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In Sections 16-5 and 16-6 we have presented basic types of Shewhart control charts. A major disadvantage of any Shewhart control chart is that the chart is relatively insensitive to small shifts in the process, say, on the order of about 1.5 or less. One reason for this relatively poor performance in detecting small process shifts is that the Shewhart chart makes use of only the information in the last plotted point, and it ignores the information in the sequence of points. This problem can be addressed, to some extent by adding criteria such as the Western Electric rules to a Shewhart chart, but the use of these rules reduces the simplicity and ease of interpretation of the chart. These rules would also cause the in-control average run length of a Shewhart chart to drop below 370. This increase in the false alarm rate can have serious practical consequences. A very effective alternative to the Shewhart control chart is the cumulative sum control chart (or CUSUM). This chart has much better performance (in terms of ARL) for detecting small shifts than the Shewhart chart, but it does not cause the in-control ARL to drop signi cantly. This section will illustrate the use of the CUSUM for sample averages and individual measurements. The CUSUM chart plots the cumulative sums of the deviations of the sample values from a target value. For example, suppose that samples of size n 1 are collected, and Xj is the

Channel includes all nodes that are owned by the task instance. This value is used in conjunction with the nodes-per-instance or area-perinstance values of the Instantiation annotation of the abstract task (see Fig. 2.10 for an example) Channel applies to the parent of the node hosting the task instance; in the virtual tree topology imposed on the network by the runtime system. Channel applies to all children of the node hosting the task instance; in the virtual tree topology imposed on the network by the runtime system.

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//kernel does image1 -> pixel4 var invec:Vector.<Number> = new <Number>[-4, 2, 100, 0.238]; //out-of-range floats will be respected var input:ShaderInput = shader.data.inimg; trace(input.channels); //1 input.input = invec; input.width = 4; //let s use the input like a long strip input.height = 1; var outvec:Vector.<Number> = new Vector.<Number>(); //must be initialized var job:ShaderJob = new ShaderJob(shader); job.target = outvec; job.width = 4; job.height = 1; //or try mirroring the input //job.width = input.width //job.height = input.height job.start(true); trace(outvec.length); //16, 4 by 1 by 4 channels

average of the jth sample. Then if 0 is the target for the process mean, the cumulative sum control chart is formed by plotting the quantity a 1Xj

You can also send or receive batches of oating-point data using a ByteArray. As with Vectors, you won t lose accuracy in the process. Use writeFloat() and readFloat() and the ByteArray cursor to prepare and extract single points of data, keeping in mind that a 32-bit oat is 4 bytes. This method is useful when the data you need to process is already tightly packed oats, or when the result of the kernel is to be used in this format, such as a sound sample. You ll again have to instruct Pixel Bender of the intended dimensions when using a ByteArray as input or output of a kernel.

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//kernel does image4 -> pixel4 var input:ShaderInput = shader.data.inimg; trace(input.channels); //4 input.width = 4; input.height = 1; var inbytes:ByteArray = new ByteArray(); for (var i:int = 0; i < input.width * input.height * input.channels; i++) { inbytes.writeFloat(i % 4); } input.input = inbytes; var outbytes:ByteArray = new ByteArray(); var job:ShaderJob = new ShaderJob(shader, outbytes, 4, 1); job.start(true); trace(outbytes.length / 4); //16 floats (4 is length of a float) for (outbytes.position = 0; outbytes.position < outbytes.length;) { trace(outbytes.readFloat()); //0,1,2,3,0,1,2,3,0,1,2,3,0,1,2,3 4 by 1 by 4 channels }

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