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  2. Moving horizon estimation - Wikipedia

    en.wikipedia.org/wiki/Moving_Horizon_Estimation

    Moving horizon estimation uses a sliding time window. At each sampling time the window moves one step forward. It estimates the states in the window by analyzing the measured output sequence and uses the last estimated state out of the window, as the prior knowledge.

  3. Exponentiation by squaring - Wikipedia

    en.wikipedia.org/wiki/Exponentiation_by_squaring

    For example, to calculate the exponent 398, which has binary expansion (110 001 110) 2, we take a window of length 3 using the 2 k-ary method algorithm and calculate 1, x 3, x 6, x 12, x 24, x 48, x 49, x 98, x 99, x 198, x 199, x 398.

  4. Moving average - Wikipedia

    en.wikipedia.org/wiki/Moving_average

    Smoothing of a noisy sine (blue curve) with a moving average (red curve). In statistics, a moving average (rolling average or running average or moving mean [1] or rolling mean) is a calculation to analyze data points by creating a series of averages of different selections of the full data set.

  5. Construction estimating software - Wikipedia

    en.wikipedia.org/wiki/Construction_estimating...

    Construction cost estimating software is computer software designed for contractors to estimate construction costs for a specific project. A cost estimator will typically use estimating software to estimate their bid price for a project, which will ultimately become part of a resulting construction contract.

  6. Window - Wikipedia

    en.wikipedia.org/wiki/Window

    A window is an opening in a wall, door, roof, or vehicle that allows the exchange of light and may also allow the passage of sound and sometimes air.Modern windows are usually glazed or covered in some other transparent or translucent material, a sash set in a frame [1] in the opening; the sash and frame are also referred to as a window. [2]

  7. Sliding DFT - Wikipedia

    en.wikipedia.org/wiki/Sliding_DFT

    In applied mathematics, the sliding discrete Fourier transform is a recursive algorithm to compute successive STFTs of input data frames that are a single sample apart (hopsize − 1). [1] The calculation for the sliding DFT is closely related to Goertzel algorithm .

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