Best Practice in .NET

Creator QR Code in .NET Best Practice

3: Working with Sketches
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Building an SOA Data Store with Web Services
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T I P Keep basically static file systems separate (i.e., make sure they are separate file systems), and you will rarely have to back them up.
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You are prompted with the Burn to Disc dialog box, as shown in Figure 9-8.
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Part III Developing with SQL Server
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The Menu Bar
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Stein (1956) discovered the possibility of devising nonparametric efficient tests and estimates, Later, several authors, in particular Takeuchi (1971), Beran (1974, 1978), Sacks (1973, and Stone (1979, described specific location estimates that are asymptotically efficient for all sufficiently smooth symmetric densities. Since we may say that these estimates adapt themselves to the underlying distribution, they have become known under the name of adaptive procedures. See also the review article by Hogg (1974). In the mid- 1970s adaptive estimates-attempting to achieve asymptotic efficiency at all well-behaved error distributions-were thought by many to be the ultimate robust estimates. Then Klaassen (1980) proved a disturbing result on the lack of stability of adaptive estimates. In view of his result, I conjectured at that time that an estimate cannot be simultaneously adaptive in a neighborhood of the model and qualitatively robust at the model; to my knowledge, this conjecture still stands. Adaptive procedures typically are designed for symmetric situations, and their behavior for asymmetric true underlying distributions is practically unexplored. In any case, adaptation to asymmetric situations does not make sense in the robustness context. The point is: if a smooth model distribution is contaminated by a tightly concentrated asymmetric contaminant, then Fisher information is dominated by the latter. But since that contaminant may be a mere bundle of gross errors, any information derived from it is irrelevant for the location parameter of interest. The connection between adaptivity and robustness is paradoxical also for other reasons. In robustness, the emphasis rests much more on stability and safety than on efficiency. For extremely large samples, where at first blush adaptive estimates look particularly attractive, the statistical variability of the estimate falls below its potential bias (caused by asymmetric contamination and the like), and robustness
50- input. This means that the antenna must be corrected for a 50- output at a given field strength for each frequency. Antenna factors are provided by the antenna vendor, which allows one to make necessary corrections. In addition, system sensitivity and polarization are important factors when using any antenna. Antenna Factors. This is one of the most important parameters when using any antenna. Each antenna is provided by the manufacturer with a table of antenna factors, usually in dB/m versus frequency. To convert the measured voltage at the receiver s input into actual field strength, one must add the antenna factor to the measured reading per Eq. (5.6). The test engineer must perform this calculation using a calculator or software. In addition to the antenna factor being a function of frequency, the interconnecting coax is also frequency sensitive. A problem with antenna factors is that the antenna is usually calibrated at only a particular distance from a source transmitter. When using the antenna at a different test distance, the calibration chart from the vendor may be invalid for this particular test. Efs = Eant + AF + CL where Efs = actual field strength of signal Eant = measured field strength directly from antenna AF = antenna factor CL = cable loss (amount of attenuation within coax) System Sensitivity. Serious problems can occur with spectrum analyzers during radiated emissions testing. This lies in the measurement distance used and the sensitivity of the receiver. Typically, the test distance for radiated emissions is specified at 10 m. Sometimes, it is only possible to take data at a closer distance. The minimal level that will be determined by the spectrum analyzer will be the noise floor of the equipment. For 120-kHz bandwidth, the noise floor is typically +13 dB V. To this measured value, antenna factor and cable attenuation must be added to derive the overall measurement system sensitivity. For test environments where the noise floor is greater than the specification limit, an alternative method of recording radiated energy must be performed. There are three options to overcome this sensitivity problem: 1. Reduce the distance to 3 m. Although the specification limit is increased by +10.5 dB, significant error may be present because the antenna may now be in the near field. This closer measurement distance still may not ascertain if an RF signal exists within the noise floor or is hidden in the ambient noise. 2. Use a low-noise preamplifier or preselector. These two items lower the effective system noise floor by a factor equal to the preamplifier gain less its noise figure. Typical gain is approximately 20 26 dB. 3. Use a test receiver. A receiver (not spectrum analyzer) has enhanced sensitivity with an extremely low noise floor. (5.6)
This opens the Speech Reference Card page.
which is independent of the chip pulse shape. The signal-to-interference ratio (SIR) for the case of wideband interference is b,WI = Eb W Pav = I0 Rb Iav (10.4.16)
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