floating-point operation การใช้

• Floating - point operations never throw an exception
• If one or both operands of a floating - point operation are
• If a floating - point operation is invalid , the result of the operation is
• Floating - point operations return
• For example , single - precision floating - point operations may not be rounded to single precision
• Floating - point operations never throw an exception they produce nans or infinities instead
• Any floating - point operation like addition or multiplication is achieved in a few discrete steps
• Instead , in exceptional situations the result of a floating - point operation is zero , infinity , or nan , as described below
• If the result of a floating - point operation is too small for the destination format , the result of the operation is zero
• If the magnitude of the result of a floating - point operation is too large for the destination format , the result of the operation is
• Is available for programming languages that do not support the concepts of infinity and not - a - number in floating - point operations
• For this reason , you may experience some loss of precision , and some floating - point operations may produce unexpected results
• Double is the most efficient data type , because the processors on current platforms perform floating - point operations in double precision
• A processor usually has two sets of general - purpose registers , one optimized for floating - point operations and the other for integer operations
• Round toward negative infinity means round the result of a floating - point operation to the representable value nearest to and no greater than the result
• Double is the most efficient of the fractional data types , because the processors on current platforms perform floating - point operations in double precision
• This is a mathematical calculator designed for single - hand operation , a key figure refers to each wide support floating - point operations . press vivid and lively design
• Fprs are the source and destination operands of all floating - point operations and can contain 32 - bit and 64 - bit signed and unsigned integer values , as well as single - precision and double - precision floating - point values
• The floating - point status and control register fpscr captures status and exceptions resulting from floating - point operations , and the fpscr also provides control bits for enabling specific exception types , as well as for selecting one of the four rounding modes
• In the coarse step , an improved klt ( kanade - lucas - tomasi ) algorithm is applied in feature extraction , and a new acceleratingalgorithm of template matching is presented to match features , and also a feasible feature validitytesting method is given . in the precise step , the “ regional counting method based onjoint - histogram ” is used for reference to make out its own improved method , which achieves “ sub - pixel ” precision as well as avoids heavy floating - point operation by clever definition ofcriterion function