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During the daily operation of many industrial systems and complex machines, such as chemical and metallurgical plants, trucks, hydraulic excavators, and other construction machines, large data sets can be collected from a number of sensors These data sets are later used by maintenance engineers and experts to discover any signi cant difference or change in the operating conditions, which points to a possible faulty condition of the machine or system If this is the case, quick countermeasures by the operator are needed to prevent further deterioration in the system performance The above is only a small example of the many typical cases in which the human operator is faced with the problem of similarity analysis of large multidimensional data sets.

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Evaluating the similarity between a given set or pairs of large data sets and discovering the differences between them is a very fundamental step that is used for further classi cation and extracting a kind of knowledge about the system The complexity of the problem of similarity analysis arises from the fact that we have to analyze and compare directly the large data sets to each other rather than just classifying single patterns (single data points) in the prede ned feature space, as in the standard classi cation problem The problem of similarity exists also when different types of pictorial information should be properly compared and classi ed Here, the similarity analysis of a large number of images is considered to be a very important step in the more general procedure for unsupervised classi cation [1, 2] and knowledge acquisition from many types of pictorial information.

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This is a very speci c and important task, where humans still perform better than the currently available computerized systems Among the reasons for such results are the obvious complexity and the vagueness (subjective nature) of the problem itself, as well as the various possible criteria that could be used for the similarity analysis Nevertheless, a good (ie, true and plausible) solution to the problem of similarity evaluation is a key factor for success in many applications, such as fast visual search through a large amount of image information for proper sorting and classi cation In this chapter, we present a special fuzzy rule-based scheme for similarity analysis The results from this analysis, in the form of similarity degrees, can be later used for unsupervised classi cation of images.

There is a simple way to do this part. 15.23 Multiplicative multiple-access channel . Find and sketch the capacity region of the following multiplicative multiple-access channel:

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After investigating several possible features for a plausible similarity analysis, we have nally selected two of them as inputs of the fuzzy decision procedure These are the center of gravity of the image and the weighted average size of the image, computed in two versions The features are extracted from the raw pixel data information through respective algorithms Especially, the image volume is approximately evaluated as the number of information granules (neurons) that represent the original raw data set (pixels) The number of neurons, as well as their proper locations in the three-dimensional RGB space, is automatically de ned by a specially introduced growing unsupervised learning algorithm Finally, extended simulations are performed over a test example, consisting of 18 images of different owers They show the merits of the proposed fuzzy rule-based similarity analysis, which can be successfully applied to solving real-world classi cation.

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The last phase in the life of a virtual call is the clearing (takedown). Either the DTE or the network can clear a virtual call. The clear applies only to the logical channel that was used for that call. Three different packet types are involved in the call-clearing phase. The clear request packet is issued by the DTE initiating the clear. The remote DTE receives it as a clear indication packet. Both the DTE and DCE then issue clear con rmation packets to acknowledge receipt of the clear packets. Flow control is called out by the following packet types: receiver ready (RR), receive not ready (RNR), and reject (Rej). Each of these packets is normally three octets long, or four octets long using the extended version. Sequence numbering also assists ow control (Ref. 1).

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