Sensor stochastic

H. Bayley and P. S. Cremer, Stochastic sensors inspired by biology, Nature 413, 226-230 (2001). 

Whereas the previous example relies on a series of channels etched into the membrane, typical single-channel stochastic sensors can be created in a closely related way.91 For such sensors, usually one protein (e.g., a-hemolysin) is introduced into a lipid bilayer. However, the problem here is that lipid bilayers are rather fragile objects. As an alternative, Martin and coworkers embedded a single gold nanotube... 

In the case of pulverized coal flow measurement, the concentration of the pulverized coal is measured by low-power, low-frequency microwave sensors. The variation in the microwave transmission characteristic (dielectric load) is caused by the changing coal concentration, which produces shifts in measurement frequency. The resulting quantifiable values indicate the coal density. This concentration measurement is performed by a microwave transmitter and a microwave receiver, as shown in Figure 3.90. The velocity of the pulverized coal is measured by two identical microwave devices by crosscorrelation. Here, the pair of sensors detect the stochastic signals resulting from the charged coal particles, which are nearly identical but shifted by the time the pulverized coal gets from one sensor to the other. 

The most common way to deal with the problem of stochastic drift is to modulate the exposure of the analyte to the sensor and to synchronously detect the sensor response. When the analyte is off (i.e., the sensor is zeroed ), the sensor signal can be recorded as the baseline value. Drift-corrected signals can be obtained by subtracting the baseline signal from that recorded when the analyte is on. If the frequency of the on/off modulation is much higher than the frequency of the baseline drift, then this scheme results in dramatically improved stability in the measured data. An implicit requirement in this measurement strategy is that the response kinetics of the sensitive film/analyte combination be sufficiently fast to allow on/off modulation at the desired frequency. 

The interaction between a chemical sensor and the molecules it detects is a dynamic stochastic process. The resultant fluctuations carry a stochastic fingerprint . Conventional sensing methods measure average values and thereby ignore the stochastic component of the signal these sensors are several orders of magnitude less sensitive than the nose of a canine or even a human. 

ABSTRACT Advances of electronic integration and radio communication have led to the emergency of new kind of safety systems. Wireless Sensor Network (WSN) belongs to this type of new systems. There are many papers dealing with WSN topic but usually to model the performances of such a system. The present paper gives a modelling framework using stochastic petri nets with the aim to assess WSN rehabihty. Some importance measures are also used to improve this latter one. 

The structural variability of these materials leads to the appearance of new chemical and physical properties - variable magnetism [1, 2], efficient catalysis, adsorption and high ion-exchange capacity [3]. The use of such nanocomposites promises to improve the mechanical and thermal stability of polymers in which composites have been dispersed [4], and also opens up the possibility for a creation of new optoelectronic devices (stochastic lasers, LEDs, sensors [5-7]), including a variety of diagnostic purposes [8]. Layered materials have potential application in pharmacology and medical science [9],... 

All methods described above can be categorized as automated case-by-case simulations based on accidents. There are two more aspects which are of importance for a sound system evaluation during the pre-crash phase. Many processes involved are deterministic, e.g., the participants dynamics, the technical functions implemented, as well as many physical boundary conditions. However, some of the key processes do have a stochastic nature for example, the driver action and reaction as well as some characteristics, e.g., of the sensors modeled. Due to the sensitivity of the results to those processes, stochastic elements are an important feature of any representative evaluation (see also Sect. 3.4). 

Kalman filter is well known as an efficient recursive filter that can optimally estimate the states of linear dynamic systems from a series of noisy measurements [20]. For nonlinear systems, extended Kalman filters [21,22] have been developed and validated by many studies to be effective in real applications [17, 23-25]. Unlike model-based estimators which heavily rely upon the plant models, a specific feature of a Kalman filter is that it finds the stochastic relations between model predictions and sensor measurements, and then estimates system states in an optimal approach. By utilizing this feature of the Kalman filter, a slowly time-varying state can be treated as a constant and its variation can be estimated by comparing the model predictions and measurements in a stochastic manner. 

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