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The physical quantity q that is observed by a sensor is mainly a function of the size of the sensor, of its orientation, of its location, and of time t Since the size of a sensor is nonzero, sensors can typically only observe the accumulation (eg, weighted average) of a physical quantity over a certain area or volume If we assume that size, orientation, and location are constant properties of a sensor i, we can denote the time-dependent physical quantity observed by the sensor as qi(t) Often, q is a real-valued scalar function (eg, for temperature sensors), but may also be more complex (eg, for a location sensor, q might return triples (x, y, z)) The output of a sensor i under stimulus qi(t) is denoted as hi(t).

Note that hi(t) for a given qi(t) typically depends on a number of parameters, for example, fabrication tolerance of the sensor, environmental parameters such as temperature and humidity, and wear of the sensor In analogy to software clocks, we introduce a software sensor as a function c that maps a sensor output h(t) to c(h(t)) Software sensors are typically introduced to map a sensor output to a standard scale (eg, the Celsius scale) Here, the goal of calibration is to nd a suitable c for a given scale Often, q does refer to such a standard scale, in which case the goal of calibration could be to nd a c that approximates ci(hi(t)) qi(t) for all t As mentioned in the previous section, calibration may also be applied to actuators.

Paull, G, & Glencross, D (1997) Expert perception and decision making in baseball International Journal of Sport Psychology, 28, 35 56 Reilly, T, Williams, A M, Nevill, A, & Franks, A (2000) A multidisciplinary approach to talent identification in soccer Journal of Sports Sciences, 18, 695 702 Rodrigues, S, Vickers, J, & Williams, A M (2002) Eye, head, and arm co-ordination in table tennis Journal of Sports Sciences, 20, 187 200 Salmela, J H, & Fiorito, P (1979) Visual cues in ice hockey goaltending Canadian Journal of Applied Sport Sciences, 4, 56 59 Sanderson, F H (1981) Visual acuity and sports performance In I M Cockerill & W W MacGillivary (Eds), Vision in sport (pp 64 79) Cheltenham, England: Stanley Thorne Savelsbergh, G J P, van der Kamp, J, Oudejans, R R D, & Scott, M A (2004).

An actuator can be considered a reverse sensor that accepts a digital value h(t) as input and produces a physical quantity q(t) as output For example, a heater may accept a temperature speci cation as input and heat until this temperature is reached A software actuator c21 then maps a given value v to c21 (v), which can be used as an input to the actuator to produce a certain physical quantity (eg, such that q(t) v) 774 Classes of Calibration.

what time, and from where the calls were made, etc Classifying the users into groups will help our system to easily identify if a specific call does not correspond to a mobile phone owner There are several types of impostors our SIPI system can identify: (i) those who had changed the mobile phone s owner call patterns; (ii) those who bought a mobile phone only for one month (already convinced not to pay); and (iii) those who bought mobile phones using other names Thus, when the call made using the genuine/cloned phone is terminated, the system will check to see if the characteristics of the call are within the client patterns saved in the file A warning message could be sent to the client if a fraud was detected.

This immediate notification, instead of waiting until the end of monthly bill cycle, will help to reduce losses to the carrier and to the owner of the mobile phone that has been cloned To identify these types of impostors as soon as possible instead of at the end of the month, avoiding more losses for the carrier, we propose the use of a radial basis function (RBF) network in its more basic form [25] to partition the users into classes and create the log files that we refer to as baseline files The architecture of the RBF network consists of an entry layer, a hidden layer, and an output layer The nodes of the output layer form a linear combination of the radial basis function using the kernel classifier The RBF function in the hidden layer produces a response for the stimulus of input (pattern).