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This code is based on work by Conor and a few others from the Arduino forums, so thanks to those involved. Also, version 0019 of the Arduino IDE came in an SPI example sketch for the SCP1000 using the SPI.h library by Tom Igoe. The code above does things the hard way again so that you can see exactly how the SCP1000 communicates with the Arduino. Once you understand it, you can make life easier by using the SPI library. After you have uploaded the code, open up the serial monitor window and ensure that your baud rate is set to 38400. You will see a stream of data from the sensor showing the pressure in hPa (hectopascals) and in atmospheres. Hectopascals are the unit of pressure commonly used in weather forecasts. You will also see the temperature in Celsius and Fahrenheit.

Summary

The code starts off with a set of defines for your pins on the Mega: #define #define #define #define SLAVESELECT 53 SPICLOCK 52 DATAOUT 51 //MOSI DATAIN 50 //MISO

Plug-ins, first introduced in Struts 1.1, are a good way to extend or amend the functionality of Struts. A plug-in has to implement the org.apache.struts.action.PlugIn interface. This interface has two functions: init(), which is called when the PlugIn implementation is instantiated, and destroy(), which is called when Struts shuts down. Additional parameters may be passed to the plug-in before initialization using <set-property> tags in the plug-in declaration. Each <set-property> must have a corresponding setXXX defined on the plug-in implementation.

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In Figure 7-2, the bottom curved line represents optimal risk tolerance the amount of risk a company is willing to accept. You can see that high-impact risks have a low likelihood, and high-likelihood risks have low impact. The top line represents inherent risk. This is the amount of risk before any mitigating controls are applied. The space in the middle represents the amount of risk a company s controls must mitigate to reduce the risk to a tolerable level. The residual risk is represented by the bottom line. This is the risk left after mitigating controls have been applied. It s possible for your residual risk to be above your risk tolerance. When this is the case, discussions must take place to determine the best way to address the situation (for example, develop remediation plans, accept the risk, etc.). These metrics are very important because they enable you to measure and do trending analysis on the effectiveness of your controls. For example, dashboards can show you the areas where controls are the weakest (that is, where your residual risk is greater than your risk tolerance) so that you can identify areas for controls redesign. IT organizations should consider having defined risk tolerances that are based on metrics. Some organizations work by the seat of their pants and

n this chapter, we ll take a look at a web application technology called JavaServer Faces (JSF) that will play a huge role in the Java webapp framework scene for many years to come. We ll also preview a newer technology called Struts Shale, which apart from the common moniker, shares little with Struts as you know it. Struts Shale, or Shale, for short, is the first among many new webapp frameworks that have a Struts heritage but embrace the advantages of JSF. Besides these two, we ll also give you a peek at the Struts-Faces integration library, which allows you to use JSF alongside classic Struts (that is, Struts as described in this book). Before we delve into the exciting details, I d like to give you a bird s-eye view of the two main players in this chapter, JSF and Shale.

If you are using a Duemilanove, the pins will be: #define #define #define #define SLAVESELECT 10 SPICLOCK 13 DATAOUT 11 //MOSI DATAIN 12 //MISO

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