Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

基于调谐频率与分频处理的高分辨率反演技术

讨论了基于调谐频率与分频处理的高分辨率反演技术，给出了方法的基本原理，即利用地震数据的调谐频率和分频处理分析，半定量地确定储层厚度，并将其作为地震反演的约束条件。方法的实现过程是：通过层状地质模型的地震正演模拟，建立不同主频子波时地层厚度与调谐频率的关系；利用地震资料时-频分析技术，实现地震资料分频处理；利用合成地震记录标定地震子波主频；利用分频处理结果，借助于调谐频率分析与调谐振幅分析，给出地震反演约束条件；利用反演算法消除地震子波影响，提高和拓展地震资料的频带范围，实现储层的高分辨率成像。普光地区的应用实例表明，调谐频率与分频处理的高分辨率反演方法无需对储层空间变化规律进行假设，适用于强非均质碳酸盐岩储层的高分辨率成像。     

MULTICARRIER DS—CDMA WITH ADAPTIVE MODULATION AND POWER ALLOCATION

Adaptive modulation and power allocation is introduced into the multicarrier DSCDMA system to improve the system performance and bandwidth efficiency.First,the system design appropriate for adaptive modulation and power allocation is given,then the algorithm of adaptive modulation and power allocation is applied.Simulation results demonstrate great performance improvement compared with the fixed modulated one.     

智能天线对移动通信网性能的影响

智能天线是目前移动通信中的热点话题之一，也是提升网络容量和改善传输质量的关键技术。本文简要介绍了智能天线基本概念，原理及其在无线网络中的重大作用。     

Gap modulation in MCu[Q1−xQ′x]F (M=Ba, Sr; Q, Q′=S, Se, Te) and related materials

BaCuQF (Q=S, Se, Te) materials exhibit band gaps that allow transmission of much of the visible spectrum. BaCuSF is transparent in thin-film form with a band gap of 3.1 eV. Band gap estimates for powders of the solid solution series BaCuS_1−xSe_xF were obtained from wavelength-dependent diffuse-reflectance measurements using an integrating sphere. The band gap can be tuned by the substitution of Se for S to 2.9 eV for BaCuSeF. The decrease scales almost linearly with the increase in the volume of the tetragonal unit cell, which is determined primarily by the expansion of the a lattice parameter; the overall volume increase is 7.0% from x=0 to 1. Further reduction of the band gap is observed in BaCuSe_1−xTe_xF solid solutions, where a unit cell volume increase of 5.5% produces a band gap of 2.7 eV in BaCuSe_0.5Te_0.5F. Powders and films of BaCuSF exhibit strong red luminescence under ultraviolet excitation, which is suppressed by K doping. Additional tuning of band gap and electrical properties (the materials are p-type conductors) can be achieved by replacing Ba with Sr.     

自适应阵对角线加载研究

本文分析了对角线加载对自适应阵性能的影响，提出了加载门限的概念，以及加载门限的计算公式，为加载量的选取提供了方法和依据，并将加载研究的结果加以推广，提出了有效的ＬＳ自适应波束形成算法的非零初始化方法。     

Adaptive motion control using neural network approximations

In this paper, we present a new adaptive technique for tracking control of mechanical systems in the presence of friction and periodic disturbances. Radial Basis Functions (RBFs) are used to compensate for the effects of nonlinearly occurring parameters in the friction and periodic disturbance model. Theoretical analysis, such as stability and transient performance, is provided. Furthermore, the performance of the adaptive RBF controller and its non-adaptive counterpart are compared.     

低阈值宽调谐PPLN光参量振荡

用半导体激光 (LD)抽运的声光调QNd∶YVO4 激光器做抽运源 ,实现了准位相匹配的光参量输出 ,其调谐范围为 1 4 36～ 1 7μm。非线性光学介质是多周期极化的LiNbO3 (PPLN)。光参量振荡阈值 10 3μJ(脉宽 2 2ns) ,在抽运光达到阈值 3 3倍的条件下 ,信号光输出能量 4 2 5 μJ ,斜效率 12 5 %。     

Adaptive user modelling in intelligent telephone and email assistants

With the burgeoning complexity and capabilities of modern information appliances and services, user modelling is becoming an increasingly important research area. Simple user profiles already personalise many software products and consumer goods such as digital TV recorders and mobile phones. A user model should be easy to initialise, and it must adapt in the light of interaction with the user. In many cases, a large amount of training data is needed to generate a user model, and adaptation is equivalent to retraining the system. This paper briefly outlines the user modelling problem and work done at BTexact on an intelligent personal assistant (IPA) which incorporates a user profile. We go on to describe FILUM, a more flexible method of user modelling, and show its application to the telephone assistant and email assistant components of the IPA, with tests to illustrate its usefulness.