uwp barcode scanner c#

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database but the performance gap is quite small. Overall, the MLDA variants based on scatter ratio again obtained better results than the MLDA variants based on scatter difference. As in Section 2.4.2, we focus on the scatter-ratio-based variants, UMLDA and DATER, for the comparison between MLDA-TTP and MLDA-TVP. From Figure 2.9 and Table 2.3, UMLDA consistently outperforms DATER signi cantly on this database, especially for a smaller L, although the performance of UMLDA deteriorates when the number of features exceeds a certain number. Thus, on this database the UMLDA, an MLDA-TVP approach extracting uncorrelated features, shows its advantage against the MLDA-TTP approach, where the features can be viewed to be extracted through interdependent EMPs. Regarding the comparison between LDA and MLDA, there is an interesting observation from this experiment. For the Fisherface (LDA) approach, when L increases from 20 to 30, the recognition rate ironically decreases, as seen in Table 2.3. For the ULDA, when L increases from 10 to 20, the recognition rate surprisingly decreases too, as in Table 2.3. This is in contrary with our belief that more training samples should result in better recognition performance. On the other hand, all the four MLDA variants do not have this problem on this database, with recognition rate increasing as L increases, showing that MLDA approaches are more stable and consistent. Furthermore, the UMLDA algorithm outperforms the LDA and ULDA signi cantly, especially for a larger L, demonstrating again the bene ts of extracting features directly from the natural 2-D representation of face images rather than from their vectorized representation.

<cfcomponent extends= com.herdomain.calcbonus.Emp > <cfset This.managersOwnProperty = Just for us Managers > <cffunction name= ManagerBonus access= public returntype= numeric > <cfargument name= SSN type= string required= yes > <cfreturn Bonus(Arguments.SSN) * 1.15> </cffunction> </cfcomponent>

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In summary, through the comparison in Figures 2.8 and 2.9 and in Tables 2.2 and 2.3, it can be seen that by treating face images in their natural 2-D representation, the MLDA solution UMLDA achieves very good recognition results consistently on two very challenging face databases, for various number of training samples per subjects (L = 5, 10, 20, and 30). It is also observed that the MLDA variants based on scatter ratio generally outperform the MLDA variants based on scatter difference; and with scatter ratio as the separation criterion, the overall performance of MLDA-TVP is better than that of MLDA-TTP. In addition, MLDA variants are shown to be more stable and consistent than LDA approaches. Considering the short period of research and development in multilinear learning solutions for biometric signal recognition, the empirical evaluation results presented here are very encouraging and we believe that there is still great potential in further development of multilinear learning algorithms that operate directly on natural tensorial representations. The materials provided in this chapter represent a good starting point for newcomers to this eld; and the taxonomy of various multilinear projections and MLDA variants, together with discussions on their connections, is also bene cial for researchers already working in this eld.

Notice also that the preceding code declares a property directly in Manager, named managersOwnProperty. Manager inherits Emp s properties, but the process is a one-way street, so managersOwnProperty is available only in the Manager component and, not in the Emp component. The same principles of inheritance that work for component properties also work for component functions. The Emp component contains a Bonus() function that calculates the bonuses of nonmanagerial employees, but managers make an additional 15 percent bonus. To calculate manager bonuses, you extend Manager s functionality by declaring a function named ManagerBonus(), as shown in Listing 22-10. To retrieve the normal bonus for an employee, ManagerBonus() must call the Bonus() function that is declared in the Emp component. But Manager extends Emp, so Bonus() is available as one of Manager s own functions; all ManagerBonus() must do is return Bonus() multiplied by 1.15, as follows:

P [AB] = P [A|B] P [B] = P [B|A] P [A]

Listing 22-11 shows inheritance in action. Notice how the properties and functions of both Emp and Manager are directly accessible from Manager.

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<cfscript> managerServices = CreateObject( Component , com.herdomain.calcbonus.Manager ); </cfscript> <cfoutput> <p>These are all accessed through the Manager component only:</p> <p>Manager Bonus: <b>#managerServices.ManagerBonus( 012-34-5678 )#</b><br> managersOwnProperty: <b>#managerServices.managersOwnProperty#</b></p> <p>Standard Bonus: <b>#managerServices.Bonus( 012-34-5678 )#</b><br> empProperty: <b>#managerServices.empProperty#</b></p> </cfoutput>

The result of executing Listing 22-11 is shown in Figure 22-2.

for 02 s across 075-s overlapping windows Double peaks or missing peaks are revealed using autocorrelation with previous and following heartbeats Heartbeats are then cropped using the RR interval, the distance between the R peaks of adjacent heartbeats Figure 178 is a waterfall diagram where the heartbeats for each individual in a group are averaged by task [4, 5] Differences among individuals are clearly greater than the task-to-task differences, which indicate that human identi cation using ECG heartbeat information is possible Fiducial Points After alignment, the ECG ducial positions were located The standard medical ducial labels do not fully characterize the entire heartbeat trace From pattern recognition science, additional feature attributes are rarely completely correlated or independent However, additional attributes generally improve the scalability to larger populations [29] at the cost of reducing the tolerance to intra-subject variability.

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