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success = true; } } } } } catch(Exception e) { logmsg( connect failed: +e); port = null; input = null; output = null; } return success; } public boolean send(int b) { // will also cover char or byte try { output.write(b & 0xff); // for good measure do the & } catch (Exception e) { // null pointer or serial port dead errorMessage( send , e); return false; } return true; } public void startup() { send(START); } public void control() { send(CONTROL); } public void pause(int millis) { try { Thread.sleep(millis); } catch(Exception e) { } } // Roomba ROI opcodes // these should all be bytes, but Java bytes are signed public static final int START = 128; // 0 bytes of data public static final int BAUD = 129; // 1 byte of data public static final int CONTROL = 130; // 0 // ...

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As a 32-bit operating system, Windows CE defines a 4 GB address space. Two gigabytes of this space are reserved by Windows CE for hardware access. The other 2 GB block is the virtual memory space shared by all applications. Windows CE divides 1.056 GB of this virtual address space into 33 process slots. Each of these slots occupies 32 MB of the 2 GB application virtual address space. Except for a small amount reserved by Windows CE, the rest of the virtual address space is used for memory mapped files. The Windows CE API provides a number of functions for determining the global state of a Windows CE device. These functions can be used to determine the current physical and virtual memory usage, as well as current object store memory status.

EXECUTIVE INSIGHT (CONTINUED) uniquely agged by the statistical model. For example, linking other e-mail or shipping addresses associated with transactions agged by the model may result in the uncovering of additional fraudulent activity. Merchants should be aware that fraud detection models require constant care and feeding in order to maintain an acceptable level of effectiveness. The evolving nature of fraud, coupled with the ongoing evolution of a merchant s product and service suite, means that, in addition to the up-front investment required for model creation, merchants must also budget for ongoing model maintenance, as well as refresh and rebuild activities. There may also be peripheral work required to begin the modeling process, such as ensuring that all historical fraud data is accurately associated with original transaction data, or cleaning up historical data in order to ensure consistency across all data elements. The time and cost associated with these activities should be considered within the total cost of ownership required to operate the model, which, of course, should be compared against the monetary savings that the model brings to the enterprise. The construction and maintenance of these models can often be complex and costly, but if executed correctly, they can bring additional fraud detection capabilities above and beyond other fraud detection tools, techniques, and technologies used by merchants today.

Note. b is the unstandardized regression coef cient; b* is the standardized regression coef cient; sb is the estimate of the standard error of the regression coef cient; 95% con dence interval is on the unstandardized regression coef cient.

This may seem to the reader somewhat redundant to our introductory explanation of dBs. The purpose of this section is to instill the concepts of insertion loss and insertion gain. If we say that that waveguide section had an insertion loss of 3.4 dB, we know that the power would drop 3.4 dB from the input to the output of that waveguide section. If we said that the LNA (low noise ampli er) had an insertion gain of 30 dB, we would expect the output to have a power 30 dB greater than the input.

tan h L(b j )/2 tan h L(b j )/2 (75)

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