103 research outputs found

    Structural Decomposition of STGs

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    Specification of asynchronous circuit behaviour becomes more complex as the complexity of today’s System-On-a-Chip (SOC) design increases. This also causes the Signal Transition Graphs (STGs) – interpreted Petri nets for the specification of asynchronous circuit behaviour – to become bigger and more complex, which makes it more difficult, sometimes even impossible, to synthesize an asynchronous circuit from an STG with a tool like petrify [CKK+96] or CASCADE [BEW00]. It has, therefore, been suggested to decompose the STG as a first step; this leads to a modular implementation [KWVB03] [KVWB05], which can reduce syn- thesis effort by possibly avoiding state explosion or by allowing the use of library elements. A decomposition approach for STGs was presented in [VW02] [KKT93] [Chu87a]. The decomposition algorithm by Vogler and Wollowski [VW02] is based on that of Chu [Chu87a] but is much more generally applicable than the one in [KKT93] [Chu87a], and its correctness has been proved formally in [VW02]. This dissertation begins with Petri net background described in chapter 2. It starts with a class of Petri nets called a place/transition (P/T) nets. Then STGs, the subclass of P/T nets, is viewed. Background in net decomposition is presented in chapter 3. It begins with the structural decomposition of P/T nets for analysis purposes – liveness and boundedness of the net. Then STG decomposition for synthesis from [VW02] is described. The decomposition method from [VW02] still could be improved to deal with STGs from real applications and to give better decomposition results. Some improvements for [VW02] to improve decomposition result and increase algorithm efficiency are discussed in chapter 4. These improvement ideas are suggested in [KVWB04] and some of them are have been proved formally in [VK04]. The decomposition method from [VW02] is based on net reduction to find an output block component. A large amount of work has to be done to reduce an initial specification until the final component is found. This reduction is not always possible, which causes input initially classified as irrelevant to become relevant input for the component. But under certain conditions (e.g. if structural auto-conflicts turn out to be non-dynamic) some of them could be reclassified as irrelevant. If this is not done, the specifications become unnecessarily large, which intern leads to unnecessarily large implemented circuits. Instead of reduction, a new approach, presented in chapter 5, decomposes the original net into structural components first. An initial output block component is found by composing the structural components. Then, a final output block component is obtained by net reduction. As we cope with the structure of a net most of the time, it would be useful to have a structural abstraction of the net. A structural abstraction algorithm [Kan03] is presented in chapter 6. It can improve the performance in finding an output block component in most of the cases [War05] [Taw04]. Also, the structure net is in most cases smaller than the net itself. This increases the efficiency of the decomposition algorithm because it allows the transitions contained in a node of the structure graph to be contracted at the same time if the structure graph is used as internal representation of the net. Chapter 7 discusses the application of STG decomposition in asynchronous circuit design. Application to speed independent circuits is discussed first. Af- ter that 3D circuits synthesized from extended burst mode (XBM) specifications are discussed. An algorithm for translating STG specifications to XBM specifi- cations was first suggested by [BEW99]. This algorithm first derives the state machine from the STG specification, then translates the state machine to XBM specification. An XBM specification, though it is a state machine, allows some concurrency. These concurrencies can be translated directly, without deriving all of the possible states. An algorithm which directly translates STG to XBM specifications, is presented in chapter 7.3.1. Finally DESI, a tool to decompose STGs and its decomposition results are presented

    Compositional approach to design of digital circuits

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    PhD ThesisIn this work we explore compositional methods for design of digital circuits with the aim of improving existing methodoligies for desigh reuse. We address compositionality techniques looking from both structural and behavioural perspectives. First we consider the existing method of handshake circuit optimisation via control path resynthesis using Petri nets, an approach using structural composition. In that approach labelled Petri net parallel composition plays an important role and we introduce an improvement to the parallel composition algorithm, reducing the number of redundant places in the resulting Petri net representations. The proposed algorithm applies to labelled Petri nets in general and can be applied outside of the handshake circuit optimisation use case. Next we look at the conditional partial order graph (CPOG) formalism, an approach that allows for a convenient representation of systems consisting of multiple alternative system behaviours, a phenomenon we call behavioural composition. We generalise the notion of CPOG and identify an algebraic structure on a more general notion of parameterised graph. This allows us to do equivalence-preserving manipulation of graphs in symbolic form, which simplifies specification and reasoning about systems defined in this way, as displayed by two case studies. As a third contribution we build upon the previous work of CPOG synthesis used to generate binary encoding of microcontroller instruction sets and design the corresponding instruction decoder logic. The proposed CPOG synthesis technique solves the optimisation problem for the general case, reducing it to Boolean satisfiability problem and uses existing SAT solving tools to obtain the result.This work was supported by a studentship from Newcastle University EECE school, EPSRC grant EP/G037809/1 (VERDAD) and EPSRC grant EP/K001698/1 (UNCOVER). i

    Hierarchical hidden Markov structure for dynamic correlations: the hierarchical RSDC model.

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    This paper presents a new multivariate GARCH model with time-varying conditional correlation structure which is a generalization of the Regime Switching Dynamic Correlation (RSDC) of Pelletier (2006). This model, which we name Hierarchical RSDC, is building with the hierarchical generalization of the hidden Markov model introduced by Fine et al. (1998). This can be viewed graphically as a tree-structure with different types of states. The first are called production states and they can emit observations, as in the classical Markov-Switching approach. The second are called abstract states. They can't emit observations but establish vertical and horizontal probabilities that define the dynamic of the hidden hierarchical structure. The main gain of this approach compared to the classical Markov-Switching model is to increase the granularity of the regimes. Our model is also compared to the new Double Smooth Transition Conditional Correlation GARCH model (DSTCC), a STAR approach for dynamic correlations proposed by Silvennoinen and Teräsvirta (2007). The reason is that under certain assumptions, the DSTCC and our model represent two classical competing approaches to modeling regime switching. We also perform Monte-Carlo simulations and we apply the model to two empirical applications studying the conditional correlations of selected stock returns. Results show that the Hierarchical RSDC provides a good measure of the correlations and also has an interesting explanatory power.Multivariate GARCH; Dynamic correlations; Regime switching; Markov chain; Hidden Markov models; Hierarchical Hidden Markov models

    The Quantitative Genetics of Neurodevelopment: A Magnetic Resonance Imaging Study of Childhood and Adolescence

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    Understanding the causes of individual differences in brain structure may give clues about the etiology of cognition, personality, and psychopathology, and also may identify endophenotypes for molecular genetic studies on brain development. We performed a comprehensive statistical genetic study of anatomic neuroimaging data from a large pediatric sample (N=600+) of twins and family members from the Child Psychiatry Branch at the NIMH. These analyses included variance decomposition of structural volumetric endophenotypes at several levels of resolution, voxel-level analysis of cortical thickness, assessment of gene by age interaction, several multivariate genetic analyses, and a search for genetically-mediated brain-behavioral relationships. These analyses found strong evidence for a genetic role in the generation of individual differences in brain volumes, with the exception of the cerebellum and the lateral ventricles. Subsequent multivariate analyses demonstrated that most of the genetic variance in large volumes shares a common source. More subtle analyses suggest that although this global genetic factor is the principal determinant of neuroanatomic variability, genetic factors also mediate regional variability in cortical thickness and are different for gray and white matter volumes. Models using graph theory show that brain structure follows small-world architectural rules, and that these relationships are genetically-determined. Structural homologues appeared to be strongly related genetically, which was further confirmed using novel methods for semi-multivariate quantitative genetic analysis at the voxel level. Studies on interactions with age were mixed. We found evidence of gene by age interaction on frontal and temporal lobar volumes, indicating that the role of genetic factors on these structures is dynamic during childhood. Analyses on cortical thickness at a finer scale, however, showed that environmental factors are more important in childhood, and environmental changes were responsible for most of the changes in heritability over this age range. When assessing the relationship between brain and behavior, we found weak negative genetic correlations and positive environmental correlations between IQ and cortical thickness, which appear to partially cancel each other out. More complex models allowing for age interactions suggest that high and low IQ groups have different patterns of gene by age interactions in concordance with prior literature on cortical phenotypes

    Computational Complexity of Strong Admissibility for Abstract Dialectical Frameworks

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    Abstract dialectical frameworks (ADFs) have been introduced as a formalism for modeling and evaluating argumentation allowing general logical satisfaction conditions. Different criteria used to settle the acceptance of arguments arecalled semantics. Semantics of ADFs have so far mainly been defined based on the concept of admissibility. Recently, the notion of strong admissibility has been introduced for ADFs. In the current work we study the computational complexityof the following reasoning tasks under strong admissibility semantics. We address 1. the credulous/skeptical decision problem; 2. the verification problem; 3. the strong justification problem; and 4. the problem of finding a smallest witness of strong justification of a queried argument

    Local Binary Patterns applied to Face Detection and Recognition

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    Nowadays, applications in the field of surveillance, banking and multimedia equipment are becoming more important, but since each application related to face analysis demands different requirements on the analysis process, almost all algorithms and approaches for face analysis are application dependent and a standardization or generalization is quite difficult. For that reason and since many key problems are still not completely solved, the face analysis research community is still trying to cope with face detection and recognition challenges. Although emulating human vision system would be the ideal solution, it is a heuristic and complicated approach which takes into account multiple clues such as textures, color, motion and even audio information. Therefore, and due to the fast evolution of technology that makes it possible, the recent trend is moving towards multimodal analysis combining multiple approaches to converge to more accurate and satisfactory results. Contributions to specific face detection and recognition applications are helpful to enable the face analysis research community to continue building more robust systems by concatenating different approaches and combining them. Therefore, the aim of this research is to contribute by exploring the Local Binary Patterns operator, motivated by the following reasons. On one hand, it can be applied to face detection and recognition and on the other hand due to its robustness to pose and illumination changes. Local Binary Patterns were first used in order to describe ordinary textures and, since a face can be seen as a composition of micro textures depending on the local situation, it is also useful for face description. The LBP descriptor consists of a global texture and a local texture representation calculated by dividing the image into blocks and computing the texture histogram for each one. The global is used for discriminating the most non-face objects (blocks), whereas the second provides specific and detailed face information which can be used not only to select faces, but also to provide face information for recognition. The results will be concatenated in a general descriptor vector, that will be later used to feed an adequate classifier or discriminative scheme to decide the face likeness of the input image or the identity of the input face in case of face recognition. It is in that stage where this research will focus, first evaluating more simple classification methods and then trying to improve face detection and recognition ratios by trying to eliminate features vector redundancy

    Determinate STG decomposition of marked graphs

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    STGs give a formalism for the description of asynchronous circuits based on Petri nets. To overcome the state explosion problem one may encounter during circuit synthesis, a nondeterministic algorithm for decomposing STGs was suggested by Chu and improved by one of the present authors. To find the best possible result the algorithm might produce, it would be important to know to what extent nondeterminism influences the result, i.e. to what extent the algorithm is determinate. The result of the algorithm clearly depends on the partition of output signals that has to be chosen initially. In general, it also depends on the order of computation steps. We prove that for live and bounded marked graphs - a subclass of Petri nets of definite practical importance in the area of circuit design - the decomposition result depends only on the signal partition. In the proof, we also characterize redundant places in these marked graphs as shortcut places; this easy graph-theoretic characterization is of independent interest
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