Blind equalization of quadrature partial response‐trellis coded modulated signals in Rician fading

In this paper, a blind maximum‐likelihood channel estimation algorithm is developed for quadrature partial response‐trellis coded modulated (QPR‐TCM) signals propagating through a Rician fading environment. A hidden Markov model (HMM) formulation of the problem is introduced and the Baum–Welch parameter estimation algorithm is modified to provide a computationally efficient solution to the resulting optimization problem. Performance analysis of the proposed method is carried out through the evaluation of bit‐error probability upper bound for Rician fading channels. Also, some illustrative simulations are presented. Copyright © 2001 John Wiley & Sons, Ltd.     

Effect of tempering temperature and microstructure on the corrosion behavior of a tempered steel

In this study, a tempered martensitic matrix was obtained in a low carbon steel, by applying austenization, quenching and tempering heat treatments. After austenization at 1000°C for 30 minutes, steel samples were quenched in water and then tempered at 200, 540 and 600°C for 30 minutes. Hardness measurements were done and then immersion tests were carried out in a 3.5 wt % NaCl solution for periods ranging between 1–7 days. Weight losses of the samples were determined after each immersion period and microstructural studies were performed on the corroded surfaces. Corrosion rates were calculated using weight loss data and verified by potentiodynamic tests. Results revealed that corrosion behavior of the experimental steels was directly affected by tempering temperature, hardness and microstructure.     

High output impedance current-mode lowpass,bandpass and highpass filters using current controlled conveyors

Three new current-mode second-order filter configurations which employ one or two current controlled conveyors (CCCIIs) and three passive elements are presented. Each proposed filter can realize one of the bandpass, lowpass or highpass responses all at high impedance outputs. The proposed filters offer current-control of pole angular frequency ω_o without disturbing the parameter ω_o /Q. No component matching is required and all the passive and active sensitivities are low. PSPICE simulation results are given to confirm the theoretical analysis.     

Modifying of Cotton Fabric Surface with Nano-ZnO Multilayer Films by Layer-by-Layer Deposition Method

ZnO nanoparticle–based multilayer nanocomposite films were fabricated on cationized woven cotton fabrics via layer-by-layer molecular self-assembly technique. For cationic surface charge, cotton fabrics were pretreated with 2,3-epoxypropyltrimethylammonium chloride (EP3MAC) by pad-batch method. XPS and SEM were used to examine the deposited nano-ZnO multilayer films on the cotton fabrics. The nano-ZnO films deposited on cotton fabrics exhibited excellent antimicrobial activity against Staphylococcus aureus bacteria. The results also showed that the coated fabrics with nano-ZnO multilayer films enhanced the protection of cotton fabrics from UV radiation. Physical tests (tensile strength of weft and warp yarns, air permeability and whiteness values) were performed on the fabrics before and after the treatment with ZnO nanoparticles to evaluate the effect of layer-by-layer (LbL) process on cotton fabrics properties.     

Forecasting of entropy production due to buoyant convection using support vector machines (SVM) in a partially cooled square cross-sectional room

Forecasting of entropy generation of laminar natural convection in a partially cooled square cross-sectional room has been performed using support vector machines (SVM). The two-dimensional room was modeled as floor heating story with a window. Values of temperature and velocities were obtained by solving governing equations of natural convection with finite difference technique and using these values entropy generation was calculated with its definition. Forecasting of entropy generation due to fluid friction irreversibility (FFI) and heat transfer irreversibility (HTI) were made with known values for unknown parameters using SVM. Thus, calculation time was extremely reduced and values were obtained even for non-convergence cases. It was observed that the SVM was a strong method to predict the entropy generation without computational fluid dynamic analysis for all cases.     

Effect of Boron and Heat Treatment on Mechanical Properties of White Cast Iron for Mining Application

 Heat treatment methods were applied to white cast iron for improving the impact and wear resistance. Additionally, chemical composition optimization was made. Furthermore, the effect of boron addition on such applications was investigated. Samples were investigated by using optical and electron microscope methods. Hardness, wear and impact tests were conducted. The results showed that the secondary carbides in the standard alloy were iron-enriched, needle-like carbides M3C when the boron-added alloy contained Fe23(C,B)6 type, globular secondary carbides. It was concluded that heat treatment B provided higher wear and hardness properties, compared to the standard heat treatment. Optimum mechanical properties were obtained by lower destabilisation temperatures and increasing temperature reduced the wear resistance and hardness.     

A decision support system for locating weapon and radar positions in stationary point air defence

In this study, a decision support system (DSS) based on the interactive use of location models and geographical information systems (GIS) was developed to determine the optimal positions for air defence weapons and radars. In the location model, the fire units are considered as the facilities to be located and the possible approach routes of air vehicles are treated as demand points. Considering the probability that fire by the units will miss the targets, the objective of the problem is to determine the positions that provide coverage of the approach routes of the maximum number of weapons while considering the military principles regarding the tactical use and deployment of units. In comparison with the conventional method, the proposed methodology presents a more reliable, faster, and more efficient solution. On the other hand, owing to the DSS, a battery commander who is responsible for air defence becomes capable of determining the optimal weapon and radar positions, among the alternative ones he has identified, that cover the possible approach routes maximally. Additionally, he attains the capability of making such decisions in a very short time without going to the field over which he will perform the defence and hence without being subject to enemy threats. In the decision support system, the digital elevation model is analysed using Map Objects 2.0, the mathematical model is solved using LINGO 4.0 optimization software, and the user interface and data transfer are supported by Visual Basic 6.0.     

Nonlinear transformation of term frequencies for term weighting in text categorization

In automatic text categorization, the influence of features on the decision is set by the term weights which are conventionally computed as the product of term frequency and collection frequency factors. The raw form of term frequencies or their logarithmic forms are generally used as the term frequency factor whereas the leading collection frequency factors take into account the document frequency of each term. In this study, it is firstly shown that the best-fitting form of the term frequency factor depends on the distribution of term frequency values in the dataset under concern. Taking this observation into account, a novel collection frequency factor is proposed which considers term frequencies. Five datasets are firstly tested to show that the distribution of term frequency values is task dependent. The proposed method is then proven to provide better F₁ scores compared to two recent approaches on majority of the datasets considered. It is confirmed that the use of term frequencies in the collection frequency factor is beneficial on tasks which does not involve highly repeated terms. It is also shown that the best F₁ scores are achieved on majority of the datasets when smaller number of features are considered.