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The output of statistical methods is an estimate of mobile location coordinates, and changes in the shape of the region of uncertainty are not revealed by this method When both region-of-uncertainty information and an estimate of the location are needed, both geometric and statistical triangulation algorithms are used [Pah02] Example 134: LS and RGWH Algorithms for Indoor Geolocation In [Kan04a] the performance of least squares (LS) and residual weighted (RGWH) LS algorithms in a square area with four RPs in the corners of the room is evaluated The LS algorithm is a simple traditional gradient algorithm used in basic GPS systems which iteratively minimizes the square of the error in estimation of the position For two-dimensional applications, considered in this example, it needs a minimum of three RPs With more RPs the algorithm is expected to provide more accurate positioning.



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The RGWH, originally designed for cellular positioning [Che99a, Che99b], is another version of the LS algorithm, which calculates all possible solutions for positioning, in our case all possible 3RF combinations and the 4RF solution The nal estimate of the location is made by weighted averaging of all the estimates The weighting factor is the inverse of the residual error Thus less reliable positioning with higher residual error is counted with less emphasis Using a random-number generator, a uniformly distributed location in the room is selected Using the distance measurement errors model provided in Example 132, the estimated distances from the four RPs when a TOA system is used are determined These distances are then used by LS or RGWH algorithms to determine the estimated location of the terminal in the area The difference between the estimated location and the actual randomly selected location is used as the positioning error.

These contribute to the short-circuit current value in the same way as the generators since, upon occurrence of a short-circuit, they work as a generator. They

By repeating this experiment, the statistics of the positioning error associated.

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CRYOGENIC MICROCALORIMETERS This is achieved in two different ways, reducing the size of the detector and/or reducing its working temperature. Typical working temperatures in the range of tens of mK and volumes of the order of 10 3 mm3 are used for best energy resolution. The temperature variations to be detected may be of the order of K, so good sensors are also necessary to detect them. Although these are stringent requirements, several groups working in the eld have obtained impressive results. Requirements for a good microcalorimeter are an absorber with small heat capacity able to convert the energy of the incident radiation into phonons and/or electrons in thermal equilibrium quickly and with high ef ciency, and a sensor with low heat capacity and high sensitivity to temperature variations. We discuss these characteristics, together with the performance of cryogenic microcalorimeters in the following.

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o to switch to the buffer listing window. Use the arrow keys to move to the buffer area you want to use, and hit the Enter key. C-x b to type in the name of the buffer area you want to switch to.

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may have multiple optima (s 4 and 8) or no optimum at all ( 10), either as a consequence of the functional form of the equation to be solved or as a consequence of numerical issues There are a number of methods to nd an optimum of a nonlinear problem, and as we have seen repeatedly, two implementations of even the same algorithms may nd different solutions to the same problem One should choose a statistical package that allows one to vary optimization algorithms, supply analytical derivatives, and otherwise control the nonlinear and maximum likelihood solver, such as modifying convergence criteria and starting values We recommend using analytic derivatives where possible, and where not, using central differences rather than forward differences for numerical derivatives, as central differences are generally more accurate (see 8).

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