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The GNSS-based indoor positioning systems mentioned above would have been nice solutions as no infrastructure would have been required. Unfortunately there is clearly a need for other solutions. The next two solutions, pseudolites and repeaters, use GNSS-like signals but require a local infrastructure in order to provide indoor positioning. The proposed accuracies are usually better than those proposed by outdoor GNSS, even in very good outdoor conditions (no multipath, good propagation conditions, high received power levels, no obstructions, and a large number of satellites in view). An important point to note at this time is that using pseudolites and repeaters for indoor positioning is still under research (although pseudolites already exist to complement outdoor GNSS systems). The basic idea underlying the use of a local infrastructure is somehow to reconstruct a constellation associated with the local building. One could call the local infrastructure a terrestrial satellite constellation. As the problem of indoor reception arises mainly because of low signal levels, using generators can effectively solve the problem by providing higher level signals, that is, those that are powerful enough. The practical implementation of the pseudolite approach consists in building complete signal generators. The shrewdness in the application of pseudolites is their use of similar signals to GNSS. In this way, standard receivers are already enabled to decode such signals without any hardware updates. Only software updates are required in order to tell the receiver to search for and to acquire these new terrestrial satellites. For the GPS constellation, which is currently the only one that is fully operational, the C/A codes are reserved and it is not possible to use them. Thus,

Behavioral Methods The prescienti c history of psychology aside, the formal implementation of behavioral methods for assessing personality is usually traced to the World War II activities of the United States Of ce of Strategic Services (OSS), the predecessor organization to the Central Intelligence Agency Once again the winds of war instigated advances in the methods of behavioral science, just as they have in the biological and physical sciences To aid in selecting operatives for covert intelligence missions, the OSS observed how recruits behaved in a variety of contrived problem-solving and stress-inducing situations and on this basis predicted the likely quality of their performance in the eld (Of ce of Strategic Services Assessment Staff, 1948; see also Handler, 2001).

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Online learning of eTS fuzzy models, which is based on Takagi-Sugeno rules, considers their online identi cation to be subject to gradually evolving rules The procedure in the present work includes recursive subtractive fuzzy clustering, for determination of the antecedent parameters, and recursive least-squares estimation, for determination of the consequent parameters Angelov and Filev [4] developed an interesting technique for recursive clustering and rule creation that is the starting and fulcral point for the present proposal The procedure can be summarized as follows: Step 1: Initialization of the rule base The rule base is initially empty Rules are created around the centers of the input output clusters, in the subtractive clustering framework Let xk and yk be the input and output vectors at instant k, respectively.

1, 2, 3, 4

When the rst input output data is coming, z1 x1 ; y1 , it is the single existing cluster in the input output space and at the same time its own center z* P1(z1 ) is the energy potential of the 1 cluster center z* to be used for subtractive clustering The cluster center z* is a 1 1 multidimensional point (the number of dimensions is equal to the number of inputs plus the number of outputs) and each input is an antecedent of each fuzzy rule To obtain from it a fuzzy set for each antecedent, one needs to project the cluster center into each input dimension, and this projection gives the center of the fuzzy set of each antecedent So the rule base is initialized with the rst data sample, as in (6.

A gap of more than 20 years followed before the OSS methods led to a clearly de ned approach to assessment, mainly because the emergence of systematic behavioral assessment techniques had to await new ways of conceptualizing personality for assessment purposes Of many contributions to the literature that reconceptualized personality in ways that fostered the development of behavioral assessment, two can be singled out for their clarity and in uence In 1968, Walter Mischel published Personality and Assessment (Mischel, 1968), a book in which he argued that personality traits are semantic ctions, that continuity in behavior across time and place exists only as a function of similarity across situations, and that assessment of behavior should accordingly focus on its situational determinants A few years later, Goldfried and Kent (1972) drew a sharp distinction between traditional and behavioral assessment.

(9.93)

1): k 1; R 1; x* xk ; P1 z* 1 1 1 6:1 where k is the actual sampling point, R is the actual number of rules, z* is the rst 1 cluster center; x* is the focal point of the antecedent of rst rule, obtained by 1 projecting z* on the input dimensions 1 In a mixed-batch real-time learning approach, the rule base can be initialized by existing expert knowledge or based on previous of ine identi cation, de ning.

(11.173)

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