Nowadays in the fifth generation (5G) communication systems, millimeter wave (mmWave) has aroused interest to not only industrial use but also network operators due to the massive amount of bandwidth available at mmWave frequencies. Initial access in cellular systems is an essential procedure in which new mobile user equipment (UE) establishes a connection with a base station (BS). However, mmWave relies on highly directional beamforming (BF) to overcome its severe path loss, while the initial access requires a wide beam to obtain sufficient information for beamforming. So the challenge is to handle the balance between highly directional mmWave and fast and reliable initial access. The high path loss of millimetre wave transmission dictates that multiple BSs may be closer and interfere more with each other. We focus our study on two BS parameters under the random search method. In our study, the beamwidth can be different for each BS, but a uniform number of slot limits needs to be chosen for all BSs. Our objective is to obtain the best parameters for each BS in a reasonable period of time. We build a systemlevel simulation in MATLAB and explored a variety of methods to select the best parameters, including reinforcement learning, supervised learning, and genetic algorithms. It is identified that the main challenge of applying reinforcement learning and supervised learning is the exponentially growing variety of BS parameters. A genetic algorithm is able to derive approximate best values in complex relational species. Therefore the genetic algorithm is considered to be able to be applied in scenarios with a high number of BSs. The result shows that reinforcement learning has great performance in a few BS cases, and the genetic algorithm is able to provide a large improvement over most of the BS methods with the same parameters. / I den femte generationens kommunikationssystem har millimetervågor väckt intresse, inte bara inom industrin utan även hos nätverksoperatörer, på grund av den enorma bandbredd som finns tillgänglig vid mmWave-frekvenser. Initial access i cellulära system är ett viktigt förfarande där ny mobil användarutrustning upprättar en anslutning till en basstation. mmWave är dock beroende av starkt riktad strålformning för att övervinna den allvarliga vägförlusten, medan den inledande åtkomsten kräver en bred stråle för att få tillräcklig information för strålformning. Utmaningen består alltså i att hantera balansen mellan mycket riktgivande mmWave och snabb och tillförlitlig initial access. Den höga vägförlusten för millimetervågsöverföring innebär att flera stationära basstationer kan vara närmare varandra och störa varandra mer. Vi fokuserar vår studie på två parametrar för BS med hjälp av metoden för slumpmässig sökning. I vår studie kan strålbredden vara olika för varje BS, men ett enhetligt antal slotgränser måste väljas för alla BS. Vårt mål är att få fram debästa parametrarna för varje BS på en rimlig tidsperiod. Vi bygger upp en simulering på systemnivå i MATLAB och utforskade en rad olika metoder för att välja de bästa parametrarna, bland annat förstärkningsinlärning, övervakad inlärning och genetiska algoritmer. Det konstateras att de största utmaningarna vid tillämpning av förstärkningsinlärning och övervakad inlärning är det exponentiellt växande utbudet av parametrar för BS. Genetisk algoritm kan härleda ungefärliga bästa värden i komplexa relationella arter. Därför anses den genetiska algoritmen kunna tillämpas i scenarier med ett stort antal BSs. Resultatet visar att förstärkningsinlärning har stor prestanda i ett fåtal BS-fall och att genetisk algoritm kan ge en stor förbättring jämfört med de flesta BS-metoder med samma parametrar.

Approved for public release; distribution is unlimited / An increasing number of unmanned vehicles (UV) are being incorporated into maritime operations as organic elements of Expeditionary and Carrier Strike Groups for development of the recognized maritime picture. This thesis develops an analytically-based planning aid for allocating UVs to missions. Inputs include the inventory of UVs, sensors, their performance parameters, and operational scenarios. Operations are broken into mission critical functions: detection, identification, and collection. The model output assigns aggregated packages of UVs and sensors to one of the three functions within named areas of interest. A spreadsheet model uses conservative time-speed-distance calculations, and simplified mathematical models from search theory and queuing theory, to calculate measures of performance for possible assignments of UVs to missions. The spreadsheet model generates a matrix as input to a linear integer program assignment model which finds the best assignment of UVs to missions based on the user inputs and simplified models. The results provide the mission planner with quantitatively-based recommendations for unmanned vehicle mission tasking in challenging scenarios. / Lieutenant, United States Navy

This thesis presents a study of impedance optimisation of active microstrip patch antennas to multiple frequency points. A single layered aperture coupled microstrip patch antenna has been optimised to match the source reflection coefficient of a transistor in designing an active antenna. The active aperture coupled microstrip patch antenna was optimised to satisfy Global Positioning System (GPS) frequency specifications. A rudimentary aperture coupled microstrip patch antenna consists of a rectangular antenna element etched on the top surface of two dielectric substrates. The substrates are separated by a ground plane and a microstrip feed is etched on the bottom surface. A rectangular aperture in the ground plane provides coupling between the feed and the antenna element. This type of antenna, which conveniently isolates any circuit at the feed from the antenna element, is suitable for integrated circuit design and is simple to fabricate. An active antenna design directly couples an antenna to an active device, therefore saving real estate and power. This thesis focuses on designing an aperture coupled patch antenna directly coupled to a low noise amplifier as part of the front end of a GPS receiver. In this work an in-house software package, dubbed ACP by its creator Dr Rod Waterhouse, for calculating aperture coupled microstrip patch antenna performance parameters was linked to HP-EEsof, a microwave computer aided design and simulation package by Hewlett-Packard. An ANSI C module in HP-EEsof was written to bind the two packages. This process affords the client the benefit of powerful analysis tools offered in HP-EEsof and the fast analysis of ACP for seamless system design. Moreover, the optimisation algorithms in HP-EEsof were employed to investigate which algorithms are best suited for optimising patch antennas. The active antenna design presented in this study evades an input matching network, which is accomplished by designing the antenna to represent the desired source termination of a transistor. It has been demonstrated that a dual-band microstrip patch antenna can be successfully designed to match the source reflection coefficient, avoiding the need to insert a matching network. Maximum power transfer in electrical circuits is accomplished by matching the impedance between entities, which is generally acheived with the use of a matching network. Passive matching networks employed in amplifier design generally consist of discrete components up to the low GHz frequency range or distributed elements at greater frequencies. The source termination for a low noise amplifier will greatly influence its noise, gain and linearity which is controlled by designing a suitable input matching network. Ten diverse search methods offered in HP-EEsof were used to optimise an active aperture coupled microstrip patch antenna. This study has shown that the algorithms based on the randomised search techniques and the Genetic algorithm provide the most robust performance. The optimisation results were used to design an active dual-band antenna.

Economic dispatch is an important optimization challenge in power systems. It helps to find the optimal output power of a number of generating units that satisfy the system load demand at the cheapest cost, considering equality and inequality constraints. Many nature inspired algorithms have been broadly applied to tackle it such as particle swarm optimization. In this dissertation, two improved particle swarm optimization techniques are proposed to solve economic dispatch problems. The first is a hybrid technique with Bat algorithm. Particle swarm optimization as the main optimizer integrates bat algorithm in order to boost its velocity and to adjust the improved solution. The second proposed approach is based on Cuckoo operations. Cuckoo search algorithm is a robust and powerful technique to solve optimization problems. The study investigates the effect of levy flight and random search operation in Cuckoo search in order to ameliorate the performance of the particle swarm optimization algorithm. The two improved particle swarm algorithms are firstly tested on a range of 10 standard benchmark functions and then applied to five different cases of economic dispatch problems comprising 6, 13, 15, 40 and 140 generating units. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

Este trabalho tem como objetivo apresentar soluções de sistemas não-lineares com raízes múltiplas, através de um algoritmo híbrido. Para esta finalidade foi desenvolvido e implementado um algoritmo de busca aleatória baseado no método proposto por Luus e Jaakola (1973) como etapa de busca aleatória dos pontos iniciais, que são refinados através do algoritmo de Hooke e Jeeves. O diferencial deste trabalho foi propor um algoritmo híbrido, utilizando as características dos algoritmos Luus-Jaakola e Hooke e Jeeves como etapas de busca e refinamento respectivamente. Para isso, os algoritmos acima são encapsulados em funções no algoritmo híbrido. Além destas duas etapas, o algoritmo híbrido possui duas outras características importantes, que é a execução repetida até que se alcance um número suficiente de soluções distintas, que são então submetidas a um processo de classificação de soluções por intervalo, onde cada intervalo gera um conjunto de soluções próximas, que por sua vez, são submetidas à etapa final de minimização, resultando em apenas um valor de solução por classe. Desta forma cada classe produz uma única solução, que faz parte do conjunto final de soluções do problema, pois este algoritmo é aplicado a problemas com múltiplas soluções. Então, o algoritmo híbrido desenvolvido foi testado, tendo como padrão, vários problemas clássicos de programação não-linear, em especial os problemas irrestritos com múltiplas soluções. Após os testes, os resultados foram comparados com o algoritmo Luus-Jaakola, e o Método de Newton Intervalar / Bisseção Generalizada (IN/GB - Interval Newton/Generalized Bisection), com a finalidade de se obter uma análise quantitativa e qualitativa de seu desempenho. Por fim comprovou-se que o algortimo Híbrido obteve resultados superiores quando comparados com os demais. / This paper aims to present solutions for nonlinear systems with multiple roots, using a hybrid algorithm. For this purpose was developed and implemented an algorithm based on random search method proposed by Luus and Jaakola (1973) as a step in search of random starting points, which will be refined through the algorithm of Hooke and Jeeves. The differential of this work is to propose a hybrid algorithm, using the characteristics of the Luus-Jaakola algorithm and Hooke and Jeeves as a search and refinement stages respectively. For this, the above algorithms are encapsulated in functions in the hybrid algorithm. Besides these two steps, the hybrid algorithm has two other important characteristics, which is the execution repeated until to reach a sufficient number of distinct solutions, which is then undergo a process of classification of solutions by interval, where each interval generates a set solutions to close, which in turn is subject to the final stage of minimization, resulting in only one value per class of solution. Thus each class provides a unique solution, which is part of the final set of solutions of the problem, because this algorithm is applied to problems with multiple solutions. So, the hybrid algorithm developed was tested, with the standard, several problems of classical non-linear programming, in particular the unrestricted problems with multiple solutions. After the tests, the results were compared with algorithm Luus-Jaakola, and the Interval Newton/Generalized Bisection method (IN/GB), in order to obtain a quantitative and qualitative analysis of their performance. Finally it was found that the hybrid algortimo achieved higher when compared to the others.

Este trabalho tem como objetivo apresentar soluções de sistemas não-lineares com raízes múltiplas, através de um algoritmo híbrido. Para esta finalidade foi desenvolvido e implementado um algoritmo de busca aleatória baseado no método proposto por Luus e Jaakola (1973) como etapa de busca aleatória dos pontos iniciais, que são refinados através do algoritmo de Hooke e Jeeves. O diferencial deste trabalho foi propor um algoritmo híbrido, utilizando as características dos algoritmos Luus-Jaakola e Hooke e Jeeves como etapas de busca e refinamento respectivamente. Para isso, os algoritmos acima são encapsulados em funções no algoritmo híbrido. Além destas duas etapas, o algoritmo híbrido possui duas outras características importantes, que é a execução repetida até que se alcance um número suficiente de soluções distintas, que são então submetidas a um processo de classificação de soluções por intervalo, onde cada intervalo gera um conjunto de soluções próximas, que por sua vez, são submetidas à etapa final de minimização, resultando em apenas um valor de solução por classe. Desta forma cada classe produz uma única solução, que faz parte do conjunto final de soluções do problema, pois este algoritmo é aplicado a problemas com múltiplas soluções. Então, o algoritmo híbrido desenvolvido foi testado, tendo como padrão, vários problemas clássicos de programação não-linear, em especial os problemas irrestritos com múltiplas soluções. Após os testes, os resultados foram comparados com o algoritmo Luus-Jaakola, e o Método de Newton Intervalar / Bisseção Generalizada (IN/GB - Interval Newton/Generalized Bisection), com a finalidade de se obter uma análise quantitativa e qualitativa de seu desempenho. Por fim comprovou-se que o algortimo Híbrido obteve resultados superiores quando comparados com os demais. / This paper aims to present solutions for nonlinear systems with multiple roots, using a hybrid algorithm. For this purpose was developed and implemented an algorithm based on random search method proposed by Luus and Jaakola (1973) as a step in search of random starting points, which will be refined through the algorithm of Hooke and Jeeves. The differential of this work is to propose a hybrid algorithm, using the characteristics of the Luus-Jaakola algorithm and Hooke and Jeeves as a search and refinement stages respectively. For this, the above algorithms are encapsulated in functions in the hybrid algorithm. Besides these two steps, the hybrid algorithm has two other important characteristics, which is the execution repeated until to reach a sufficient number of distinct solutions, which is then undergo a process of classification of solutions by interval, where each interval generates a set solutions to close, which in turn is subject to the final stage of minimization, resulting in only one value per class of solution. Thus each class provides a unique solution, which is part of the final set of solutions of the problem, because this algorithm is applied to problems with multiple solutions. So, the hybrid algorithm developed was tested, with the standard, several problems of classical non-linear programming, in particular the unrestricted problems with multiple solutions. After the tests, the results were compared with algorithm Luus-Jaakola, and the Interval Newton/Generalized Bisection method (IN/GB), in order to obtain a quantitative and qualitative analysis of their performance. Finally it was found that the hybrid algortimo achieved higher when compared to the others.

The aim of this master thesis is to study algorithms for automatically tuning antenna parameters to improve the performance of the radio access part of a telecommunication network and user experience. There are four dierent optimization algorithms, Stepwise Minimization Algorithm, Random Search Algorithm, Modied Steepest Descent Algorithm and Multi-Objective Genetic Algorithm to be applied to a model of a radio access network. The performances of all algorithms will be evaluated in this thesis. Moreover, a graphical user interface which is developed to facilitate the antenna tuning simulations will also be presented in the appendix of the report.

The Master Thesis deals with logistic module innovation of information system ERP. The principle of innovation is based on implementation of heuristic algorithms which solve Travel Salesman Problems (TSP). The software MATLAB is used for analysis and tests of these algorithms. The goal of Master Thesis is the comparison of selections algorithm, which are suitable for economic purposes (accuracy of solution, speed of calculation and memory demands).

This diploma thesis deals with evolutionary algorithms used for travelling salesman problem (TSP). In the first section, there are theoretical foundations of a graph theory and computational complexity theory. Next section contains a description of chosen optimization algorithms. The aim of the diploma thesis is to implement an application that solve TSP using evolutionary algorithms.

This project is about security of computer networks using Intrusion Detection Systems. IDS contain rules for detection expressed with regular expressions, which are for detection represented by finite-state automata. The complexity of this detection with non-deterministic and deterministic finite-state automata is explained. This complexity can be reduced with help of regular expressions grouping. Grouping algorithm and approaches for speedup and improvement are introduced. One of the approches is Genetic algorithm, which can work real-time. Finally Random search algorithm for grouping of regular expressions is presented. Experiment results with these approches are shown and compared between each other.