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Figure 4.2 Magnetic lineations either side of the midAtlantic ridge south of Iceland. Positive anomalies in black (after Heirtzler et al., 1966, in Deep Sea Research 13, 428, with permission from Pergamon Press. Copyright Elsevier 1966).



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Non-spatial attributes, sometimes referred to as aspatial data, are those properties of one or more spatial entities that are used to graphically represent a feature. An attribute is a property of an entity, usually used to refer to a nonspatial quali cation of a spatially referenced entity [DOE, 1987]. Figure 1.6 displays an area feature with associated attributes. The attributes for a land parcel feature could include ownership, freehold and leasehold details, or for a road feature they could be width, name, kind, etc.





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That is, provided that we transmit at a rate less than capacity, arbitrarily low probabilities of error can be obtained, if a sufficiently long error correction code is employed The capacity is thus the amount of information that can be transmitted reliably through the channel per channel use A converse to the channel coding theorem states that for a channel with capacity C , if R > C , then the probability of error is bounded away from zero: reliable transmission is not possible The channel coding theorem is an existence theorem; it tells us that codes exist that can be used for reliable transmission, but not how to find practical codes Shannon s remarkableproof used random codes But as the code gets long, the decoding complexity of a truly random (unstructured)code goes up exponentiallywith the length of the code.

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Since Shannon sproof, engineers and mathematicianshave been looking for ways of constructing codes that are both good (meaning they can correct a lot of errors) and practical, meaning that they have some kind of structure that makes decoding of sufficiently low complexity that decoders can be practically constructed Figure 122 shows a comparison of the capacity of the AWGNC and the BAWGNC channels as a function of E , / a 2 (an SNR measure) In this figure, we observe that the capacity of the AWGNC increases with S N R beyond one bit per channel use, while the BAWGNC asymptotes to a maximum of one bit per channel use - if only binary data is put into the channel, only one bit of useful information can be obtained It is always the case that > CAWGNC CBAWGNC.

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Over all possible input distributions, the Gaussian distribution is information maximizing, so CAWGNC an upper bound on capacity for any modulation or coding that might be is employed However, at very low SNRs, CAWGNC CBAWGNC very nearly equal and are Figure 122 also shows the capacity of the equivalent BSC, with crossover probability p = Q ( , / W ) and capacity CBSC = 1 - H2(p) This corresponds to hard-input decoding Clearly, there is some loss of potential rate due to hard-input decoding, although the loss diminishes as the SNR increases 1124 Proof of the Channel Coding Theorem In this section we present a proof of the channel coding theorem While mathematically accurate, it is not complete The arguments can be considerably tightened, but are sufficient to show the main ideas of coding Also, the proof is only presented for the discrete.

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