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Color Models In the previous section we saw that in a bitmap image each pixel has a value that specifies its color So, how does a numerical value get translated to a color There are many ways to represent colors numerically A system for representing colors is called a color model Color models are usually designed to take advantage of a particular type of display device On most color monitors there are three phosphors (red, green, and blue), or light emitters, for each pixel Adjusting the intensity of the individual phosphors controls the color of the pixel When all three phosphors are at their minimum intensity the pixel appears black At their maximum intensity the pixel appears white If the red phosphor is the only one active, the pixel appears red When the red and green phosphors are on they combine to produce shades of yellow, and when all three phosphors are at full intensity the pixel appears white he most common color model used in computer applications is known as RGB (Red-Green-Blue) The RGB model mimics the operation of computer displays In RGB, colors are composed of three component values that represent the relative intensities of red, green, and blue Figure 15 shows the relationship of colors in the RGB color model The range of colors that can be represented by a color model is known as a colorspace In Figure 15, the RGB colorspace is the cube in the diagram In mathematical discussions of color, component values are often represented as real numbers normalized to the range 00 to 10 In programming and image formats, unsigned integer component values are almost always used The range of values for a color component is determined by the sample precision, which is the number of bits used to represent a component For photographic.

achieve lower costs through rationing assets (eg, ambulatory service and real-time forecasting of resources) The purpose of this case was to develop and test a valid survey instrument for measuring user evaluations of VAMTA, as a novel means to expedite smart data delivery, in preventive healthcare [3] The ndings of the case study were used to test the instrument that was used in a preliminary assessment of the effectiveness of the VAMTA system and the applicability of smart data in the TTF model to the VAMTA application The development of the instrument was carried out in two stages The rst stage was item creation The objective of this rst stage was to ensure face and content validity of the instrument An item pool was generated by interviewing two end-users of IT, obtained from a pool of medical technicians The end-users were given training on the module for 2 days and invited to participate in the study These subjects were selected for reasons of geographical proximity of the sample and, in many cases, the existence of personal contacts onboard ship An interview was also conducted with one of the authors of this study, who has approximately 10 years experience as an IT end-user In addition, the domain coverage of the developed pool of items was assessed by three other end-users from three different ship environments covered in the survey None of the end-users, who were a part of the scale development, completed the nal survey instrument All the items were measured on a ve-point Likert scale ranging from strongly agree to strongly disagree Next, the survey instrument was utilized in a pilot study in which end-users tested the VAMTA This pilot study provided face validity, in that the perceptions of end-users could be measured, and evaluation of the system from a conceptual viewpoint could be documented A total of 33 end-users were used in this phase to test the VAMTA They reported their perceptions of the VAMTA in the survey instrument, which was provided after their training and testing The pilot study results were analyzed using SPSS and Microsoft Excel to determine whether TTF, along with individual characteristics, had an impact on user evaluations of the VAMTA For the case, the original TTF model was modi ed to ensure adequate domain coverage of medical and preventive healthcare applications The third smart data dimension revolves around data-aligned methods and algorithms that utilize data with pointers for best practices, data with built-in intelligence, autonomics, an automated regulatory environment (ARE), and an automated contracting environment (ACE) Our case used data-aligned methods for the 33 individuals who participated in the testing of the VAMTA, including 11 females and 22 males, with various levels of experience and education Experience ranged from test subjects who had never seen the application before they tested it, to developers who built the application A number of test subjects had limited medical backgrounds Education ranged from high school graduates to people with doctorate degrees Data with pointers for best practices was tilized so that each test subject was shown a demonstration of the VAMTA prior to testing Test subjects were then required to build a new user account and speech pro le Subjects ran through the application once using a test script to become familiar with.

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the application Next, the test subjects went through the application again while being videotaped No corrections were made to dictated text during the videotaping This allowed the tracking of voice dictation accuracy for each user with a new speech pro le Test subjects completed the entire process in an average of 2 hours Afterward, each test subject completed a questionnaire to determine user demographics, utility and quality performance, system interface, hardware, information management, and overall system satisfaction and importance This survey instrument also allowed the test subjects to record any problems and suggest improvements to the system Data with built-in intelligence techniques recorded any problems that the test subjects encountered in the survey instrument Autonomics were employed by the VAMTA development team, to document bugs in the tracking system, and targeted by the test architect for the appropriate problem solving action The performance of VAMTA during testing was measured in terms of voice accuracy, voice accuracy total errors, duration with data entry by voice, and duration with data entry by keyboard and mouse Viewed together, these statistics provide a snapshot of the accuracy, speed, and overall effectiveness of the VAMTA system Each test subject s printout was compared with a test script printout for accuracy When discrepancies occurred between the subject s printout and the est script, the printouts were compared with the automated video recordings to determine whether the test subjects said the words properly, stuttered or mumbled words, and/or followed the test script properly Misrecognitions occurred when the test subject said a word properly and the speech program recorded the wrong word This automated regulatory environment provided a solid baseline for the lab experiments and led to the following results The accuracy of voice recognition, con rmed by videotaped records of test sessions, averaged 976%, with 6 misrecognitions (Table 41) The minimum average voice recognition was 85%, with 37 misrecognitions The maximum average voice recognition was 996% with 1 misrecognition Median voice recognition was 984%, with 4 misrecognitions.

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A common way of de ning a halftone screen is by specifying a frequency, angle, and spot function The frequency is the number of halftone cells per inch; the angle indicates the orientation of the grid lines relative to the device coordinate system As a cell s desired gray level varies from black to white, individual pixels within the cell change from black to white in a well-de ned sequence: if a particular gray level includes certain white pixels, lighter grays will include the same white pixels along with some additional ones The order in which pixels change from black to white for increasing gray levels is determined by a spot function, which speci es that order in an indirect way that minimizes interactions with the screen frequency and angle Consider a halftone cell to have its own coordinate system: the center of the cell is the origin and the corners are at coordinates 10 horizontally and vertically Each pixel in the cell is centered at horizontal and vertical coordinates that both lie in the range 10 to +10 For each pixel, the spot function is invoked with the pixel s coordinates as input and must return a single number in the range 10 to +10, de ning the pixel s position in the whitening order The speci c values the spot function returns are not signi cant; all that matters are the relative values returned for different pixels As a cell s gray level varies from black to white, the rst pixel whitened is the one for which the spot function returns the lowest value, the next pixel is the one with the next higher spot function value, and so on If two pixels have the same spot function value, their relative order is chosen arbitrarily PDF provides built-in de nitions for many of the most commonly used spot functions A halftone can simply specify any of these prede ned spot functions by name instead of giving an explicit function de nition For example, the name SimpleDot designates a spot function whose value is inversely related to a pixel s distance from the center of the halftone cell This produces a dot screen in which the black pixels are clustered within a circle whose area is inversely proportional to the gray level The prede ned function Line is a spot function whose value is the distance from a given pixel to a ine through the center of the cell, producing a line screen in which the white pixels grow away from that line.

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