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.

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Developers often spend much of their valuable development time on software debugging and bug finding. In addition, software defects cost software industry as a whole hundreds or even a trillion of US dollars. As a result, many fault localization (FL) techniques for localizing bugs automatically, have been proposed. Despite its popularity, adopting FL in industrial environments has been impractical due to its undesirable accuracy and high runtime overhead cost. Motivated by the real-world challenges of FL applicability, this dissertation addresses these issues by proposing two main enhancements to the existing FL. First, it explores different strategies to combine a variety of program execution information with Information Retrieval-based fault localization (IRFL) techniques to increase FL's accuracy. Second, this dissertation research invents and experiments with the unconventional techniques of Instant Fault Localization (IFL) using the innovative concept of triggering modes. Our empirical evaluations of the proposed approaches on various types of bugs in a real software development environment shows that both FL's accuracy is increased and runtime is reduced significantly. We find that execution information helps increase IRFL's Top-10 by 17–33% at the class level, and 62–100% at the method level. Another finding is that IFL achieves as much as 100% runtime cost reduction while gaining comparable or better accuracy. For example, on single-location bugs, IFL scores 73% MAP, compared with 56% of the conventional approach. For multi-location bugs, IFL's Top-1 performance on real bugs is 22%, just right below 24% that of the existing FL approaches. We hope the results and findings from this dissertation help make the adaptation of FL in the real-world industry more practical and prevalent. / Doctor of Philosophy / In software engineering, fault localization (FL) is a popular technique to automatically find software bugs, which cost a huge loss of hundreds of billions of US dollars on the software industry. Despite its high demanding and popularity, adopting FL in industrial software companies remains impractical. To help resolve this applicability problem, this dissertation proposed enhanced techniques to localize bugs more accurately and with less overhead runtime expenses. As a result, FL becomes more practical and efficient for software companies.

[pt] Espera-se que o desenvolvimento de suítes de teste a partir de modelos possa contribuir substancialmente para a redução do esforço humano e aumentar a eficácia dos testes gerados. Entende-se por eficácia (ideal) dos testes o percentual de defeitos existentes encontrados a partir desses testes. Uma grande parte dessas técnicas baseia-se em máquinas de estado e quase sempre estão voltadas para o teste de funcionalidade. Entretanto, existe a necessidade de se poder testar sistemas altamente interativos, tais como smartphones e tablets, a partir de uma descrição de sua interface humano-computador. O objetivo da dissertação é efetuar uma primeira avaliação de uma técnica voltada para a geração de suítes de teste visando o teste de interfaces gráficas. Para tal,desenvolvemos e avaliamos a eficácia de uma ferramenta, chamada Easy, que utiliza casos de uso tabulares e máquina de estados para a geração automática da suíte de testes. Os casos de uso são descritos em linguagem natural restrita. A partir dessa descrição, a ferramenta constrói uma máquina de estado e, a seguir, a utiliza para gerar cenários. Por construção os cenários estarão em conformidade com os casos de uso. Cada cenário corresponde a um caso de teste. Os cenários são apresentados ao usuário em linguagem natural restrita, permitindo a visualização destes antes da geração dos scripts finais de testes. Os scripts gerados são destinados a uma ferramenta de execução automatizada voltada para o teste de interfaces gráficas. Neste trabalho, utilizou-se a ferramenta UI Automation, responsável pela execução de testes em aplicações destinadas ao iOS, sistema operacional de iPhone, iPad e iPod Touch. A eficácia do processo foi avaliada em uma aplicação real, disponível na loja virtual de aplicativos App Store. Além disso, foram realizados testes de IHC afim de avaliar a influência no custo da produção da suíte de teste. / [en] It is expected that the development of test suites from models can contribute substantially to reducing the human effort and to increase the effectiveness of the generated tests. Means for tests’ effectiveness (ideal) the percentage of existing defects found by these tests. Most of these techniques is based on state machines and mostly directed to testing the functionality. However, there is a need to be able to test highly interactive systems, such as smartphones and tablets, from a description of its human computer interface. The goal of the dissertation is to make a first evaluation of a technique aimed to generate test suites for test of human computer graphic interface. For this purpose was developed and evaluated its effectiveness, a tool called Easy, using use cases tabular and state machine for the automatic generation of the suite tests. The use cases are described in natural language restricted. From this description, the tool builds a state machine, and then uses this to generate scenarios. By construction scenarios will be in accordance with the use cases. Each scenario corresponds to a test case. The scenarios are presented to the user in natural language, allowing the visualization of them before the generation of the final scripts tests. The generated scripts are intended to a running automated tool geared to testing graphical interfaces. In this work, we used the UI Automation tool, responsible for running tests on applications for the iOS, operational system for iPhones, iPads and iPod touchs. The effectiveness of the procedure was evaluated in a real application, available in the online store applications App Store. In addition, HCI tests were performed in order to evaluate the influence on the cost of production of the test suite.