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Name Mouse port Keyboard port Serial port Parallel port Audio out FireWire USB Qty 1 1 1 1 1 or 2 1 or 2 4-8 Appear Often Often Often Often Sometimes Sometimes Usually Used for DIN-9 or PS/2 connector for mouse plug-in DIN-9 or PS/2 connector for keyboard plug-in Male 9-pin D connector (sometimes on optional cable) Female 25-pin D connector SPDIF optical and/or coax audio out for entertainment center link-up Standard IEEE 1394 connector for networking, camcorders, video and TV, storage, printers, scanners, and so on Standard USB connector for storage, flash drives, keyboard, mouse, infrared links, video and TV, printers, scanners, and so on Ethernet 10/100 or 10/100/1000 network links (most mobos have 1, some have 2) 6-jack sound card inputs and outputs Driving VGA and/or DVI devices, sometimes also S-Video or TV
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Nonlinear black-box models can be constructed in the frequency domain; they nd their natural application within nonlinear analysis algorithms of the spectral balance type ( 1) [118, 119]. The basic principle requires the de nition of the current in a nonlinear element as an extension of the quasi-static formula: i(t) = ig (v(t)) + dq (1) (v(t)) d2 q (2) (v(t)) d3 q (3) (v(t)) + + + dt dt 2 dt 3 (3.91)
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0, then = + j , where 1 + Tan 1 4 2 2 2 2 2 . (12.129)
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Quantum computing as a eld has its roots very rmly planted in major theoretical developments in the 1980s and 1990s. The early musings of Feynman on how e ciently quantum mechanics could be simulated on a computer, Deutsch s de nition of quantum Turing machines and quantum circuits, Deutsch and Jozsa s algorithm, and the study of quantum complexity theory by Bernstein and Vazirani showing that quantum Turing machines violate the modi ed Church Turing thesis all led up to Shor s remarkable polynomial (P) time quantum algorithms for factoring and discrete logarithm. These algorithms provided the killer applications that brought QC in the limelight. However, before any serious e ort by experimentalists to realize quantum computers, another seemingly insurmountable hurdle had to be overcome by theoreticians. Quantum states are fragile and subject to decoherence that is continuous rather than discrete. This and the no-cloning theorem seemed to rule out the application of error correction techniques. The invention of quantum error-correcting codes by Calderbank, Shor, and Steane overturned conventional wisdom in quantum mechanics and paved the way for fault-tolerant QC and the threshold result that was independently obtained by Aharonov and Ben-Or; Knill, LaFlamme, and Zurek; and Gottesman and Preskill. Theoretical work has played a similarly central role in quantum cryptography (QCRYPT), where the 1984 protocol for quantum key distribution (QKD) due to Bennett and Brassard provided the major moving force for the eld. For the last decade, QC has brought about a remarkable collaboration between theoreticians and experimentalists. This collaboration has resulted in the elucidation of viable designs for quantum computers. The establishment by DiVincenzo and Barenco and co-workers of elementary universal families of one and two-qubit quantum gates for QC did much to simplify the quantum circuit model that the physical design needed to implement. Theorists, notably Lloyd, Cirac, and Zoller and DiVincenzo proposed the rst potentially viable designs for quantum computers using ion traps and electromagnetic resonance techniques. The rst prototypes of quantum computers were built by experimentalists, notably Wineland, Kimble, Cory, and Chuang working closely with the theorists. As the technological program of experimentally realizing quantum computers advances toward its goals, what is the future role of theory in QC The outline below identi es some of the grand-challenge theoretical problems where progress is essential to both the success of the experimental e orts as well as the impact of QC. In addition, as the experimental e ort accelerates, the collaboration between theory and experiment must continue to grow and evolve.
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The beta distribution of the first kind [5, p. 91] is limited to the range zero to unity. The scale is limited and many shapes are possible with the two shape parameters g and c as shown in Figures 6.19 and 6.20. The beta probability distribution function is given by p x C c g c 1 g 1 x 1 x C c C g when x > 0 (67) elsewhere
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Figure 9-18: Watching a program while using a Windows XP application.
At the nanoscale level, thin lms are 2D nanostructures. Thin- lm nanocomposites are typically composed of nanocrystalline grains with sizes in the range 3 10 nm of a hard, ceramic material in a matrix of an amorphous or crystalline material. Compared with existing technologies, thin nanocomposite lms o er a much wider variety of opportunities for structural control: The structure may be tailored not only at the molecular and microlevel but also at intermediate scales. This o ers the potential of hierarchical control similar to the more sophisticated morphologies present in materials synthesized in nature [210]. For example, the architecture of mother-of-pearl, consisting of alternating tablets of aragonite (a few hundreds of nanometers thick) and thin organic lms (a few tens of nanometers thick) gives the mollusk shell exceptional strength without the brittleness associated with pure inorganic phases. While this construction model has been of interest to material scientists, research involving organic/inorganic interfaces, thin layers, and lamellar heterostructures has expanded beyond their mechanical properties to include structural, electronic, and optical properties of mesoscale composites [211]. There are major potential application opportunities for thin nanocomposite lms in the areas of new paints, coatings, di usion barriers, and functional polymer lms; also, a large variety of processing strategies are possible, including solvent casting, water suspensions, and UV and thermal curing [210]. For example, there is a lot of interest in studying ferroelectric/oxide thin- lm structures that are nanopatterned and/or compositionally graded. The properties of these ferroelectric lms are expected
the existing RIS and the new PACS to transfer the data. Therefore a commercial broker is purchased and implemented to act as an interface between the two systems. The RIS can output HL7 messages triggered by particular events in the radiology work ow (e.g., exam scheduled, exam dictated, exam completed). The broker receives the HL7 messages from the RIS. Following the speci cations provided by the RIS, the broker has been precon gured to map the incoming HL7 message data into particular elds of its own database tables. The PACS components can now communicate directly with the broker to make requests for information. Some important data that are requested by PACS are as follows: 1. A worklist of scheduled exams for an acquisition modality. 2. Radiology reports and related patient demographic and exam information for PACS viewing workstations. 3. Patient location for automatic distribution of PACS exams to WSs in the wards. 4. Scheduled exam information for prefetching by the PACS server to PACS WSs. These requests are in addition to general patient demographic data and exam data that are needed to populate a DICOM header of a PACS exam. 10.6 IMAGE PREPROCESSING In addition to receiving images from imaging devices, the gateway computer performs certain image preprocessing functions before images are sent to the PACS server or WSs. There are two categories of preprocessing functions. The rst relates to the image format for example, a conversion from the manufacturer s format to a DICOM-compliant format of the PACS. This category of preprocessing involves mostly data format conversion, as was described in Section 9.4.1. The second category of preprocessing prepares the image for optimal viewing at the PACS WS. To achieve optimal display, an image should have the proper size, good initial display parameters (a suitable lookup table; see 12), and proper orientation; any visual distracting background should be removed. Preprocessing function is modality speci c in the sense that each imaging modality has a speci c set of preprocessing requirements. Some preprocessing functions may work well for certain modalities but poorly for others. In the remainder of this section we discuss preprocessing functions according to each modality. 10.6.1 Computed Radiography (CR) and Digital Radiography (DR) 10.6.1.1 Reformatting A CR image have three popular sizes (given here in inches) depending on the type of imaging plates used: L = 14 17, H = 10 12, or B = 8 10 (high-resolution plate). These plates give rise to 1760 2140, 1670 2010, and 2000 2510 matrices, respectively (or similar matrices dependent on manufacturers). There are two methods of mapping a CR image matrix size to a given size LCD monitor. First, because display monitor screens vary in pixel sizes, a reformatting of the image size from these three different plate dimensions may be necessary in order to t a given monitor. In the reformat preprocessing function, because both the image and the monitor size are known, a mapping between the size
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