Fuzzy C-means based support vector machine for channel equalisation

This paper proposes a new classification network, the fuzzy C-means based support vector machine (FCM–SVM) and applies it to channel equalisation. In contrast to a kernel-based SVM, the FCM–SVM has a smaller number of parameters while retaining the SVM's good generalisation ability. In FCM–SVM, input training data is clustered by FCM. The output of FCM–SVM is a weighted sum of the degrees where each input data belongs to the clusters. To achieve high generalisation ability, FCM–SVM weights are learned through linear kernel based SVM. Computer simulations illustrate the performance of the suggested network, where the FCM–SVM is used as a channel equaliser. Simulations with white Gaussian and coloured Gaussian noise are performed. This paper also compares simulation results from the FCM–SVM, the Gaussian kernel based SVM and the optimal equaliser.     

Psoriasis image identification using k-means clustering with morphological processing

We present a preliminary design and experimental results of psoriasis objects tracking method for color-skin images that utilizes k-means clustering with morphological processing technique. The method is capable of solving unable exactly contoured psoriasis objects problem in color-skin image by adding the morphological reconstruction operation. The key idea of the proposed image processing procedure is the k-means clustering method helps the rough segmentation, then the dilation and erosion method are adapted to refine previous results. In this paper we investigate the possibility of employing this approach for psoriasis image application. The application of the proposed method for tracking psoriasis is demonstrated to help pathologists distinguish exactly its size and region. In this paper, we propose a psoriasis image segmentation procedure to improve the accuracy. The experimental results demonstrate that the misclassification error is very small between the proposed result and hand drawing.     

Applying likelihood on Hopfield neural network for radar tracking

Abstract

The multiple‐target tracking (MTT) algorithm plays an important role in radar systems. Data association is the most important technique to solve the tracking problems associating dense measurements with existing tracks. A new approach applying Likelihood to measurements and existing tracks in a radar system based on Neural Network computation is investigated in this paper. The proposed algorithm will solve both the data association and the target tracking problems simultaneously. With this approach, the matching between radar measurements and existing target tracks can achieve global relevance. Computer simulation results indicate the ability of this algorithm to keep track of targets under various conditions.     

Do the VOCs that evaporate from a heavily polluted river threaten the health of riparian residents?

To understand the potential threat of volatile organic compounds (VOCs) to the health of residents living close to a heavily polluted river, this study investigated the species and the concentration of VOCs evaporating from a river and surveyed the health condition of the nearby residents. Air samples were taken seasonally at the upstream, midstream, and downstream water surfaces of the river, and at different locations at certain distances from the river. These samples were analyzed qualitatively and quantitatively through gas chromatography and electron capture detector (GC/ECD) for chlorinated organic compounds, and through gas chromatography and flame ionization detector (GC/FID) for ordinary hydrocarbons. The health data obtained from valid health questionnaires of 908 residents were analyzed through Statistical Package for Social Science (SPSS) software. Twenty-six species of VOCs were identified in the environment adjacent the river, many of which are carcinogenic or believed to be carcinogenic to humans. However, results of this study shows that the VOCs evaporating from the polluted river have not been definitively identified as a major factor of cancer in the residents. However, the risk of suffering from certain chronic diseases may increase in residents living less than 225 m away from the river due to the high levels of evaporated VOCs. Residents living less than 225 m away from the river and with nearby specific industries are 3.130 times more at risk of suffering from chronic diseases than those with no nearby specific industries.     

The Use of Fuzzy Logic in the Taguchi Method for the Optimisation of the Submerged Arc Welding Process

The use of fuzzy logic in the Taguchi method to optimise the submerged arc welding process with multiple performance characteristics is reported in this paper. An orthogonal array, the signal-to-noise ratio, multiresponse performance index, and analysis of variance are employed to study the performance characteristics in the submerged arc welding process. The process parameters, namely arc current, arc voltage, welding speed, electrode protrusion, and preheat temperature are optimised with considerations of the performance characteristics, including deposition rate and dilution. Experimental results are provided to confirm the effectiveness of this approach.     

A synchronization scheme using self-pulsating laser diodes inoptical chaotic communication

A synchronization scheme using self-pulsating laser diodes in optical chaotic communication is proposed. Optical chaotic light can be obtained by injecting a sinusoidal electronic signal into a self-pulsating laser diode. To synchronize between two identical chaotic systems with different initial conditions, a drive and response system model is constructed according to Pecora and Carroll's theory. Synchronization can be achieved for optical simplex and duplex transmissions provided that the conditional Lyapunov exponents for the drive and response systems are all negative. This approach offers a key step toward realizing optical chaotic modulation and demodulation     

Simplified Procedure for Developing Joint Distribution of amax and Mw for Probabilistic Liquefaction Hazard Analysis

This paper presents an innovative approach for a probabilistic analysis of liquefaction hazard at a given site in a specified exposure time based on the U.S. Geological Survey (USGS) National Seismic Hazard Maps data. The foundation of this new approach is a simplified procedure for obtaining a joint distribution of peak ground surface acceleration (amax) and moment magnitude of earthquake (Mw) at any given locality in the United States based on the USGS website data. The developed joint probability distribution of amax and Mw can be readily combined with the conditional probability of liquefaction (given a pair of amax and Mw) to obtain the total probability of liquefaction at a given site in a specified exposure time. Detailed formulation of the new approach is presented and demonstrated with examples.     

Scanned Probe Oxidation on p-GaAs(100) Surface with an Atomic Force Microscopy

Locally anodic oxidation has been performed to fabricate the nanoscale oxide structures on p-GaAs(100) surface, by using an atomic force microscopy (AFM) with the conventional and carbon nanotube (CNT)-attached probes. The results can be utilized to fabricate the oxide nanodots under ambient conditions in noncontact mode. To investigate the conversion of GaAs to oxides, micro-Auger analysis was employed to analyze the chemical compositions. The growth kinetics and the associated mechanism of the oxide nanodots were studied under DC voltages. With the CNT-attached probe the initial growth rate of oxide nanodots is in the order of ~300 nm/s, which is ~15 times larger than that obtained by using the conventional one. The oxide nanodots cease to grow practically as the electric field strength is reduced to the threshold value of ~2 × 10⁷ V cm⁻¹. In addition, results indicate that the height of oxide nanodots is significantly enhanced with an AC voltage for both types of probes. The influence of the AC voltages on controlling the dynamics of the AFM-induced nanooxidation is discussed.