17971442
Exogenous connectors for software components
In existing component models, control originates in components, and connectors are channels for passing on the control to other components. This provides a mechanism for message passing, which allows components to invoke one another's operations by method calls (or remote procedure calls) either directly or indirectly via a channel such as a bus. Thus components in these models mix computation with control, since in performing their computation they also initiate method calls and manage their returns, via connectors. Consequently, in terms of control, components are not loosely coupled. In this paper, we propose exogenous connectors, and demonstrate their use in a small example. In contrast to connectors in existing component models, exogenous connectors initiate calls and manage their returns, and are used to encapsulate control in a component model we are working on. In the example, we demonstrate the feasibility of exogenous connectors, and compare them with connectors in closely related architecture description languages.
10182592
A Software Component Model and Its Preliminary Formalisation
A software component model should define what components are, and how they can be composed. That is, it should define a theory of components and their composition. Current software component models tend to use objects or port-connector type architectural units as components, with method calls and port-to-port connections as composition mechanisms. However, these models do not provide a proper composition theory, in particular for key underlying concepts such as encapsulation and compositionality. In this paper, we outline our notion of these concepts, and give a preliminary formalisation of a software component model that embodies these concepts.
14492161
A taxonomy of software component models
CBSE currently lacks a universally accepted terminology. Existing component models adopt different component definitions and composition operators. We believe that for future research it would be crucial to clarify and unify the CBSE terminology, and that the starting point for this endeavour should be a study of current component models. In this paper, we take this first step and present and discuss a taxonomy of these models. The purpose of this taxonomy is to identify the similarities and differences between them with respect to commonly accepted criteria, with a view to clarification and/or potential unification.
2059324
Choquet integrals and natural extensions of lower probabilities
Abstract Both the Choquet integral with respect to monotone set functions and the natural extensions of lower probabilities are generalizations of the Lebesgue integral with respect to σ-additive measures. The relation between these generalizations is investigated. We show that the Choquet integral with respect to a belief measure is always greater than or equal to the corresponding natural extension. Also, we compare some concepts introduced in the theory of imprecise probabilities with concepts established in fuzzy measure theory, capacity theory, and Dempster-Shafer theory.
17592799
Blind separation of slow waves and spikes from gastrointestinal myoelectrical recordings
Myoelectrical recordings of the gut contain slow waves (slow rhythmicity) and spikes (fast rhythmicity). While the slow wave determines the frequency and propagation of gastrointestinal contractions, spike activities are directly associated with the contractions. Traditionally, spikes are extracted from the myoelectrical recording using high-pass/bandpass filters. Due to the sharp rising edge (high-frequency component) of the slow wave, the conventional method is not accurate in the separation of the slow wave and spikes, although it has been used for years. In this paper, a novel and fast blind source separation algorithm is developed. Experimental results show that the proposed method was able to accurately extract spike activities from the myoelectrical recordings obtained in dogs and that its performance was not affected by the high-frequency components of the slow wave.
28306753
Low hardware complexity parallel turbo decoder architecture
Turbo decoders inherently have low throughput and long latency because of iterative decoding. Parallel processing is a powerful technique for high-throughput applications but often comes with large hardware complexity penalties. In this paper, we present a generic low hardware complexity parallel turbo decoder architecture, which enables multiple soft-input soft-output (SISO) decoders to process one data frame simultaneously. It can achieve multiple times the throughput of a typical serial decoder with a small fraction of hardware overhead. The proposed architecture works for any type of random interleavers, which makes it extremely useful in practical applications where turbo interleave patterns are not free to design. Based on estimation, the illustrated low hardware complexity 2-level parallel turbo decoding architecture can achieve twice the throughput of a typical serial decoding architecture with less than 20% hardware overhead when applied to 3rd generation CDMA systems.
1885677
Learning nonlinear multiregression networks based on evolutionary computation
This paper describes a novel knowledge discovery and data mining framework dealing with nonlinear interactions among domain attributes. Our network-based model provides an effective and efficient reasoning procedure to perform prediction and decision making. Unlike many existing paradigms based on linear models, the attribute relationship in our framework is represented by nonlinear nonnegative multiregressions based on the Choquet integral. This kind of multiregression is able to model a rich set of nonlinear interactions directly. Our framework involves two layers. The outer layer is a network structure consisting of network elements as its components, while the inner layer is concerned with a particular network element modeled by Choquet integrals. We develop a fast double optimization algorithm (FDOA) for learning the multiregression coefficients of a single network element. Using this local learning component and multiregression-residual-cost evolutionary programming (MRCEP), we propose a global learning algorithm, called MRCEP-FDOA, for discovering the network structures and their elements from databases. We have conducted a series of experiments to assess the effectiveness of our algorithm and investigate the performance under different parameter combinations, as well as sizes of the training data sets. The empirical results demonstrate that our framework can successfully discover the target network structure and the regression coefficients.
16274687
Measuring Intra- and Inter-Observer Agreement in Identifying and Localizing Structures in Medical Images
Inter- and intra-observer variability exists in any measurements made on medical images. There are two sources of variability. The first occurs when the observers identify and localize the object of interest, and the second happens when the observers make appropriate measurement on the object of interest. A number of statistical methods are available to quantify the degree of agreement between measurements made by different observers. However, little has been done to develop metrics for quantifying the variability in identifying and localizing the objects of interest prior to measurement. In this paper, we propose to use the complex wavelet structural similarity index (CW-SSIM) method to measure the variability in identifying and localizing structures on images. Performance comparisons using simulated images as well as real mammography images demonstrate the effectiveness and robustness of the CW-SSIM method.