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Specify first point: 8 ,3 4 Specify next point or [Undo]: Move the mouse cursor down from the start point of the line. You see a temporary drag line. Then type the following length of the line. 2 8-7/16 Note You can see what you type in the Dynamic Input tooltip as you are typing. Therefore, you can check that you ve typed the right numbers before you press Enter. Specify next point or [Undo]: Move the cursor horizontally to the right and type 28 . Specify next point or [Close/Undo]: Now try entering the distance using decimal notation, instead of feet and inches. Move the cursor up and type 32.4375 Specify next point or [Close/Undo]: .

< xml version= 1.0 > <xsql:query connection= demo xmlns:xsql= urn:oracle-xsql > SELECT ename as employee_name , to_char(hiredate, YYYY-MON ) AS hiredate FROM emp </xsql:query>

You can save brush sets as well as load and edit them either by choosing commands on the palette menu in the Brush drop-down palette (see Figure 5-16, earlier in this chapter) or via the Brushes panel of the Preset Manager dialog box, new to Photoshop 6. To check out the dialog box, choose Edit Preset Manager. Figure 5-25 gives you a look at the Preset Manager with the Brushes panel at the forefront. If you re already working in the Preset Manager, you can press Ctrl+1 to get to the Brushes panel.

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5.1 BASICS OF 4-D, MULTIMODALITY, AND FUSION OF MEDICAL IMAGING In 2 we introduced the concept of multidimensional medical images from 2-D, 3-D, to 4-D. Figure 5.1A revisits two types of 3-D image with z and t as the third dimension, respectively. The details of 3-D imaging were discussed in 4. This chapter presents 4-D imaging, which can take on several forms. One possible extension from 3-D imaging to 4-D imaging would be adding the fourth dimension of time t to the 3-D (x, y, z) spatial imaging shown in Figure 5.1B . Another form of 4-D imaging is the fusion of two modality anatomical structures, or one anatomical and one physiological modality 3-D image sets depicted in Figure 5.1C . Section 5.1 presents the basics of 4-D imaging, including its formation, multimodality image registration, fusion, and display. Section 5.2 discusses speci c 4-D imaging for diagnostic and therapy purposes in daily clinical operation. Section 5.3 introduces multimodality imaging and methods of image registration. Section 5.4 considers the relationship between multimodality image registration with image fusion and display, and some applications. The many examples given in Sections 5.2, 5.3, and 5.4 demonstrate applications of the concepts described in Section 5.1. 5.1.1 From 3-D to 4-D Imaging

The design of the LTVS prototype in the outpatient radiological imaging center of the Healthcare Consultation Center II (HCCII), Radiology Department, USC is based on the work ow study results shown in Table 27.1. 27.4.2 System Infrastructure

In case network element load exceeds the service rate, a single queue at each internal congestion point is no longer su cient. Instead, a di erent queue for each type of service (PDP context type) is needed to which independent latency, jitter and packet loss characteristics apply. Figure 6.3 shows the QoS tra c management functions in the 3G-SGSN. When IP packets arrive at the 3G SGSN from the GGSN, IP input scheduling is performed. IP input scheduling prioritises and schedules packets from ingress packet queues based on a DSCP. In Figure 6.4, an example of IP scheduling is given. In this example six queues are shown: one expedited forwarding (EF), four assured forwarding (AF) and one best-e ort (BE). For more details on these IETF schemes see Section 6.3. When the packet arrives at the hardware driver, the access control list classi es the packet based on the DSCP eld in the IP header. Then, the packet is sent to the proper queue. Each queue has a queue management function which is responsible for establishing and maintaining queue behaviour within the 3G SGSN and involves four basic actions:

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Property 2 (Jennrich and Moore) A general expressionfor the Fisher score The Fisher score vector of exponential family distributed observations is described by

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