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It should be emphasized, however, that despite the lack of dependence of the optimum decision metric on knowledge of the channel fading amplitude, the error probability performance of this receiver does indeed depend on the fading amplitude statistics and will, of course, be worse for the fading channel than for the pure AWGN channel On the other hand, for nonconstant envelope signal sets (eg, M-QAM), the second term in (712) cannot be ignored and optimum performance requires perfect knowledge of the channel fading amplitude (typically provided by an AGC) Finally, note that if in the generalized fading channel model all paths have equal noise PSD (ie, Nl = N0 , l = 1, 2, , Lp ), then, the decision metric of (78) reduces to that of (710) 7.

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Exact relational division nds exact matches without any remainder. It takes the basic question of relational division with remainder and tightens the method so that the divisor will have no extra rows that cause a remainder. In practical terms it means that the example question now asks, Who has ordered only every toy If you address this query with a modi ed form of Joe Celko s method, the pseudocode becomes For whom is the number of toys ordered equal to the number of toys available, and also equal to the total number of products ordered If a customer has ordered additional products other than toys, then the third part of the question eliminates that customer from the result set. The SQL code contains two primary changes to the previous query. One, the outer query must nd both the number of toys ordered and the number of all products ordered. It does this by nding the toys purchased in a derived table and joining the two data sets. Two, the HAVING clause must be modi ed to compare the number of toys available with both the number of toys purchased and the number of all products purchased, as follows:

connection manager, choosing the mining structure and highlighting the mining model to be queried On the Query tab, click the Build New Query button and map columns in the data ow to the columns of the model (a default mapping is created based on column name) Then specify the columns to be added to the data ow in the lower half of the pane (usually a prediction function) and give the output an appropriate name (Alias) Derived Column: Uses expressions to generate values that can either be added to the data ow or replace existing columns Within the editor, construct Integration Services expressions to produce the desired value, using type casts to change data types as needed Assign each expression to either replace an existing column or be added as a new column Give new columns an appropriate name and data type.

2 THE CASE OF KNOWN PHASES AND DELAYS BUT UNKNOWN AMPLITUDES When the amplitudes are unknown, then the conditional probability of (75) must be averaged over their joint PDF to arrive at the decision metric Assuming indeLp pendent amplitudes with rst-order PDFs pal ( l ) l=1 , we obtain.

Export Column: Writes large object data types (DT_TEXT, DT_NTEXT, or DT_IMAGE) to le(s) speci ed by a lename contained in the data ow For example, large text objects could be extracted into different les for inclusion in a website or text index Within the editor, specify two columns for each extract de ned: a large object column and a column containing the target lename A le can receive any number of objects Set Append/Truncate/Exists options to indicate the desired le create behavior Fuzzy Grouping: Identi es duplicate rows in the data ow using exact matching for any data type and/or fuzzy matching for string data types (DT_STR and DT_WSTR) Con gure the task to examine the key columns within the data ow that identify a unique row.

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