Orientador: Niederauer Mastelari / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-24T18:11:18Z (GMT). No. of bitstreams: 1 Cardoso_RaulMariano_M.pdf: 9927638 bytes, checksum: f9633c7839661e2f914d88b7d438913e (MD5) Previous issue date: 2014 / Resumo: Os computadores como conhecemos não são mais a maioria de dispositivos que se comunicam utilizando a Internet. Há alguns anos uma tendência pode ser observada neste sentido: "coisas" recebem pequenos computadores, e são representadas virtualmente, gerando informações de valor aos seus usuários. Esta nova fase de desenvolvimento da sociedade da informação é conhecida como "Internet das Coisas" e tem afetado tanto a relação das pessoas com a vida cotidiana, quanto os modelos de negócio correntes. Este trabalho mostra como esta tendência tecnológica, inserida no contexto dos Sistemas de Transportes, pode gerar aplicações de grande relevância. Com este objetivo, metodologias que pudessem embasar o projeto foram buscadas. Um modelo arquitetural de referência foi utilizado para embasar o projeto e design de um sistema distribuído focado em aplicações específicas. A tecnologia RFID foi utilizada, com leitores e etiquetas eletrônicas, para projetar um arranjo de dispositivos que identifique os veículos e detecte sua presença nas entradas e saídas de um local restrito de interesse. Microcontroladores de baixo custo, de código aberto, tanto em hardware quanto em software, e com capacidade de se comunicar através da Internet também foram definidos por este projeto. Localmente, estes dispositivos filtram informações, mantêm bases de dados reduzidas, monitoram sensores e controlam atuadores. Para gerenciar os dispositivos desta rede, é proposta a utilização de serviços na rede, estabelecendo assim a possibilidade de uma arquitetura em nuvem. Este servidor, por sua vez, deve coordenar os dispositivos distribuídos, obtendo informações da rede que devem sincronizar os bancos de dados e também prover informações de estado dos controladores. O foco principal é o estabelecimento de uma arquitetura e infraestrutura que possam suportar as aplicações propostas. Um estudo de caso foi realizado considerando o campus da Universidade Estadual de Campinas. A primeira aplicação viável de um projeto como este é o controle de acesso veicular ao campus universitário, através da implantação de leitores RFID nas portarias, em conjunto com controladores, cancelas automáticas e sensores de presença. Isso motivou o desenvolvimento de uma prova de conceito. A instrumentação das portarias permite que os veículos sejam automaticamente identificados quando adentram o espaço restrito do campus, gerando informações de valor para outras aplicações possíveis. Dentre as aplicações previstas estão: controle de estacionamento privativo automático, sinalização interativa através de placas eletrônicas, informação de horários de transporte coletivo e elaboração de relatórios periódicos sobre o trânsito. Para que todas estas aplicações sejam disponibilizadas a comunidade, serão necessários trabalhos futuros de pesquisa e desenvolvimento / Abstract: Computers as we know are no longer the majority of devices that communicate using the Internet. A few ago years a trend can be observed in this direction: "things" get small computers, and are represented virtually generating information of value to its users. This new phase of development of the information society is known as the "Internet of Things" and has affected both people's relationship with everyday life, as the current business models. This work shows how this technological trend, inside the context of Transportation Systems, can generate highly relevant applications. With this objective, methodologies that could base the project were sought. An architectural reference model was used to support the project and design of a distributed system focused on specific applications. RFID technology was used with electronic tags and readers, to develop a devices arrangement that identifies vehicles and detect their presence in the inputs and outputs of a restricted location of interest. Low cost, open source and Internet ready devices were also described for work¿s interest. Locally, these devices filter information, maintain reduced data bases, monitor sensors and control actuators. To manage the devices was proposed the use of network services, establishing the possibility of cloud architecture. The server must coordinate the distributed devices, getting information from the network that must synchronize the databases and also provide status information of the controllers. The main focus is to establish an architecture and infrastructure that can support the proposed applications. A case study was designed aiming the campus of the State University of Campinas. The first viable implementation of such a project is the control of vehicular access to the campus, through the installation of RFID readers, controllers, automatic barriers and presence sensors. That result in a deployed proof of concept. The instrumentation of the campus access allows vehicles to be automatically identified when they enter the restricted space of the campus, generating valuable information for other possible applications. Among the intended applications include: automatic control of private parking, signaling through interactive electronic boards, information on public transportation schedules and reporting generation about transit. In order to all these applications are available for the community, needed further in research and development / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestre em Engenharia Mecânica

The iterative learning control (ILC) problem considers control tasks that perform a specific tracking command, and the command is to be performed is many times. The system returns to the same initial conditions on the desired trajectory for each repetition, also called run, or iteration. The learning law adjusts the command to a feedback system based on the error observed in the previous run, and aims to converge to zero-tracking error at sampled times as the iterations progress. The ILC problem is an inverse problem: it seeks to converge to that command that produces the desired output. Mathematically that command is given by the inverse of the transfer function of the feedback system, times the desired output. However, in many applications that unique command is often an unstable function of time. A discrete-time system, converted from a continuous-time system fed by a zero-order hold, often has non-minimum phase zeros which become unstable poles in the inverse problem. An inverse discrete-time system will have at least one unstable pole, if the pole-zero excess of the original continuous-time counterpart is equal to or larger than three, and the sample rate is fast enough. The corresponding difference equation has roots larger than one, and the homogeneous solution has components that are the values of these poles to the power of k, with k being the time step. This creates an unstable command growing in magnitude with time step. If the ILC law aims at zero-tracking error for such systems, the command produced by the ILC iterations will ask for a command input that grows exponentially in magnitude with each time step. This thesis examines several ways to circumvent this difficulty, designing filters that prevent the growth in ILC. The sister field of ILC, repetitive control (RC), aims at zero-error at sample times when tracking a periodic command or eliminating a periodic disturbance of known period, or both. Instead of learning from a previous run always starting from the same initial condition, RC learns from the error in the previous period of the periodic command or disturbance. Unlike ILC, the system in RC eventually enters into steady state as time progresses. As a result, one can use frequency response thinking. In ILC, the frequency thinking is not applicable since the output of the system has transients for every run. RC is also an inverse problem and the periodic command to the system converges to the inverse of the system times the desired output. Because what RC needs is zero error after reaching steady state, one can aim to invert the steady state frequency response of the system instead of the system transfer function in order to have a stable solution to the inverse problem. This can be accomplished by designing a Finite Impulse Response (FIR) filter that mimics the steady state frequency response, and which can be used in real time. This dissertation discusses how the digital feedback control system configuration affects the locations of sampling zeros and discusses the effectiveness of RC design methods for these possible sampling zeros. The sampling zeros are zeros introduced by the discretization process from continuous-time system to the discrete-time system. In the RC problem, the feedback control system can have sampling zeros outside the unit circle, and they are challenges for the RC law design. Previous research concentrated on the situation where the sampling zeros of the feedback control system come from a zero-order hold on the input of a continuous-time feedback system, and studied the influence of these zeros including the influence of these sampling zeros as the sampling rate is changed from the asymptotic value of sample time interval approaching zero. Effective RC design methods are developed and tested based for this configuration. In the real world, the feedback control system may not be the continuous-time system. Here we investigate the possible sampling zero locations that can be encountered in digital control systems where the zero-order hold can be in various possible places in the control loop. We show that various new situations can occur. We discuss the sampling zeros location with different feedback system structures, and show that the RC design methods still work. Moreover, we compare the learning rates of different RC design methods and show that the RC design method based on a quadratic fit of the reciprocal of the steady state frequency response will have the desired learning rate features that balance the robustness with efficiency. This dissertation discusses the steady-state response filter of the finite-time signal used in ILC. The ILC problem is sensitive to model errors and unmodelled high frequency dynamics, thus it needs a zero-phase low-pass filter to cutoff learning for frequencies where there is too much model inaccuracy for convergence. But typical zero-phase low-pass filters, like Filtfilt used by MATLAB, gives the filtered results with transients that can destabilize ILC. The associated issues are examined from several points of view. First, the dissertation discusses use of a partial inverse of the feedback system as both learning gain matrix and a low-pass filter to address this problem The approach is used to make a partial system inverse for frequencies where the model is accurate, eliminating the robustness issue. The concept is used as a way to improve a feedback control system performance whose bandwidth is not as high as desired. When the feedback control system design is unable to achieve the desired bandwidth, the partial system inverse for frequency in a range above the bandwidth can boost the bandwidth. If needed ILC can be used to further correct response up to the new bandwidth. The dissertation then discusses Discrete Fourier Transform (DFT) based filters to cut off the learning at high frequencies where model uncertainty is too large for convergence. The concept of a low pass filter is based on steady state frequency response, but ILC is always a finite time problem. This forms a mismatch in the design process, and we seek to address this. A math proof is given showing the DFT based filters directly give the steady-state response of the filter for the finite-time signal which can eliminate the possibility of instability of ILC. However, such filters have problems of frequency leakage and Gibbs phenomenon in applications, produced by the difference between the signal being filtered at the start time and at the final time, This difference applies to the signal filtered for nearly all iterations in ILC. This dissertation discusses the use of single reflection that produced a signal that has the start time and end times matching and then using the original signal portion of the result. In addition, a double reflection of the signal is studied that aims not only to eliminate the discontinuity that produces Gibbs, but also aims to have continuity of the first derivative. It applies a specific kind of double reflection. It is shown mathematically that the two reflection methods reduce the Gibbs phenomenon. A criterion is given to determine when one should consider using such reflection methods on any signal. The numerical simulations demonstrate the benefits of these reflection methods in reducing the tracking error of the system.

This paper presents a theoretical development of adaptive cruise control models and platoon operation logic for Automated Highway Systems in the Advanced Vehicle Control Systems (AVeS). Three control modes, constant speed, emergency and vehicle-following, are defined based on the minimum safe stopping distance, and applied to the platoon operations. Desired acceleration model is built for the different cruise control mode by considering the relative velocity, the difference between the relative distance and desired spacing, and the acceleration of the preceding vehicle. A control system model is proposed based on the analysis of vehicle dynamics. The contribution of uncontrolled forces from the air, slop and friction to the vehicle acceleration is considered. Application of control models for two successive vehicles is simulated under the situations of speed transition and emergency stopping. Proper control parameters are determined for different operation mode subject to the conditions: collision avoidance and stability. Same criteria are utilized to the platoon simulation in which the operation logic is regulated so that the platoon leader is operated under either emergency mode or constant speed mode depending upon the . distance from the downstream vehicle, while the intraplatoon vehicles are forced to operate under vehicle-following mode. Three cases under speed transition, emergency stopping and platoon leader splitting are simulated to determine the stable control parameters. Lane capacity analysis shows the tradeoff between safety and efficiency for platoon. modal operations on freeway with guideline or automated highway. / Master of Science

Thesis (PhD)--University of Stellenbosch, 2004. / 271 Leaves printed single pages, preliminary pages i-xviii and 253 numberd pages. Includes bibliography. List of figures, List of tables. / ENGLISH ABSTRACT: A Neurocontrol Paradigm for Intelligent Process Control using Evolutionary Reinforcement Learning Balancing multiple business and operational objectives within a comprehensive control strategy is a complex configuration task. Non-linearities and complex multiple process interactions combine as formidable cause-effect interrelationships. A clear understanding of these relationships is often instrumental to meeting the process control objectives. However, such control system configurations are generally conceived in a qualitative manner and with pronounced reliance on past effective configurations (Foss, 1973). Thirty years after Foss' critique, control system configuration remains a largely heuristic affair. Biological methods of processing information are fundamentally different from the methods used in conventional control techniques. Biological neural mechanisms (i.e., intelligent systems) are based on partial models, largely devoid of the system's underlying natural laws. Neural control strategies are carried out without a pure mathematical formulation of the task or the environment. Rather, biological systems rely on knowledge of cause-effect interactions, creating robust control strategies from ill-defined dynamic systems. Dynamic modelling may be either phenomenological or empirical. Phenomenological models are derived from first principles and typically consist of algebraic and differential equations. First principles modelling is both time consuming and expensive. Vast data warehouses of historical plant data make empirical modelling attractive. Singular spectrum analysis (SSA) is a rapid model development technique for identifying dominant state variables from historical plant time series data. Since time series data invariably covers a limited region of the state space, SSA models are almost necessarily partial models. Interpreting and learning causal relationships from dynamic models requires sufficient feedback of the environment's state. Systemisation of the learning task is imperative. Reinforcement learning is a computational approach to understanding and automating goal-directed learning. This thesis aimed to establish a neurocontrol paradigm for non-linear, high dimensional processes within an evolutionary reinforcement learning (ERL) framework. Symbiotic memetic neuro-evolution (SMNE) is an ERL algorithm developed for global tuning of neurocontroller weights. SMNE is comprised of a symbiotic evolutionary algorithm and local particle swarm optimisation. Implicit fitness sharing ensures a global search and the synergy between global and local search speeds convergence.Several simulation studies have been undertaken, viz. a highly non-linear bioreactor, a rigorous ball mill grinding circuit and the Tennessee Eastman control challenge. Pseudo-empirical modelling of an industrial fed-batch fermentation shows the application of SSA for developing partial models. Using SSA, state estimation is forthcoming without resorting to fundamental models. A dynamic model of a multieffect batch distillation (MEBAD) pilot plant was fashioned using SSA. Thereafter, SMNE developed a neurocontroller for on-line implementation using the SSA model of the MEBAD pilot plant. Both simulated and experimental studies confirmed the robust performance of ERL neurocontrollers. Coordinated flow sheet design, steady state optimisation and nonlinear controller development encompass a comprehensive methodology. Effective selection of controlled variables and pairing of process and manipulated variables were implicit to the SMNE methodology. High economic performance was attained in highly non-linear regions of the state space. SMNE imparted significant generalisation in the face of process uncertainty. Nevertheless, changing process conditions may necessitate neurocontroller adaptation. Adaptive neural swarming (ANS) allows for adaptation to drifting process conditions and tracking of the economic optimum online. Additionally, SMNE allows for control strategy design beyond single unit operations. SMNE is equally applicable to processes with high dimensionality, developing plant-wide control strategies. Many of the difficulties in conventional plant-wide control may be circumvented in the biologically motivated approach of the SMNE algorithm. Future work will focus on refinements to both SMNE and SSA. SMNE and SSA thus offer a non-heuristic, quantitative approach that requires minimal engineering judgement or knowledge, making the methodology free of subjective design input. Evolutionary reinforcement learning offers significant advantages for developing high performance control strategies for the chemical, mineral and metallurgical industries. Symbiotic memetic neuro-evolution (SMNE), adaptive neural swarming (ANS) and singular spectrum analysis (SSA) present a response to Foss' critique. / AFRIKAANSE OPSOMMING: 'n Neurobeheer paradigma vir intelligente prosesbeheer deur die gebruik van evolusionêre versterkingsleer Dit is 'n komplekse ontwikkelingstaak om menigte besigheids- en operasionele doelwitte in 'n omvattende beheerstrategie te vereenselwig. Nie-lineêriteite en vele komplekse prosesinteraksies kombineer om ingewikkelde aksie-reaksie verwantskappe te vorm. Dit is dikwels noodsaaklik om hierdie interaksies omvattend te verstaan, voordat prosesbeheer doelwitte doeltreffend gedoen kan word. Tog word sulke beheerstelsels dikwels saamgestel op grond van kwalitatiewe kriteria en word ook dikwels staatgemaak op historiese benaderings wat voorheen effektief was (Foss, 1973). Dertig jaar na Foss se kritiek, bly prosesbeheerstelsel ontwerp 'n heuristiese saak. Die biologiese prosessering van informasie is fundamenteel verskillend van metodes wat gebruik word in konvensionele beheertegnieke. Biologiese neurale meganismes (d.w.s., intelligente stelsels) word gebaseer op gedeeltelike modelle, wat grotendeels verwyderd is van die onderskrywende natuurwette. Neurobeheerstrategieë word toegepas sonder suiwer wiskundige formulering van die taak of die omgewing. Biologiese stelsels maak eerder staat op kennis van aksie-reaksie verhoudings en skep robuuste beheerstrategieë van swak gedefineerde dinamiese stelsels. Dinamiese modelle is of fundamenteel of empiries. Fundamentele modelle word ontwikkel vanaf eerste beginsels en word tipies uit algebraïese en differensiële vergelykings saamgestel. Modellering vanaf eerste beginsels is beide tydrowend en duur. Groot databasisse van historiese aanlegdata maak empiriese modellering aantreklik. Singuliere spektrumanalise (SSA) maak die vinnige ontwerp van empiriese modelle moontlik, waardeur dominante veranderlikes vanaf historiese tydreekse onttrek kan word. Aangesien tydreeksdata slegs 'n gedeelte van die prosesomgewing verteenwoordig, is SSA modelle noodwendig gedeeltelike modelle. Die interpretasie en aanleer van kousale verhoudings vanaf dinamiese modelle vereis voldoende terugvoer van omgewingstoestande. Die leertaak moet sistematies uitgevoer word. Versterkingsleer is 'n ramingsbenadering tot 'n doelwit-gedrewe leerproses. Hierdie tesis bewerkstellig 'n neurobeheerparadigme vir nie-lineêre prosesse met hoë dimensies binne 'n evolusionêre versterkingsleer (EVL) raamwerk. Simbiotiese, memetiese neuro-evolusie (SMNE) is 'n EVL algoritme wat ontwikkel is vir globale verstelling van die gewigte van ‘n neurobeheerder. SMNE is saamgestel uit 'n simbiotiese evolusionêre algoritme en 'n lokale partikelswerm-algoritme. Implisiete fiksheidsdeling verseker 'n globale soektog en die sinergie tussen globale en lokale soektogte bespoedig konvergensie.Verskeie simulasie studies is onderneem, o.a. die van 'n hoogs nie-lineêre bioreaktor, 'n balmeulaanleg en die Tennessee Eastman beheer probleem. Empiriese modellering van 'n industriële enkelladingsfermentasie demonstreer die aanwending van SSA vir die ontwikkeling van gedeeltelike modelle. SSA benader die toestand van 'n dinamiese stelsel sonder die aanwending van fundamentele modellering. 'n Dinamiese model van 'n multi-effek-enkelladingsdistillasie (MEBAD) proefaanleg is bewerkstellig deur die gebruik van SSA. Daarna is SMNE gebruik om 'n neurobeheerder te skep vanaf die SSA model vir die beheer van die MEBAD proefaanleg. Beide simulasie en eksperimentele studies het die robuuste aanwending van EVL neurobeheerders bevestig. Die gekoördineerde ontwerp van vloeidiagramme, gestadigde toestand-optimering en nie-lineêre beheerderontwikkeling vereis 'n omvattende metodologie. Beheerveranderlikes en die koppeling van proses- en uitvoerveranderlikes is implisiet en effektief. Maksimale ekonomiese aanwins was moontlik in hoogs nie-lineêre dele van die toestandsruimte. SMNE het besondere veralgemening toegevoeg tot neurobeheerderstrategieë ten spyte van prosesonsekerhede. Nietemin, veranderende prosestoestande mag neurobeheerderaanpassing genoodsaak. Aanpasbare neurale swerm (ANS) algoritmes pas neurobeheerders aan tydens veranderende proseskondisies en volg die ekonomiese optimum, terwyl die beheerder die proses beheer. SMNE bewerkstellig ook die ontwikkeling van beheerstrategieë vir prosesse met meer as een eenheidsoperasie. SMNE skaal na prosesse met hoë dimensionaliteit vir die ontwikkeling van aanlegwye beheerstrategieë. Talle kwelvrae in konvensionele aanleg-wye prosesbeheer word deur die biologies gemotiveerde benadering van die SMNE algoritme uit die weg geruim. Toekomstige werk sal fokus op die verfyning van beide SMNE en SSA. SMNE en SSA bied 'n nie-heuristiese, kwantitatiewe benadering wat minimale ingenieurskennis of oordeel vereis. Die metodologie is dus vry van subjektiewe ontwerpsoordeel. Evolusionêre versterkingsleer bied talle voordele vir 'n ontwikkeling van effektiewe beheerstrategieë vir die chemiese, mineraal en metallurgiese industrieë. Simbiotiese memetiese neuro-evolusie (SMNE), aanpasbare neurale swerm metodes (ANS) en singulêre spektrum analise (SSA) gee antwoord op Foss se kritiek.

A research report submitted to the faculty of Engineering and the built environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering. Johannesburg, August 2016 / There has been much interest in using the Microsoft Xbox 360 Kinect cameras for visual servo control applications. It is a relatively cheap device with expected shortcomings. This work contributes to the practical considerations of using the Kinect for visual servo control applications. A comprehensive characterisation of the Kinect is synthesised from existing literature and results from a nonlinear calibration procedure. The Kinect reduces computational overhead on image processing stages, such as pose estimation or depth estimation. It is limited by its 0.8m to 3.5m practical depth range and quadratic depth resolution of 1.8mm to 35mm, respectively. Since the Kinect uses an infra-red (IR) projector, a class one laser, it should not be used outdoors, due to IR saturation, and objects belonging to classes of non- IR-friendly surfaces should be avoided, due to IR refraction, absorption, or specular reflection. Problems of task stability due to invalid depth measurements in Kinect depth maps and practical depth range limitations can be reduced by using depth map preprocessing and activating classical visual servoing techniques when Kinect-based approaches are near task failure. / MT2017

Orientador: Paulo Roberto de Aguiar / Banca: Eduardo Carlos Bianchi / Banca: Rosemar Batista da Silva / Resumo: A necessidade de reduções de custos aliada ao aumento de qualidade das peças produzidas requer a implementação de sistemas inteligentes em ambientes industriais. O controle dos danos causados no processo de retificação é de interesse direto da indústria dependente desse processo. O objetivo deste trabalho é a proposição de modelos fuzzy empregados na detecção da queima de peças de aço SAE 1020 no processo de retificação plana. Foram realizados doze testes para diferentes condições de usinagem. Para cada teste foram coletados dados referentes a potência elétrica e emissão acústica (sinal puro). Os níveis de queima das peças foram analisados visualmente e com o auxílio computacional. A partir dos sinais de emissão acústica, potência de corte e parâmetros utilizando esses dois sinais, regras linguísticas foram estabelecidas para as diversas situações de queima (leve, média, severa) com a aplicação da lógica nebulosa utilizando-se o Toolbox do MATLAB. Quatro modelos práticos de sistema fuzzy foram desenvolvidos. O primeiro modelo com duas entradas apenas resultam num processo de simples análise. O segundo modelo possui a entrada adicional da estatística do desvio do valor médio (MVD), associando uma nova informação e precisão. Esse modelo é baseado em um sistema de inferência de três entradas, combinados dois a dois. O terceiro modelo, com 64 regras, baseia-se nas mesmas três entradas utilizadas no segundo modelo, combinadas três a três. Esses dois modelos diferem entre si pela base de regras desenvolvidas. O quarto modelo difere do terceiro devido ao número de regras e a entrada adicional baseada na potência de corte, do desvio padrão da mesma e do sinal RMS de emissão acústica. Apresentando respostas válidas, os quatro modelos desenvolvidos mostraram eficiência, precisão, confiabilidade e... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The need of costs reduction and quality increase of the produced pieces requires the implementation of intelligent systems in industrial environments. The control of damages caused during the grinding process is interesting to the industry that depends on such process. This work uses fuzzy logic as tool to classify and estimate burn levels in the grinding process in order to help controlling such process. Twelve tests were performed for different grinding conditions. For each test, data were concerning electrical power and acoustic emission (raw signal). The levels of burning parts were analyzed visually and with computer assistance. Based on acoustic emission signals, cutting power, and statistics using these two signals, liguistic rules were established for the various burn situations (slight, intermediate, sever) by applying fuzzy logic using the MATLAB toolbox. Four practical fuzzy system models were developed. This first model with two inputs resulted only in a simple analysis process. The second model has an additional MVD statistic input, associating information and precision. This model is base d on an inference system of three inputs, combined two by two. The third model with 64 rules is based on the same three inputs used in the second model, differ by the rule base developed. The forth model is different from the third one due to the number of rules, the additional input based on the cutting power, the standard deviation and the acoustic emission RMS signal. The four developed models presented valid responses, proving effective, accurate, reliable and easy to use for the determination of ground workpiece burn. In this analysis... (Complete abstract click electronic access below) / Mestre

Design of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based algorithms in a sequential, single processor machine like Pascal, C or C++ takes several hours or even days. But in this thesis, the GMDH algorithm was modified and implemented into a software tool written in Verilog HDL and tested with specific application (XOR) to make the simulation faster. The purpose of the development of this tool is also to keep it general enough so that it can have a wide range of uses, but robust enough that it can give accurate results for all of those uses. Most of the applications of neural networks are basically software simulations of the algorithms only but in this thesis the hardware design is also developed of the algorithm so that it can be easily implemented on hardware using Field Programmable Gate Array (FPGA) type devices. The design is small enough to require a minimum amount of memory, circuit space, and propagation delay. / by Mukul Gupta. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

Options for tracking dynamic underwater targets using optical methods is currently limited. This thesis examines optical reflectance intensities utilizing Lambert’s Reflection Model and based on a proposed underwater laser tracking system. Numerical analysis is performed through simulation to determine the detectable light intensities based on relationships between varying inputs such as angle of illumination and target position. Attenuation, noise, and laser beam spreading are included in the analysis. Simulation results suggest optical tracking exhibits complex relationships based on target location and illumination angle. Signal to Noise Ratios are a better indicator of system capabilities than received intensities. Signal reception does not necessarily confirm target capture in a multi-sensor network. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Several technologies are being made available for the blind and the visually impaired with the use of infrared and sonar sensors, Radio Frequency Identification, GPS, Wi-Fi among others. Current technologies utilizing microprocessors increase the device's power consumption. In this project, a Verilog Hardware Language (VHDL) designed handheld device that autonomously guides a visually impaired user through an obstacle free path is proposed. The goal is to minimize power consumption by not using the usual microcontroller and replacing it with components that can increase its speed. Utilizing six infrared sensors, the handheld device is modeled after current technologies which use IR and sonar sensors which are reviewed in this project. By using behavioral modeling, an algorithm for obstacle avoidance and the generation of the obstacle free path is reduced using a K-map and implemented using a multiplexer. / by Ernesto Cividanes. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

One of the basic problems which have to be solved by Unmanned Automated Vehicles (UAV) involves the computation of a motion plan that would enable the system to reach a target given a set of initial conditions in presence of uncertainties on the vehicle dynamics and in the environment. Recent research efforts in this area have relied on deterministic models. To address the problem of inevitable uncertainties, a low-level control layer is typically used to ensure proper robust trajectory tracking. Such decision-tracking algorithms correct model disturbances a posteriori, while the whole movement planning is done in a purely deterministic fashion. We argue that the decision making process that takes place during movement planning, as performed by experienced human pilots, is not a purely deterministic operation, but is heavily influenced by the presence of uncertainties and reflects a risk-management policy. This research aims at addressing these uncertainties and developing an optimal control strategy that would account for the presence of system uncertainties. The underlying description of UAV trajectories will be based on a modeling language, the Maneuver Automaton, that takes into full account the vehicle dynamics, and hence guarantees flyable and trackable paths and results in a discretized solution space. Two optimal control problems, a nominal problem omitting uncertainties and a robust problem addressing the presence of uncertainties, will be defined and compared throughout this work. The incorporation of uncertainties, will ensure that the generated motion planning policies will maximize the probability to meet mission goals, weighing risks against performance.