A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

Maximum Data Generation Rate Routing Protocol Based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

For rechargeable wireless sensor networks, limited energy storage capacity, dynamic energy supply, low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario. Therefore, before data delivery, a sensor has to update its waking schedule continuously and share them to its neighbors, which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets. In this work, we propose the maximum data generation rate routing protocol based on data flow controlling technology. For a sensor, it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors. Hence, the energy consumption for time synchronization, location information and waking schedule shared will be reduced significantly. The saving energy can be used for improving data collection rate. Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.     

Probing the degenerate pattern growth of {100}<011> orientation in a directionally solidified Al-4.5 wt% Cu alloy

Degenerate pattern is a seemingly disordered morphology but it exhibits the inherently ordered crystal connected with tip-splitting and limited stability which makes it difficult to observe in the metallic system. Here we employ (100)[011] orientated planar-front seeds using directional solidification and reveal the fundamental origins of the degenerate pattern growth in an Al-4.5 wt% Cu alloy. We find that the spacing of the tip-splitting (λ) in the degenerate of the alloys followed a power law, λ∝V^−0.5, and the frequency (f) of the splitting was related to the growth velocity (V) by ƒ∝V^1.5. The dimensionless growth direction (θ/θ₀) increased monotonously and approached 0.6 with faster velocity, attributed to its anisotropy in the interface kinetics. Once growth velocity exceeded a threshold, two types of pattern transitions from degenerate to regular dendrites were proposed. One of them exhibited a random and chaotic mode and the other underwent a rotation in growth direction.     

Computational modelling of water flow inside laboratory Knelson concentrator bowl

Enhanced gravity concentrators such as Knelson concentrator (KC) are extensively used in the mineral processing industry. The complexities of KC bowl geometry and variation of feed characteristics have forced process engineers to design empirically new units using laboratory and pilot-scale Knelson concentrators. However, numerical modelling methods such as computational fluid dynamics (CFD) and discrete element method (DEM) provide a better insight of flow behaviour of fluid and particulate solid phases inside these processing units. This article reports findings of CFD simulations for single-phase water flow inside the laboratory KC. An available standard 7.5-cm laboratory KC bowl was numerically simulated using realisable k-ε turbulence model to resolve the turbulence dispersion of existing transitional flow regime. The effects of relative centrifugal force (RCF) intensity and bed fluidisation water flow rate on the water velocity and pressure distributions were studied. Simulations confirmed the swirling flow pattern governing inside the bowl. The results revealed that the impact of RCF intensity on the water field values is greater than that of bed fluidisation water flow rate. Both velocity and pressure variations inside the bowl rings followed a linear trend.     

Magnetic Nanoparticle Arrays Self-Assembled on Perpendicular Magnetic Recording Media

We study magnetic-field directed self-assembly of magnetic nanoparticles onto templates recorded on perpendicular magnetic recording media, and quantify feature width and height as a function of assembly time. Feature widths are determined from Scanning Electron Microscope (SEM) images, while heights are obtained with Atomic Force Microscopy (AFM). For short assembly times, widths were ~150 nm, while heights were ~14 nm, a single nanoparticle on average with a 10:1 aspect ratio. For long assembly times, widths approach 550 nm, while the average height grows to 3 nanoparticles, ~35 nm; a 16:1 aspect ratio. We perform magnetometry on these self-assembled structures and observe the slope of the magnetic moment vs. field curve increases with time. This increase suggests magnetic nanoparticle interactions evolve from nanoparticle–nanoparticle interactions to cluster–cluster interactions as opposed to feature–feature interactions. We suggest the aspect ratio increase occurs because the magnetic field gradients are strongest near the transitions between recorded regions in perpendicular media. If these gradients can be optimized for assembly, strong potential exists for using perpendicular recording templates to assemble complex heterogeneous materials.     

Reduced texaphyrin: A ratiometric optical sensor for heavy metals in aqueous solution

We report here a water-soluble metal cation sensor system based on the as-prepared or reduced form of an expanded porphyrin, texaphyrin. Upon metal complexation, a change in the redox state of the ligand occurs that is accompanied by a color change from red to green. Although long employed for synthesis in organic media, we have now found that this complexation-driven redox behavior may be used to achieve the naked eye detectable colorimetric sensing of several number of less-common metal ions in aqueous media. Exposure to In(III), Hg(II), Cd(II), Mn(II), Bi(III), Co(II), and Pb(II) cations leads to a colorimetric response within 10 min. This process is selective for Hg(II) under conditions of competitive analysis. Furthermore, among the subset of response-producing cations, In(III) proved unique in giving rise to a ratiometric change in the ligand-based fluorescence features, including an overall increase in intensity. The cation selectivity observed in aqueous media stands in contrast to what is seen in organic solvents, where a wide range of texaphyrin metal complexes may be prepared. The formation of metal cation complexes under the present aqueous conditions was confirmed by reversed phase high-performance liquid chromatography, ultra-violet-visible absorption and fluorescence spectroscopies, and high-resolution mass spectrometry.     

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。     

Performance evaluation of noise reduction method during on-line monitoring of MV switchgear for PD measurements by non-intrusive sensors

Partial Discharge (PD) measurement is a globally accepted method for insulation diagnosis of electrical assets. The consequences of insulation breakdown are well known. The trend is to move from conventional offline testing to online monitoring for insulation life prediction, which results in the inclusion of high frequency noise in the captured signals. Therefore de-noising is of paramount importance in online monitoring to obtain useful information from the signal.In this research, a 20 kV switchgear panel has been subjected to PD faults in the laboratory and measurements have been carried out by using different non-intrusive sensors including a novel sensor, the D-dot sensor and recorded by a high frequency oscilloscope. The measured results show the effective applicability of sensors for switchgear. The Discrete Wavelet Transform (DWT) has been used to de-noise PD signals in this paper. Time domain and frequency domain comparison of original and de-noised PD signals reveals the significance of this technique for online monitoring of Medium Voltage (MV) switchgear. Finally, an adaptive online de-noising concept, based on automatic de-noising is also proposed in this paper.     

Flow pattern identification and measurement techniques in gas-liquid-liquid three-phase flow: A review

The simultaneous flow of gas, oil, and water forms various flow patterns due to the complex interfacial relationships. Three-phase flow patterns are classified as the gas-liquid and liquid-liquid flow patterns. Pressure drop, void fraction, liquid holdup, and phase distribution are important characteristics of the three-phase flow. These characteristics are generally associated with the three-phase flow patterns. Hence, the knowledge about flow patterns can help to predict the overall behavior of the three-phase flow. Studies have been conducted to identify three-phase flow pattern and their characteristics at various superficial velocities of gas, oil, and water. The major purpose of the studies is to gather information about the three-phase co-current flow and use it for improvement of the efficiency of the flow systems. Therefore, the accuracy of the measurement technique is critical. Several types of flow pattern identification and measurement techniques have been developed to improve accuracy and provide high-quality results. In this article, classical and advanced techniques used for the three-phase flow identification and measurement have been reviewed. The survey will help the researchers working in the area of multiphase flow to choose the right technique based on the objectives of the studies.     

一款紧凑增强型可变阻抗电磁带隙结构

基于传统AI-EBG结构,提出了一种小尺寸的增强型电磁带隙结构,实现了从0.5~9.4 GHz的宽频带-40 dB噪声抑制深度,且下截止频率减少到数百MHz,可有效抑制多层PCB板间地弹噪声。文中同时研究了EBG结构在高速电路应用时的信号完整性问题,使用差分信号方案可改善信号完整性。