We show how to construct precoding and decoding vectors at the PHY layer so that each data stream can be transported free of interference. Based on the set of IA constraints, we study a user throughput maximization problem and show the throughput improvement over two other schemes via numerical results. For the downlink, we find that we can exploit the uplink IA constraints to the downlink case simply by reversing the roles of user and base station. Further, the downlink user throughput maximization problem has the exactly same formulation as the uplink problem and thus can be solved in the exactly same way. In the temporal domain, we study IA for UWA networks. A fundamental issue in UWA networks is large propagation delays due to slow signal speed in water medium. But temporal IA has the potential to turn the adverse effect of large propagation delays into something beneficial. We propose a temporal IA scheme based on propagation delays, nicknamed PD-IA, for multi-hop UWA networks. We first derive a set of PD-IA constraints to guarantee PD-IA feasibility at the PHY layer. Then we develop a distributed PD-IA scheduling algorithm, called Shark-IA, to maximally overlap interference in a multi-hop UWA network. We show that PD-IA can turn the adverse propagation delays to throughput improvement in multi-hop UWA networks. In the third part, we study IN for multi-hop single-antenna networks with full cooperation among the nodes. The fundamental problem here is node selection for IN in a multi-hop network environment. We first establish an IN reference model to characterize the IN capability at the PHY layer. Based on this reference model, we develop a set of constraints that can be used to quickly determine whether a subset of links can be active simultaneously. By identifying each eligible neutralization node as a neut, we study IN in a multi-hop network with a set of sessions and derive the necessary constraints to characterize neut selection, IN, and scheduling. These constraints allow us to study IN problems from a networking perspective but without the need of getting into signal design issues at the PHY layer. By applying our IN model and constraints to study a throughput maximization problem, we show that the use of IN can generally increase network throughput. In particular, throughput gain is most significant when there is a sufficient number of neuts that can be used for IN. In summary, this dissertation offers a comprehensive investigation of three interference management techniques (IC, IA, and IN) from a networking perspective. Theoretical and algorithmic contributions of this dissertation encompass characterization of interference exploitation capabilities at the PHY layer, derivation of tractable interference models, development of feasibility proof for each interference model, formulation of throughput maximization problems, design of distributed IC and PD-IA scheduling algorithms, and development of near-optimal solutions with a performance guarantee. The results in this dissertation offer network-level understanding of the three interference management techniques and lay the groundwork for future research on interference management in wireless networks. / Ph. D.

Over the past decade, wireless data communication has experienced a phenomenal growth, which is driven by the popularity of wireless devices and the growing number of bandwidth hungry applications. During the same period, various advanced communication technologies have emerged to improve network throughput. Some examples include multi-input multi-output (MIMO), full duplex, cognitive radio, mmWave, among others. An important research direction is to understand the impacts of these new technologies on network throughput performance. Such investigation is critical not only for theoretical understanding, but also can be used as a guideline to design algorithms and network protocols in the field. The goal of this dissertation is to understand the impact of some advanced technologies on network throughput performance. More specifically, we investigate the following three technologies: MIMO, full duplex, and mmWave communication. For each technology, we explore the performance envelope of wireless networks by studying a throughput maximization problem. / Ph. D.

No / Ethnicity is one of the most salient clues to face identity. Analysis of ethnicity-specific facial data is a challenging problem and predominantly carried out using computer-based algorithms. Current published literature focusses on the use of frontal face images. We addressed the challenge of binary (British Pakistani or other ethnicity) ethnicity classification using profile facial images. The proposed framework is based on the extraction of geometric features using 10 anthropometric facial landmarks, within a purpose-built, novel database of 135 multi-ethnic and multi-racial subjects and a total of 675 face images. Image dimensionality was reduced using Principle Component Analysis and Partial Least Square Regression. Classification was performed using Linear Support Vector Machine. The results of this framework are promising with 71.11% ethnic classification accuracy using a PCA algorithm + SVM as a classifier, and 76.03% using PLS algorithm + SVM as a classifier.