Real time flood disaster monitoring based on energy efficient ensemble clustering mechanism in wireless sensor network

In this article, energy efficient ensemble clustering method (EECM) with black widow optimization (EECM-BWO) algorithm is proposed for effective data transmission with the help of real time flood disaster monitoring wireless sensor network (WSN). Initially, unified scalable ensemble clustering algorithm based on ensemble generation and consensus function is proposed for selecting the optimal routing path among the node using BWO algorithm. Then, biologically inspired routing black widow spiders optimization algorithm is proposed to trade off the nodes energy level, self-organization, and self-configuration in the WSN. The simulation is performed using NS2 simulator for validating the performance of the proposed EECM-BWO method. Here, in node, low delay achieves 24.07%, 72.58%, 51.36%, 81.75%, 77.74%, high packet delivery ratio achieves 70.83%, 53.93%, 90.23%, 43.58%, 24.58%, low packet drop attains 77.93%, 72.76%, 61.56%, 51.87%, 34.35%, low energy consumption attains 75.9%, 52.94%, 65.81%, 58%, 41.2% compared with existing energy-efficient clustering approach consolidated game theory as well as dual-cluster-head mode for WSNs energy-aware clustering by cuckoo optimization approach (EECM-COA), energy-aware clustering-based routing using multi-path reliable transmission with routing and control board (EECM-RCB-MRT), adaptive repair algorithm with temporally ordered routing algorithms for flood control strategy (EECM-AR-TORA-FCS), passive multi-hop clustering algorithm (EECM-PMC), dynamic source routing protocol based on genetic algorithm-bacterial foraging optimization (DSR-GA-BFO).     

基于宇称-时间对称光子晶体结构的磁场传感器研究

提出了一种基于宇称-时间(parity-time,PT) 对称结构的磁场检测微腔模型,利用水 基Fe₃O₄磁流体介质层的磁光效应和PT对称结构的光放大效应,当磁场强度发生变化时 ,引 起结构禁带中缺陷模位置的改变。因此,可以通过检测缺陷模位置实现磁场强度的检测。本 文利用传递矩阵法(transfer matrix method,TMM)从理论上分析了结构的透射光谱,在此 基础上对结构周期、介质层厚度、PT对称单元中宏观洛伦兹振荡强度等参数进行了数值优化 。优化后的仿真结果表明,该传感器能够实现有效的磁场磁强检测,磁场强度在0—500Oe 的 范围内,传感器的灵敏度可以达到97.14 nm/RIU,优值和检测极限分 别可以达到10⁴和10－7 RIU。同时, 传感器的分辨率数值最小可以达到0.05 Oe和最 大可以达到2Oe。本文所设计的传感器结构可用于高综合性能磁场强度检测设备的设计。     

A path planning model based on nested regular hexagons using weighted centroid localization

In many wireless sensor network (WSN) applications, the location of a sensor node is crucial for determining where the event or situation of interest occurred. Therefore, localization is one of the critical challenges in WSNs. Mobile anchor node assisted localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL localization is that the path planning of the mobile anchor (MA) node should be done so that the localization error in the network will be minimal and that all unknown nodes in the network are covered. This paper proposes a new path planning approach called nested hexagons curves (NHexCurves) for MANAL. NHexCurves guarantees that it will receive messages from at least three non-collinear anchors to locate all unknown nodes in the network. The proposed model has compared six different path planning schemes in the literature using weighted centroid localization (WCL). In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Second, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) is observed. Then, with similar approaches, the standard deviation of errors, localization ratio, scalability performances, and finally, path lengths of the models are examined. The simulation results show that the NHexCurves static path planning model proposed in this study stands out compared to other models with high localization error and localization ratio performance, especially at low resolutions, due to its path design. At the same time, the lowest error values according to σ are obtained with the proposed model among all models considered.     

Biodegradable Microparticles for Simultaneous Detection of Counterfeit and Deteriorated Edible Products

In an era of globalized trade relations where food and pharmaceutical products cross borders effortlessly, consumers face counterfeit and deteriorated products at elevated rates. This paper presents multifunctional, biodegradable hydrogel microparticles that can provide information on the authenticity and the potential deterioration of the tagged food or pharmaceutical formulations. These microparticles integrate spatially patterned authenticity code with two sensors—the first one detects possible presence of pathogenic microbes through monitoring pH while the second one identifies products stored above optimal temperatures via optical monitoring of the microparticle degradation. Particles are synthesized from a biocompatible polymer and a photoinitiator, dextran modified with 2‐hydroxyethylmethacrylate and riboflavin, respectively, using a continuous, high throughput method stop‐flow lithography. The proposed synthesis approach also enables crosslinking with visible light bringing about additional flexibility to flow lithography. Model liquid and solid food and pharmaceutical products are successfully labeled with microparticles and the functionality of the sensors in aqueous solutions is demonstrated.     

Gold immunochromatographic sensor for the rapid detection of twenty-six sulfonamides in foods

A gold immunochromatographic sensor (GICS) was developed for the rapid detection of 26 sulfonamides in honey samples.The sensor was based on a group-specific monoclonal antibody (mAb) that can recognize all 26 sulfonamides.Three haptens (hapten 1 with a thiazole ring,hapten 2 with a benzene ring,and hapten 3 with a straight carbon chain) were used for antigen preparation.With hybridoma technology,a group-specific mAb was screened with a 50％ maximal inhibitory concentration (IC50) against sulfathizole (STZ) and the other 25 analogues ranging from 0.08 to 90.18 ng/mL.Mono-dispersed gold nanoparticles were conjugated with the mAb to develop the lateral immunochromatographic strip.A labeled antibody concentration of 0.1 μg/mL and a coating antigen concentration of 0.2 μg/mL in the test line were chosen for strip preparation.Under optimized conditions,the visual limits of detection (vLOD) for the concentrations of STZ,sulfamethoxazole,sulfamethizole,sulfadiazine,sulfamerazine,sulfadimethoxine,sulfamonomethoxine,sulfameter,sulfamethoxypyridazine,and sulfachloropyridazine were 5,0.25,0.25,10,5,10,25,2.5,5,0.25,and 10 μg/kg,respectively.Scanner analysis in honey samples revealed good performance for detection of the 26 sulfonamides.Commercial honey samples were tested with the sensor and positive results were confirmed with high-performance liquid chromatography.The proposed strip sensor provides a convenient method for the rapid and reliable determination of sulfonamides pollutants in honey samples.     

Gold nanoparticle-based paper sensor for ultrasensitive and multiple detection of 32 (fluoro)quinolones by one monoclonal antibody

For rapid and simultaneous detection of (fluoro)quinolones, a broadly specific monoclonal antibody (mAb) that recognizes 32 (fluoro)quinolone antibiotics was prepared using a mixture of a norfloxacin derivative and a sarfloxacin derivative as the hapten. An immunochromatographic strip based on gold nanoparticles (AuNPs) was then assembled with goat anti-mouse antibody and antigen (sarfloxacin coupled to ovalbumin), used to form the C line and T line, respectively. This antigen competes with the (fluoro)quinolones in a sample incubated with mAbs labeled with AuNPs. The strip can detect 32 (fluoro)quinolones including oxolinic acid, nalidixic acid, miloxacin, pipemidic acid, piromidic acid, rosoxacin, cinoxacin, norfloxacin, pefloxacin, lomfloxacin, enofloxacin, fleroxacin, ciprofloxacin, enrofloxacin, dafloxacin, orbifloxacin, sparfloxacin, gemifloxacin, besifloxacin, balofloxacin, gatifloxacin, moxifloxacin, nadifloxacin, ofloxacin, marbofloxacin, flumequine, pazufloxacin, prulifloxacin, sarafloxacin, difloxacin, trovafloxacin, and tosufloxacin in milk within 10 min with the naked eye. The cut-off values of the strip range from 1 to 100 ng/mL and the limits of detection are 0.1–10 ng/mL. The strip does not cross-react with antibiotics including tetracycline, sulfamethazine, ampicillin, erythromycin, aflatoxin B1, or gentamicin. In short, this immunochromatographic strip is a very useful tool for the primary screening of (fluoro)quinolones in milk.

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Medium access control layer management for saving energy in wireless sensor networks routing algorithms

Wireless sensor networks (WSNs) consist of small nodes that are capable of sensing, computing, and communication. One of the greatest challenges in WSNs is the limitation of energy resources in nodes. This limitation applies to all of the protocols and algorithms that are used in these networks. Routing protocols in these networks should be designed considering this limitation. Many papers have been published examining low energy consumption networks. One of the techniques that has been used in this context is cross-layering. In this technique, to reduce the energy consumption, layers are not independent but they are related to each other and exchange information with each other. In this paper, a cross-layer design is presented to reduce the energy consumption in WSNs. In this design, the communication between the network layer and medium access layer has been established to help the control of efforts to access the line to reduce the number of failed attempts. In order to evaluate our proposed design, we used the NS2 software for simulation. Then, we compared our method with a cross-layer design based on an Ad-hoc On-demand Distance Vector routing algorithm. Simulation results show that our proposed idea reduces energy consumption and it also improves the packet delivery ratio and decreases the end-to-end delay in WSNs.     

基于Kinect传感器的移动机器人环境检测及行为学习

研究了一种基于深度图像和强化学习算法的移动机器人导航行为学习方法。该方法利用机器人装配的Kinect传感器检测工作环境信息,然后对获取的深度图像数据和视频图像进行处理、融合和识别,并由此构建机器人任务学习的状态空间,最终利用强化学习方法实现移动机器人的导航任务的自学习。该方法的有效性通过实验得到验证。实验表明,该方法能够使机器入具有较强的环境感知能力,并能够通过自学习的方式掌握行为能力。     

石墨烯/银纳米颗粒复合材料的制备及其对双氧水的电化学检测

以氧化石墨烯(GO)和硝酸银为原材料,聚乙烯吡咯烷酮(PVP)为还原剂和稳定剂,通过水热法制备出还原氧化石墨烯/银纳米颗粒(rGO/AgNPs)复合材料。采用透射电子显微镜(TEM)、X射线衍射(XRD)及紫外-可见分光光度计(UV-Vis)对rGO/AgNPs复合材料的形貌、组成和结构进行表征。同时,将rGO/AgNPs复合材料修饰到玻碳电极表面制备出过氧化氢(H_2O_2)电化学传感器,通过循环伏安法(CV)和计时安培响应法(i-t)对传感器进行电化学性能测试。实验结果表明:制备的rGO/AgNPs传感器具有较好的电化学性能,其对H_2O_2检测的灵敏度为340.6μA·(mmol/L)~(-1)·cm~(-2),响应时间为3s,最低检测极限为7.5μmol/L(S/N=3),线性检测范围为20~4950μmol/L(线性相关系数为R=0.9973)。     

旋转状态下微波叶尖间隙传感器校准影响因素分析

主要针对应用于发动机叶尖间隙测量的微波叶尖间隙传感器的校准需求,对测量原理、传感器的校准原理等进行了详细阐述,并分析了转速和回缩值等对校准结果的影响。为微波叶尖间隙传感器的进一步优化设计及应用提供了数据依据。