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You save a layer index for a drawing that you expect to use as an xref to create an index of all of the layers in the drawing. As with a spatial index, the saving process takes a little longer, but you save time at the other end when you load an xref with frozen or turned off layers. To create a layer index in AutoCAD, choose File Save As to open the Save Drawing As dialog box. From the Tools menu at the upper-right corner of the dialog box, choose Options to open the Saveas Options dialog box, shown in Figure 19-10. From the Index type drop-down list, choose Layer or Layer & Spatial. Click OK, and then click Cancel. In AutoCAD LT, set the INDEXCTL system variable to 1 for just a layer index, or to 3 for both layer and spatial indexes. After you create a layer index, each time you save the drawing, you see the following message:
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Bushman T., Development of a Holographic Computing System, Proc. SPIE, 1162, 66 77 (1989). Butters, J. N. and J. A. Leendertz, Speckle Pattern and Holographic Techniques in Engineering Metrology, Opt. Laser Tech., 3, 26 30 (1971). Case S. K., J. A. Jalkio, and R. C. Kim, 3-D Vision System Analysis and Design, in ThreeDimensional Machine Vision, Takeo Kanade, Ed., Kluwer Academic Publishers, Norwell, MA, 1987, pp. 63 95. Chiang, F.-P., Moire Methods of Strain Analysis, in Manual on Experimental Stress Analysis, A. S. Kobayashi, Ed., Soc. for Exp. Stress Anal., Brook eld Center, CT, 1983, pp. 51 69. Creath K., Averaging Double-Exposure Speckle Interferograms, Opt. Lett., 10, 582 584 (1985c). Creath K., Phase-Shifting Speckle Interferometry, Appl. Opt., 24, 3053 3058 (1985b). Creath K., Digital Speckle Pattern Interferometry, Ph.D. Dissertation, Optical Sciences Center, University of Arizona, Tucson AZ, University Micro lms, Ann Arbor MI, 1985a. Creath K., Speckle: Signal or Noise Proceedings FRINGE 93 Bremen 1993, W. Jueptner and W. Osten (Eds.), Akademie Verlag, Berlin, pp. 97 102. Creath, K., Measuring Step Heights Using an Optical Pro ler, Proc. SPIE, 661, 296 301 (1986). Creath K. and G. E. Schwartz, The Dynamics of Life: Imaging Temperature and Refractive Index Variations Surrounding Material and Biological Systems with Dynamic Interferometry , Fringe 2005: The 5th International Workshop on Automatic Processing of Fringe Patterns. W. Osten (Eds.), Springer-Verlag, Berlin, 2005, pp. 588 596. Dandliker R., The Story of Speckles in Interferometry, Interferometry in Speckle Light, P. Jacquot and J.-M. Fournier (Eds.), Springer Verlag, Berlin, 2000, pp. 3 10. D Apuzzo N., Overview of 3D Surface Digitization Technologies in Europe, Proc. SPIE 6056, 605605:1 13, (2006). DeMattia P. and V. Fossati-Bellani, Holographic Contouring by Displacing the Object and the Illumination Beam, Opt. Commun., 26, 17 21 (1978). Der Hovanesian J. and Y. Y. Yung, Moire Contour-Sum Contour-Difference, and Vibration Analysis of Arbitrary Objects, Appl. Opt., 10, 2734 2738 (1971). Durelli A. J. and V. J. Parks, Moire Analysis of Strain, Prentice-Hall, Englewood Cliffs, NJ, 1970. Ennos A., Speckle Interferometry, in Laser Speckle and Related Phenomena, J. C. Dainty, Ed., Springer-Verlag, New York, 1975, pp. 203 253. Erf R. K., Speckle Metrology, Academic Press, New York, 1978. Federico A., G. H. Kaufmann, E. P. Serrano, Speckle Noise Reduction in ESPI Fringes Using Wavelet Shrinkage, Interferometry in Speckle Light, P. Jacquot and J.-M. Fournier (Eds.), Springer Verlag, Berlin, 2000, pp. 397 404. Feit E., Electronic Holography for Non-Destructive Testing, Advanced Imaging, January, 42 45, (1989). Frankowski G., M. Chen, T. Huth, Real-Time Shape Measurement With Digital Stripe Projection by Texas Instruments Micromirror Devices DMDTM, Proc. SPIE 3958, 90 105 (2000). Fujii H. and J. W. Lit, Surface Roughness Measurement Dichromatic Speckle Patterns: An Experimental Study, Appl. Opt., 17, 2690 2694 (1978).
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26.7.3.1 Statistical Analysis Statistical analyses was performed using computer software (SPSS, version 15.0 for Windows, SPSS Inc., Chicago, Illinois). Graphs were generated by third-party software groups (KaleidaGraph 3.5, Synergy Software, Reading, Pennsylvania). Two types of analyses, paired-samples t-test and ANOVA (analysis of variance), were conducted using the chronologic age as the reference standard. Age 0 group was not used for analysis. Two-cycle data were combined for each race and a given gender with the entire ages ranging from 1 to 18; then paired-samples t-tests were performed case by case to nd the mean difference between the average of two readings and the chronologic age. The results were eight categories for comparison: Asian girls, Asian boys, African-American girls, African-American boys, Caucasian girls, Caucasian boys, Hispanic girls, and Hispanic boys (ASF, ASM, AAF, AAM, CAF, CAM, HIF, and HIM) each depicting the overall view of differences between the radiologists average bone age reading against the chronologic age for subjects of each race and sex. On the basis of the effects of growth factor and sexual hormones, as well as our observations from phalangeal, carpal, and wrist joint regions, we divided the entire growth age ranging from age 1 year to 18 years into four age subsets, shown in Figure 26.16 (right column). These subsets were used to study the differences in growth patterns of children of different races for a given subset. ANOVA (analysis of variance) was used to study the cross-racial comparisons for a given subset of growth range based on the differences between chronologic age and bone age. 26.7.3.2 Radiologists Interpretation Table 26.5 shows the mean difference in age between the average bone age reading by two radiologists and the chronologic age for each of the eight categories by race and sex. Since we collected data in children with normal skeletal development, the differences, shown with asterisks in Table 26.5, are within two standard deviations between the normal chronologic age
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Outdoor macro MIMO: Figure 4.5 illustrates an outdoor scenario where there are L multipaths from the base antenna array having distinct angles. Here the base-station is placed high above ground and is not surrounded by local scatters. In contrast, the user station is assumed t o have uncorrelated fading at the antennas due to the fact that usually a large number of local scatters exists around the mobile antenna.Hence the outdoor downlink channel matrix has uncorrelated rows and correlated columns, whereas the uplink channel matrix has uncorrelated columns and correlated rows. Distributed MIMO: a few other systems can be modeled as MIMO channels. One example is the relay network where a source signal is delivered to destination(s) through multiple relay nodes [8]. Other examples in9 clude distributed sensor networks 1 1 and single-frequency broadcasting networks.
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Appendix B: Con guring PHP
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3.2. CHANNEL ESTIMATION
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N2 Quadratic equations N3 Cubic equations
The equation also represents the sum of noise powers.
Figure A-2: When you open AutoCAD, by default, this screen appears so that you can choose a workspace.
The WAN lines are usually leased from a telecommunications provider. Sometimes they cannot provide a high degree of reliability and performance. The distance imposes a significant time lag or latency, which creates an issue with data consistency between the two sites.
1. J. Carey and B. Carlson. Framework Process Patterns: Lessons Learned Developing Application Frameworks. Addison-Wesley, 2002. 2. H. C. Edwards. Managing complexity in massively parallel, adaptive, multiphysics applications. Eng. Comput., 22:135 155, 2006. 3. J. R. Stewart and H. C. Edwards. A framework approach for developing parallel adaptive multiphysics applications. Finite Elem. Anal. Des., 40:1599 1617, 2004. 4. H. C. Edwards. Sierra framework for massively parallel adaptive multiphysics applications. In Proceedings from the NECDC 2004, Livermore, CA. Lawrence Livermore National Laboratory, October 2004. 5. H. C. Edwards, J. R. Stewart, and John D. Zepper. Mathematical abstractions of the SIERRA computational mechanics framework. In H. A. Mang, F. G. Rammerstorfer, and J. Eberhardsteiner, editors, Proceedings of the 5th World Congress on Computational Mechanics, Vienna, Austria, July 2002. 6. H. C. Edwards and J. R. Stewart. Sierra, a software environment for developing complex multiphysics applications. In K. J. Bathe, editor, Computational Fluid and Solid Mechanics. Proceedings of the First MIT Conference, Cambridge, MA. Elsevier, Oxford, UK, 2001, pp. 1147 1150. 7. H. C. Edwards. Sierra framework version 3: core services theory and design. Technical report SAND2002-3616, Sandia National Laboratories, Albuquerque, NM, November 2002. 8. J. R. Stewart. Sierra framework version 3: master element subsystem. Technical report SAND20030529, Sandia National Laboratories, Albuquerque, NM, February 2003. 9. J. R. Stewart. Sierra framework version 3: transfer services design and use. Technical report SAND2003-0249, Sandia National Laboratories, Albuquerque, NM, January 2003. 10. J. R. Stewart and H. C. Edwards. Sierra framework version 3: h-adaptivity design and use. Technical report SAND2002-4016, Sandia National Laboratories, Albuquerque, NM, December 2002. 11. A. B. Williams. Sierra framework version 4: solver services. Technical report SAND2004-6428, Sandia National Laboratories, Albuquerque, NM, February 2005. 12. S.P. Domino, C. D. Moen, S. P. Burns, and G. H. Evans. Sierra/fuego: a multimechanics re environment simulation tool. In 41st Aerospace Sciences Meeting, Reno, NV. AIAA Paper No. 2003-0149, January 2003. 13. S. W. Bova and R. Lober. An approach to coupled, multiphysics thermal analysis. In K. J. Bathe, editor, Computational Fluid and Solid Mechanics. Proceedings of the First MIT Conference, Cambridge, MA. Elsevier, Oxford, UK, 2001. 14. J. R. Koteras, A. S. Gullerud, V. L. Porter, W. M. Scherzinger, and K. H. Brown. Presto : impact dynamics with scalable contact using the sierra framework. In K. J. Bathe, editor, Computational Fluid and Solid Mechanics. Proceedings of the First MIT Conference, Cambridge, MA. Elsevier, Oxford, UK, 2001. 15. J. A. Mitchell, A. S. Gullerud, W. M. Scherzinger, J. R. Koteras, and V. L. Porter. Adagio: non-linear quasi-static structural response using the sierra framework. In K. J. Bathe, editor, Computational Fluid and Solid Mechanics. Proceedings of the First MIT Conference, Cambridge, MA. Elsevier, Oxford, UK, 2001. 16. T. Smith, C. Ober, and A. Lorber. Sierra/premo a new general purpose compressible ow simulation code. In 32nd AIAA Fluid Dynamics Conference and Exhibit, AIAA St. Louis, Missouri. AIAA-20023292, 2002.
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