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A major goal in blind source separation to identify and separate sources is to model their inherent characteristics. While most state-of- the-art approaches are supervised methods trained on large datasets, interest in non-data-driven approaches such as Kernel Additive Modelling (KAM) remains high due to their interpretability and adaptability. KAM performs the separation of a given source applying robust statistics on the time-frequency bins selected by a source-specific kernel function, commonly the K-NN function.

Project-based learning is a form of active learning where large-scale projects provide context for technical learning. Along with background information, this paper examines teaching and learning of signals and systems in the context of two ABET accredited project-based learning programs. Examples of projects, deep learning activities and classroom activities are provided.

We propose a rate-distortion optimized framework for estimating
illumination changes (lighting variations, fade in/out
effects) in a highly scalable coding system. Illumination
variations are realized using multiplicative factors in the image
domain and are estimated considering the coding cost
of the illumination field and input frames which are first
subject to a temporal Lifting-based Illumination Adaptive
Transform (LIAT). The coding cost is modelled by an L1-
norm optimization problem which is derived to approximate

Recurrent neural networks have become increasingly popular for the task of language modeling achieving impressive gains in state-of-the-art speech recognition and natural language processing (NLP) tasks. Recurrent models exploit word dependencies over a much longer context window (as retained by the history states) than what is feasible with n-gram language models.