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Figure 5.4. Line-outs of the spatial pro les at the focus of the 200-cm lens for eight consecutive return pulses from the N2 SBS cell using (a) the full quasi-Gaussian temporal pro le and (b) the fast-rise-time temporal pro le. The input energy was *200 mJ corresponding to .16 the SBS threshold.
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lot of companies are working on a nonvolatile form of RAM. This will speed up the boot-up and shutdown times of a device, and will save energy as well. As more and more companies are releasing green -friendly devices, this technology may debut soon (maybe even before this book is released).
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for the bottleneck before other related problems are considered. This is a good strategy, since the overall problem is strongly restricted by the bottleneck. The bottleneck in terms of the shunting problem is the planning period at which the problem is most tightened. Due to the scarce capacity at the marshaling yard and the setup during the morning, the last planning period is probably the tightest one. After generating a blueprint of departing trains of the last planning period, the heuristic considers the arrivals of that period in order to match them onto the blueprint. Since the heuristic follows a backward approach, all arrivals are matched but some departing train units of the blueprint remain unmatched. These unmatched train units are matched when matching the arrivals of a planning period prior to the current planning period. The unmatched material can be seen as a debit that is moved to the planning period to be considered next. Thus, the generated blueprint of one planning period may restrict the problem in planning period(s) prior to the one just solved. In nding a blueprint to another planning period, the heuristic might get stuck by the debited restrictions. This means that based on the positioning of trains planned so far, it becomes impossible to nd a feasible positioning of a train that is treated later in the planning process. In practice this is not likely to occur, since the marshaling yard is not heavily used during the day. However, in case the heuristic gets stuck, we assume a procedure that slightly adapts the blueprint by swapping and shifting planned positions of train units. Of course it is best to apply these swapping and shifting operations on planned positions of material that has not yet arrived physically, and hence is not parked at the sidings; otherwise swapping implies additional shunting. By following a backward approach starting at the tightest planning period, the heuristic saves time, since there is a reduced chance of getting stuck. Figure 16.11 illustrates the multiple-period heuristic. It shows how the two algorithms Blueprint and Matching of the one-period heuristic are incorporated. As stated before, the branch of the Blueprint algorithm that is entered when T 1 requires more research for good division, swapping, and shifting strategies. The reader who is familiar with Dynamic Programming clearly sees similarities between the multiple-period heuristic and Dynamic Programming (DP). In Section 16.3 we provide the DP-formulation and discuss these similarities. 16.2.3. Illustration of the Multiple-Period Heuristic Before relating the heuristic to DP, let us illustrate how the multiple-period heuristic works. We focus on the process of copying a blueprint from one planning period to another. Consider the following example. Example 1. Consider a planning horizon in which a set of 19 trains need to be shunted to and collected from the marshaling yard. The marshaling yard consists of two sidings (labeled t1 and t2) of 150 meters each. Details about the timetable can be found in Table 16.2. The letter a or d precedes the train number, meaning that the train is an arrival or a departure, respectively. For simplicity, each train is composed of only one
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As you can see, you can interrogate any variable while debugging the function. 10. To add a new expression to the Watch window, select the variable aList and press Ctrl+W. 11. To change the value of aList, return to the Console window and type the following:
Dificulty i n obtaining the TX spatial signature: the RX spatial signature in (4.3) and the TX spatial signature (4.10) are both time and frequency dependent, and therefore different in general. While the RX spatial signature, aR,can be estimated along with the received signals, the T X spatial signature, aT, can only be obtained indirectly. In a TDD (time-division multiplexing) system for example, one can estimate aT as aT = aR. However such an assumption is only valid if the TDD time frames are short
Figure 8.25. Rise time from 0 to 90% of the maximum output energy of the double-pass MOPA versus incident density of energy on the photorefractive crystal (repetition rate of 10 Hz). Black markers: ring phase conjugate mirror. White markers: internal loop phase conjugate mirror.
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