传感器论文英文文献

辛里有个宝儿益久传感器--是中国最专业的传感器在线交易，以及传感器供求信息发布网站。2011-2-27 19:27:25你百度搜下 益久传感器 就可以找到。可以在线发布你的传感器供求信息。希望我的回答能帮你解答关于：“急求有关传感器的外文文献翻译，包括英文全文和中文翻译”的问题。

与此案的具体情况,用小双谱PM-LPG捏造的具体情况采用高折射率、气孔结构表现出强烈的熔覆地区的分裂(43海里)的两个谐振(slow-axis和fast-axis共振蘸)为同一层模式[6]。这增加了两个共振的光谱分离与同一层模式为时不时地告诉我们,如果双折射的具体情况是增加,熔覆结构的具体情况,我们可以适当选择两个谐振(slow-axis和fast-axis resonantdips)具有不同的层模式比那些接近顺序相同的层模式。在我们的具体情况提出了传感器系统的选择是熊猫型资料(日本)具有高折射率玻璃,熔覆元素扮演的角色,应用纤维芯的应变层区域。在这个由刻画了液化石油气(具体情况,两个共振共鸣和fast-axis slow-axis沉入)具有不同的层模式可以被密切座落在狭窄的波长(ocm 50海里)和被选入了波长的兴趣。它已经报道的波长偏移的共振蘸一液化石油气由于紧张或温度是不同于层模式取决于他们命令[7]。因此,温度和应变响应的较低的(例如,浸蘸波长较短的共鸣的PM-LPG制作可以不同于那些在下降(不再共振波长的区别),因为复模式。特别是如果两个共振蘸在线性应用应力和温度,同时测量应力和温度可以达到通过以下方程:(两个公式),分别为:波长转换上下共振蘸由于应用温度变化、应变变化规律,并和温度系数的上下共振跌落,respectively.系数和紧张的上下共振跌落。第三次世界大战。实验和讨论图1显示透射光谱的PM-LPG制作。从图,两个共振波长的(低)和共振浸蘸上共振化学方面的)可在正交极化条件(RLP可线性偏光系统)。PM-LPG是虚构的刻画了液化石油气以在一个具体情况(双折射。~ 的* 10负4方,B-Ge codoped)。准分子激光光束的KrF 248海里发光在球场,经480—μm振幅的纵向长度的30毫米,在40-mm-long资料所H2-loaded 100℃时在100条为7天。实验原理图的安装,同时测量在图2。我们用两个水平轴向应变翻译阶段分离的PM-LPG而使用温度室之间的阶段加热光栅独立。

中，英己送出A transducer is a device that converts one type of energy to another. The conversion can be to/from electrical, electro-mechanical, electromagnetic, photonic, photovoltaic, or any other form of energy. While the term transducer commonly implies use as a sensor/detector, any device which converts energy can be considered a transducer. Transducers are electric or electronic devices that transform energy from one manifestation into another. Most people, when they think of transducers, think specifically of devices that perform this transformation in order to gather or transfer information, but really, anything that converts energy can be considered a that detect or transmit information include common items such as microphones, Geiger meters, potentiometers, pressure sensors, thermometers, and antennae. A microphone, for example, converts sound waves that strike its diaphragm into an analogous electrical signal that can be transmitted over wires. A pressure sensor turns the physical force being exerted on the sensing apparatus into an analog reading that can be easily represented. While many people think of transducers as being some sort of technical device, once you start looking for them, you will find transducers everywhere in your everyday transducers have an inverse that allows for the energy to be returned to its original form. Audio cassettes, for example, are created by using a transducer to turn the electrical signal from the microphone pick-up – which in turn went through a transducer to convert the sound waves into electrical signal – into magnetic fluctuations on the tape head. These magnetic fluctuations are then read and converted by another transducer – in this case a stereo system – to be turned back into an electrical signal, which is then fed by wire to speakers, which act as yet another transducer to turn the electrical signal back into audio wavesOther transducers turn one type of energy into another form, not for the purpose of measuring something in the external environment or to communicate information, but rather to make use of that energy in a more productive manner. A light bulb, for example, one of the many transducers around us in our day-to-day lives, converts electrical energy into visible light. Electric motors are another common form of electromechanical transducer, converting electrical energy into kinetic energy to perform a mechanical task. The inverse of an electric motor – a generator – is also a transducer, turning kinetic energy into electrical energy that can then be used by other in all energy conversions, some energy is lost when transducers operate. The efficiency of a transducer is found by comparing the total energy put into it to the total energy coming out of the system. Some transducers are very efficient, while others are extraordinarily inefficient. A radio antenna, for example, acts as a transducer to turn radio frequency power into an electromagnetic field; when operating well, this process is upwards of 80% efficient. Most electrical motors, by contrast, are well under 50% efficient, and a common light bulb, because of the amount of energy lost as heat, is less than 10% is the Difference Between Transducers and Sensors Transducers are machines used to change one type of energy into another. They can often be found as a component of more complex devices. Sensors are explicitly intended to measure and express levels of measurement. Quite often, sensors are composed of transducers; therefore, one can see how easy it can be to confuse the , transducers come in basic varieties of which there are almost endless applications. The first variety is contact transducers. This type is categorized by a single point of contact used to detect energy. There is generally a coupling material, such as water or oil, employed in order to prevent distortion between the source of energy and the point of sensors utilize contact transducers in order to detect energy levels and convert that into an electrical energy which would then influence a display meter. One type of contact transducer that was almost ubiquitous in the late 1980s and early 1990s were tape heads. These were found in any cassette player, touching the magnetic tape and reading the magnetic information that was on it. This information was then converted to an electric signal that was carried by wire to speakers or headphones and then converted back into sound second most common type of transducers is the immersion type. These are intended to work in liquid environments. This type is effective at measuring sound, pressure, or other forms of mechanical energy. Paintbrush transducers are used like immersion types are, but they work in open environments and have highly sensitive crystals to detect even the faintest levels of energy. Antennae for radio waves are paintbrush types as they collect the broadcast radio waves and convert them into electrical energy that is converted back into sound by a radio’s Transducer Current Transducer Capacitive Transducers MEMS Sensors

这是一篇 的论文，谈论有关 无线传感网络 的，你看下，是否符合你需要，如果类型都不一致，那就没必要翻译了。Mechanisms for energy conservation in wireless sensor networksSupervisor: Maurizio BonuccelliThesis commettee: Paolo Ferraggina, Piero MaestriniExternal referees: Stefano Basagni, Mani SrivastavaNational commettee: Bugliesi, Meo, and Panzieri December 27, 2005 AbstractIn this thesis we address the problem of reducing energy consumption in wireless sensor networks. We propose a suit of techniques andstrategies imported from other research areas that can be applied to design energy-efficient protocols in sensor networks. They includetime series forecasting, quorums systems, and the interaction between sensor properties and protocol design. We apply these techniques to the time synchronization problem, to efficiently collecting data from a sensor network, and to ensuring stronger data consistency guarantees in mobile show in [1,2,3,4] that time series forecasting techniques, and in particular autoregressive (AR) models, can be applied to sensor networks to conserve energy. We study a simple type of time series models with a short prediction window. We have chosen this model because it is capableof predicting data produced by real-world sensors measuring physical phenomena, and it is computationally tractable on modern-generation sensor networks. We apply these models to solve two relevant problems in sensor networks: the problem of efficiently collecting sensor data at the sink, and the time synchronization propose an energy-efficient framework, called SAF Similarity--based Adaptable query Framework [1,2] ), for approximate querying and detecting outlier values in sensor networks. The idea is to combine local AR models built at each node into a global model stored at the root of the network(the sink) that is used to approximately answer user queries. Our approach uses dramatically fewer transmissions than previous approximate approaches by using AR models and organizing the network into clusters based on data similarity between nodes. Our definition of data similarity is based on the coefficients of the local AR models stored at the sink, which reduces energy consumption over techniques that directly compare data values, and allows us to derive an efficient clustering algorithm that is provably optimal in the number of clusters formed by the network. Our clusters have several interesting features that make them suitable also for mobile networks: first, they can capture similarity between nodes that are not geographically adjacent; second, cluster membership adapts at no additional cost; third, nodes within a cluster are not required to track the membership of other nodes in the cluster. Furthermore, SAF provides provably correct error bounds and allows the user to dynamically tune answer quality to answer queries in an energy and resource efficient addition, we apply the AR models to solve the time synchronization problem from a novel perspective which is complementary to the well-studied clock synchronization problem [3,4]. More precisely, we analyze the case in which a sensor node decides to skip one or more clock adjustments to save energy, or it is temporarily isolated, but still requires an accurate estimate of the time. We propose a provably correct clock method based on AR models, which returns a time estimate within a constant (tunable) error bound and error probability. This method is highly adaptable and allows the sensor to decide how manyclock adjustments it can skip while maintaining the same time accuracy, thus saving energy. In addition, we propose a suit of deterministic methods that reduce the time estimation error by at least a factor 2. More precisely, we propose a provably correct deterministic clock reading method, called the DCR method, which exploits information regarding the sign of the clock deviation, and can be applied to reduce by half the frequency of the periodic clock adjustments, while maintaining the same error bound [3,4]. This method is of both practical and theoretical interest. In fact, it leads to a noticeable energy saving, and shows that a stronger but realistic clock model can lead to a refinement of the optimality bound for the maximum deviation of a clock that is periodically synchronized. In addition, we propose a generalized version of the DCR method that enhances its accuracy depending on the clock stability, and a method that guarantees the monotonicity of the time values analyze for the first time quorum system techniques in the context of sensor networks: we redesign them and show their benefits in terms of energy consumption [6]. Quorum systems have the potential to save energy in sensor networks since they can reduce noticeably the amount of communication, improve the load balance among sensor nodes, and enhance the scalability of the system. However, previous quorum systems and quorum metrics, proposed for wired networks, are unsuitable for sensor networks since they do not address their properties and limitations. These observations have motivated us to redesigning quorum systems and their metrics, taking into account the limitations and characteristics of sensors (., transmission costs, limited energysource, physical radio broadcast), and the network topology. More precisely, we redefine the following quorum metrics: load balance, access cost and quorum capacity, and devise some strategies based on some characteristics of sensor networks that reduce the amount of communication when designing quorum systems for sensor networks. We apply these strategies to design a family of energy-efficient quorum systems with high resiliency. In particular, we propose a quorum construction that reduces the quorum access cost, and propose an energy-efficient data diffusion protocol built on top of it that reduces the energy consumption by reducing the amount of transmissions and addition, we analyze quorum systems in case of high node mobility. More precisely, we study the difficult problem of guaranteeing the intersection between two quorums in case nodes move continuously along unknown paths [7]. We address this problem by defining a novel mobility model that provides a minimum set of constraints sufficient to derive strong data guarantees in highly mobile networks. Also in this case, we show the unsuitability of previous quorum systems, and provide a condition which is necessary to guarantee data availability and atomic consistency under high node mobility. We propose a new classof quorum systems, called Mobile Dissemination (MD) quorums, suitable for highly mobile networks, and propose a quorum construction which is optimal with respect to the quorum size (., message transmissions) [7]. Then, we apply the MD quorum system to implement a provably correct atomic read/write shared memory for mobile and sparse [1] D. Tulone, S. Madden. PAQ: Time series forecasting for approximate query answeringin sensor networks. In Proc. of the 3rd European Workshop on Wireless Sensor Networks, pp. 21-37, Feb 2006.[2] D. Tulone, S. Madden. An energy-efficient querying framework in sensor networks for detecting node to conference.[3] D. Tulone. On the feasibility of global time estimation under isolation conditions in wireless sensor appear in Algorithmica.[4] D. Tulone. A resource-efficient time estimation for wireless sensor networks. In Proc. of the 4th Workshop of Principles of Mobile Computing, pp. 52-59, Oct 2004.[5] D. Tulone. How efficiently and accurately can a process get the reference time? Intl. Symp. on Distributed Computing, Oct2003. Brief announcement, pp. 25-32.[6] , E. D. Demaine. Redesigning quorum systems for wireless sensor networks. Submitted to conference.[7] D. Tulone. Is it possible to ensure strong data guarantees in highly mobile networks? Submitted to conference.

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