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Effective Denoising and Adaptive Equalization of Indoor Optical Wireless Channel With Artificial Light Using the Discrete Wavelet Transform and Artificial Neural Network

Presents a new theoretical model based on Discrete Wavelet Transform (DWT) and Artificial Neural Network based receiver architecture for indoor optical wireless communications. Model includes sensitivity to Artificial Light Interference (ALI), one of the dominant sources of performance degradation. It is demonstrated that the model can effectively reduce ALI by proper selection of DWT coefficients, resulting in improved receiver performance. Professor Angelova was the mathematical lead on this interdisciplinary research, which the Unit converted into a specialist Wavelet Analysis workshop (CPD event), part sponsored by IEEE, and attended by (registration-fee paying) external clients and industry members in 2010 and 2011.