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.     

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。     

麻柳场嘉二3储层酸化工艺室内实验研究

储层酸化改造是低渗碳酸盐岩油气藏增产的主要措施之一。由于储层性质不同，改造方案的实施效果差异较大。对麻柳场嘉二^3储层酸化工艺室内实验研究和现场实践发现，胶束酸多级注入酸化-闭合酸化工艺对川南麻柳场嘉陵江组的储层改造行之有效。     

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.     

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.     

Evaluating the transfer of technology between application domains: a critical evaluation of the human component in the system

Technology can take many forms from the development of sophisticated electronics to the application of basic procedural principles. Technology transfer can also take several forms, however, in this case, it is the transfer of technological principles between application domains that is of primary interest. This paper presents a framework (the Five ‘M’s framework) that can be used to consider the likely success of technology transfer. The framework is based upon a sociotechnical system approach. The framework is illustrated with an evaluation of the introduction of intelligent cruise control into motor vehicles. It is argued that this is a technological concept first developed as an aerospace application in the form of the autopilot. In this case, it is argued that the transfer of this technology in its current form may potentially compromise safety.     

Decentralized adaptive coordinated control of multiple robot arms without using a force sensor

This paper presents a distributed adaptive coordinated control method for multiple robot arms grasping a common object. The cases of rigid contact and rolling contact are analyzed. In the proposed controller, the dynamic parameters of both object and robot arms are estimated adaptively. The desired motions of the robot arms are generated by an estimated object reference model. The control method requires only the measurements of the positions and velocities of the object and robot arms, but not the measurements of forces and moments at contact points. The asymptotic convergence of trajectory is proven by the Lyapunov-like Lemma. Experiments involving two robot arms handling a common object are shown.