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The value-fusion method consists of two phases: (i) exchanging the measured values and (ii) arriving to a consensus by computing the average of values and comparing it to a threshold In the presence of faulty sensors, in order to preserve precision and accuracy, the extreme values are dropped from the set that is going to be averaged In contrast, in the decision-fusion method, each device.



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For comparison, we will also solve the problem using nodal analysis. The presence of the voltage sources indicates that this method could be simpler. Treating the 3-V source and its connecting nodes as a supemode and writing the KCL equation for this supemode yields

The output y of the perceptron is 1 if v > 0, or 0 otherwise. 1 and 0 represent two classes in a binary classi cation. The linear equation





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Vrij and Heaven (1999) found in their study that variations of lie complexity are responsible for these con icting ndings When the lie was easy to fabricate, a decrease in speech errors and stutters occurred, whereas the opposite pattern occurred when the lie was dif cult to fabricate Vrij and Heaven (1999) suggested that, in line with the attempted behavioural control approach, liars will try to avoid making speech errors and stutters while lying However, they only achieve this when the lie is easy to formulate When the lie is dif cult to fabricate an increase in speech errors and stutters occurs, due to the cognitive load required to fabricate the lie Perhaps the most interesting aspect of the literature review was the absence of several signs of nervousness as indicators of deception For example, nervous behaviours.

For this exercise, use the Desk.obj file found in the Photoshop CD containing the bonus content. To access this file, insert the bonus content CD and navigate to Goodies 3D models Desk.

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With the Markov properties discussed in the preceding section we have clear notions when and how a graph can represent a set of conditional independence statements that hold in a given distribution. We also know, from the different examples discussed in Section 4.1.4, that sometimes a graph can represent a set of conditional independences perfectly (cf. the notion of a perfect map, Definition 4.1.15 on page 103), while in other circumstances it is impossible to find a such graph (of a given type, that is directed or undirected; cf. Figures 4.3 and 4.4). What we have not considered yet is whether two distinct graphs can represent exactly the same set of conditional independence statements. Such a consideration is relevant for learning the structure of graphical models from data, as we will consider it in 7, because it obviously influences whether we can expect a unique structure as a learning result or not. Formally, this question employs the notion of Markov equivalence: Definition 4.1.21 Two (directed or undirected) graphs G1 = (U, E1 ) and G2 = (U, E2 ) with the same set U of nodes are called Markov equivalent iff they satisfy the same set of node separation statements (with d-separation for directed graphs and u-separation for undirected graphs), or formally, iff X, Y, Z U : X|Z|Y

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1. Open an image in Photoshop. 2. Use the Pen tool to create a path around an object in the image, as shown in Figure 17.36.

Is fuzzi cation frequently carried out when the answer is a number, or when the answer is a word, or both The members of a discrete fuzzy set are usually words. When is such a set a linguistic variable, and when is it not What is a good data type for output when the answer is in words What is the difference between an ambiguity and a contradiction What are the major steps in arriving at a conclusion expressed in words As a program proceeds from earlier-to-later reasoning steps, what should be done about resolving ambiguities About resolving contradictions

0.890 0.545 1.078 3.30 0.341 0.627 0.796 1.99

Nanotechnology, Encyclop dia Britannica Online, March 9, 2007. <http://www.britannica. M. Singer, HP s Crossbar Latch to Replace Transistors Internet News, February 2, 2005.

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