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Found 4 papers, 0 papers with code
Neuromorphic Sampling of Sparse Signals
We develop a kernel-based sampling approach, which allows for perfect reconstruction with a sample complexity equal to the rate of innovation of the signal.
Tight-frame-like Analysis-Sparse Recovery Using Non-tight Sensing Matrices
We consider the analysis-sparse l1-minimization problem with a generalized l2-norm-based data-fidelity and show that it effectively corresponds to using a tight-frame sensing matrix.
Neuromorphic Sampling of Signals in Shift-Invariant Spaces
We present an iterative technique for perfect reconstruction subject to the events satisfying a density criterion.
Time Encoding of Finite-Rate-of-Innovation Signals
Time-encoding of continuous-time signals is an alternative sampling paradigm to conventional methods such as Shannon's sampling.
