This paper presents a new model for adaptive filter with the least-mean-square (LMS) scheme to train the mask operation on low resolution images. The adaptive filter theory with adaptive least-mean-square scheme (ALMSS) uses the training mask for moving object detection and tracking. However, the successful moving objects detection in a real surrounding environment is a difficult task due to noise issues such as fake motion or Gaussian noise. Many approaches have been developed in constrained environments to detect and track moving objects. On the other hand, the ALMSS approach can effectively reduce the noise with low computing cost in both fake motion and Gaussian noise environments. The experiments on real scenes indicate that the proposed ALMSS method is effective for moving object detection and tracking in real-time.
Islam and Wahid proposed an area-and powerefficient design of Daubechies wavelet transforms. However, it was found that the matrix decompositions of the folded algebraicinteger-quantization scheme for DAUB4 and DAUB6 are incorrect and do not correspond with their architecture designs. We propose modifications not only to the algorithm but also to its implementation. Compared with the original method, the proposed design requires fewer adders and maintains the same critical path.Index Terms-Algebraic integer quantization (AIQ), Daubechies wavelet, error-free algorithm, folded mapping.
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