基于MF-R和AWS密钥管理机制的物联网健康监测大数据分析系统

带有传感器的可穿戴式医疗设备不断生成大量数据，由于数据的复杂性，难以通过处理和分析大数据来找到有价值的决策信息。为了解决这个问题，提出了一种新的物联网体系结构，用于存储和处理医疗应用的可扩展传感器数据（大数据）。所提出的架构主要由两个子架构组成：Meta Fog重定向（MF-R）架构和AWS密钥管理机制。MF-R架构使用Apache Pig和Apache HBase等大数据技术来收集和存储不同传感器设备生成的传感器数据，并利用卡尔曼滤波消除噪声。AWS密钥管理机制使用密钥管理方案，目的是保护云中的数据，防止未经授权的访问。当数据存储在云中时，所提出的系统能够使用随机梯度下降算法和逻辑回归来开发心脏病的预测模型。仿真实验表明，和其他几种算法相比，提出的算法具有更小的误差，且在吞吐量、准确度等方面具有一定的优越性。     

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.     

郭庄煤业选煤厂振动筛筛选形式的优化研究

针对郭庄煤业选煤厂振动筛振动筛分方式可靠性差、筛分效率低下、物料筛分率低的难题,本文提出了三种新的振动筛分方案,对不同筛分方案的实际应用效果进行了分析。结果表明三移动一摆动的振动筛分方式对物料的综合分散度最高,稳定性好,该筛分方案已在郭庄煤业选煤厂煤炭筛分过程中得到了全面的应用。     

Kernel-based fourth-order diffusion for image noise removal

The fourth-order partial differential equations have good performance on noise smoothing and edge preservation without creating blocky effects on smooth regions. However, for low signal-to-noise ratio images, the discrimination between edges and noise is a challenging problem. A novel kernel-based fourth-order diffusion is proposed in this paper. It introduces a kernelized gradient operator in the fourth-order diffusion process, which leads to more effective noise removal capability. Experiment results show that this method outperforms several previous anisotropic diffusion methods for noise removal and edge preservation.     

Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

Physics‐based spectral sharpening through filter‐chart calibration

The spectral overlap of color‐sampling filters increases errors when using a diagonal matrix transform, for color correction and reduces color distinction. Spectral sharpening is a transformation of colors that was introduced to reduce color‐constancy errors when the colors are collected through spectrally overlapping filters. The earlier color‐constancy methods improved color precision when the illuminant color is changed, but they overlooked the color distinction. In this article, we introduce a new spectral sharpening technique that has a good compromise of color precision and distinction, based on real physical constraints. The spectral overlap is measured through observing a gray reference chart with a set of real and spectrally disjoint filters selected by the user. The new sharpening method enables to sharpen colors obtained by a sensor without knowing the camera response functions. Experiments with real images showed that the colors sharpened by the new method have good levels of color precision and distinction as well. The color‐constancy performance is compared with the data‐based sharpening method in terms of both precision and distinction. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 564–576, 2015     

Passive target detection and tracking from electromagnetic field measurements

This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to detect and track a possible target by resorting to a particle Bernoulli filter (PBF). To comply with real‐time operation, the PBF works along with an artificial neural network (ANN) model of the environment trained offline via finite elements (FEs). The performance of the proposed algorithm is assessed via simulation experiments.     

轻质滤料滤池的试验与研究

轻质滤料滤池是以比水密度略小的滤料为滤床的滤池，通过以净水厂的原水和污水处理厂的二级处理出水为过滤试验对象，所取得的结果表明：下向流轻质滤料滤池对浊度和悬浮固体（SS）的去除率分别可以达到80％和45％，而滤床的水头损失不超过15cm。因此轻质滤料滤池可以以预过滤的形式配置在过滤处理的净水和污水的处理工艺中。     

超短波水文自动测报系统的抗干扰方法

介绍集中解决超短波水文自动测报系统抗干扰问题的方法，重点推荐一种空腔窄带滤波器。     

低插损窄带型10.7MHz压电陶瓷滤波器的研制

介绍了低插损窄带型10.7 MHz压电陶瓷滤波器的一种设计制作方法。结合压电陶瓷能陷模理论,通过对滤波器分割电极设计和制造工艺控制技术的研究,强调了工艺控制的重要性。产品达到日本村田同类产品水平,对国产化窄带型10.7 MHz压电陶瓷滤波器的研发和生产具有十分重要的意义。认为选用高Qm值压电陶瓷材料、分割电极和耦合电容的设计、焊接和点蜡工艺的控制等是研制低插损窄带型10.7 MHz压电陶瓷滤波器的重点。