Some results on generalized residual entropy

Shannon’s entropy plays an important role in the context of information theory. Since this entropy is not applicable to a system which has survived for some units of time, the concept of residual entropy has been developed in the literature. Here we generalize the residual entropy by choosing a convex function ? with ?(1) = 0. In this paper, some orderings and aging properties have been defined in terms of the generalized residual entropy function and their properties have been studied. Quite a few results available in the literature have been generalized and some distributions (viz. uniform, exponential, Pareto, power series, finite range) have been characterized through the generalized residual entropy.     

Symbolic dynamic filtering and language measure for behavior identification of mobile robots

This paper presents a procedure for behavior identification of mobile robots, which requires limited or no domain knowledge of the underlying process. While the features of robot behavior are extracted by symbolic dynamic filtering of the observed time series, the behavior patterns are classified based on language measure theory. The behavior identification procedure has been experimentally validated on a networked robotic test bed by comparison with commonly used tools, namely, principal component analysis for feature extraction and Bayesian risk analysis for pattern classification.     

Recent Advances in Boron Nitride Based Hybrid Polymer Nanocomposites

Boron nitride (BN) is an eminent inorganic compound having many interesting characteristics such as improved oxidation resistance, mechanical strength, good thermal conductivity (TC), higher bandgap, high chemical stability, thermal stability, high hydrophobicity, and electrical insulation. The use of BN as a filler in polymers is a well-established strategy to tailor the properties of polymer composites. Recent studies depict an interesting urge to reap the synergistic effect of various nanofillers with BN in the form of hybrids. Hence the consolidation of the works on BN based hybrid fillers would definitely attract researchers so that these new filler systems could be transformed into useful polymer nanocomposites in future. This review article focuses on the synthesis and characterization of various boron nitride based hybrids in detail. Moreover, the review also throws light on different BN hybrid reinforced polymer nanocomposites (PNCs) and their thermal, electrical, electronic as well as biomedical applications in a detailed manner. Thus the review anticipates serving as a tool toward understanding the recent trends in the field of boron nitride hybrid based ternary polymer composites.     

Evaluation of Concurrent Flaw Populations in Silicon Carbide in Terms of a Modified Weibull Distribution Function

Concurrent surface- and edge-flaw populations are often observed in strength testing of ceramics The overall strength distribution in such ceramic samples has been analyzed in terms of a modified Weibull distribution function. The junction provides an upper and lower strength limit and is characterized by two shape and location parameters. To evaluate the parameters of the distribution function, the type of flaw that causes failure need not be identified. The applicability of the function has been evaluated in terms of experimental results on SiC specimens. The analysis also shows that the relative values of shape parameters determine which flaw type dominates.     

Modeling of microstrip antennas using neural networks techniques: A review

Artificial neural networks modeling have recently acquired enormous importance in microwave community especially in analyzing and synthesizing of microstrip antennas (MSAs) due to their generalization and adaptability features. A trained neural model estimates response very fast, which is nearly equal to its measured and/or simulated counterpart. Thus, it completely bypasses the repetitive use of conventional models as these models need rediscretization for every minor changes in the geometry, which itself is a time‐consuming exercise. The purpose of this article is to review this emerging area comprehensively for both analyzing and synthesizing of the MSAs. During reviewing process, some untouched cases are also observed, which are essentially required to be resolved for antenna designers. Unique and efficient neural networks‐based solutions are suggested for these cases. The proposed neural approaches are validated by fabricating and characterizing of the prototypes too. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:747–757, 2015.     

Modeling of electromagnetic band gap structures: A review

This article represents a comprehensive review of the research carried out on analytical and numerical methods modeling of electromagnetic band‐gap (EBG) structures used in around last two decades. Because of the unique characteristics of the surface wave reduction as well as perfect magnetic conductor (PMC) like behavior, the EBG structures have created their separate existence in antenna engineering society. These structures are being widely used in designing of several microwave planar circuits including printed antennas, printed microwave filters, etc. The purpose of this article is to present an inclusive review of analytical methods as well as numerical methods in the context of modeling of EBG‐structures. Such a review process is rarely carried out in the open literature to the best of authors' knowledge. The review exercise might be helpful to the researchers working on modeling of EBG‐structures as well as of EBG‐structured printed antennas, microwave planar filters, etc.     

Rényi information measure for a used item

Recently, in the literature, a measure of tail heaviness has been proposed based on Rényi entropy. This measure is very useful in the sense that it can be used to measure tail heaviness even for the distributions for which the kurtosis measure does not exist. Nadarajah and Zografos [Nadarajah, Zografos, Information Sciences 153 (2003), 119-138] have derived the expression for this measure for different univariate continuous distributions. But, this measure can only be used for the lifetime of a new item. In case of used item, this measure needs some modification. In this paper, we have modified the measure accordingly so that it can be used in the case of used item and also for the new item. We have also derived expressions for this measure for sixteen different univariate distributions and ten other standard distributions derived from the general distributions used in reliability and survival analysis. The most of the results obtained in the literature in this direction can be obtained as particular cases of our general results.     

Identification of statistical patterns in complex systems via symbolic time series analysis

Identification of statistical patterns from observed time series of spatially distributed sensor data is critical for performance monitoring and decision making in human-engineered complex systems, such as electric power generation, petrochemical, and networked transportation. This paper presents an information-theoretic approach to identification of statistical patterns in such systems, where the main objective is to enhance structural integrity and operation reliability. The core concept of pattern identification is built upon the principles of Symbolic Dynamics, Automata Theory, and Information Theory. To this end, a symbolic time series analysis method has been formulated and experimentally validated on a special-purpose test apparatus that is designed for data acquisition and real-time analysis of fatigue damage in polycrystalline alloys.     

Optimal supervisory control of finite state automata

This paper presents optimal supervisory control of dynamical systems that can be represented by deterministic finite state automaton (DFSA) models. The performance index for the optimal policy is obtained by combining a measure of the supervised plant language with (possible) penalty on disabling of controllable events. The signed real measure quantifies the behaviour of controlled sublanguages based on a state transition cost matrix and a characteristic vector as reported in earlier publications. Synthesis of the optimal control policy requires at most n iterations, where n is the number of states of the DFSA model generated from the unsupervised plant language. The computational complexity of the optimal control synthesis is polynomial in n. Syntheses of the control algorithms are illustrated with two application examples.     

Signed real measure of regular languages for discrete-event automata

This paper presents the concept and formulation of a signed real measure of regular languages for analysis of discrete-event supervisory control systems. The measure is constructed based upon the principles of language theory and real analysis for quantitative evaluation and comparison of the controlled behaviour for discrete-event automata. The marked (i.e. accepted) states of finite-state automata are classified in different categories such that the event strings terminating at good and bad marked states have positive and negative measures, respectively. In this setting, a controlled language attempts to disable as many bad strings as possible and as few good strings as possible. Different supervisors may achieve this goal in different ways and generate a partially ordered set of controlled languages. The language measure creates a total ordering on the performance of the controlled languages, which provides a precise quantitative comparison of the controlled plant behaviour under different supervisors. Total variation of the language measure serves as a metric for the space of sublanguages of the regular language.