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The averaging in Equation (16.7) can be seen as a low pass filter that stabilises the control process. Hence, the auto-tuning process is guided by the indicator trends and not by short time chaotic fluctuations. The filtering in Equation (16.7) is done using samples from the last 100 seconds. MD blocking based model. The first FLC model, denoted herein as MD blocking based model, utilises MD blocking (i.e. macrodiversity blocking rate) as input to the FLC and will be described here using the decision matrix formulation. Three decision matrices in two levels are used: the first level comprises two matrices, and their corresponding corrections are aggregated by a third (second level) matrix (see Figure 16.8). The zeros in the squares correspond to no correction, and the + + + and to strong increase and decrease of the add_win of the central BS respectively. The first matrix based on local reasoning utilises the maximum MD blocking of the neighbouring BSs and MD blocking of the central BS as input indicators. The correction of this matrix should account for the neighbouring BS in the worst situation. For example, when the central BS suffers from high MD blocking while all neighbouring BSs experience low MD blocking, then a strong increase of

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Table 4-1: Writing to Addresses ADDRESS Write to X Write to X+1 Write to X+2 Write to X+3 Memory after four writes 0x01 X 0x01 X+1 0x01 0x02 0x03 0x04 0x02 X+2 0x01 0x02 0x03 0x04 0x03 X+3 0x01 0x02 0x03 0x04 0x04 0x02 0x03 0x04 0x04 0x04 0x04 X+4 X+5 X+6

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From a user s perspective, a portlet [33] is a window (Figure 8.7) in a portal that provides a speci c service, e.g. a calendar or news feed. From an application development perspective, a portlet is a software component written in Java, managed by a portlet container, which handles user requests and generates dynamic contents. Portlets, as pluggable user interface components, can pass information to a presentation layer of a portal system. The content

Perl Get QUERY_STRING from %ENV; Loop extracting and decoding name/value pairs: If name is act : record action value. If name is self : record requestor value. If name is other : record desired value. If name is email : record email value. If name is interests : add interest value to a list. Check that action, own type, other type and interest list have defined values; if not, use a subroutine to print an HTML error page. Check that email is defined if action is add. Check that values for own type and wanted type are in legal sets (MALE, FEMALE, EPERSON) and (MALE, FEMALE, EPERSON, ANY). Check that five interests from the standard list were supplied, and convert from names to numeric values for entry in database. If action is add: Execute SQL insert statement with data values from form. Generate a page welcoming latest E-Pal. If action is search: Retrieve all records, checking each for suitability. Generate HTML text with details of matches.

A major computational requirement for the presented algorithms is associated with the singular value decomposition step, which is an iterative algorithm with computational order O(N 3 ) per iteration. In some cases, however, we are interested in estimating the parameters of only a few strongest paths; therefore, computing the full SVD of the data matrix Ys is not necessary. In such a case, one can use simpler methods to nd principal singular vectors, which have lower computational requirements and converge very fast to the desired solution [8, 13]. We rst give an outline of the Power method [13], that can be used to compute only one dominant right (or left) singular vector of Ys . This can be of interest for initial synchronization or in applications such as ranging or positioning. Later, we present its extended version applicable to the general case of estimating Md . 1 principal singular vectors. Power Method Consider a matrix F Ys Ys H of size P P, and suppose that F is diagonizable, that is, L 1 FL diag(l1 , . . . , lP ) with L y1 , . . . , yP and jl1 j . jl2 j ! jlP j. Given y(0) , the Power method produces a sequence of vectors y(k) in the following way: z(k) Fy(k 1) , y(k) z(k) =kz(k) k2 : (2:38)

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CLDC API introduces a new Generic Connection Framework in the package javax.microedition.io. This framework is necessitated by the fact that different devices