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to Figure 21.2, except that it shows the experts estimates of the maximum and minimum additional needed ll. In some cases these estimates do encompass the actual event, but the values are clearly not consistent. Some maximum and minimum estimates actually fall within an expert s 50% con dence limits. Hynes and Vanmarcke state, It is clear that there are wide differences among engineers in the way they interpret the terms minimum and maximum. These widely used terms are essentially meaningless unless related to relative likelihood or probability. It should also be noted that those in the audience at

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17

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764 764 764 765 765

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face, ngerprint, and iris, heartbeat information can only be collected from a living individual, which makes it dif cult to deceive or defeat. In order to deceive a heartbeat collection sensor, an intruder must mask the signal of his/her own body. Then, he/she must emanate the heartbeat of the target individual across electrical potential, sound, and re ection/absorption of light. Masking strategies often leave characteristic signatures themselves, such as a change in the noise oor, which would also ag the system to an intruder. This chapter extends previous work in three areas. Previous research [4] focused mainly on analysis of the electrocardiogram (ECG). We extend the ECG results by applying our processing methods to a larger and more diverse set of individuals, demonstrating that performance remains high for a larger and a more diverse population. Second, we reviewed alternative sensing methods, using blood pressure and pulse oximetry [5], are presented and their corresponding performance is documented. Finally, we examine fusion of heartbeat information across the three modalities and quantify performance. The next section discusses the phenomenology and sensing modalities for monitoring cardiovascular function [6]. Then, we describe the experiments and data collection. Finally, we present the results and conclude with a summary of the ndings and recommendation for future investigations.

<cffunction name= FlushCompanyToDatabase returntype= void > <cflock name= #This.lockName# timeout= 10 throwontimeout= Yes type= READONLY > <cfif This.isNewCompany> <cfquery name= insCompany datasource= #Request.MainDSN# > INSERT INTO Company( CompanyName, Address, City, State, ZipCode, Comments ) VALUES ( #Trim(This.companyName)# , #Trim(This.address)# , #Trim(This.city)# , #Trim(This.state)# , #Trim(This.zipCode)# , #Trim(This.comments)# ) </cfquery> <cfelse> <cfquery name= updCompany datasource= #Request.MainDSN# > UPDATE Company SET CompanyName = #Trim(This.companyName)# , Address = #Trim(This.address)# , City = #Trim(This.city)# , State = #Trim(This.state)# , ZipCode = #Trim(This.zipCode)# , Comments = #Trim(This.comments)# WHERE CompanyID = #This.companyID# </cfquery> </cfif> </cflock> <cfreturn> </cffunction>

the seminar were also invited to give their estimates and their 50% intervals capture[d] the true value 62% of the time. Slovic et al. (1982) and others have cited these and similar ndings in suggesting that substantive expertise, or capability within one s specialized knowledge domain, has no necessary relationship to normative expertise or ability to provide coherent and unbiased probability judgments. On the other hand, at least some evidence with experts assessing probabilities about professional subjects with which they are familiar suggests that they may be more calibrated than are non-experts (viz., weather forecasters (Winkler and Murphy 1968) and auditors (Smith and Kida 1991)). Similar over-con dence effects have been shown in subjective estimates of probability distributions provided by geotechnical engineers. Folayan et al. (1970) obtained estimated distributions for compressibility parameters of San Francisco bay mud from engineers with up to 17 years of experience. Baecher (1972) further analyzed these prior distributions in comparison to that obtained from subsequent laboratory tests. As shown in Figure 21.4, the estimated means were lower than that measured, but, more signi cantly, overcon dence produced distributions too narrow to encompass most of the measured data. The one exception was subject 5, a graduate student whose estimate showed gross under-con dence. Why over-con dence There are many suggestions in the literature. Keren (1994) suggests that anchoring and adjustment may be to blame. People may anchor on a probability estimate re ecting intermediate dif culty, say 75%, and adjust up or down but not suf ciently depending of the perceived dif culty of the estimation task. Ferrell and McGoey (1980) present a similar argument, but the literature contains other attempted explanations as well (McClelland and Bolger 1994).

17.2 17.2.1

Notice that you need only a ReadOnly lock here because your are reading only from the This scope. Now that your locking problems are solved, you can tackle another prevalent problem: function variable leakage.

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