A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

Process optimisation in sunflower oil extraction by supercritical CO2

A continuous process for the extraction of sunflower oil using supercritical CO₂, featuring multiple extractors, one oil separator and three cascaded CO₂ recovery vessels operating at different pressures, was devised and studied. For every single equipment of the plant making up the process a mathematical model was built. Experimental tests—consisting in measurements of oil solubility in supercritical CO₂—were carried out in a laboratory-scale apparatus to characterise the behaviour of sunflower oil in the separation from the supercritical fluid. The mathematical model of the whole process was coded in the commercial gPROMS process modelling environment where both its simulation and optimisation—this latter assuming the overall oil production cost as the objective function—were carried out. The process- and economics-related results are discussed and compared with those obtained with traditional and cold-pressing extraction.     

Kinetics of discontinuous precipitation in a Zn-2.5 at % Cu alloy

The morphology and growth kinetics of discontinuous precipitation in a Zn-2.5 at % Cu alloy have been studied in the temperature range 383–583 K by optical and scanning electron microscopy. The precipitate phase has a lamellar morphology, and maintains a statistically constant interlamellar spacing under isothermal growth conditions. The interlamellar spacing increases with an increase in temperature. The isothermal growth kinetics in terms of reaction front migration rate is maximum at 523 K. The upper temperature limit for the occurrence of reaction in this alloy has been predicted to be 643 K. A detailed kinetic analysis of the experimental data using several analytical models has confirmed discontinuous precipitation in this system to be a boundary diffusion controlled reaction, and enabled the determination of the grain boundary chemical diffusivity of Cu in a Zn-rich Zn-Cu alloy in the temperature range studied. The corresponding activation energy values determined in this study, range between 65 to 86 kJ/mol^–1, which compare well with the relevant data in the literature.     

High power GaN‐HEMT SPDT switches for microwave applications

In this article, the design, fabrication, and on‐wafer test of X‐Band and 2–18 GHz wideband high‐power SPDT MMIC switches in AlGaN/GaN technology are presented. The switches have demonstrated state‐of‐the‐art performance and RF fabrication yield better than 65%. Linear and power measurements for different control voltages have been reported and an explanation of the dependence of the power performances on the control voltage is given. In particular, the X‐band switch exhibits a 0.4 dB compression level at 10 GHz when driven by a 38 dBm input signal. The wideband switch shows a compression level of 1 dB at an input drive higher than 38 dBm across the entire bandwidth. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

Energy sector planning using multiple-index pinch analysis

Pinch analysis was initially developed as a methodology for optimizing energy efficiency in process plants. Applications of pinch analysis applications are based on common principles of using stream quantity and quality to determine optimal system targets. This initial targeting step identifies the pinch point, which then allows complex problems to be decomposed for the subsequent design of an optimal network using insights drawn from the targeting stage. One important class of pinch analysis problems is energy planning with footprint constraints, which began with the development of carbon emissions pinch analysis; in such problems, energy sources and demands are characterized by carbon footprint as the quality index. This methodology has been extended by using alternative quality indexes that measure different sustainability dimensions, such as water footprint, land footprint, emergy transformity, inoperability risk, energy return on investment and human fatalities. Pinch analysis variants still have the limitation of being able to use one quality index at a time, while previous attempts to develop pinch analysis methods using multiple indices have only been partially successful for special cases. In this work, a multiple-index pinch analysis method is developed by using an aggregate quality index, based on a weighted linear function of different quality indexes normally used in energy planning. The weights used to compute the aggregate index are determined via the analytic hierarchy process. A case study for Indian power sector is solved to illustrate how this approach allows multiple sustainability dimensions to be accounted for in energy planning.     

Cryptanalysis of RSA with two decryption exponents

In this paper, we consider RSA with N=pq, where p,q are of same bit size, i.e., q<p<2q. We study the weaknesses of RSA when multiple encryption and decryption exponents are considered with same RSA modulus N. A decade back, Howgrave-Graham and Seifert (CQRE 1999) studied this problem in detail and presented the bounds on the decryption exponents for which RSA is weak. For the case of two decryption exponents, the bound was N^0.357. We have exploited a different lattice based technique to show that RSA is weak beyond this bound. Our analysis provides improved results and it shows that for two exponents, RSA is weak when the RSA decryption exponents are less than N^0.416. Moreover, we get further improvement in the bound when some of the most significant bits (MSBs) of the decryption exponents are same (but unknown).     

Reconstruction Based Recognition of Scenes with Multiple Repeated Components

This paper proposes an invariance based recognition scheme for scenes with multiple repeated components. The scheme considers three component subsets which characterize the scene completely. Each such three component subset is reconstructed using single image based information. We have developed a mathematical framework for the projective reconstruction based on relative affine structure of each such three component building block. This is extended to the case when each of the components is a quadric. A set of projective invariants of three quadrics has also been obtained by us. Although the reconstruction scheme is general and applicable to all multiple repeated components, it requires the computation of infinite homography. The infinite homography and hence the reconstruction scheme are only image computable with the given information in the case of translational repetition. We therefore develop a recognition strategy for the specific case of translationally repeated quadrics. As a recognition strategy for scenes with multiple translationally repeated quadric components, we propose to compute and store invariant values for each such three component subsets. Experiments on real data have shown the applicability of this approach for recognition of aerial images of power plants. The discriminatory power of the invariants and the stability of the recognition results have also been experimentally demonstrated.     

Verifying Temporal Properties of Reactive Systems: A STeP Tutorial

We review a number of formal verification techniques supported by STeP, the Stanford Temporal Prover, describing how the tool can be used to verify properties of several versions of the Bakery Mutual exclusion algorithm for mutual exclusion. We verify the classic two-process algorithm and simple variants, as well as an atomic parameterized version. The methods used include deductive verification rules, verification diagrams, automatic invariant generation, and finite-state model checking and abstraction.