A three-dimensional surface measurement system implemented with Gaussian process based adaptive sampling

Self-adaptive surface measurements that can reduce data redundancy and improve time efficiency are in high demand in many fields of science and technology. For this purpose, a system implemented with Gaussian process (GP) adaptive sampling is developed. The non-parametric GP model is applied to reconstruct the topography and guide the subsequent sampling position, which is determined from the inference uncertainty estimation. A criterion is proposed to terminate the GP adaptive measurement automatically without any prior model or data of the topography. Experiments on typical surfaces validate the intelligence, adaptability, and high accuracy of the GP method along with the stabilization of the automatic iteration termination. Compared with traditional raster sampling, data redundancy is reduced and the time efficiency is improved without sacrificing the surface reconstruction accuracy. The proposed method can be implemented in other systems with similar measurement principles, thus benefitting surface characterizations.     

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.     

Optimal filtering and control for wireless networked closed-loop control systems

This article studies the optimal filtering and control for wireless networked control systems (WNCSs). In WNCSs, packets may be lost in both control and feedback channels and user datagram protocol is usually used to improve the performance of the real-time control. Relevant literature indicates that the conventional optimal filtering for such a system cannot be applied in practice due to the complex calculation with Gaussian mixtures. This paper proposes a novel scheme to realize the optimal filtering and the linear quadratic Gaussian control for WNCSs, in which the controlled node performs a local estimation and the remote-control node performs the final estimation and control, and a synchronization of two estimators is guaranteed by a communication mechanism. An optimal filtering algorithm is developed, the stability condition of the filtering error covariance is obtained, optimal finite-horizon and infinite-horizon control are derived, and the stability of the closed-loop control system is proved. Numerical simulations show the validity and feasibility of the theoretical results.     

Enhanced thermoelectric properties of atomic-layer-deposited Hf:Zn16O/18O superlattice films by interface-engineering

This study examines three novel approaches for enhancing the thermoelectric (TE) properties of atomic-layer-deposited (ALD) ZnO thin films: 1) Hf-doping, which preserved the crystallinity of ZnO and provided effective phonon scattering owing to Hf's similar atomic radius to and large mass difference with Zn, leading to high power factor (PF) and low thermal conductivity (κ); 2) controlling the distribution of Hf into an alternating scattered phase/clustered phase superlattice, which balanced the high PF of the scattered phases with the low κ of the clustered phases, while providing significant energy-filtering effect to raise the Seebeck coefficient; 3) introducing ¹⁸O/¹⁶O periodicity into the Hf:ZnO films—by alternately using H₂¹⁶O and H₂¹⁸O as oxidants in the ALD processes, which further suppressed κ without compromising PF. The combination of the three approaches resulted in a maximum improvement in ZT of ~1600% over that of the undoped ZnO.     

AT89C2051芯片在车载产品中的应用

本文论述了采用单片机技术研制成功的泊车用超声波测距仪的基本原理,用AT89C2051作为控制器的实现方案,以及采用软件校正的方法,提高了测量精度和整机的可靠性.     

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.     

基于矢量平均的信道估计算法

提出一种适用于DS-CDMA无线通信系统的低计算量而性能良好的信道估计算法。在DS-CDMA系统中，当接收信号经过解扩或多用户检测等时域预处理后，可以认为干扰大大减弱、期望信号在处理后的信号中占主要地位。这样，用预处理后信号相关阵的最大特征值对应的特征矢量可以很好地近似期望信号的信道矢量。但是，直接特征分解需要很大的计算量，特征跟踪计算量较低，但瞬态性能较差。本文提出一种基于相关矩阵列矢量平均的信道估计算法，该算法不需要特征分解或跟踪。仿真结果表明：新算法在降低计算量的情况下可以获得同直接特征分解方法几乎相同的性能。     

具有控制项的限定记忆卡尔曼滤波器

在已有的限定记忆卡尔曼滤波器的基础上 ,将确定性先验信息作为控制项加以应用 ,推导出了完整的限定记忆卡尔曼滤波公式 ,从而在记忆长度确定的情况下 ,有效地减小了模型不准的误差 ,降低了滤波总误差。仿真同样表明了该方法的有效性