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Yes / Regular language inference, initiated by Angluin, has many developments, including applications in software engineering and testing. However, the capability of finite automata to model the system data is quite limited and, in many cases, extended finite state machine formalisms, that combine the system control with data structures, are used instead. The application of Angluin-style inference algorithms to extended state machines would involve constructing a minimal deterministic extended finite state machine consistent with a deterministic 3-valued deterministic finite automaton. In addition to the usual, accepting and rejecting, states of finite automaton, a 3-valued deterministic finite automaton may have “don't care” states; the sequences of inputs that reach such states may be considered as accepted or rejected, as is convenient. The aforementioned construction reduces to finding a minimal deterministic finite automaton consistent with a 3-valued deterministic finite automaton, that preserves the deterministic nature of the extended model that also handles the data structure associated with it. This paper investigates fundamental properties of extended finite state machines in relation to Angluin's language inference problem and provides an inference algorithm for such models.

A navegação autônoma é uma tarefa fundamental na robótica móvel. Para que esta tarefa seja realizada corretamente é necessário um sistema inteligente de controle e navegação associado ao sistema sensorial. Este projeto apresenta o desenvolvimento de um sistema de controle para a navegação de veículos e robôs móveis autônomos. A abordagem utilizada neste trabalho utiliza Redes Neurais Artificiais para o aprendizado de Autômatos Finitos de forma que os robôs possam lidar com os dados provenientes de seus sensores mesmo estando sujeitos a imprecisões e erros e ao mesmo tempo permite que sejam consideradas as diferentes situações e estados em que estes robôs se encontram (contexto). Dessa forma, é possível decidir como agir para realizar o controle da sua movimentação, e assim executar tarefas de controle e navegação das mais simples até as mais complexas e de alto nível. Portanto, esta dissertação visa utilizar Redes Neurais Artificiais para reconhecer o estado atual (contexto) do robô em relação ao ambiente em que está inserido. Uma vez que seja identificado seu estado, o que pode inclusive incluir a identificação de sua posição em relação aos elementos presentes no ambiente, o robô será capaz de decidir qual a ação/comportamento que deverá ser executado. O sistema de controle e navegação irá implementar um Autômato Finito que a partir de um estado atual define uma ação corrente, sendo capaz de identificar a mudança de estados, e assim alternar entre diferentes comportamentos previamente definidos. De modo a validar esta proposta, diversos experimentos foram realizados através do uso de um simulador robótico (Player-Stage), e através de testes realizados com robôs reais (Pioneer P3-AT, SRV-1 e veículos automatizados) / Autonomous navigation is a fundamental task in mobile robotics. In order to accurately perform this task it is necessary an intelligent navigation and control system associated to the sensorial system. This project presents the development of a control system for autonomous mobile robots and vehicles navigation. The adopted approach uses Artificial Neural Networks for Finite State Machine learning, allowing the robots to deal with sensorial data even when this data is not precise and correct. Simultaneously, it allows the robots to consider the different situations and states they are inserted in (context detection). This way, it is possible to decide how to proceed with motion control and then execute navigation and control tasks from the most simple ones until the most complex and high level tasks. So, this work uses Artificial Neural Networks to recognize the robots current state (context) at the environment where it is inserted. Once the state is detected, including identification of robots position according to environment elements, the robot will be able to determine the action/- behavior to be executed. The navigation and control system implements a Finite State Machine deciding the current action from current state, being able to identify state changes, alternating between different previously defined behaviors. In order to validade this approach, many experiments were performed with the use of a robotic simulator (Player-Stage), and carrying out tests with real robots (Pioneer P3-AT, SRV-1 and autonomous vehicles)

A navegação autônoma é uma tarefa fundamental na robótica móvel. Para que esta tarefa seja realizada corretamente é necessário um sistema inteligente de controle e navegação associado ao sistema sensorial. Este projeto apresenta o desenvolvimento de um sistema de controle para a navegação de veículos e robôs móveis autônomos. A abordagem utilizada neste trabalho utiliza Redes Neurais Artificiais para o aprendizado de Autômatos Finitos de forma que os robôs possam lidar com os dados provenientes de seus sensores mesmo estando sujeitos a imprecisões e erros e ao mesmo tempo permite que sejam consideradas as diferentes situações e estados em que estes robôs se encontram (contexto). Dessa forma, é possível decidir como agir para realizar o controle da sua movimentação, e assim executar tarefas de controle e navegação das mais simples até as mais complexas e de alto nível. Portanto, esta dissertação visa utilizar Redes Neurais Artificiais para reconhecer o estado atual (contexto) do robô em relação ao ambiente em que está inserido. Uma vez que seja identificado seu estado, o que pode inclusive incluir a identificação de sua posição em relação aos elementos presentes no ambiente, o robô será capaz de decidir qual a ação/comportamento que deverá ser executado. O sistema de controle e navegação irá implementar um Autômato Finito que a partir de um estado atual define uma ação corrente, sendo capaz de identificar a mudança de estados, e assim alternar entre diferentes comportamentos previamente definidos. De modo a validar esta proposta, diversos experimentos foram realizados através do uso de um simulador robótico (Player-Stage), e através de testes realizados com robôs reais (Pioneer P3-AT, SRV-1 e veículos automatizados) / Autonomous navigation is a fundamental task in mobile robotics. In order to accurately perform this task it is necessary an intelligent navigation and control system associated to the sensorial system. This project presents the development of a control system for autonomous mobile robots and vehicles navigation. The adopted approach uses Artificial Neural Networks for Finite State Machine learning, allowing the robots to deal with sensorial data even when this data is not precise and correct. Simultaneously, it allows the robots to consider the different situations and states they are inserted in (context detection). This way, it is possible to decide how to proceed with motion control and then execute navigation and control tasks from the most simple ones until the most complex and high level tasks. So, this work uses Artificial Neural Networks to recognize the robots current state (context) at the environment where it is inserted. Once the state is detected, including identification of robots position according to environment elements, the robot will be able to determine the action/- behavior to be executed. The navigation and control system implements a Finite State Machine deciding the current action from current state, being able to identify state changes, alternating between different previously defined behaviors. In order to validade this approach, many experiments were performed with the use of a robotic simulator (Player-Stage), and carrying out tests with real robots (Pioneer P3-AT, SRV-1 and autonomous vehicles)

Two-dimensional scan design is an effective BIST architecture that uses multiple scan chains in parallel to test the Circuit Under Test (CUT). Linear Finite State Machines (LFSMs) are often used as on-board Pseudo Random Pattern Generators (PRPGs) in two-dimensional scan designs. However, linear dependencies present in the LFSM generated test-bit sequences adversely affect the resultant fault coverage in two-dimensional scan designs. In this work, we present methods that improve the resultant fault coverage in two-dimensional scan designs through the minimization of linear dependencies. Currently, metric of channel separation and matrix-based metric are used in order to estimate linear dependencies in a CUT. When the underlying sub-circuit (cone) structure of a CUT is available, the matrix-based metric can be used more effectively. Fisrt, we present two methods that use matrix-based metric and minimize the overall linear dependencies in a CUT through explicitly minimizing linear dependencies in the highest number of underlying cones of the CUT. The first method minimizes linear dependencies in a CUT through the selection of an appropriate LFSM structure. On the other hand, the second method synthesizes a phase shifter for a specified LFSM structure such that the overall linear dependencies in a CUT are minimized. However, the underlying structure of a CUT is not always available and in such cases the metric of channel separation can be used more effectively. The metric of channel separation is an empirical measure of linear dependencies and an ad-hoc large channel separation is imposed between the successive scan chains of a two-dimensional scan design in order to minimize the linear dependencies. Present techniques use LFSMs with additional phase shifters (LFSM/PS) as PRPGs in order to obtain desired levels of channel separation. We demonstrate that Generalized LFSRs (GLFSRs) are a better choice as PRPGs compared to LFSM/PS and obtain desired levels of channel separations at a lower hardware cost than the LFSM/PS. Experimental results corroborate the effectiveness of the proposed methods through increased levels of the resultant fault coverage in two-dimensional scan designs.

The traffic signal affects the life of virtually everyone every day. The effectiveness of signal systems can reduce the incidence of delays, stops, fuel consumption, emission of pollutants, and accidents. The problems related to rapid growth in traffic congestion call for more effective traffic signalization using robust feedback control methodology. Online traffic-responsive signalization is based on real-time traffic conditions and selects cycle, split, phase, and offset for the intersection according to detector data. A robust traffic feedback control begins with assembling traffic demands, traffic facility supply, and feedback control law for the existing traffic operating environment. This information serves the input to the traffic control process which in turn provides an output in terms of the desired performance under varying conditions. Traffic signalization belongs to a class of hybrid systems since the differential equations model the continuous behavior of the traffic flow dynamics and finite-state machines model the discrete state changes of the controller. A complicating aspect, due to the state-space constraint that queue lengths are necessarily nonnegative, is that the continuous-time system dynamics is actually the projection of a smooth system of ordinary differential equations. This also leads to discontinuities in the boundary dynamics of a sort common in queueing problems. The project is concerned with the design of a feedback controller to minimize accumulated queue lengths in the presence of unknown inflow disturbances at an isolated intersection and a traffic network with some signalized intersections. A dynamical system has finite L₂-gain if it is dissipative in some sense. Therefore, the H<SUB>infinity</SUB>-control problem turns to designing a controller such that the resulting closed loop system is dissipative, and correspondingly there exists a storage function. The major contributions of this thesis include 1) to propose state space models for both isolated multi-phase intersections and a class of queueing networks; 2) to formulate H<SUB>infinity</SUB> problems for the control systems with persistent disturbances; 3) to present the projection dynamics aspects of the problem to account for the constraints on the state variables; 4) formally to study this problem as a hybrid system; 5) to derive traffic-actuated feedback control laws for the multi-phase intersections. Though we have mathematically presented a robust feedback solution for the traffic signalization, there still remains some distance before the physical implementation. A robust adaptive control is an interesting research area for the future traffic signalization. / Ph. D.

O objetivo deste trabalho é tornar possível a inserção de um robô humanoide para auxiliar pacientes em sessões de fisioterapia. Um sistema robótico é proposto que utiliza um robô humanoide, denominado NAO, visando analisar os movimentos feitos pelos pacientes e corrigi-los se necessário, além de motivá-los durante uma sessão de fisioterapia. O sistema desenvolvido permite que o robô, em primeiro lugar, aprenda um exercício correto de fisioterapia observando sua execução por um fisioterapeuta; em segundo lugar, que ele demonstre o exercício para que um paciente possa imitá-lo; e, finalmente, corrija erros cometidos pelo paciente durante a execução do exercício. O exercício correto é capturado por um sensor Kinect e dividido em uma sequência de estados em dimensão espaço-temporal usando k-means clustering. Estes estados então formam uma máquina de estados finitos para verificar se os movimentos do paciente estão corretos. A transição de um estado para o próximo corresponde a movimentos parciais que compõem o movimento aprendido, e acontece somente quando o robô observa o mesmo movimento parcial executado corretamente pelo paciente; caso contrário o robô sugere uma correção e pede que o paciente tente novamente. O sistema foi testado com vários pacientes em tratamento fisioterapêutico para problemas motores. Os resultados obtidos, em termos de precisão e recuperação para cada movimento, mostraram-se muito promissores. Além disso, o estado emocional dos pacientes foi também avaliado por meio de um questionário aplicado antes e depois do tratamento e durante o tratamento com um software de reconhecimento facial de emoções e os resultados indicam um impacto emocional bastante positivo e que pode vir a auxiliar pacientes durante tratamento fisioterapêuticos. / This dissertation develops a robotic system to guide patients through physiotherapy sessions. The proposed system uses the humanoid robot NAO, and it analyses patients movements to guide, correct, and motivate them during a session. Firstly, the system learns a correct physiotherapy exercise by observing a physiotherapist perform it; secondly, it demonstrates the exercise so that the patient can reproduce it; and finally, it corrects any mistakes that the patient might make during the exercise. The correct exercise is captured via Kinect sensor and divided into a sequence of states in spatial-temporal dimension using k-means clustering. Those states compose a finite state machine that is used to verify whether the patients movements are correct. The transition from one state to the next corresponds to partial movements that compose the learned exercise. If the patient executes the partial movement incorrectly, the system suggests a correction and returns to the same state, asking that the patient try again. The system was tested with multiple patients undergoing physiotherapeutic treatment for motor impairments. Based on the results obtained, the system achieved high precision and recall across all partial movements. The emotional impact of treatment on patients was also measured, via before and after questionnaires and via a software that recognizes emotions from video taken during treatment, showing a positive impact that could help motivate physiotherapy patients, improving their motivation and recovery.

A realização de atividades de teste é indispensável para a garantia da qualidade de um produto e para a identificação de defeitos, diminuindo custos de manutenção e evitando ao máximo o risco do cliente encontrar esses defeitos. Nessa linha, testes baseados em modelos têm se mostrado atrativos, pois o custo de geração de casos de testes e de correção de defeitos tende a ser menor. Devido à sua simplicidade conceitual e expressividade na descrição do comportamento de um sistema, um dos modelos mais usados e pesquisados na área de teste baseado em modelos são as Máquinas de Estados Finitos (MEFs). Por meio de MEFs e com apoio de ferramentas apropriadas, a geração de casos de testes para avaliar os comportamentos esperados de um sistema é automatizada, reduzindo tanto o custo da geração e da manutenção quanto as falhas humanas. Desta forma, a aplicabilidade de métodos de geração de casos de teste baseados em modelos no contexto de sistemas embarcados vem sendo investigada. O objetivo deste trabalho de mestrado consiste em investigar a aplicabilidade dos métodos de geração em cenários de teste reais, com foco em sistemas embarcados, identificando as difi- culdades e limitações do processo, bem como os requisitos essenciais para a adequação dos métodos de geração propostos na literatura e de ferramentas de apoio à atividade de teste. O foco principal do projeto é a implementação de mecanismos que atendam aos requisitos levantados, visando a usabilidade, segurança e portabilidade da ferramenta / Test activities are essential to ensure the quality of products and identify faults to reduce maintenance costs and avoid that the client finds these faults. In this sense, model-based tests have been proved useful, because the cost of generating test cases and fault correction tend to be smaller. Due to its conceptual simplicity and expressiveness in describing the behavior of a system, Finite State Machines (FSM) have been used and researched in the model-based testing area. FSMs, employed with the support of appropriate tools, enable the generation of test cases in an automated way to assess the expected behavior of a system, reducing both the generation and maintenance costs and human failures. Thus, the applicability of test cases generation methods based on models in the context of embedded systems should be investigated. Test cases generation methods based on FSM are designed to derive test cases from the model. In this context, this work aims to investigate the applicability of generation methods in real-world scenarios, focusing embedded systems. It should identify the difficulties and limitations of the process, as well as the essential requirements for the adequacy of generation methods proposed in the literature and tools to support the test activity. The main focus of the project is the implementation of mechanisms that meet the elicited requirements in order to provide usability, security and tool portability