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BE-AAPSA is based on a previously developed system, where two adaptive background models based on weight arrays with temporal learning mechanism identify dynamic objects within a video scene.
In BE-AAPSA, the objective is to create a robust background estimation model where the learning rates for each pixel are calculated according to the results of the two adaptive weight arrays and the module where the video is classified.
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Briefly, we create multiple copies of photon weight array at all nodes for each time gate of the simulation.
The earliest SS sensors were the Weight Matrix Model (WMM) [ 51], Weight Array Model (WAM) [ 52], and Windowed second-order WAM model (WWAM) [ 3].
To facilitate the computation of Φ, we use a 3D Per-Pixel-Weight array P of size W×H×(2w+1).
Weights were assigned to each array in order to give less weight to arrays of lesser quality [ 77].
The second type of structure is the time sequence phase weighting (TSPW) antenna array [18, 19], in which the combined phase weighted array signals are sequentially sampled by a single RF-receiving channel.
A crucial result needed for proving these results is a central limit lemma for weighted degenerate U statistics where the weights are arrays of some non-random real numbers.
An ultralight-weight photovoltaic array technology seems to be for the moment, the most promising option but photothermal power systems that could also be taken into account.
Sais-lite is a light-weight suffix array construction algorithm that takes linear time and space (Nong et al., 2009).
The weighted ANOVA model assigned array weights to smoothly up or down-weight the importance of a particular array, based on how similar that array is to others of the same type [ 49].
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