Generalised compositionality in graph transformation

We present a notion of composition applying both to graphs and to rules, based on graph and rule interfaces along which they are glued. The current paper generalises a previous result in two different ways. Firstly, rules do not have to form pullbacks with their interfaces; this enables graph passing between components, meaning that components may “learn” and “forget” subgraphs through communication with other components. Secondly, composition is no longer binary; instead, it can be repeated for an arbitrary number of components

Application of ASTAR/precession electron diffraction technique to quantitatively study defects in nanocrystalline metallic materials

Nanocrystalline metallic materials have the potential to exhibit outstanding performance which leads to their usage in challenging applications such as coatings and biomedical implant devices. To optimize the performance of nanocrystalline metallic materials according to the desired applications, it is important to have a decent understanding of the structure, processing and properties of these materials. Various efforts have been made to correlate microstructure and properties of nanocrystalline metallic materials. Based on these research activities, it is noticed that microstructure and defects (e.g., dislocations and grain boundaries) play a key role in the behavior of these materials. Therefore, it is of great importance to establish methods to quantitatively study microstructures, defects and their interactions in nanocrystalline metallic materials. Since the mechanisms controlling the properties of nanocrystalline metallic materials occur at a very small length scale, it is fairly difficult to study them. Unfortunately, most of the characterization techniques used to explore these materials do not have the high enough spatial resolution required for the characterization of these materials. For instance, by applying complex profile-fitting algorithms to X-ray diffraction patterns, it is possible to get an estimation of the average grain size and the average dislocation density within a relatively large area. However, these average values are not enough for developing meticulous phenomenological models which are able to correlate microstructure and properties of nanocrystalline metallic materials. As another example, electron backscatter diffraction technique also cannot be used widely in the characterization of these materials due to problems such as relative poor spatial resolution (which is ~90 nm) and the degradation of Kikuchi diffraction patterns in severely deformed nano-size grain metallic materials. In this study, ASTAR/precession electron diffraction is introduced as a relatively new orientation microscopy technique to characterize defects (e.g., geometrically necessary dislocations and grain boundaries) in challenging nanocrystalline metallic materials. The capability of this characterization technique to quantitatively determine the dislocation density distributions of geometrically necessary dislocations in severely deformed metallic materials is assessed. Based on the developed method, it is possible to determine the distributions and accumulations of dislocations with respect to the nearest grain boundaries and triple junctions. Also, the competency of this technique to study the grain boundary character distributions of nanocrystalline metallic materials is presented

Timing analysis of synchronous data flow graphs

Consumer electronic systems are getting more and more complex. Consequently, their design is getting more complicated. Typical systems built today are made of different subsystems that work in parallel in order to meet the functional re- quirements of the demanded applications. The types of applications running on such systems usually have inherent timing constraints which should be realized by the system. The analysis of timing guarantees for parallel systems is not a straightforward task. One important category of applications in consumer electronic devices are multimedia applications such as an MP3 player and an MPEG decoder/encoder. Predictable design is the prominent way of simultaneously managing the design complexity of these systems and providing timing guarantees. Timing guarantees cannot be obtained without using analyzable models of computation. Data flow models proved to be a suitable means for modeling and analysis of multimedia applications. Synchronous Data Flow Graphs (SDFGs) is a data flow model of computation that is traditionally used in the domain of Digital Signal Processing (DSP) platforms. Owing to the structural similarity between DSP and multimedia applications, SDFGs are suitable for modeling multimedia applications as well. Besides, various performance metrics can be analyzed using SDFGs. In fact, the combination of expressivity and analysis potential makes SDFGs very interesting in the domain of multimedia applications. This thesis contributes to SDFG analysis. We propose necessary and sufficient conditions to analyze the integrity of SDFGs and we provide techniques to capture prominent performance metrics, namely, throughput and latency. These perfor- mance metrics together with the mentioned sanity checks (conditions) build an appropriate basis for the analysis of the timing behavior of modeled applications. An SDFG is a graph with actors as vertices and channels as edges. Actors represent basic parts of an application which need to be executed. Channels represent data dependencies between actors. Streaming applications essentially continue their execution indefinitely. Therefore, one of the key properties of an SDFG which models such an application is liveness, i.e., whether all actors can run infinitely often. For example, one is usually not interested in a system which completely or partially deadlocks. Another elementary requirement known as boundedness, is whether an implementation of an SDFG is feasible using a lim- ited amount of memory. Necessary and sufficient conditions for liveness and the different types of boundedness are given, as well as algorithms for checking those conditions. Throughput analysis of SDFGs is an important step for verifying throughput requirements of concurrent real-time applications, for instance within design-space exploration activities. In fact, the main reason that SDFGs are used for mod- eling multimedia applications is analysis of the worst-case throughput, as it is essential for providing timing guarantees. Analysis of SDFGs can be hard, since the worst-case complexity of analysis algorithms is often high. This is also true for throughput analysis. In particular, many algorithms involve a conversion to another kind of data flow graph, namely, a homogenous data flow graph, whose size can be exponentially larger than the size of the original graph and in practice often is much larger. The thesis presents a method for throughput analysis of SD- FGs which is based on explicit state-space exploration, avoiding the mentioned conversion. The method, despite its worst-case complexity, works well in practice, while existing methods often fail. Since the state-space exploration method is akin to the simulation of the graph, the result can be easily obtained as a byproduct in existing simulation tools. In various contexts, such as design-space exploration or run-time reconfigu- ration, many throughput computations are required for varying actor execution times. The computations need to be fast because typically very limited resources or time can be dedicated to the analysis. In this thesis, we present methods to compute throughput of an SDFG where execution times of actors can be param- eters. As a result, the throughput of these graphs is obtained in the form of a function of these parameters. Calculation of throughput for different actor exe- cution times is then merely an evaluation of this function for specific parameter values, which is much faster than the standard throughput analysis. Although throughput is a very useful performance indicator for concurrent real-time applications, another important metric is latency. Especially for appli- cations such as video conferencing, telephony and games, latency beyond a certain limit cannot be tolerated. The final contribution of this thesis is an algorithm to determine the minimal achievable latency, providing an execution scheme for executing an SDFG with this latency. In addition, a heuristic is proposed for optimizing latency under a throughput constraint. This heuristic gives optimal latency and throughput results in most cases

Saying Hello World with GROOVE - A Solution to the TTC 2011 Instructive Case

This report presents a solution to the Hello World case study of TTC 2011 using GROOVE. We provide and explain the grammar that we used to solve the case study. Every requested question of the case study was solved by a single rule application.Comment: In Proceedings TTC 2011, arXiv:1111.440

Modelling and Analysis Using GROOVE

In this paper we present case studies that describe how the graph transformation tool GROOVE has been used to model problems from a wide variety of domains. These case studies highlight the wide applicability of GROOVE in particular, and of graph transformation in general. They also give concrete templates for using GROOVE in practice. Furthermore, we use the case studies to analyse the main strong and weak points of GROOVE

An Integrated Approach to Determine Phenomenological Equations in Metallic Systems

It is highly desirable to be able to make predictions of properties in metallic materials based upon the composition of the material and the microstructure. Unfortunately, the complexity of real, multi-component, multi-phase engineering alloys makes the provision of constituent-based (i.e., composition or microstructure) phenomenological equations extremely difficult. Due to these difficulties, qualitative predictions are frequently used to study the influence of microstructure or composition on the properties. Neural networks were used as a tool to get a quantitative model from a database. However, the developed model is not a phenomenological model. In this study, a new method based upon the integration of three separate modeling approaches, specifically artificial neural networks, genetic algorithms, and monte carlo was proposed. These three methods, when coupled in the manner described in this study, allows for the extraction of phenomenological equations with a concurrent analysis of uncertainty. This approach has been applied to a multi-component, multi-phase microstructure exhibiting phases with varying spatial and morphological distributions. Specifically, this approach has been applied to derive a phenomenological equation for the prediction of yield strength in a+b processed Ti-6-4. The equation is consistent with not only the current dataset but also, where available, the limited information regarding certain parameters such as intrinsic yield strength of pure hexagonal close-packed alpha titanium

Graph Passing in Graph Transformation

Graph transformation works under the whole world assumption. Therefore, in realistic systems, both the individual graphs and the set of all such graphs can grow very large. In reactive formalisms such as process algebra, on the other hand, each system is split into smaller components which continually interact; the interactions pass information such as names or locations between components. The state spaces for the separate components are typically much smaller, and much efficiency can be gained by analysing system behaviour on this level.In this paper we present a framework for  compositional graph transformation inspired by name-passing calculi, in which (knowledge about) subgraphs can be passed between components. Essentially, we define graph-passing (reactive) component rules and their composition into traditional (reductive) whole-world rules. This extends previous work in which a simpler form of composition was proposed. The main result is a soundness and completeness result for the composition, showing that the transformations induced by the component rules and their whole-world counterparts are equivalent

Incremental pattern matching for regular expressions

Graph pattern matching lies at the heart of any graph transformation-based system. Incremental pattern matching is one approach proposed for reducingthe overall cost of pattern matching over successive transformations by preserving the matches that stay relevant after a rule application. An important issue in any matching scheme, is the ability to properly and consistently deal with various facilities that add to the expressiveness of a GT-tool’s rule language. One such feature is the support for regular path expressions, which would let two nodes to be consideredas a “match”, if a certain path of edges exists between them. In this paper, the incorporation of regular expression support into incremental pattern matching is discussed within the context of the GROOVE tool set. This includes laying down a formal foundation for incremental pattern matching for regular expressions which is then used to justify the extension proposed to add regular expression support to a well-known pattern matching algorithm

Incremental Pattern Matching in Graph-Based State Space Exploration

Graph pattern matching is among the most costly operations in any graph transformation system. Incremental pattern matching aims at reducing this cost by incrementally updating, as opposed to totally recalculating, the possible matches of rules in the graph grammar at each step of the transformation. In this paper an implementation of one such algorithm is discussed with respect to the GROOVE toolset, with a special emphasis put on state space exploration. Specifically, we shall discuss exploration strategies that could better harness the positive aspects of incremental pattern matching in order to gain better performance

Pulse electrochemical deposition and photo-electrochemical characterization of CuInSe2 thin films.

Direct band gap and high absorption coefficient of copper indium diselenide (CIS) make it as one of the most studied ternary chalcogenides for energy conversion. Low cost methods, such as electrochemical deposition are very beneficial because of large scale production possibility, minimum waste of components and no requirement of pure starting materials. The pulse electrodeposition allows independent variation of duty cycle. In this study pulse electrodeposition of polycrystalline thin film of CuInSe2 (CIS) onto ITO glass substrates from aqueous solution containing CuSO4, In2(SO4)3 and SeO2 was carried out. The probable potential for deposition was determined as -0.9 V from cyclic voltammogram. The deposited film was annealed at 400°C under nitrogen gas flow to provide neutral atmosphere to improve the crystalline quality and remove excess selenium. The film was analyzed using X-ray diffraction which confirmed that CIS deposit has tetragonal structure. The chalcopyrite formation and consistency in terms of stoichiometry in the deposit were proved. The optical property of the thin film was determined base on the measurement by using UV-Vis spectrophotometer. The direct band gap for the thin film is around 1.21 eV. As a result, the deposited CIS thin film is a potential candidate to be used in solar cell devices as an energy convertor. Atomic force microscope was employed to monitor the effect of duty cycles on the morphology of the thin film. It is revealed that with increasing duty cycle the surface morphology shift from smooth to dendrite structure. Photo-electrochemical characterization was performed under chopped white light in acidic redox media. It was showed that CIS film is a photosensitive material and stands as p and n-p type semiconductors by adjusting different duty cycles. The photoactivity of the films was highly affected by their surface morphology