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.     

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。     

ECISER: Efficient Clip-art Image SEgmentation by Re-rasterization

Clip-art image segmentation is widely used as an essential step to solve many vision problems such as colorization and vectorization. Many of these applications not only demand accurate segmentation results, but also have little tolerance for time cost, which leads to the main challenge of this kind of segmentation. However, most existing segmentation techniques are found not sufficient for this purpose due to either their high computation cost or low accuracy. To address such issues, we propose a novel segmentation approach, ECISER, which is well-suited in this context. The basic idea of ECISER is to take advantage of the particular nature of cartoon images and connect image segmentation with aliased rasterization. Based on such relationship, a clip-art image can be quickly segmented into regions by re-rasterization of the original image and several other computationally efficient techniques developed in this paper. Experimental results show that our method achieves dramatic computational speedups over the current state-of-the-art approaches, while preserving almost the same quality of results.     

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.     

An ideal host-guest system to accomplish high-performance greenish yellow and hybrid white organic light-emitting diodes

Greenish yellow organic light-emitting diodes (GYOLEDs) have steadily attracted researcher's attention since they are important to our life. However, their performance significantly lags behind compared with the three primary colors based OLEDs. Herein, for the first time, an ideal host-guest system has been demonstrated to accomplish high-performance phosphorescent GYOLEDs, where the guest concentration is as low as 2%. The GYOLED exhibits a forward-viewing power efficiency of 57.0 lm/W at 1000 cd/m², which is the highest among GYOLEDs. Besides, extremely low efficiency roll-off and voltages are achieved. The origin of the high performance is unveiled and it is found that the combined mechanisms of host-guest energy transfer and direct exciton formation on the guest are effective to furnish the greenish yellow emission. Then, by dint of this ideal host-guest system, a simplified but high-performance hybrid white OLED (WOLED) has been developed. The WOLED can exhibit an ultrahigh color rendering index (CRI) of 92, a maximum total efficiency of 27.5 lm/W and a low turn-on voltage of 2.5 V (1 cd/m²), unlocking a novel avenue to simultaneously achieve simplified structure, ultrahigh CRI (>90), high efficiency and low voltage.     

Development of LTCC micro-hotplate with PTC temperature sensor for gas-sensing applications

Low temperature co-fired ceramic (LTCC) micro-hotplates show wide applications in gas sensors and micro-fluidic devices. It is easily structured in three-dimensional structures. This paper presents the low power consumption micro-hotplates which were developed with PTC (positive temperature coefficient) temperature sensor and inter-digitated electrodes. The paper presents two different structures for micro-hotplate with platinum as a heating element. The PTC temperature sensor using two different materials viz. PdAg and platinum paste are developed with micro-hotplates. The simulation has been achieved through COMSOL for LTCC and alumina micro-hotplates. The temperature variation with power consumption has been measured for the developed LTCC micro-hotplates. The change in resistance of PTC temperature sensors was measured with micro-hotplate temperature. The aim of this study was to place a temperature sensor with the gas sensor module to measure and control the temperature of micro-hotplate. A SnO₂ sensing layer is coated on LTCC micro-hotplate using screen printing and characterized for the sensing of carbon monoxide gas (CO). This study will be beneficial for designing hotplates based on LTCC technology with low power consumption and better stability of temperature for gas-sensing applications.     

Inferring kinetic dissolution of NaCl in aqueous glycol solution using a low-cost apparatus and population balance model

An experimental methodology for inferring brine dissolution rate in monoethylene glycol (MEG) solutions at different temperatures using a webcam combined with a mathematical model is presented. The measurement system is designed to track the RGB (red, green, and blue) colour variations during the dissolution process. A dynamic model augmented with the population balance equation is applied to describe the dissolution process. Moreover, the dissolution rate is consistently related to the temperature and MEG concentration through the driving force based on the Gibbs energy and chemical affinity. The applied low-cost measurement apparatus proved to be a useful resource for tracking the dissolution dynamics in a wide range of undersaturation.     

Highly stretchable self-sensing actuator based on conductive photothermally-responsive hydrogel

Soft robots built with active soft materials have been increasingly attractive. Despite tremendous efforts in soft sensors and actuators, it remains extremely challenging to construct intelligent soft materials that simultaneously actuate and sense their own motions, resembling living organisms’ neuromuscular behaviors. This work presents a soft robotic strategy that couples actuation and strain-sensing into a single homogeneous material, composed of an interpenetrating double-network of a nanostructured thermo-responsive hydrogel poly(N-isopropylacrylamide) (PNIPAAm) and a light-absorbing, electrically conductive polymer polypyrrole (PPy). This design grants the material both photo/thermal-responsiveness and piezoresistive-responsiveness, enabling remotely-triggered actuation and local strain-sensing. This self-sensing actuating soft material demonstrated ultra-high stretchability (210%) and large volume shrinkage (70%) rapidly upon irradiation or heating (13%/°C, 6-time faster than conventional PNIPAAm). The significant deswelling of the hydrogel network induces densification of percolation in the PPy network, leading to a drastic conductivity change upon locomotion with a gauge factor of 1.0. The material demonstrated a variety of precise and remotely-driven photo-responsive locomotion such as signal-tracking, bending, weightlifting, object grasping and transporting, while simultaneously monitoring these motions itself via real-time resistance change. The multifunctional sensory actuatable materials may lead to the next-generation soft robots of higher levels of autonomy and complexity with self-diagnostic feedback control.