Field-programmable smart-pixel arrays: design, VLSI implementation, and applications

A smart-pixel array is a two-dimensional array of optoelectronic devices that combine optical inputs and outputs with electronic processing circuitry. A field-programmable smart-pixel array (FP-SPA) is a smart-pixel array capable of having its electronic functionality dynamically programmed in the field. Such devices could be used in a diverse range of applications, including optical switching, optical digital signal processing, and optical image processing. We describe the design, VLSI implementation, and applications of a first-generation FP-SPA implemented with the 0.8-microm complementary metal-oxide semiconductor-self-electro-optic effect device technology made available through the Lucent Technologies-Advanced Research Projects Agency Cooperative (Lucent/ARPA/COOP) program. We report spice simulations and experimental results of two sample applications: In the first application, we configure this FP-SPA as an array of free-space optical binary switches that can be used in optical multistage networks. In the second, we configure the device as an optoelectronic transceiver for a dynamically reconfigurable free-space intelligent optical backplane called the hyperplane. We also describe the testing setup and the electrical and the optical tests that demonstrate the correct functionality of the fabricated device. Such devices have the potential to reduce significantly the need for custom design and fabrication of application-specific optoelectronic devices in the same manner that field-programmable gate arrays have largely eliminated the need for custom design and fabrication of application-specific gate arrays, except in the most demanding applications.     

Parameterized complexity of basic decision problems for tree automata

There are many decision problems in automata theory (including membership, emptiness, inclusion and universality problems) that are NP-hard for some classes of tree automata (TA). The study of their parameterized complexity allows us to find new bounds of their nonpolynomial time algorithmic behaviours. We present results of such a study for classical TA, rigid tree automata, TA with global equality and disequality and t-DAG automata. As parameters we consider the number of states, the cardinality of the signature, the size of the term or the t-dag and the size of the automaton.     

Identification of defect-prone classes in telecommunication software systems using design metrics

The goal of this paper is to investigate the relation between object-oriented design choices and defects in software systems, with focus on a real-time telecommunication domain. The design choices are measured using the widely accepted metrics suite proposed by Chidamber and Kemerer for object oriented languages [S.R. Chidamber, C.F. Kemerer, A metrics suite for object oriented design, IEEE Transactions on Software Engineering 20 (6) (1994) 476-493].This paper reports the results of an extensive case study, which strongly reinforces earlier, mainly anecdotal, evidence that design aspects related to communication between classes can be used as indicators of the most defect-prone classes.Statistical models applicable for the non-normally distributed count data are used, such as Poisson regression, negative binomial regression, and zero-inflated negative binomial regression. The performances of the models are assessed using correlations, dispersion coefficients and Alberg diagrams.The zero-inflated negative binomial regression model based on response for a class shows the best overall ability to describe the variability of the number of defects in classes.     

A fuzzy ensemble of parallel polynomial neural networks with information granules formed by fuzzy clustering

In this paper, we introduce a new category of fuzzy models called a fuzzy ensemble of parallel polynomial neural network (FEP²N²), which consist of a series of polynomial neural networks weighted by activation levels of information granules formed with the use of fuzzy clustering. The two underlying design mechanisms of the proposed networks rely on information granules resulting from the use of fuzzy C-means clustering (FCM) and take advantage of polynomial neural networks (PNNs).The resulting model comes in the form of parallel polynomial neural networks. In the design procedure, in order to estimate the optimal values of the coefficients of polynomial neural networks we use a weighted least square estimation algorithm. We incorporate various types of structures as the consequent part of the fuzzy model when using the learning algorithm. Among the diverse structures being available, we consider polynomial neural networks, which exhibit highly nonlinear characteristics when being viewed as local learning models.We use FCM to form information granules and to overcome the high dimensionality problem. We adopt PNNs to find the optimal local models, which can describe the relationship between the input variables and output variable within some local region of the input space.We show that the generalization capabilities as well as the approximation abilities of the proposed model are improved as a result of using polynomial neural networks. The performance of the network is quantified through experimentation in which we use a number of benchmarks already exploited within the realm of fuzzy or neurofuzzy modeling.     

Fostering scholarly productivity through academic and internship research training environments.

How can training programs foster counseling and clinical psychologists' scholarly productivity? This study examined the impact of academic and internship research training environments (RTEs) on the scholarly activity of 223 early career professionals. Results supported the construct validity of the Internship Research Training Environment scale through cross-validation and confirmatory factor analysis. Findings revealed differences in perceptions of the internship RTE and scholarly productivity as a function of academic training philosophy and internship training philosophy. Finally, results suggested that academic and internship RTEs could influence research interest and scholarly productivity indirectly by enhancing research self-efficacy and research outcome expectations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Rough-fuzzy collaborative clustering.

In this study, we introduce a novel clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined by exchanging prototypes of the subsets of data and by moving prototypes of the corresponding clusters toward each other. Thereby, the required communication links are established at the level of cluster prototypes and partition matrices, without hampering the security concerns. A detailed clustering algorithm is developed by integrating the advantages of both fuzzy sets and rough sets, and a measure of quantitative analysis of the experimental results is provided for synthetic and real-world data.     

A Human–Computer Cooperation Fuzzy c‐Means Clustering with Interval‐Valued Weights

In this paper, a fuzzy c‐means clustering algorithm based on interval‐valued weights is proposed for improving clustering performance. In the proposed algorithm, the interval‐valued weights are first constructed by synergy of the ReliefF algorithm and the analytic hierarchy process (AHP) method, and then they are transformed into a constraint condition associating with each weight variable in the weighted clustering objective function. In the sequence, the weighted clustering objective function is solved by combining the Lagrange multiplier method with the gradient‐based iteration computation. In the whole process of algorithm iteration, a compulsion strategy with human–computer cooperation is adopted to ensure each weight variable satisfies interval constraint itself. Three well‐known data set are used to perform profound experiments. Experimental results clearly show that the proposed algorithm has better clustering performance than other the weighted fuzzy c‐means clustering algorithm.