This thesis deals with methods for optimization in image processing. There is described some of optimization techniques and some applications in image processing. There is also described detailed procedure and realization of bee algorithm, genetic algorithm, PSO algorithm and their realization in image registration, matched filtering, image segmentation and image reconstruction. Algorithms and their efficiencies are compared in the particular application for image processing.

In the literature, various Mobile Ad hoc NETwork (MANET) routing protocols proposed. Each performs the best under specific context conditions, for example under high mobility or less volatile topologies. In existing MANET, the degradation in the routing protocol performance is always associated with changes in the network context. To date, no MANET routing protocol is able to produce optimal performance under all possible conditions. The core aim of this thesis is to solve the routing problem in mobile Ad hoc networks by introducing an optimum system that is in charge of the selection of the running routing protocol at all times, the system proposed in this thesis aims to address the degradation mentioned above. This optimisation system is a novel approach that can cope with the network performance’s degradation problem by switching to other routing protocol. The optimisation system proposed for MANET in this thesis adaptively selects the best routing protocol using an Artificial Intelligence mechanism according to the network context. In this thesis, MANET modelling helps in understanding the network performance through different contexts, as well as the models’ support to the optimisation system. Therefore, one of the main contributions of this thesis is the utilisation and comparison of various modelling techniques to create representative MANET performance models. Moreover, the proposed system uses an optimisation method to select the optimal communication routing protocol for the network context. Therefore, to build the proposed system, different optimisation techniques were utilised and compared to identify the best optimisation technique for the MANET intelligent system, which is also an important contribution of this thesis. The parameters selected to describe the network context were the network size and average mobility. The proposed system then functions by varying the routing mechanism with the time to keep the network performance at the best level. The selected protocol has been shown to produce a combination of: higher throughput, lower delay, fewer retransmission attempts, less data drop, and lower load, and was thus chosen on this basis. Validation test results indicate that the identified protocol can achieve both a better network performance quality than other routing protocols and a minimum cost function of 4.4%. The Ad hoc On Demand Distance Vector (AODV) protocol comes in second with a cost minimisation function of 27.5%, and the Optimised Link State Routing (OLSR) algorithm comes in third with a cost minimisation function of 29.8%. Finally, The Dynamic Source Routing (DSR) algorithm comes in last with a cost minimisation function of 38.3%.

Submitted by Edisangela Bastos (edisangela@ufpa.br) on 2014-01-13T19:54:55Z No. of bitstreams: 2 license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) Dissertacao_ImputacaoDadosBaseado.pdf: 1208259 bytes, checksum: 2e7b9d1f0b1637d5e64621ecdbc0f82f (MD5) / Approved for entry into archive by Ana Rosa Silva(arosa@ufpa.br) on 2014-01-17T14:29:14Z (GMT) No. of bitstreams: 2 license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) Dissertacao_ImputacaoDadosBaseado.pdf: 1208259 bytes, checksum: 2e7b9d1f0b1637d5e64621ecdbc0f82f (MD5) / Made available in DSpace on 2014-01-17T14:29:14Z (GMT). No. of bitstreams: 2 license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) Dissertacao_ImputacaoDadosBaseado.pdf: 1208259 bytes, checksum: 2e7b9d1f0b1637d5e64621ecdbc0f82f (MD5) Previous issue date: 2013 / Durante o processo de extração do conhecimento em bases de dados, alguns problemas podem ser encontrados como por exemplo, a ausência de determinada instância de um atributo. A ocorrência de tal problemática pode causar efeitos danosos nos resultados finais do processo, pois afeta diretamente a qualidade dos dados a ser submetido a um algoritmo de aprendizado de máquina. Na literatura, diversas propostas são apresentadas a fim de contornar tal dano, dentre eles está a de imputação de dados, a qual estima um valor plausível para substituir o ausente. Seguindo essa área de solução para o problema de valores ausentes, diversos trabalhos foram analisados e algumas observações foram realizadas como, a pouca utilização de bases sintéticas que simulem os principais mecanismos de ausência de dados e uma recente tendência a utilização de algoritmos bio-inspirados como tratamento do problema. Com base nesse cenário, esta dissertação apresenta um método de imputação de dados baseado em otimização por enxame de partículas, pouco explorado na área, e o aplica para o tratamento de bases sinteticamente geradas, as quais consideram os principais mecanismos de ausência de dados, MAR, MCAR e NMAR. Os resultados obtidos ao comprar diferentes configurações do método à outros dois conhecidos na área (KNNImpute e SVMImpute) são promissores para sua utilização na área de tratamento de valores ausentes uma vez que alcançou os melhores valores na maioria dos experimentos realizados. / During the knowledge discovery in database process some problems may be found, e.g. some instance of one attribute may be missing. Such issue can even cause harmful effects to the final results of the process, since directly affects the data quality of a database which some machine learning algorithm may be applied to. In the literature are some proposals to solve such harm; among them is the data imputation process that estimates a plausible value to fill in the missing one. Inside the area of missing value treatment, some researches were analyzed and observations were raised such as, a few utilization of synthetic datasets that simulates the main mechanisms of missingness and a tendency to use bioinspired algorithm to treat the missing values. From this scenario, the present dissertation analyses an imputation method based on particle swarm optimization, an underexplored one, and applies it to the treatment of synthetics datasets generated considering the main mechanisms of missingness, MAR, MCAR and NMAR. The results obtained when comparing the algorithm against different configurations of itself and another two treatments known in the area (KNNImpute and SVMImpute) are promising for its use as missing value treatment whereas the bioinspired method reached the bests values for the major of the experiments.

Artificial intelligence techniques have been widely used for prediction in various areas of sciences and engineering. In the thesis, applications of AI techniques are studied to predict the dilution of industrial outfall discharges. The discharge of industrial effluents from the outfall systems is broadly divided into two categories on the basis of density. The effluent with density higher than the water receiving will sink and called as negatively buoyant jet. The effluent with density lower than the receiving water will rise and called as positively buoyant jet. The effluent discharge in the water body creates major environmental threats. In this work, negatively buoyant jet is considered. For the study, ANFIS model is taken into consideration and incorporated with algorithms such as GA, PSO and FFA to determine the suitable model for the discharge prediction. The training and test dataset for the ANFIS-type models are obtained by simulating the jet using the realizable k-ε turbulence model over a wide range of Froude numbers i.e. from 5 to 60 and discharge angles from 20 to 72.5 degrees employing OpenFOAM platform. Froude number and angles are taken as input parameters for the ANFIS-type models. The output parameters were peak salinity (Sm), return salinity (Sr), return point in x direction (xr) and peak salinity coordinates in x and y directions (xm and ym). Multivariate regression analysis has also been done to verify the linearity of the data using the same input and output parameters. To evaluate the performance of ANFIS, ANFIS-GA, ANFIS-PSO, ANFIS-FFA and multivariate regression model, some statistical parameters such as coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE) and average absolute deviation in percentage are determined. It has been observed that ANFIS-PSO is better in predicting the discharge characteristics.

The great innovations of the last century have ushered continuous progress in many areas of technology, especially in the form of miniaturization of electronic circuits. This progress shows a trend towards consistent decreases in power requirements due to miniaturization. According to the ITRS and industry leaders, such as Intel, the challenge of managing and providing power efficiency still persist as scaling down of devices continues. A variety of power sources can be used in order to provide power to low power applications. Few of these sources have favorable characteristics and can be designed to deliver maximum power such as the novel mini notched turbine used as a source in this work. The MiNT is a novel device that can be used as a feasible energy source when integrated into a system and evaluated for power delivery as investigated in this work. As part of this system, a maximum power point tracking system provides an applicable solution for capturing enhanced power delivery for an energy harvesting system. However, power efficiency and physical size are adversely affected by the characteristics and environment of many energy harvesting systems and must also be addressed. To address these issues, an analysis of mini notched turbine, a RF rectenna, and an enhanced maximum power point tracking system is presented and verified using simulations and measurements. Furthermore, mini notched energy harvesting system, RF rectenna energy harvesting system, and enhanced maximum power point tracking system are developed and experimental data analyzed. The enhanced maximum power point tracking system uses a resistor emulation technique and particle swarm optimization (PSO) to improve the power efficiency and reduce the physical size. This new innovative design improves the efficiency of optimized power management circuitry up to 7% compared to conventional power management circuits over a wide range of input power and range of emulated resistances, allowing more power to be harvested from small energy harvesting sources and delivering it to the load such as smart sensors. In addition, this is the first IC design to be implemented and tested for the patented mini notched turbine (MiNT) energy harvesting device. Another advantage of the enhanced power management system designed in this work is that the proposed approach can be utilized for extremely small energy sources and because of that the proposed work is valid for low emulated resistances. and systems with low load resistance Overall, through the successful completion of this work, various energy harvesting systems can have the ability to provide enhanced power management as the IC industry continues to progress toward miniaturization of devices and systems.

A Wireless Sensor Network (WSN) is a group of spatially dispersed sensors that monitor the physical conditions of the environment and collect data at a central location. Sensor deployment is one of the main design aspects of WSNs as this a ffects network coverage. In general, WSN deployment methods fall into two categories: planned deployment and random deployment. This thesis considers planned sensor deployment of a Mobile Wireless Sensor Network (MWSN), which is defined as selectively deciding the locations of the mobile sensors under the given constraints to optimize the coverage of the network. Metaheuristic algorithms are powerful tools for the modeling and optimization of problems. The Krill Herd Algorithm (KHA) is a new nature-inspired metaheuristic algorithm which can be used to solve the sensor deployment problem. A Multi-Agent System (MAS) is a system that contains multiple interacting agents. These agents are autonomous entities that interact with their environment and direct their activity towards achieving speci c goals. Agents can also learn or use their knowledge to accomplish a mission. Multi-agent systems can solve problems that are very difficult or even impossible for monolithic systems to solve. In this work, a modification of KHA is proposed which incorporates MAS to obtain a Multi-Agent Krill Herd Algorithm (MA-KHA). To test the performance of the proposed method, five benchmark global optimization problems are used. Numerical results are presented which show that MA-KHA performs better than the KHA by finding better solutions. The proposed MA-KHA is also employed to solve the sensor deployment problem. Simulation results are presented which indicate that the agent-agent interactions in MA-KHA improves the WSN coverage in comparison with Particle Swarm Optimization (PSO), the Firefly Algorithm (FA), and the KHA. / Graduate

This dissertation focuses on daily volt/var control in distribution networks with feeder capacitors, substation capacitors and transformers equipped with on-load tap changers. A hybrid approach is proposed to solve the daily volt/var control problem. To reduce the computational requirements of the problem, this approach combines two methods, namely heuristic and optimal scheduling for the substation and feeder sub-problems respectively. The feeder capacitor dispatch schedule is determined based on a heuristic reactive power setpoint method. At this stage the objective is to minimize the reactive power flow through the substation bus in every time-interval. And as such, mathematical modeling of the distribution network components is adapted to suit time-varying conditions. Furthermore, an optimization model to determine a proper dispatch schedule of the substation devices is formulated. The objective of this model is to minimize the daily total energy loss and voltage deviations. Additionally, the reference voltage of the substation secondary bus and the transformer tap position limits are modified to adapt to given load profiles. The optimization model is solved with a discrete particle swarm optimization algorithm, which incorporates Newton’s method to determine the power-flow solution. The proposed method is applied to a time-varying distribution system and evaluated under different operational scenarios. It is also compared to on-line volt/var control with various settings. Simulation results show that the proposed approach minimizes both the voltage deviations and the total energy loss, while on-line control prioritizes one objective over the other depending on the specified settings. / Dissertation (MEng)--University of Pretoria, 2015. / Electrical, Electronic and Computer Engineering / Unrestricted

Même si les anticorps sont plus connus pour leur capacité à neutraliser les agents infectieux en se liant aux antigènes via leurs paratopes, leurs fragments cristallisables (Fc) sont aussi impliqués dans la signalisation des réponses immunitaires en se liant à des récepteurs spécifiques. Les interactions entre les récepteurs et les anticorps sont reconnues pour être affectées par la glycosylation des anticorps. Pour observer la cinétique de telles interactions biologiques, une des méthodes les plus utilisées est la spectroscopie de résonance des plasmons de surface (SPR). La synthèse et l’analyse, via SPR, d’anticorps en laboratoire sont un procédé long et exigeant si toutes les étapes sont réalisées manuellement. La possibilité d’effectuer le couplage d’un appareil SPR commercial à un bioréacteur pourrait être envisagée, mais le coût d’achat et d’opération d’un tel appareil SPR limiterait l’utilité d’un tel projet pour une possible utilisation à plus grande échelle. C’est pourquoi le couplage d’un appareil SPR de faible coût et d’un bioréacteur serait avantageux. Cela permettrait de superviser la synthèse des anticorps et leur affinité au récepteur en temps ‘’réel’’. Ce mémoire de maîtrise explorera le développement de deux des composantes nécessaires pour la réalisation de ce couplage entre le SPR et un bioréacteur, soit la chimie de surface pour passiver le capteur SPR et un algorithme d’optimisation par nuée de particules (Particles swarm Optimisation) évolutive. L’algorithme réalisera une corrélation du signal obtenue d’un instrument P4-SPR aux équations cinétiques décrivant les interactions entre les anticorps et leurs récepteurs dans le but d’obtenir les constantes cinétiques et thermodynamiques (Kd,kon,koff). De plus, ce mémoire présentera une étude qui a été réalisée afin de minimiser l’adsorption non spécifique des molécules composant le biocapteur et maximiser le signal de l’anticorps Trastuzumab (TZM), utilisé dans le couplage de l’instrument P4-SPR au bioréacteur, sur des monocouches de composition variée. / While antibodies are best known to help the neutralization of pathogens by binding to the antigens with their paratope, their crystallizable fragment region (Fc region) is also used to trigger immune response by binding to specific receptors. Interactions between receptors and antibodies are known to be affected by the glycosylation the antibodies. To observe the kinetic of those interactions, one of the favored method is surface plasmon resonance (SPR). However, a substantial time may have elapsed between synthesis of a modified antibody and its test in a SPR apparatus as the two are not coupled and oftentimes in different laboratories. The coupling of a SPR and a bioreactor would accelerate the process, but using a commercial instrument would limit it usefulness due to the high price and high cost of use of these SPR instruments. This is why the coupling of a low-cost SPR to a bioreactor is of interesting in the context of glycosylated antibody production. This could permit to monitor the synthesis of the antibody and it affinity to the target receptor in near real time. This masters’ thesis will show the development of two of the essential components, consisting in the surface chemistry to passivate the SPR chip and an algorithm using an evolving PSO (Particles Swarm Optimisation), to estimate kinetic and thermodynamics constants (Kd,kon,koff) by correlating the signal obtained of a P4-SPR instrument to the kinetic and thermodynamics equations describing the interactions between antibodies and their receptors. The thesis also presents the results of the tests while trying to minimize nonspecific adsorption of the molecules used for the biosensor on multiple self-assembled monolayers (SAM) and maximize signal of the antibody named Trastuzumab (TZM) and used in the coupling of the P4-SPR to the bioreactor.

The subjects of perfect information games, machine learning and computational intelligence combine in an experiment that investigates a method to build the skill of a game-playing agent from zero game knowledge. The skill of a playing agent is determined by two aspects, the first is the quantity and quality of the knowledge it uses and the second aspect is its search capacity. This thesis introduces a novel representation language that combines symbols and numeric elements to capture game knowledge. Insofar search is concerned; an extension to an existing knowledge-based search method is developed. Empirical tests show an improvement over alpha-beta, especially in learning conditions where the knowledge may be weak. Current machine learning techniques as applied to game agents is reviewed. From these techniques a learning framework is established. The data-mining algorithm, ID3, and the computational intelligence technique, Particle Swarm Optimisation (PSO), form the key learning components of this framework. The classification trees produced by ID3 are subjected to new post-pruning processes specifically defined for the mentioned representation language. Different combinations of these pruning processes are tested and a dominant combination is chosen for use in the learning framework. As an extension to PSO, tournaments are introduced as a relative fitness function. A variety of alternative tournament methods are described and some experiments are conducted to evaluate these. The final design decisions are incorporated into the learning frame-work configuration, and learning experiments are conducted on Checkers and some variations of Checkers. These experiments show that learning has occurred, but also highlights the need for further development and experimentation. Some ideas in this regard conclude the thesis. / Dissertation (MSc)--University of Pretoria, 2007. / Computer Science / MSc / Unrestricted

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