Information is often stored and transmitted through electrical signals. This information may need refinement, which may be done by processing and altering the electrical signals, in which it is transmitted. When refining a signal, a frequency selective filter is often used. It can be implemented through digital signal processing (DSP). DSP is a concept where signals are refined using a digital compute system. Digital systems are designed to replace their analog counterpart, mitigating their flaws in scalability, complexity and cost. A DSP system is typically implemented using software on a small computer, while analog systems are implemented through various electronic components. The objective of this project is to design a DSP system that filters analog input data using automatically synthesised filters from user-defined input specifications. The DSP system is implemented using a microcontroller. The system designed the filters and found the filter coefficients. It then uses analog to digital converter (ADC) to sample an input signal and applies the filter. Lastly, it uses the digital to analog converter (DAC) to reconstruct a filtered, analog result. A user interface is not designed for the system, and only a limited number of filters are implemented. However, the system is successful in designing filters and finding their coefficients.

Yes / In this paper, the Chebychev spectral collocation method is applied for the thermal analysis of convective-radiative straight fins with the temperature-dependent thermal conductivity. The developed heat transfer model was used to analyse the thermal performance, establish the optimum thermal design parameters, and also, investigate the effects of thermo-geometric parameters and thermal conductivity (nonlinear) parameters on the thermal performance of the fin. The results of this study reveal that the rate of heat transfer from the fin increases as the convective, radioactive, and magnetic parameters increase. This study establishes good agreement between the obtained results using Chebychev spectral collocation method and the results obtained using Runge-Kutta method along with shooting, homotopy perturbation, and adomian decomposition methods.

The complexity of numerical ocean circulation models requires careful checking with a variety of test problems. The purpose of this paper is to develop a test problem involving Rossby and gravity waves in a two-layer fluid in a channel. The goal is to compute very accurate solutions to this test problem. These solutions can then be used as a part of the checking process for numerical ocean circulation models. Here, Chebychev pseudo-spectral methods are used to solve the governing equations with a high degree of accuracy. Chebychev pseudo-spectral methods can be described in the following way: For a given function, find the polynomial interpolant at a particular non-uniform grid. The derivative of this polynomial serves as an approximation to the derivative of the original function. This approximation can then be inserted to differential equations to solve for approximate solutions. Here, the governing equations reduce to an eigenvalue problem with eigenvectors and eigenvalues corresponding to the spatial dependences of modal solutions and the frequencies of those solutions, respectively. The results of this method are checked in two ways. First, the solutions using the Chebychev pseudo-spectral methods are analyzed and are found to exhibit the properties known to belong to physical Rossby and gravity waves. Second, in the special case where the two-layer model degenerates to a one-layer system, some analytic solutions are known. When the numerical solutions are compared to the analytic solutions, they show an exponential rate of convergence. The conclusion is that the solutions computed using the Chebychev pseudo-spectral methods are highly accurate and could be used as a test problem to partially check numerical ocean circulation models. / Graduation date: 2012

Problems in the public policy decision-making environments are typically complex and continuously evolve. In a resource-constrained environment, several alternatives, criteria, and conflicting objectives must be considered. As a result, solutions to these types of problems cannot be modelled solely using single-criteria techniques. It has been observed that most techniques used to shape energy policy and planning either produce sub-optimal solutions or use strong assumptions about the preferences of decision-maker(s). This difficulty creates a compelling need to develop novel techniques that can handle several alternatives, multiple criteria and conflicting objectives to support public sector decision-making processes. First, the study presents a novel scenario-based multi-objective optimisation framework based on the augmented Chebychev goal programming (GP) technique linked to a value function for analysing a decision environment underlying energy choice among low-income households in isolated rural areas and informal urban settlements in South Africa. The framework developed includes a multi-objective optimisation technique that produced an approximation of a Pareto front linked to an a priori aggregation function and a value function to select the best alternatives. Second, the study used this model to demonstrate the benefits of applying the framework to a previously unknown subject in public policy: a dynamic multi-technology decision problem under uncertainty involving multiple stakeholders and conflicting objectives. The results obtained suggest that while it is cost-optimal to pursue electrification in conjunction with other short-term augmentation solutions to meet South Africa's universal electrification target, sustainable energy access rates among low-income households can be achieved by increasing the share of clean energy generation technologies in the energy mix. This study, therefore, challenges the South African government's position on pro-poor energy policies and an emphasis on grid-based electrification to increase energy access. Instead, the study calls for a portfolio-based intervention. The study advances interventions based on micro-grid electrification made up of solar photovoltaics (PV), solar with storage, combined cycle gas turbine (CCGT) and wind technologies combined with either bioethanol fuel or liquid petroleum gas (LPG). The study has demonstrated that the framework developed can benefit public sector decision-makers in providing a balanced regime of technical, financial, social, environmental, public health, political and economic aspects in the decision-making process for planning energy supply interventions for low-income households. The framework can be adapted to a wide range of energy access combinatorial problems and in countries grappling with similar energy access challenges.

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