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Creation Quick Response Code in Software Write one master program that is intelligent enough to sort out the needs of the

Write one master program that is intelligent enough to sort out the needs of the
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overall process and then assign as many cogs as are necessary to the various tasks that have to be undertaken automatically. At this time, the software (meaning the sophistication) to do this does not exist within the Propeller/Spin system. Let a human programmer take a close look at the program requirements and decide what each of the cogs will be assigned to do. This is the way we will undertake our tasks because this is what the Spin language is designed to do. This means that we have to have a good understanding of exactly what needs to be done to solve the
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statiC VersUs dynamiC
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problem under consideration. Although this may seem trivial at first, it is a relatively sophisticated undertaking complicated by the fact that the solution is not unique. When there are many ways to solve a problem and the best way is not always apparent, things can get complicated. It takes a long time and lots of experience to get good at solving problems in a parallel environment. You have to develop a certain amount of expertise. Even so, we will come up with some basic guidelines about how to proceed as we learn more about the cogs and how to use them in their parallel environment.
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Static Versus Dynamic
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Basically, the types of tasks we are interested in can be divided into two major categories: those tasks that do not change as we process the data and those that do. According to the classifications I will define, tasks involving data that does not change are classified as static tasks and those handling data that changes in real time are classified as dynamic.
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A large number of tasks handle large amounts of data that either does not change or changes only marginally over short periods of time. An example of such a task would be a list of customers an insurance company maintains. We may need to sort this list from time to time, in any number of different ways, to get information about its contents for our current needs, but there is no need to respond to something critical in real time. For example, we might be looking for everyone over 90 years of age to send them a wellness greeting, and if we can send the information out by the end of the day it will in most probability be okay. Here are some other examples of static systems:
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Mailing lists Census data Payrolls Calendars
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In general, these databases do not change over short periods of time and their basic use involves the data itself rather than something dynamic that is happening within the system. You may be interested in the demographics of the U.S. population in 1873 and there is not much that is going to change within the database that represents that population in the next few days (other than newly discovered historical data that may be added from time to time). Handling static tasks requires speed but not necessarily parallel processing, although we had agreed earlier that almost every task could be done faster in a parallel environment. Even speed is not paramount in that a large mailing list can be sorted overnight, and for most purposes that would be acceptable.
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Parallel processing is not particularly well suited to handling such static systems, although as time goes on, and massively parallel systems become available in both hardware and software, parallel engines will be used to handle the kind of databases we have been discussing.
dynamiC systems
On the other hand, a number of other systems contain information that is changing constantly, and in many cases we need to read and manage the various properties of such a system in real time so that we can control the system to get the results we have specified. Almost every industrial control situation is a dynamic system with more than one variable. By definition, in any dynamic system things change constantly. One aspect of interacting with a dynamic system is looking at (reading) the variables that are changing. We are interested in these variables because they define the operation of the system. In most cases, the data of interest is represented by the many displays on the control panels that manage what is going on in the system. These displays are designed for human observation and response. In most cases, the management of the system itself is undertaken by some type of automatic mechanism executing a complicated algorithm. Each control loop has its own feedback system and has to be managed by some sort of machine intelligence. Today, this intelligence is provided by computers, both small and large. Such systems are particularly well suited for management by parallel systems where any number of variables may affect one outcome. Complex formulas, fuzzy logic, and machine intelligence play their role in controlling these systems. (In the old days, complicated systems were handled by experienced operators who had long-term experience with the systems. The operators were accepted as being experts in their fields.) An example of a simple dynamic control system is a domestic hot water heater. The controller turns the heat on at a certain temperature and turns it off at a higher temperature. If the temperature gets too high, another control might override the heat input or a relief valve might be used to release the built-up energy in a safe way. We do not need a sophisticated control system here. The available systems are reliable, durable, and safe, and the system response is fairly slow. On the other hand, an automated baking line in a modern bakery needs a very sophisticated control system, which has to be manipulated constantly to get the perfect cookies we expect on our grocery shelves. Machine intelligence and fuzzy logic have application here. The inputs of such a system include the following:
The temperature in the oven The speed of the conveyors in the oven The temperature history for the last hour along the long linear oven The ambient external temperature The humidity at the oven door The color and variety of the flour being used and the year the grain was harvested in The color of the finished product, top and bottom The results of the last batch Conditions halfway down the baking oven
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