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That is, aggregation takes no effect and all the data have to be transmitted to a centralized node for further processing Function MEDIUM falls into this category 15412 Redundancy Suppression Due to the fact that correlation exists in sensor data both in the spatial and temporal domains, one of the most important datafusion functions is to eliminate data redundancy, or in a more concrete term, to exploit the correlation structure that exists in sensor data via distributed source coding Chou et al [35] proposed incorporating both distributed source coding and adaptive signal processing in data fusion and exploiting the correlation structure in sensor data to reduce the number of bits transmitted The system architecture consists of a data-gathering node (sink) and sensors The data-gathering node sends queries to sensors sequentially to obtain certain information that pertains to the entire eld.

Entropy rates of Markov chains (a) Find the entropy rate of the two-state Markov chain with transition matrix P = 1 p01 p01 p10 1 p10 .

The design objective here is to devise a computationally inexpensive encoding operation that supports multiple compression rates in the sensors while allowing a more complex decoding procedure in the data-gathering node The theoretical base used is the Slepian Wolf theorem [40]: if two discrete random variable X and Y are correlated, then X can be losslessly compressed using H(XjY) bits without access to Y, where H(XjY) is the conditional entropy of X given the information of Y Based on the Slepian Wolf theorem, the authors propose a blind compression method to achieve the theoretical limit given in the theorem Suppose that the datagathering node has full information of Y (side information) and the difference between X and Y is less than 2i 1 D, where D is the sampling resolution of the sensor readings.

conditioned in participants without asthma For example, mental arithmetic, a task that can elicit increased respiratory resistance (although note that Lehrer, Hochron, Carr, et al, 1996, found mental arithmetic to decrease respiratory resistance), was preceded by the display of a speci c color and a different color preceded the appearance of a clear slide (ie, no demand for mental arithmetic); increased respiratory resistance was demonstrated in response to the speci c color (D Miller & Kotses, 1995) In their debrie ng, 90% of participants recalled correctly which color preceded the arithmetic, but only 17% guessed correctly that the purpose of the experiment was to examine changes in breathing in anticipation of performing mental arithmetic, suggesting that conditioning occurred without subjects awareness.

In this gure, we create the Department table rst This table has a clause that speci es the D# column as the primary key of the table In the Employee table, SSN is de ned as the primary key column of the table MgrSSN, on the other hand, is de ned as a foreign key that corresponds to the SSN column of the same table This column keeps track of the manager of each employee This constraint is inherited from the ER model, and it corresponds to the fact that in the real world an employee may be the manager of one or more employees In the relational model, this is implemented by the RI constraint between the SSN and MgrSSN columns of the Employee table.

Rietveld, van Beest, and Everaerd (2000) exposed adolescents with asthma to placebo, citric acid at levels that induced cough, or citric acid at 50% of cough-inducing levels The purpose of their experiment was to examine the role of expectations: some participants were led to believe the experiment was about asthma, others were told the experiment was about evaluating avors Cough frequency was greater among participants who were told the experiment was about asthma than among those who were told the experiment was about evaluating avors Expectancies are important variables in cognitive explanatory models, and the results of this study suggest that expectancies about the research topic may in uence symptom perception and reporting An additional interpretation of the results is in terms of classical conditioned effects.

With their proposed tree-based codebook, one can encode X with only i bits without any knowledge of Y and the decoder can fully recover the information of X An example that shows the encoding and decoding operations with the tree-based codebook is depicted in Figure 1510 The sampling resolution, D, in this example is set to 01 If the sink has collected the full information of sensor data Y (eg, Y 04) and that the difference between the value of X and Y is less than 02, then sensor X only needs to use i ( 2) bits to encode its value without the knowledge of Y The deterministic encoding function for X is f (X) index(X) mod 2i , where the index function converts the value to the index in the tree-based codebook, for example, f (0.