Asymptotic performance of single parity-check product codes

This article investigates the asymptotic performance of single parity-check (SPC) product codes (PCs) from a decoding point of view. Specifically, the probability of bit error is bounded before and after the decoding of each dimension, similar to the analysis of "iterated codes" by Elias (1954). It is shown that the asymptotic probability of bit error can be driven to zero as the number of dimensions, and hence the block length, increases at signal-to-noise ratios (SNRs) within 2 dB of capacity over the additive white Gaussian noise (AWGN) channel.     

Comparison of total and faecal coliforms as faecal indicator in eutrophicated surface water.

The aim of this study was to evaluate the use of total coliforms (TC) and faecal coliforms (FC) using a membrane filtration method for precise monitoring of faecal pollution in Korean surface water. The samples were collected in Korea from both main rivers and their tributaries. Presumptive TC * FC were enumerated. The ratios of presumptive FC to TC were not constant, but varied widely, and TC were difficult to enumerate because of overgrowth by background colonies. For FC this was not the case. Seven hundred and three purified strains of presumptive TC * FC and their background colonies were biotyped using API 20E. Among 272 presumptive TC, non-faecal related species, Aeromonas hydrophila dominated (34.6%) and E. coli accounted for only 5.1%. In contrast, E. coli made up 89% of the 209 presumptive FC. Furthermore, of 164 background colonies on Endo Agar LES, 54.9% was A. hydrophila, while background colonies on m-FC Agar were few (58 strains), and despite their atypical colony appearance, most of them were biotyped as enteric bacteria. These results reveal that the detection of FC rather than TC using m-FC Agar is more appropriate for faecal pollution monitoring in eutrophicated surface water located in a temperate region.     

Self-reflective segmentation of human bodily motions using recurrent neural networks

For realizing a naturalistic collaboration between the human and the robot, we have to establish the intention sharing from the series of motion data that are observed and exchanged between the human and the machine. In a word, this is a problem to detect "meanings" out of the digitized data stream. In this paper, we propose a novel approach based on semiosis, and present a method of interpreting bodily motions using recurrent neural networks called Elman networks. We made some experiments using the raw data acquired while a human performs a simple task of fetching objects by stretching and folding his/her arm, and demonstrate that the network can learn invariant features of the generalized motion concepts, classify the motion by referring to self-organized memory structure, and understand a task structure of the observed human bodily motion. These capabilities are essential for machine intelligence to establishing the human-robot shared autonomy, a new style of human-machine collaboration proposed in the area of robotics.     

Second harmonic generation from a ferroelectric film

We derive an expression for transmittivity (T_SHG) of second harmonic generation (SHG) signals from a ferroelectric (FE) film. Intensities of up and down fields in the medium are investigated in relation to T_SHG. The derivations are made based on undepletion of input fields and nonlinear wave equation derived from the Maxwell equations. We present two cases: film without mirrors and with partial mirrors. Expressions for the newly derived nonlinear susceptibility coefficients of SHG for real crystal symmetry [J. Opt. Soc. Am. B 19 (2002) 2007] are used to get more realistic results. Variations in T_SHG with respect to film thickness are illustrated.     

High-gain hybrid amplifier consisting of cascaded fluoride-based TDFA and silica-based EDFA in 1458-1540 nm wavelength region

A hybrid optical fibre amplifier is described that consists of a fluoride-based thulium-doped fibre amplifier and a silica-based erbium-doped fibre amplifier connected in a cascade. The amplifier has a gain of more than 25 dB and a noise figure of less than 9 dB over a wide wavelength region of 1458-1540 nm.     

Design procedure of class-E amplifier for off-nominal operation at 50% duty ratio

Design equations for satisfying the off-nominal operating condition [i.e., only the zero-voltage switching (ZVS) condition] of the Class-E amplifier with a linear shunt capacitance at a duty ratio D=0.5 are derived. A new parameter s (V/s), called the slope of switch voltage when the switch turns on is introduced to obtain an image of the distance from the nominal conditions. By examining off-nominal Class-E operation degree of the design freedom of the Class-E amplifier increases by one. In addition various amplifier parameters such as operating frequency, output power, and load resistance range can be set as design specifications. For example, the peak switch voltage and switch current can be taken into account in the design procedure. Examples of a design procedure of the Class-E amplifier for off-nominal operation are given. The theoretical results were verified with PSpice simulation and experiments.     

Gamma-irradiation-induced Ag/SiO2 composite films and their optical absorption properties

Small Ag particles or clusters dispersed mesoporous SiO₂ composite films were prepared by a new method: First the matrix SiO₂ films were prepared by sol-gel process combined with the dip-coating technique, then they were soaked in AgNO₃ solutions followed by irradiation of γ-ray at room temperature and in ambient pressure. The structures of these films were examined by X-ray diffraction (XRD), high-resolution transmission electron microscope (HRTEM), and optical absorption spectroscopy. It has been shown that the Ag particles grown within the porous SiO₂ films are very small, and they are isolated and dispersed from each other with very narrow size distributions. With increasing the soaking concentration and an additional annealing, an opposite peakshift effect of the surface plasmon resonance (SPR) was observed in the optical absorption measurements.     

Volumetric Change and Surface Properties of Temperature-Sensitive Polyvinylalcohol (PVA) Hydrogel

Polyvinylalcohol (PVA) polymer gel is a temperature sensitive polymeric gel, with a critical transition temperature (with H₂O) of 310 K. At higher than 310 K, this temperature sensitive polymer gel shrinks because of discharging water, whereas at lower temperatures, the gel swelled because of absorbing water. The reversibility of the gel's volume change was confirmed by temperature swing. The adsorption behavior of an organic compound onto the PVA polymer gel in water was tested at various temperatures. The amount of adsorbed organic compound increased remarkably at temperatures higher than about 310 K. Then, it was confirmed that the organic compound in PVA polymer gel could be reversibly adsorbed and desorbed by a temperature change between 293 and 323 K. The mechanism of adsorption of the organic compound onto the PVA polymer gel could be explained by hydration and dehydration of polymer gel.     

Classification of audio radar signals using radial basis function neural networks

Radial basis function (RBF) neural networks are used to classify real-life audio radar signals that are collected by a ground surveillance radar mounted on a tank. Currently, a human operator is required to operate the radar system to discern among signals bouncing off tanks, vehicles, planes, and so on. The objective of this project is to investigate the possibility of using a neural network to perform this target recognition task, with the aim of reducing the number of personnel required in a tank. Different signal classification methods in the neural net literature are considered. The first method employs a linear autoregressive (AR) model to extract linear features of the audio data, and then perform classification on these features, i.e, the AR coefficients. AR coefficient estimations based on least squares and higher order statistics are considered in this study. The second approach uses nonlinear predictors to model the audio data and then classifies the signals according to the prediction errors. The real-life audio radar data set used here was collected by an AN/PPS-15 ground surveillance radar and consists of 13 different target classes, which include men marching, a man walking, airplanes, a man crawling, and boats, etc. It is found that each classification method has some classes which are difficult to classify. Overall, the AR feature extraction approach is most effective and has a correct classification rate of 88% for the training data and 67% for data not used for training.