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  2. Template:Article-cv - Wikipedia

    en.wikipedia.org/wiki/Template:Article-cv

    The template you used to blank the article will display this code, with parameters already filled out, in its lower right quadrant. You may simply copy and paste it from there. Please also add {{subst:uw-copyright| PageName}} to the article creator's talk page. This code will also be generated by the blanking template.

  3. Template:Cv - Wikipedia

    en.wikipedia.org/wiki/Template:Cv

    {{Like resume}} - tagging a biographical article that is written like a curriculum vitae Topics referred to by the same term This is an unused template to list other templates associated with a similar title or shortcut.

  4. Array programming - Wikipedia

    en.wikipedia.org/wiki/Array_programming

    In array languages, operations are generalized to apply to both scalars and arrays. Thus, a+b expresses the sum of two scalars if a and b are scalars, or the sum of two arrays if they are arrays. An array language simplifies programming but possibly at a cost known as the abstraction penalty.

  5. Scale factor (computer science) - Wikipedia

    en.wikipedia.org/wiki/Scale_factor_(computer...

    As an example, when using an unsigned 8-bit fixed-point format (which has 4 integer bits and 4 fractional bits), the highest representable integer value is 15, and the highest representable mixed value is 15.9375 (0xF.F or 1111.1111 b). If the desired real world values are in the range [0,160], they must be scaled to fit within this fixed-point ...

  6. Local binary patterns - Wikipedia

    en.wikipedia.org/wiki/Local_binary_patterns

    VLFeat, an open source computer vision library in C (with bindings to multiple languages including MATLAB) has an implementation. LBPLibrary is a collection of eleven Local Binary Patterns (LBP) algorithms developed for background subtraction problem. The algorithms were implemented in C++ based on OpenCV.

  7. Vector processor - Wikipedia

    en.wikipedia.org/wiki/Vector_processor

    If the time taken for the vector unit to add an array of 64 numbers is 10 times faster than its equivalent scalar counterpart, r = 10. Also, if the total number of operations in a program is 100, out of which only 10 are scalar (after vectorization), then f = 0.9, i.e., 90% of the work is done by the vector unit.

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