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Offset binary, [1] also referred to as excess-K, [1] excess-N, excess-e, [2] [3] excess code or biased representation, is a method for signed number representation where a signed number n is represented by the bit pattern corresponding to the unsigned number n+K, K being the biasing value or offset.
In the base −2 representation, a signed number is represented using a number system with base −2. In conventional binary number systems, the base, or radix, is 2; thus the rightmost bit represents 2 0, the next bit represents 2 1, the next bit 2 2, and so on. However, a binary number system with base −2 is also possible.
Thus, only 10 bits of the significand appear in the memory format but the total precision is 11 bits. In IEEE 754 parlance, there are 10 bits of significand, but there are 11 bits of significand precision (log 10 (2 11) ≈ 3.311 decimal digits, or 4 digits ± slightly less than 5 units in the last place).
Thus, in order to get the true exponent as defined by the offset-binary representation, the offset of 127 has to be subtracted from the value of the exponent field. The minimum and maximum values of the exponent field (00 H and FF H ) are interpreted specially, like in the IEEE 754 standard formats.
A binary equivalent, known as well-known binary (WKB), is used to transfer and store the same information in a more compact form convenient for computer processing but that is not human-readable. The formats were originally defined by the Open Geospatial Consortium (OGC) and described in their Simple Feature Access . [ 1 ]
Parallel curves of the graph of = for distances =, …, Two definitions of a parallel curve: 1) envelope of a family of congruent circles, 2) by a fixed normal distance The parallel curves of a circle (red) are circles, too
For a double-precision number, the exponent is stored in the range 1 .. 2046 (0 and 2047 have special meanings), and is interpreted by subtracting the bias for an 11-bit exponent (1023) to get an exponent value in the range −1022 .. +1023.
NAF assures a unique representation of an integer, but the main benefit of it is that the Hamming weight of the value will be minimal. For regular binary representations of values, half of all bits will be non-zero, on average, but with NAF this drops to only one-third of all digits.