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EEREAP works from a memory dump, emulating the microprocessor, not executing instructions. Together with the memory dump you need to also feed it with a context file, where you define the values of the registers, the memory layout, and what the target for the search would be. (For more information, take a look at the presentation and readme file included in the package.) The following presents a simple EEREAP script to find all suitable addresses to use as an exception handler trampoline to jump to the code, like a pop-pop-ret or any other:

Terms: Metal working machinery, equipment and supplies, metal-cutting machinery, metal-turning equipment, metal-milling equipment, milling insert, turning insert, etc. Relations: Use, used-for, broader-term, narrowerterm, related-term

(7.8)

High capacity or long range sector macrocells High/middle capacity or long range sector macrocells Middle capacity, regular coverage urban/suburban sector cells Regular coverage suburban/rural sector cells Coverage of short range rural or isolated omnidirectional cells Indoor/pico cells

The primary use of a simulation model is to respond to what-if questions These descriptive models can produce large amounts of detailed output because they work by mimicking many parts of the real-world system One major weakness is that no automatic searching or optimizing is done by the model (Any such features must be built on top of the simulation model and must be used as a submodel) In such cases, the simulation may have to be performed many, many times while a search for the best decision parameters is under way This can be quite expensive if the simulation is complex Two major issues in simulation modeling are how long a simulation run must proceed to achieve steady state (typical behavior) and how many different runs must be performed to achieve statistical significance Inside most simulation models is a pseudo-random number generator.

Its elements are:

In order that the sample averages X (k) converge to a constant mean , equation 13.2 requires the process to be ergodic. We assume weak ergodicity from the nature of the underlying network (many subscribers, random radio and network conditions). Now we can de ne a variance within the sliding window: 2= 1 k 1

This is a mathematical subroutine that produces numbers that appear to be random These random numbers are manipulated further to represent parts of the model that are not deterministic Examples might include the arrival of customers at a ticket counter or the time of failure of an electronic circuit component These random number generators can be seeded with special input parameters to make them produce different streams of random values Repeating runs with different seed values provides a set of outputs that has a statistical distribution to be analyzed Many commercial software packages are available that can be used to build simulation models Some of these are general-purpose simulation languages that have general but powerful features such as waiting lines and resource pools that ease the modeling task.

Having faith in your abilities is good, but stubbornly refusing to accept your errors is deadly. You must manage your ego. If the indicators and the numbers say that you are wrong, accept the fact that you have made a mistake and exit your position. Prior to entering a trade, you should have carefully analyzed it. But things change, and sometimes they change quickly. The financial markets have the ability to shift in a heartbeat. One minute the scene is bullish and the next minute it is bearish. Every trader has seen dramatic changes in market sentiment over very short periods of time. Therefore, pay attention. Constantly read the indicators. If the picture changes and the trade is no longer a winning one, get out. Never sit and hold a loser. You might want prices to go higher, but the market does not care what you want. Being stubborn and refusing to accept the fact that you were wrong will not change the situation. All that will happen is that you will lose a lot of money. Therefore, be stubborn about something else, but not the markets. Temper confidence with humility and understand that even the world s best traders are sometimes wrong.

At the other extreme are tailored simulation models (such as oil refinery models) that are already built but afford the user the ability to specify input parameters to describe the precise configuration under study In between these extremes are simulation languages that are suited for a large class of models such as networks that are a formalism in which many problems can be represented..

Time Period x (x x) (x x)2

Figure 14.19 (continued)