Modeliais paremtos architektūros (MDA) technologijos panaudojimo tikslas – automatizuoti kuriamos programų sistemos kuriamų modelių transformavimą ir kodo generavimą. Norint atlikti sukurtų modelių korektišką transformavimą, reikia užtikrinti modelių pilnumą ir suderinamumą tarpusavyje. Šie modeliai aprašomi UML modeliavimo kalba. Magistriniame darbe nagrinėjama nuo platformos nepriklausančio modelio kūrimo stadija, modelio klasių ir būsenų mašinų suderimo galimybės ir būsenų mašinų korektiškumo ir išbaigtumo metodikos. Taip pat bus pateikiamas sprendimas atliktas MagicDraw aplinkoje įskiepio pagalba. / The main goal of Model Driven Architecture (MDA) is the automation of software development process. According this technology, we need to create platform independent model (PIM), after that transform it to platform specific model (PSM); from PSM model we can generate program code. To do that, we need to ensure static and dynamic completeness of PIM. All these models are described using UML modeling language. In this work correctness and completeness of PIM is achieved by analyzing compatibility of class diagrams and state machines, as well as correctness and completeness of state machines themselves. To solve this problem, algorithms were created and implemented in a plug-in for MagicDraw CASE tool.

Made available in DSpace on 2014-06-11T19:22:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-10-30Bitstream added on 2014-06-13T20:09:43Z : No. of bitstreams: 1 almeida_ts_me_ilha.pdf: 5032122 bytes, checksum: ba20bdd1ce902754e7b772b2be3cc785 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Neste trabalho, apresenta-se o desenvolvimento e a avaliação de duas ferramentas que auxiliam projetos de circuitos eletrônicos, sejam eles projetos de sistemas digitais ou de sistemas mistos (sinais digitais e sinais analógicos). A partir de um diagrama de transição de estados, modelado em ambiente Stateflow®, a primeira ferramenta, denominada SF2HDL, realiza a extração de linguagens de descrição de hardware, podendo ser VHDL ou Verilog HDL. Sendo ainda capaz de extrair uma tabela de transição de estados padronizada, que, posteriormente, foi utilizada como entrada pelo programa TABELA, o qual realiza a minimização do sistema digital. A máquina de estados finitos, alvo da tradução, pode ser descrita tanto pelo modelo de Mealy como pelo modelo de Moore. Como estudos de caso, foram utilizados quatro códigos de linhas empregados em sistemas de telecomunicações. A segunda ferramenta é um aperfeiçoamento de uma ferramenta já existente, denominada MS2SV, empregada na síntese de sistemas mistos. O MS2SV é capaz de gerar uma descrição em VHDL-AMS estrutural, a partir de um modelo descrito em alto nível de abstração no ambiente Simulink®. Toda a estrutura de projeto necessária para a simulação e análise do sistema no ambiente SystemVision™, também é gerado pelo MS2SV. Foram utilizados quatro modelos de conversor de dados do tipo DAC (Digital to Analog Conversor), para avaliar o desempenho da ferramenta. Nesse contexto, as duas ferramentas permitem maior flexibilidade ao projetista, traduzindo descrições em níveis de abstração diferentes, o que permite uma análise mais detalhada do funcionamento do sistema e facilitando a sua implementação física / In this work, it’s shown the development and evaluation of two tools to aid in electronic circuits projects, be them digital systems projects or for mixed systems (digital and analogical signs). From a states transition diagram modeled in Stateflow® environment, the first tool, named SF2HDL, performs the extraction of hardware description languages, which could be VHDL or Verilog HDL. It is also capable of extracting states transition table standardized, which later was used as a TABELA program, which accomplishes the minimization of the digital system. The target finite state machine of the translated can be described by the Mealy model as much as the Moore model. As case studies were used four code lines employed in telecommunications systems. The second tool is an improvement of an already existent tool, known as MS2SV, used in the synthesis of mixed systems. The MS2SV is able to generate a description in structural VHDL-AMS, from a model described in high level of abstraction in the Simulink® environment. The whole project structure necessary for the simulation and analysis of the system by the SystemVision™ environment is also generated by MS2SV. Four DAC (Digital to Analog Converter) were used to evaluate the tool is performance. In that context, both tools allow a greater flexibility to the planner, translating descriptions in different abstraction levels, which allows a more detailed analysis of the systems behavior and making its physical implementation easier

This thesis work investigates how to represent protocols for asynchronous communication in the Scala programming language and the Akka actor framework, to be run on Java Virtual Machine (JVM). Further restrictions from the problem domain - the coexistence of multiple protocol instances sharing the same Java thread - imply that neither an asynchronous call waiting for response nor anything else can block the underlying Java threads. A common way to represent asynchronous communication protocols is to use state machines. This thesis seeks a way to shrink the size of and to reduce the complexity of the protocol implementations by representing sequences of asynchronous communication calls (i.e. sequences of sent and received messages) as a type of procedure. The idea is find a way to make the procedures that contain asynchronous calls look like synchronous communication procedures by hiding the asynchronous details. In other words, the resulting procedure code should show what to do and not so much focus on how to overcome the impediment of the asynchronous calls. With the help of an asynchronous communication protocol toy example, this report shows how such an protocol can be implemented with a combination of a state machine and a procedure representation in Scala and Akka. The procedure representation hides away the asynchronous details by using the Scala capability to use CPS-transformed delimited continuations. As a sub-problem, this thesis also shows how to safely schedule asynchronous communication timeouts with help of Scala and Akka within the restrictions of the thesis problem domain.

This thesis is about semiautomatic network protocol diagnostics and creating protocol description from eavesdropped communication. Several network eavesdropping techniques  and some common programs for network analysis are introduced. Well-known network protocols are described, with focus on their communication messages. Some already existing methods for creating models from examples are mentioned and their characteristics defined. Next we design architecture of developed tool and some methods, that create protocol description. After that we explain implementation of this tool and finally the tool is tested and experimented with.

This thesis describes the development of control applications for the deposition machine and mass spectrometer. Thesis describes operation principles of both devices and their hardware specifications. Thesis also describes the design of developed applications. Functionality was tested on series of real measurements.

The essence of this diploma thesis is the design and implementation of software for laser ablation control. The paper describes laser ablation, hardware specifications of device and application requirements. The second part provides description of design and implementation of the actual application in NI LabView.

Biological neural systems (BNS) in general and the central nervous system (CNS) specifically exhibit a strikingly efficient computational power along with an extreme flexible and adaptive basis for acquiring and integrating new knowledge. Acquiring more insights into the actual mechanisms of information processing within the BNS and their computational capabilities is a core objective of modern computer science, computational sciences and neuroscience. Among the main reasons of this tendency to understand the brain is to help in improving the quality of life of people suffer from loss (either partial or complete) of brain or spinal cord functions. Brain-computer-interfaces (BCI), neural prostheses and other similar approaches are potential solutions either to help these patients through therapy or to push the progress in rehabilitation. There is however a significant lack of knowledge regarding the basic information processing within the CNS. Without a better understanding of the fundamental operations or sequences leading to cognitive abilities, applications like BCI or neural prostheses will keep struggling to find a proper and systematic way to help patients in this regard. In order to have more insights into these basic information processing methods, this thesis presents an approach that makes a formal distinction between the essence of being intelligent (as for the brain) and the classical class of artificial intelligence, e.g. with expert systems. This approach investigates the underlying mechanisms allowing the CNS to be capable of performing a massive amount of computational tasks with a sustainable efficiency and flexibility. This is the essence of being intelligent, i.e. being able to learn, adapt and to invent. The approach used in the thesis at hands is based on the hypothesis that the brain or specifically a biological neural circuitry in the CNS is a dynamic system (network) that features emergent capabilities. These capabilities can be imported into spiking neural networks (SNN) by emulating the dynamic neural system. Emulating the dynamic system requires simulating both the inner workings of the system and the framework of performing the information processing tasks. Thus, this work comprises two main parts. The first part is concerned with introducing a proper and a novel dynamic synaptic model as a vital constitute of the inner workings of the dynamic neural system. This model represents a balanced integration between the needed biophysical details and being computationally inexpensive. Being a biophysical model is important to allow for the abilities of the target dynamic system to be inherited, and being simple is needed to allow for further implementation in large scale simulations and for hardware implementation in the future. Besides, the energy related aspects of synaptic dynamics are studied and linked to the behaviour of the networks seeking for stable states of activities. The second part of the thesis is consequently concerned with importing the processing framework of the dynamic system into the environment of SNN. This part of the study investigates the well established concept of binding by synchrony to solve the information binding problem and to proposes the concept of synchrony states within SNN. The concepts of computing with states are extended to investigate a computational model that is based on the finite-state machines and reservoir computing. Biological plausible validations of the introduced model and frameworks are performed. Results and discussions of these validations indicate that this study presents a significant advance on the way of empowering the knowledge about the mechanisms underpinning the computational power of CNS. Furthermore it shows a roadmap on how to adopt the biological computational capabilities in computation science in general and in biologically-inspired spiking neural networks in specific. Large scale simulations and the development of neuromorphic hardware are work-in-progress and future work. Among the applications of the introduced work are neural prostheses and bionic automation systems.

The high cost of processor fabrication plants and approaching physical limits have started a new wave research in alternative computing paradigms. As an alternative to the top-down manufactured silicon-based computers, research in computing using natural and physical system directly has recently gained a great deal of interest. A branch of this research promotes the idea that any physical system with sufficiently complex dynamics is able to perform computation. The power of networks in representing complex interactions between many parts make them a suitable choice for modeling physical systems. Many studies used networks with a homogeneous structure to describe the computational circuits. However physical systems are inherently heterogeneous. We aim to study the effect of heterogeneity in the dynamics of physical systems that pertains to information processing. Two particularly well-studied network models that represent information processing in a wide range of physical systems are Random Boolean Networks (RBN), that are used to model gene interactions, and Liquid State Machines (LSM), that are used to model brain-like networks. In this thesis, we study the effects of function heterogeneity, in-degree heterogeneity, and interconnect irregularity on the dynamics and the performance of RBN and LSM. First, we introduce the model parameters to characterize the heterogeneity of components in RBN and LSM networks. We then quantify the effects of heterogeneity on the network dynamics. For the three heterogeneity aspects that we studied, we found that the effect of heterogeneity on RBN and LSM are very different. We find that in LSM the in-degree heterogeneity decreases the chaoticity in the network, whereas it increases chaoticity in RBN. For interconnect irregularity, heterogeneity decreases the chaoticity in LSM while its effects on RBN the dynamics depends on the connectivity. For {K} < 2, heterogeneity in the interconnect will increase the chaoticity in the dynamics and for {K} > 2 it decreases the chaoticity. We find that function heterogeneity has virtually no effect on the LSM dynamics. In RBN however, function heterogeneity actually makes the dynamics predictable as a function of connectivity and heterogeneity in the network structure. We hypothesize that node heterogeneity in RBN may help signal processing because of the variety of signal decomposition by different nodes.

Liquid State Machines (LSMs) exploit the power of recurrent spiking neural networks (SNNs) without training the SNN. Instead, a reservoir, or liquid, is randomly created which acts as a filter for a readout function. We develop three methods for iteratively refining a randomly generated liquid to create a more effective one. First, we apply Hebbian learning to LSMs by building the liquid with spike-time dependant plasticity (STDP) synapses. Second, we create an eligibility based reinforcement learning algorithm for synaptic development. Third, we apply principles of Hebbian learning and reinforcement learning to create a new algorithm called separation driven synaptic modification (SDSM). These three methods are compared across four artificial pattern recognition problems, generating only fifty liquids for each problem. Each of these algorithms shows overall improvements to LSMs with SDSM demonstrating the greatest improvement. SDSM is also shown to generalize well and outperforms traditional LSMs when presented with speech data obtained from the TIMIT dataset.

Accidents related to falling is a major issue in society, and it is important that a person that suffers an accident is aided as quickly as possible. The purpose of this study is to examine the possibility of using sensors available in smartphones to implement an application for fall detection. The chosen method is a literature study followed by a case study. The literature study is performed to find existing solutions for implementing fall detection in a mobile application and one solution is chosen as a starting point. The case study consists of two parts. In the first part the algorithm found during the literature study is implemented and experiments are performed with purpose to improve the solution. The second part serves to evaluate the implemented solution with respect to accuracy and battery life. The proposed solution is to use accelerometer data coming from the embedded sensors available in smartphones. This data can be fed into a finite state machine to detect possible fall candidates. Properties are extracted from the data, which is analyzed by a pre-trained neural network that perform a classification of the event. The evaluation of the accuracy shows that the iOS and Android implementation reached a success rate in classifying events correctly of 91% and 83%, respectively. The evaluation of battery life shows that this solution can be implemented without consuming to much battery power. / Olyckor relaterade till fall är ett stort problem i samhället, och det är viktigt att en person som är drabbad av en olycka får hjälp så fort som möjligt. Syftet med den här studien är att undersöka möjligheten att använda sensorer tillgängliga i smartphones för att implementera en applikation för falldetektion. Den valda metoden är en litteraturstudie följt av en fallstudie. Litteraturstudien genomförs för att hitta existerande lösningar för att implementera falldetektion i en mobilapplikation, och en lösning väljs som startpunkt. Fallstudien består av två delar. I första delen implementeras algoritmen som hittades i litteraturstudien och experiment genomförs med syftet att förbättra lösningen. Den andra delen syftar till att evaluera den implementerade lösningen med avseende på noggrannhet och batteritid. Den föreslagna lösningen är att använda accelerometerdata från den inbyggda sensorn som finns i smartphones. Data från accelerometern matas in i en finit tillståndsmaskin för att detektera möjliga händelser av fall. Egenskaper extraheras från denna data och analyseras av ett förtränat neuronnät, som genomför en klassificering av händelsen. Evalueringen av noggrannheten visar att iOSoch Androidimplementationen når en precision vid klassificering av händelser på 91% och 83%, respektive. Evalueringen av batteritid visar att lösningen kan implementeras utan för stor batteriförbrukning.