A Symlet wavelet is a nearly symmetrical orthogonal wavelet family developed by Daubechies as a modification of the Daubechies wavelets, designed to have minimal phase distortion and compact support, making them well-suited for multi-resolution signal decomposition and denoising. In WiFi/CSI sensing research, Symlet wavelets are applied to decompose raw CSI amplitude and phase signals into approximation and detail coefficients across multiple scales, enabling the separation of meaningful human-activity-induced signal variations from high-frequency noise and environmental interference. Key variants are distinguished by their order (e.g., sym4, sym8), where higher orders provide greater smoothness and symmetry at the cost of increased computational complexity, with the choice of order tuned to balance noise suppression and preservation of transient features relevant to occupancy detection and crowd counting tasks.
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