A new implementation of discrete bionic wavelet transform: Adaptive tiling

Bionic wavelet transform (BWT) is a biomodel-based adaptive time–frequency analysis technique. Due to its nonlinearity, it is difficult to realize the inverse BWT. To solve this problem, this paper introduces a new implementation for the discrete BWT (DBWT). The T-function from BWT is used to split the dyadic tiling map of DWT to obtain an adaptive DBWT tiling of the time–frequency plane. Quadrature-mirror filters, organized as the DBWT tiling map, are then employed to decompose the signal. This DBWT implementation makes the distortionless signal reconstruction possible. DBWT was used to decompose both simulated signal and actual nonstationary signals. Results show that DBWT performs better than discrete wavelet transform in demonstrating a more concentrated coefficient distribution in time–frequency plane. This proposed DBWT implementation will make BWT more applicable for the future nonstationary signal analysis.