熔接型光纤液位传感器的抗污染研究

讨论了熔接型触点式光纤液位传感器的污染问题,分析研究了传感头在水与油 2种不同介质中的污染情况;提出了在光纤传感头上利用液相沉积法(LPD):镀TiO2 薄膜来提高其抗污染能力的方法,并测试了镀TiO2 膜后的光纤传感头的抗污染性能。水中的实验表明:镀TiO2 膜后,光纤传感头的抗污染能力比镀膜前有显著提高。     

Theoretical analysis of the sensing and actuating effects of piezoelectric multimorph cantilevers

The paper presents the modeling of two types of piezoelectric microcantilevers, the two-layer and two-segment piezoelectric cantilevers, in which the two piezoelectric parts perform sensor and actuator functions, respectively. An analytic model is proposed to evaluate the sensing and actuating effects of multiporph microcantilevers formed of several layers or segments of piezoelectric materials. The analytical model is used to analyze the tip deflection of the cantilever and the piezoelectric charge generated on the sensor element as a function of the microcantilever geometry. The cantilevers of sensing and actuation are compared for the different working situations of the two-layer and two-segment cantilevers. The theoretical results can be used to optimize the design of the piezoelectric cantilever structures.     

Modeling the impact of observation conditions on localization systems

In various applications, sensor fusion has demonstrated success as means to enhance a system performance in perceiving its environment. By combing observations of different sensors, the system is able to achieve improved sensing accuracy, and potentially, expanded sensing capabilities. However, the observation conditions in the surrounding of any multi-sensor system have a considerable impact on the performance of the system. This impact can be hard to mitigate if the observation conditions are stochastic in nature. Therefore, for any sensor fusion strategy to achieve reliable and robust performance it must possess a capability to assess the quality of the observation conditions in its surrounding, and ultimately, the quality of its decisions, as a function of these conditions. One typical application where the impact of the observation conditions can cause sever deterioration of the sensing performance is vehicle localization. It is typical in this application that location measurements obtained from multiple sensors (e.g., GPS, Vision, Inertial, etc.) are combined together to compute accurate vehicle location. However, such improved accuracy can only be attained under nominal observation conditions. Therefore, real-time awareness of the observation conditions around the vehicle position is pivotal for the multi-sensor system to achieve effective fusion performance.In this paper, a Markovian model is proposed to capture the impact of observation conditions on a sensor’s localization performance and to consequently determine a reliability index with respect to the localization accuracy claimed by the sensor.The proposed model is implemented on two localization techniques: single-sensor localization and multi-sensor localization. A number of experiments are conducted to determine the different levels of localization accuracy that can be achieved by each technique under a wide range of observation conditions. The proposed reliability model is tested in a variety of real-life and simulated observation conditions scenarios. It is evident from the experimental results that the proposed model is able to estimate the reliability of location estimates produced by either one of the localization techniques. The paper discusses how such reliability model can benefit multi-sensor systems.     

上下文一致性错误及其处理对应用的影响

在普适计算环境中,上下文信息由于受到噪声等不可控因素的干扰,面临着一致性错误的问题。这些错误会影响应用的正常运行,使其表现异常甚至失效。目前已经有自动的一致性错误处理方法来保障应用所获取的上下文满足数据层面的一致性约束,然而在数据层面保持一致是否能提升应用的运行质量并不明确。系统地研究了这个问题,并基于一组真实的上下文感知机器人小车应用,设计了一系列受控实验,来分析和比较在模拟和真实世界中上下文一致性错误的处理方式对应用运行质量的改善程度及潜在的负面影响。利用该实验的分析结果,将有助于提高普适计算中上下文感知应用的运行质量。     

Preemptive cloud resource allocation modeling of processing jobs

Cloud computing allows execution and deployment of different types of applications such as interactive databases or web-based services which require distinctive types of resources. These applications lease cloud resources for a considerably long period and usually occupy various resources to maintain a high quality of service (QoS) factor. On the other hand, general big data batch processing workloads are less QoS-sensitive and require massively parallel cloud resources for short period. Despite the elasticity feature of cloud computing, fine-scale characteristics of cloud-based applications may cause temporal low resource utilization in the cloud computing systems, while process-intensive highly utilized workload suffers from performance issues. Therefore, ability of utilization efficient scheduling of heterogeneous workload is one challenging issue for cloud owners. In this paper, addressing the heterogeneity issue impact on low utilization of cloud computing system, conjunct resource allocation scheme of cloud applications and processing jobs is presented to enhance the cloud utilization. The main idea behind this paper is to apply processing jobs and cloud applications jointly in a preemptive way. However, utilization efficient resource allocation requires exact modeling of workloads. So, first, a novel methodology to model the processing jobs and other cloud applications is proposed. Such jobs are modeled as a collection of parallel and sequential tasks in a Markovian process. This enables us to analyze and calculate the efficient resources required to serve the tasks. The next step makes use of the proposed model to develop a preemptive scheduling algorithm for the processing jobs in order to improve resource utilization and its associated costs in the cloud computing system. Accordingly, a preemption-based resource allocation architecture is proposed to effectively and efficiently utilize the idle reserved resources for the processing jobs in the cloud paradigms. Then, performance metrics such as service time for the processing jobs are investigated. The accuracy of the proposed analytical model and scheduling analysis is verified through simulations and experimental results. The simulation and experimental results also shed light on the achievable QoS level for the preemptively allocated processing jobs.     

基于遥感图像的多模态小目标检测

由于遥感图像目标往往较小且容易受光线、天气等因素的影响,所以单一模态下基于深度学习的遥感图像目标检测的准确度较低.然而,不同模态间的图像信息可以相互增强提高目标检测的性能.因此,基于RGB和红外图像,提出了一种适用于遥感图像多模态小目标检测的平衡多模态深度模型.相比简单地相加、点乘和拼接的方式融合2个模态的特征信息,设...     

Design and Analysis of Sensing Scheduling Algorithms under Partial Coverage for Object Detection in Sensor Networks

Object detection quality and network lifetime are two conflicting aspects of a sensor network, but both are critical to many sensor applications such as military surveillance. Partial coverage, where a sensing field is partially sensed by active sensors at any time, is an appropriate approach to balancing the two conflicting design requirements of monitoring applications. Under partial coverage, we develop an analytical framework for object detection in sensor networks, and mathematically analyze average-case object detection quality in random and synchronized sensing scheduling protocols. Our analytical framework facilitates performance evaluation of a sensing schedule, network deployment, and sensing scheduling protocol design. Furthermore, we propose three wave sensing scheduling protocols to achieve bounded worst-case object detection quality. We justify the correctness of our analyses through rigorous proof, and validate the effectiveness of the proposed protocols through extensive simulation experiments     

Depth analysis of kinect v2 sensor in different mediums

From the last few years, RGB-D cameras are widely used by researchers in various fields. Their reasonable cost and the ability to estimate distances at a high frame rate have made these sensors recommendable for applications in gaming accessories, robotics, computer vision, etc. In addition to color, these sensors also provide depth information. Aspects like the stability, accuracy and reliability of depth-sensing cameras like Kinect v2 must also be considered before using the device for applications like that of 3D space modelling. In this paper, an analysis of the error in the depth measurement as well as calculation of Depth Entropy given by Kinect v2 sensor in different mediums viz. air, glass and water has been done. We have validated our findings using the theories of optics. The findings from error analysis are used to make an error compensation model which can correct depth at each pixel of the image. The error analysis and error compensation model proposed herewith will help in improving the accuracy of present and future depth sensing devices.

     

车载自组织网络中的数据分发机制

车载自组织网络（vehicular ad-hoc network,简称VANET）作为一种新型的移动自组织网络,应用前景广阔.鉴于长距离数据分发机制是支撑VANET应用的一个不可或缺的机制,提出了一种VANET下路边单元（roadsideunit,简称RSU）辅助的数据分发机制Ara,并在不同的交通场景下对其性能进行了评估.实验结果表明,即使在有RSU出现故障的情况下,Ara也可以保证较高的数据到达率、较小的延迟和较低的消息开销.同时,通过建立分析模型对Ara的数据传递延迟进行了理论分析.该分析模型建立在车辆的微观流模型之上,可以推导出不同RSU 部署场景下的数据传递延迟.模拟实验结果验证了模型的正确性,因此该分析模型可以对Ara数据传递的性能进行预测.     

基于剖分理论的遥感影像模板数据模型

随着数据量的增加和应用需求的扩大,遥感数据的高效组织管理和快捷处理速度已经成为制约遥感技术应用的瓶颈,地球剖分理论和高性能计算为上述问题解决提供了一种可能途径。针对上述问题,结合地球剖分模型,在遥感影像剖分面片的研究基础上,提出了剖分面片模板的概念模型及数据模型,设计了基于剖分面片模板的计算模式,构建了一个小型化剖分遥感影像模板数据库,并通过一个模板化应用的具体实例对上述模型进行了部分验证。实验结果验证了模型的可行性,提高了目标检索的效率。     

Sensing liquid density using resonant flexural plate wave devices with sol–gel PZT thin films

This study presents the design, fabrication and possible applications in liquid density sensing and biosensing of a flexure plate wave (FPW) resonator using sol–gel-derived lead zirconate titanate (PZT) thin films. The resonator has a two-port structure with a reflecting grating on a composite membrane of PZT and SiN_x. The design of the reflecting grating is derived from a SAW resonator model using COM theory to generate a sharp resonant peak. A comparison between the theoretical mass and the viscosity effects reveals the applications and the constraints of the proposed device in liquid sensing. Multiple coatings of sol–gel-derived PZT films are employed because of the cost advantage and the strong electromechanical coupling effect over other piezoelectric films. Issues of fabrication of the proposed material structure are addressed. Theoretical estimates of the mass and the viscosity effects are compared with the experimental values. The resonant frequency relates quite linearly to the density of low-viscosity liquids, revealing the feasibility of the proposed device. An erratum to this article can be found at     

结合暗通道原理和双边滤波的遥感图像增强

目的 在遥感应用如目视解译等任务中,需要提高遥感影像的视觉质量,为此提出一种基于暗通道原理和双边滤波的遥感图像增强算法。方法 由于暗通道模型的softmatting过程计算复杂性高,故使用双边滤波估计大气光幕,进而获得优化透射图,代替He算法中softmatting过程,提高了计算效率。针对将暗通道原理应用于遥感图像增强时所产生的色彩失真现象,提出透射图的改进算法,提高景深图像的取值,同时约束其最大值不大于1。最后,基于景深图像和暗通道原理获得增强后的遥感图像。结果 实验结果表明,本文算法能够有效地增加图像的对比度。与基于双边滤波单尺度Retinex图像增强、四尺度Retinex增强、直方图均衡化及MSRCR增强的结果进行了比较,实验结果验证了算法的有效性。结论 本文模型能够使处理后的遥感图像更符合视觉特性,以便于目视解译与分析。该算法适用于遥感图像的可视化增强。     

Modelling and performance analysis of multi-hop ad hoc networks

Mobile ad hoc networks are becoming very attractive and useful in many kinds of communication and networking applications. Due to the advantage of numerical analysis, analytical modelling formalisms, such as stochastic Petri nets, queuing networks and stochastic process algebra have been widely used for performance analysis of communication systems. To the best of our knowledge, there is no previous analytical study that analyses the performance of multi-hop ad hoc networks, where mobile nodes move according to a random mobility model in terms of the end-to-end delay and throughput. This work presents a novel analytical framework developed using stochastic reward nets for modelling and analysis of multi-hop ad hoc networks, based on the IEEE 802.11 DCF MAC protocol, where mobile nodes move according to the random waypoint mobility model. The proposed framework is used to analyse the performance of multi-hop ad hoc networks as a function of network parameters such as the transmission range, carrier sensing range, interference range, number of nodes, network area size, packet size, and packet generation rate. The proposed framework is organized into several models to break up the complexity of modelling the complete network, and make it easier to analyse each model as required. The framework is based on the idea of decomposition and fixed point iteration of stochastic reward nets. The proposed models are validated using extensive simulations.     

Extension to the analytical model of the interdigital electrodes capacitance for a multi-layered structure

Interdigital electrodes (IE) are one of the most used transducers in different technical and analytical applications with the particular importance in the field of chemical and biological sensors. With the recent demand for lab-on-a-chip devices and the need for sensors miniaturization it becomes common the use of such transducers in structures with several dielectric layers (either substrates or superstrates). In a previous a work we proposed a model for the computation of the capacitance of these multi-layered structures using the techniques of conformal mapping and partial capacitance. Until now, that model has been used in applications where the permittivity of consecutive layers monotonically decreases from layer to layer (as we move away from the electrodes plane) giving excellent results. New applications, such as the use of Si/SiO₂ substrates (to promote very smooth surfaces for electrodes deposition) or the use of passivation layers for the protection of electrodes (e.g. from liquids), among others, represent a new challenge for the computation of the overall capacitance since in these devices the permittivity can decrease from layer to layer. Under these conditions the original partial capacitance technique needs to be modified to include these new configurations. In this work we will discuss a new approach, splitting the concept of partial capacitance in parallel partial capacitance and serial partial capacitance where new conformal mapping transformations are proposed for the latter case. Hence this novel approach will extend our previous analytical model in order to account for the cases where there is a decrease in the permittivity from layer to layer. The results are compared with finite element simulation and experimental results.     

超像素条件随机场下的RGB-D视频显著性检测

目的 视觉显著性在众多视觉驱动的应用中具有重要作用,这些应用领域出现了从2维视觉到3维视觉的转换,从而基于RGB-D数据的显著性模型引起了广泛关注。与2维图像的显著性不同,RGB-D显著性包含了许多不同模态的线索。多模态线索之间存在互补和竞争关系,如何有效地利用和融合这些线索仍是一个挑战。传统的融合模型很难充分利用多模态线索之间的优势,因此研究了RGB-D显著性形成过程中多模态线索融合的问题。方法 提出了一种基于超像素下条件随机场的RGB-D显著性检测模型。提取不同模态的显著性线索,包括平面线索、深度线索和运动线索等。以超像素为单位建立条件随机场模型,联合多模态线索的影响和图像邻域显著值平滑约束,设计了一个全局能量函数作为模型的优化目标,刻画了多模态线索之间的相互作用机制。其中,多模态线索在能量函数中的权重因子由卷积神经网络学习得到。结果 实验在两个公开的RGB-D视频显著性数据集上与6种显著性检测方法进行了比较,所提模型在所有相关数据集和评价指标上都优于当前最先进的模型。相比于第2高的指标,所提模型的AUC（area under curve）,sAUC（shuffled AUC）,SIM（similarity）,PCC（Pearson correlation coefficient）和NSS（normalized scanpath saliency）指标在IRCCyN数据集上分别提升了2.3%,2.3%,18.9%,21.6%和56.2%;在DML-iTrack-3D数据集上分别提升了2.0%,1.4%,29.1%,10.6%,23.3%。此外还进行了模型内部的比较,验证了所提融合方法优于其他传统融合方法。结论 本文提出的RGB-D显著性检测模型中的条件随机场和卷积神经网络充分利用了不同模态线索的优势,将它们有效融合,提升了显著性检测模型的性能,能在视觉驱动的应用领域发挥一定作用。     

Modeling and control of an atomic force microscope using a piezoelectric tuning fork for force sensing

This paper investigates modeling and control issues associated with an atomic force microscope which uses a piezoelectric tuning fork for atomic force sensing. In the modeling part, the dynamics of piezoelectric tuning fork and its atomic interaction with the test sample via the scanning tip are physically characterized. The modeling results explain not only the atomic force sensing mechanism but also the important characteristics observed in experimental frequency responses. In the control part, an LTR controller is designed to maximize the controller bandwidth and yet maintain robustness against unmodeled dynamics and different operating conditions. Scanning results indicate that the LTR controller exhibits superior performance than a conventional PI controller.     

噪声鲁棒的轻量级深度遥感场景图像分类检索

目的 基于深度神经网络的遥感图像处理方法在训练过程中往往需要大量准确标注的数据,一旦标注数据中存在标签噪声,将导致深度神经网络性能显著降低。为了解决噪声造成的性能下降问题,提出了一种噪声鲁棒的轻量级深度遥感场景图像分类检索方法,能够同时完成分类和哈希检索任务,有效提高深度神经网络在有标签噪声遥感数据上的分类和哈希检索性能。方法 选取轻量级神经网络作为骨干网,而后设计能够同时完成分类和哈希检索任务的双分支结构,最后通过设置损失基准的正则化方法,有效减轻模型对噪声的过拟合,得到噪声鲁棒的分类检索模型。结果 本文在两个公开遥感场景数据集上进行分类测试,并与8种方法进行比较。本文方法在AID（aerial image datasets）数据集上,所有噪声比例下的分类精度比次优方法平均高出7.8%,在NWPU-RESISC45（benchmark created by Northwestern Polytechnical University for remote sensing image scene classification covering 45 scene classes）数据集上,分类精度比次优方法平均高出8.1%。在效率方面,本文方法的推理速度比CLEOT（classification loss with entropic optimal transport）方法提升了2.8倍,而计算量和参数量均不超过CLEOT方法的5%。在遥感图像哈希检索任务中,在AID数据集上,本文方法的平均精度均值（mean average precision,mAP）在3种不同哈希比特下比MiLaN（metric-learning based deep hashing network）方法平均提高了5.9%。结论 本文方法可以同时完成遥感图像分类和哈希检索任务,在保持模型轻量高效的情况下,有效提升了深度神经网络在有标签噪声遥感数据上的鲁棒性。     

Comparison of contact sensor localization abilities during manipulation

This paper presents an experimental comparison of tactile array versus force-torque sensing for localizing contact during manipulation. The manipulation tasks involved rotating objects and translating objects using a planar two fingered manipulator. A pin and a box were selected as limiting cases of point and line contact against a cylindrical robot finger tip. Practical implementations of the two sensor types are compared theoretically and experimentally, and three different localization algorithms for the tactile array sensor are considered. Force-torque contact sensing results suffered from difficulties in calibration, transient forces, and low grasp force. Tactile array sensing was immune to these problems and the effect of shear loading was only noticeable for a simple centroid algorithm. The results show that with care, both of these sensing schemes can determine the contact location within a millimeter during real manipulation tasks.     

An Integrated System for UAV Control Using a Neural Network Implemented in a Prototyping Board

Modern aerospace vehicles are expected to have non-conventional flight envelopes and then, in order to operate in uncertain environments, they must guarantee a high level of robustness and adaptability. A Neural Network (NN) controller, with real-time learning capability, can be used in applications with manned or unmanned aerial vehicles. In this paper a novel real-time control system, based on a NN model, in order to control the trajectories of a hexacopter is proposed. The proposed NN is optimized by the analytical calculation of the embedding parameters. The paper shows a performance evaluation, through a real experimental testbed, of the proposed approach in terms of error measures and computation of the angular velocities of the hexacopter.     

混合全变差和低秩约束下的高光谱图像复原

目的 由于高光谱遥感数据携带丰富的光谱和空间信息,使其在许多领域得以广泛关注和应用。但是高光谱遥感数据在获取过程中受到各种因素的影响,存在多种不同程度的退化,进而影响到后续的处理和应用。因此,提出一种基于低秩矩阵近似和混合全变差正则化方法来复原退化的高光谱遥感数据。方法 首先分析高光谱遥感数据的两种低秩先验：光谱低秩先验和空间低秩先验;然后利用光谱低秩先验建立低秩矩阵近似表示模型,有效抑制稀疏噪声,例如脉冲噪声、条纹噪声、死线噪声等;再利用空间低秩先验建立混合全变差正则化模型,有效去除高密度噪声,例如强高斯噪声、泊松噪声等;最后结合两种模型的优势,建立基于低秩矩阵近似和混合全变差正则化模型。结果 利用多组高光谱遥感数据,和多种相关的高光谱复原方法进行对比仿真实验,表明新模型的结果在视觉质量有很大改进。与目前最新的复原模型相比,提出的模型的平均峰值信噪比能提高1.8 dB,而平均结构相似数值指标能提高0.05。结论 新模型充分利用高光谱遥感数据的空间和光谱低秩先验,针对含有高密度噪声和稀疏异常值的高光谱遥感数据,能够有效复原出高质量的高光谱遥感数据。