To allow the algorithm to work without the user specifying the number of texture classes, at the nominal top level, each block is assigned a label randomly picked from the integer range, where is the number of blocks at the nominal top level.
This property also reduces the dynamic range of the mapped integer values.
For larger inputs, our data reduction heuristic makes it feasible to tackle problems that are well beyond the range of the basic integer programming approach.
The number of bits required for integer part is defined from the dynamic range of the data in order to avoid the occurrence of an overflow [31].
Therefore, the range of the function is any non-negative integer that is a multiple of five.
Here, Binominal (cw) = cw × uniform distribution either over 0 or 1 (50% each), where cw is an integer within the range of values for the PHY characteristics, aCWmin, and aCWmax, and aCWmin ≦ cw ≦ aCWmax.
The integers in the range of [0,M I ) can be represented in this RNS uniquely and unambiguously, where M I = ∏ v = 1 V m v is referred to as the information dynamic range, i.e., the legitimate range of the information symbol. For example, given a modulus set {7,15,16}, the information symbol R=1,538, in the information dynamic range [0, 1,680), can be presented by {5,8,2} in this RNS.
The gap between the number of collisions widens for the larger integer ranges.
Of course the copy number determined in this way is a real number: our estimate for the actual, integer copy number is the range of integers that are included in the 95% confidence interval around the ratio r line / r1.
The minimum entropy-based approach [21] for range-bin alignment is an iterative approach which is based on integer shifts of the range profiles, unlike the proposed method and the global range alignment algorithm.
