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protected var smoothRandomNoise:BitmapData; protected var pointers:Vector.<Point>; protected var rands:Vector.<Number>; public function ch38ex2() { noise = new BitmapData(stage.stageWidth, stage.stageHeight, false, 0); holder = new Sprite(); video = new Video(stage.stageWidth/2, stage.stageHeight/2); video.scaleX = video.scaleY = 2; var camera:Camera = Camera.getCamera(); camera.setMode(stage.stageWidth/2, stage.stageWidth/2, 30, false); video.attachCamera(camera); testPattern = new Loader(); testPattern.blendMode = BlendMode.ADD; testPattern.alpha = 0; holder.addChild(video); holder.addChild(testPattern); addChild(holder); stage.quality = StageQuality.LOW; smoothRandomNoise = new BitmapData(300, 10, false, 0); smoothRandomNoise.perlinNoise(Math.random()*100, Math.random()*100, 4, int((new Date()).date) * int(1000*Math.random()), true, true, 7); pointers = new Vector.<Point>(12); rands = new Vector.<Number>(12); for (var i:int = 0; i < 12; i++) pointers[i] = (new Point( Math.random()*smoothRandomNoise.width, Math.random()*smoothRandomNoise.height)); var PBJURL:String = "http://actionscriptbible.com/files/badreception.pbj"; var loader:URLLoader = new URLLoader(new URLRequest(PBJURL)); loader.dataFormat = URLLoaderDataFormat.BINARY; loader.addEventListener(Event.COMPLETE, onBytecodeLoaded); var IMGURL:String = "http://actionscriptbible.com/files/testpattern.jpg"; testPattern.load(new URLRequest(IMGURL)); testPattern.contentLoaderInfo.addEventListener(Event.COMPLETE, onImgLoad); } protected function onBytecodeLoaded(event:Event):void { shader = new Shader(URLLoader(event.target).data); shaderFilter = new ShaderFilter(shader); shader.data.noiseImage.input = noise; shader.data.srcImage.input = noise; shader.data.dimensions.value = [noise.width, noise.height]; stage.fullScreenSourceRect = noise.rect; stage.addEventListener(MouseEvent.CLICK, onFullScreen);

Computer software also produces an NP chart. This is just a control chart of nP D, the number of defectives in a sample. The points, center line, and control limits for this chart are just multiples (times n) of the corresponding elements of a P chart. The use of an NP chart avoids the fractions in a P chart.

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stage.addEventListener(Event.ENTER_FRAME, go); } protected function onImgLoad(event:Event):void { testPattern.width = noise.width; testPattern.height = noise.height; } protected function onFullScreen(event:MouseEvent):void { if (stage.displayState == StageDisplayState.FULL_SCREEN) { stage.displayState = StageDisplayState.NORMAL; } else { stage.displayState = StageDisplayState.FULL_SCREEN; } } protected function go(event:Event):void { noise.noise(getTimer(), 0, 255, 7, true); for (var i:int = 0; i < pointers.length; i++) { var p:Point = pointers[i]; p.x = (int(p.x) + 2*(1+i)) % smoothRandomNoise.width; rands[i] = smoothRandomNoise.getPixel(p.x, p.y) / 0x00ffffff; } var sd:ShaderData = shader.data; testPattern.alpha = Math.max(0, rands[6] * 3 - 2); sd.noisyHDisplace.value = [Math.pow(rands[2],4)*40]; sd.vRoll.value = [rands[0] * 80 - 20]; sd.channelSplit.value = [rands[3] * 40 - 10]; sd.sinHDisplaceAmplitudes.value = [20.0, 2.0, 1.0]; sd.sinHDisplaceFrequencies.value = [rands[2]*2+0.4, rands[10]*4+4, rands[11]*8+8]; sd.sinHDisplace.value = [Math.pow(rands[2], 8) * 100]; sd.noiseLayer.value = [Math.pow(rands[10], 4) * 0.8]; sd.blackoutThresh.value = [rands[2]*0.3]; holder.filters = [shaderFilter]; } } }

0.6 0.5 0.4 0.3 0.2 0.1 p

In the example, you use a strip of Perlin noise to produce cycles of natural-looking randomness. You have a set of points you move across the strip of noise at different speeds, sampling the cyclical noise, and storing the random numbers in the rands vector. By picking an entry from this vector with a higher index, you get faster-cycling noise. By raising these values in the range zero to one to higher powers with Math.pow(), you make the distribution of the noise cluster toward the edges more. This way, you get more extreme effects less often. Anyway, try it. The effect is akin to Figure 38-1.

16-8.2

for that type of data. The current design of the ATaG runtime allows only one output channel to be associated with a particular abstract data item; that is, there can be at most one producer task. This restriction may be eliminated in the future. Table 2.2 describes the annotations that can be associated with an abstract channel in the current version of ATaG. The abstract channel is the key to concise, flexible, and architecture-independent specification of common patterns of information flow in the network. For instance, spatial dissemination and collection patterns may be expressed using simple annotations such as 1hop, local, or all nodes, on output and input channels. More sophisticated annotations may be defined as needed or desired for a particular application

U Chart (Control Chart for Defects per Unit)

FIGURE 38-1

It is sometimes necessary to monitor the number of defects in a unit of product rather than the fraction defective. Suppose that in the production of cloth it is necessary to control the number of defects per yard or that in assembling an aircraft wing the number of missing rivets must be controlled. In these situations we may use the control chart for defects per unit, or the U chart. Many defects-per-unit situations can be modeled by the Poisson distribution. If each sample consists of n units and there are C total defects in the sample, U C n

A complex effect given by Example 38-2

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