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People and their organizations possess disciplines as arrayed in the Enterprise Performance Optimization Architecture as elements The disciplines are attributes assigned to one or more people This model says nothing about the number of individuals needed with these disciplines or about various combinations of disciplines residing among various people, as that is a function of enterprise size, scope, and complexity Determining what is best for your enterprise is a part of proprietary differentiation Furthermore, for each discipline type there are sources of best practices residing in academia and among technology vendors Terms We prepared this glossary to explain what we mean by these terms Many de nitions are from Wikipedia, Princeton, and the Department of Defense; some are our own unique offerings Performance Analysis (PA) This describes any analysis, spanning in scope from an enterprise view to process details and any combination in between Where information technologists might apply the term to investigating a program s behavior, we use the term more for understanding process performance, that is the sequence and timing of tasks and consumption of resources by enabling mechanisms to produce speci c outcomes Process modeling of the type we have used does not perform states and timing analysis, for instance There are a host of techniques supporting detailed analysis and understanding of processes and activity performance by combinations of people and technologies performing work Many techniques are embedded in drawing tools like Microsoft Visio and SmartDraw , for example More robust tools are needed for serious enterprise performance engineering and management Economic Analysis (EA) This form of analysis is also scalable to a range in scope One can use economic analysis to determine the cost effectiveness of a project or.

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Here, however, is a problem: The print command has no way to display anything that won't fit in a character or integer value, so strings can't be displayed that way Instead, you must display nullterminated string variables with the x command (think "examine"), which is intended for use in examining memory Program variables exist in memory, and hence can be displayed with the x command Here's a typical use of the x command to display a string variable: (gdb) x /s &eatmsg As with the print command, the format codes are placed first, preceded by a slash symbol (/) The name of the variable follows, preceded by an ampersand (&) C programmers will recognize the use of the ampersand here: It's the "address of" operator in C In C, the expression &eatmsg would return the address of the variable eatmsg That's just what we're doing here: We're handing the x command the address of the variable eatmsg, so that it can display memory starting at that address Note that values displayed by the x command are not retained in the value history! The format code s indicates that the x command should treat memory as a null-terminated string Starting with the address indicated by &eatmsg, x will then display memory as a null-terminated string Table 121 shows he various format codes available for use with the x command Note that two of the format codes, s and I, are available only with the x command Print does not support them You can use the address-of operator with the print command to determine the memory address of a program variable To display the address of a variable named seconds, you would issue this command: (gdb) print &seconds.

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$8 = (<data variable, no debug info> *) 0x80496d5 This will also work for code labels, to determine the address at which a label exists The x command can dump memory for a specified number of bytes beginning at a specified address The two additional tricks (over and above the format code) are the repeat count and the unit size The repeat count is a number placed immediately after the slash in the x command, and it specifies the number of units to dump The format command comes next and indicates which way the data must be displayed Finally, the unit size specifies how large each displayed unit is For a traditional memory count, this is best done as a byte, for which the code is b The address may be specified as a literal address in hex, or as the address in a register, or as the address of a specific variable name or program label Here's n example of a memory dump of 64 bytes, each formatted in hex: (gdb) x /64xb 0x8049500: 0x8049508: 0x8049510: 0x8049518: 0x8049520: 0x8049528: 0x8049530: 0x8049538: (gdb) 0x8049500 0x55 0x89 0x90 0x90 0x55 0x89 0x00 0xe8 0x04 0xa3 0x97 0x04 0x83 0xc4 0x08 0xe8.

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EXPERIMENTS AND PROGRAM EXAMPLES. QR Code integrating in .net . Add MATLAB Link for CCS code to the DTMF.m. Table 9.12 lists he MATLAB script. This MATLAB script controls the execution of DSK. The function ccsboardinfo checks the DSP development system and returns the information regarding the board and processor that it has identi ed. Bar Code recognizer on .net Using Barcode scanner for .Related: VB.NET QR Code Generating Data, QR Code Generation .NET WinForms , QR Code Generation Java

increased agility that is more responsive to change, has lower IT cost to change, and achieves continuous optimization In statistics, the terms Type I error (a error, or false positive) and Type II error (b error, or a false negative) are used to describe possible errors made in a statistical decision process When an observer makes a Type I error in evaluating a sample against its parent population, he/she is mistakenly thinking that a statistical difference exists when in truth there is no statistical difference For example, imagine that a pregnancy test has produced a positive result (indicating that the woman taking the test is pregnant); if the woman is actually not pregnant, however, we say the test produced a false positive AType II error, or a false negative, is the error of failing to reject a null hypothesis when the alternative hypothesis is the true state of nature For example, a Type II error occurs if a pregnancy test reports negative when the woman is, in fact, pregnant Most classi cation systems for predicting bankruptcy have attempted to minimize the misclassi cations The minimizing misclassi cation approach assumes that Type I and Type II error costs of misclassi cation are equal This assumption may be a fallacy Remember that wisdom applied knowledge knowledge experience There is evidence that these costs are not equal and incorporating these costs into the classi cation systems can lead to superior classi cation systems The principles of evolution can be used to develop and test genetic algorithm (GA)-based neural and genetic programming (GP)-based classi cation approaches that incorporate the asymmetric Type I and Type II error costs By applying these arti cial intelligence methods and mechanisms to decision making, it is possible to move toward the smart data wisdom paradigm of knowledge experience, in which data comprise entities that also have attributes Using simulated and real-life bankruptcy data, we compared the results of the proposed approaches with statistical linear discriminant analysis (LDA), backpropagation arti cial neural network (ANN), and a GP-based classi cation approach that does not incorporate the asymmetric misclassi cation cost In essence, we are providing a data strategy map in order to make it easier to chart the course to higher performance We are oving toward wisdom because we are investigating both a linear and nonlinear approach and comparing the two outcomes The results indicate that the proposed approaches, incorporating Type I and Type II error cost asymmetries, result in lower Type I misclassi cations when compared to LDA, ANN, and GP approaches that do not incorporate misclassi cation costs These smart data truths should be self-evident and useful as a common thread among both government and private enterprise The smart data wisdom here is to investigate both the linear and nonlinear approaches and to compare the outcomes for the one that demonstrates the most cost containment We must remember that smart data is the product of engineering, such that it presents itself to the right users at the right place and time in support of planning, problem solving, decision making, sense making, and predicting In this case, there is.

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