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where N is the number of states S, M is the number of observations O, A faij g is the matrix of state transition probabilities, B fbij g is the observation symbol probability of symbol Ok in state j, and p is the initial state distribution vector To prepare the HMM for the recognition, a number of steps have to be done: Step 1: Determine a number N of states S The states need not be directly connected with a physical meaning, but it is of great advantage to do so Therefore, M 5 states are chosen, getting the following labels: State S1: open hand State S2: half-open hand State S3: middle position State S4: half-closed hand State S5: closed hand Step 2: Generate a number M of discrete observations O.

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, Hammill, Brown, & Bryant, 1993) state that a high coef cient alpha (eg, higher than 80) is a desirable property of a test, and test construction procedures that use part-whole correlations or factor-analytic structures essentially attempt to maximize this psychometric property However, sometimes internal consistency can be too high, an issue described as the attenuation paradox by Loevinger (1954) more than 40 years ago High internal consistency indicates that all test items are measuring the same thing, which at its extreme can result in highly redundant items that address a very narrow portion of a complex construct As an example, a depression test that consists of 10 questions that all ask about dif culties in falling asleep might be highly internally consistent, but such a test would miss a considerable portion of the breadth of the depression construct, including mood and relevant cognitions.

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To generate discrete observations, one rst has to deal with the continuous observations, meaning the output cluster centers in Vgrasp and the corresponding joint angles qi It should be mentioned that the clustering process leads to vectors of cluster centers whose elements, although being labelled by a time stamp, are not sorted in increasing order of time Since clustering of several grasps is done independently of each other, the orders of time stamps of the cluster centers are in general different This makes a comparison of test clusters Vgrasp and model clusters Vmodel impossible Therefore, after time clustering has been performed, the output clusters have to be sorted in increasing order of time In the following, cluster centers are assumed to be sorted in that way Next, one has to transform the continuous output cluster centers Vmodel (i), i 1 10 of the model into discrete numbers or labels.

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The depth of content coverage refers to the need to sample across the full range of intensity or severity of a particular.

One technique used on an HFC system is to employ optical couplers (a form of power splitter), where one ber trunk feeds several hubs. A hub is a location where the optical signal is converted back to an electrical signal for transmission on coaxial cable. Two applications of optical couplers are illustrated in Figure 15.9. Keep in mind that a signal split not only includes splitting the power but also the insertion loss of the coupler.5 The values shown in parentheses in the gure give the loss in the split branches (e.g., 5.7 dB, 2 dB).

If one attached to each cluster center an individual label, one would obtain M 10 15 150 observation labels (10-number of clusters, 15-number of grasps) This number of observations is unnecessarily high because some of the.

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