Enhancing robustness and perturbation tolerance of cognitive radio engines with metacognition

A cognitive radio engine (CE) is an intelligent agent which observes the radio environment and chooses the best communication settings that best meet the application’s goal. In this process, providing reliable performance is one of the major tasks in designing CEs for wireless communication systems. The main purpose of this work is providing predictable performance and controlling the cost of intelligent algorithms based on the CE’s experience and complexity analysis respectively. In this work, we extend our meta-CE design to control the cost of computations and provide more reliable performance for providing the minimum requirement of the radio applications in different scenarios. To achieve this, we use robust training algorithm (RoTA) in two different levels alongside of the individual CE algorithms. The RoTA, enables radio to guarantee some minimum output performance based on the learning stages. RoTA uses confidence interval approximation for standard normal distribution to calculate the lower and upper bounds of CE’s expected performance to analyze the reliability of decisions. Moreover, in the case of non-stationary environments, RoTA is facilitated by forgetfulness factor to provide minimum performance guarantees. The second level of RoTA operates in meta-level to control the amount of computation complexity of intelligent algorithms in all levels with respect to the obtained performance and complexity analysis.     

Fuzzy-based artificial bee colony optimization for gray image segmentation

In this article, we have proposed an image segmentation algorithm FABC, which is a kind of unsupervised classification (clustering), where we combine the concept of artificial bee colony optimization (ABC) and the popular fuzzy C means (FCM) and named it as fuzzy-based ABC or FABC. In FABC, we have used fuzzy membership function to search for optimum cluster centers using ABC. FABC is more efficient than other optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and expectation maximization (EM) algorithms. FABC overcomes the drawbacks of FCM as it does not depend on the choice of initial cluster centers and it performs better in terms of convergency, time complexity, robustness and segmentation accuracy. FABC becomes more efficient as it takes the advantage of the randomized characteristics of ABC for the initialization of the cluster centers. The experiments with FABC, GA, PSO and EM have been done over various grayscale images including some synthetic, medical and texture images, and segmentation of such images is very difficult due to the low contrast, noise and other imaging ambiguities. The efficiency of FABC is proven by both quantitative and qualitative measures.     

Survivable elastic optical networks: survey and perspective (invited)

Elastic optical networks (EONs) have been receiving extensive attention because of their inherent flexibility and the efficiency with which they allocate fiber-bandwidth. For an EON, survivability is of crucial importance because of the very high bandwidth that it carries on each optical channel. In this article, we review the current state of the art of survivable EONs which reviews the literature to summarize the following aspects: (a) spectrum resource sharing among backup lightpaths, (b) sharing of high-speed optical transponders, (c) effect of spectrum conversion, (d) bandwidth squeezed restoration (BSR), (e) joint restoration by multiple sub-band optical channels, (f) multi-layer survivability, and (g) energy efficiency. Apart from a summary on the current research status, we also discuss open research issues which are important to survivable EONs from the perspectives of (a) impact of spectrum conversion, (b) impact of elastic optical transponder configuration, (c) impact of physical layer impairments and limitations, (d) protection-path-based spectrum defragmentation, and (e) network availability.     

A Tutorial on GrÖbner Bases With Applications in Signals and Systems

This paper is a tutorial on GrÖbner bases and a survey on the applications of GrÖbner bases in the broad field of signals and systems. A reasonably detailed review is given of several fundamental theoretical issues that occur in the use of GrÖbner bases in multidimensional signals and systems applications. These topics include the primeness of multivariate polynomial matrices, multivariate unimodular polynomial matrix completion, and prime factorization of multivariate polynomial matrices. A brief review is also presented on the wide-ranging applications of GrÖbner bases in multidimensional as well as one-dimensional circuits, networks, control, coding, signals, and systems and other related areas like robotics and applied mechanics. The impact and scope of GrÖbner bases in signals and systems are highlighted with respect to what has already been accomplished as a stepping stone to expanding future research.      

Microcomputer Control of a Residential Photovoltaic Power Conditioning System

Microcomputer-based control of a residential photovoltaic power conditioning system is described. The microcomputer is responsible for array current feedback control, maximum power tracking control, array safe zone steering control, phase-locked reference wave synthesis, sequencing control, and some diagnostics. The control functions are implemented using Intel 8751 single-chip microcomputer-based hardware and software. The controller has been tested in the laboratory with the prototype power conditioner and shows excellent performance.     

Properties determining choice of mother wavelet

Properties of wavelets with finite as well as infinite support are summarised to facilitate mother wavelet selection in a chosen application. The quantitative guidelines reduce dependence on trial-and-error schemes resorted to for selection and underscore the importance of such selection in any application of interest. In wavelet-based image sequence superresolution, studied during the last four years, the use of a B-spline mother wavelet is justified.     

Application of the dielectric constant measurements to study the influence of the small quantities of water vapor on the compressibility factor of methane

Dielectric measurements have been applied successfully to obtain accurate values of the compressibility factor of methane in the presence of the small quantities of water vapor [20, 65, and 95 ppm(v)]. Up to about 95 ppm(v) of H₂O in CH₄, the compressibility factor decreases very slightly in the presence of H₂O. The compressibility factor of dry CH₄ determined by the dielectric method is compared with the best PVT results and the Goodwin equation of state. The compressibility factor of CH₄ having small quantities of water vapor is compared with the Goodwin, GERG, and AGA8 equations.     

Thermal properties of MoSi2 and SiC whisker-reinforced MoSi2

The heat capacity, thermal conductivity and coefficient of thermal expansion of MoSi₂ and 18 vol % SiC whisker-reinforced MoSi₂ were investigated as a function of temperature. The materials were prepared by hot isostatic pressing between 1650 and 1700 °C, the hold time at temperature being 4 h. The heat capacity of MoSi₂ showed an increase from about 0.44 Wsg^–11K^–1 at room temperature to 0.53 at 700 °C. Whisker reinforcement increased heat capacity by about 10%. Thermal conductivity exhibited a decreasing trend from 0.63 Wcm^–1 K^–1 at room temperature to 0.28 Wem^–1 K^–1 at 1400°C. Whiskers reduced conductivity by about 10%. The thermal expansion coefficient increased from 7.42 °C^–1 between room temperature and 200 °C to 9.13 °C^–1 between room temperature and 1200 °C. There was a 10% decrease resulting from the whiskers. The measured data are compared with literature values. The trends in the data and their potential implications for high-temperature aerospace applications of MoSi₂ are discussed.