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Active sensors often introduce a sound or light and look for how the introduced energy reacts with the environment Some examples of this type of sensing are sonar, laser, and infrared reflective detectors Sonar detectors introduce a sound, typically higher in frequency than what humans can hear, and listen for the echo The bounced-back echo is used by sonar range finders to send out a sound pulse and then compute the time it takes for the sound to return This time is directly proportional to the distance the sound must travel to bounce off the nearest object and return The speed of sound is around 770 mph, but it can easily be measured using a moderately fast microcontroller chip like those found in many robots Infrared (IR) reflective sensor systems emit a specific wavelength of light and look for a reflection of light Since light travels so much faster than sound, it is difficult to measure the time it takes to receive the reflected light Infrared detectors are typically used to detect whether an object is present within the range of the detector rather than how far the object is from the detector Some clever infrared detectors use some simple geometry present in a triangle formed by the emitter that generates the light, the reflected object, and the detector that senses the emitted light Everyday examples of infrared detectors can be found in modern bathroom stalls in public places The mechanism that automatically flushes the toilet typically uses an infrared detector to detect the presence of a person using the toilet The system is activated when a person is in the stall for a predetermined period of time Many systems have a small flashing LED that speeds up its flashing when the time has elapsed When the person leaves the presence of the IR sensor, the toilet flushes Lasers can be used to detect where an opponent is located This type of system is fairly advanced and usually employs a CCD camera or a linear sensor array A laser beam is emitted from the robot, and the CCD camera is used to see the laser spot or line on the opponent robot Generally, a band pass filter is placed in front of the camera to filter out all wavelengths of light except for the laser beam wavelength When the laser beam and the camera orientation is known, the range and location of an object can be determined through mathematical triangulation This type of system is fairly complex, but not as complex as a true vision system, and it is beginning to be seen in robotic applications using simple microcontrollers This type of system could be used in automatic weapons firing and assisted homing in on an opponent, and it can be placed in fully autonomous robots.

Figure 6-48 shows 106% and -57% This means that 106% of the total gross profit is above the line and -57% is below the line The numbers don t exactly get back to 100% because so many of the points cut the line that it s hard to make it exact However, if the user begins dragging this slider upwards, the 106% value starts to dwindle rapidly As the slider climbs, fewer dots appear above it, representing those products that had a higher gross profit Likewise, moving the top slider to the right narrows down the products that contribute to the gross profit margin The ideal scenario would be to have as many products as possible in the upper righthand corner of the perspective, but this is rarely the case Figure 6-48 shows that the products with the highest gross profit margins tend to have smaller gross profits, which means they simply don t generate as much revenue as items with lower margin For example, some of the highest margin items include bike stands and tire tubes, which do not generate anywhere near the same gross profits as a very expensive mountain bike Many of the dots tend to cluster together, forming a jumbled mess If the user wants to see a cluttered area of the chart better, she can right-click in that area and then choose Zoom to zoom in on it Right-clicking after zooming in will now present a Zoom Out option In addition, a user can click and drag on the background to draw a rubber band around a certain area Then, upon releasing the mouse, a menu will pop up with two options: Zoom and Show Only This only works if the user clicks and drags from the upper left to the lower right Other directions will draw the rubber band but not display the menu In order to provide even more information, it s possible to make the dots more useful For example, are most of the high-margin items in the Accessories category Are they in clothing, or bikes, or some other category Fortunately, there s a way to change the color and shape of the dots to represent values at other levels of the hierarchy Right-clicking in an empty area of the perspective chart will show a menu with an option named Data Point Attributes This will show a dialog box in which the user can select a member of a hierarchy and make any dots that belong to that member have a specific color and shape In this case, the dots represent individual products Custom data point attributes could be placed at either of the two levels above product, in this case the category or subcategory Because there are so many subcategories, this example will choose just the four categories of Accessories, Bikes, Clothing, and Components The user selects one of the categories and assigns a color and a shape to it and then clicks the Add button Figure 6-49 shows what the Data Point Attributes dialog box might look like when the user is done Clicking the OK button returns the user to the perspective chart.