A fast fixed-point algorithm for independent component analysis
Neural Computation
Blind separation methods based on Pearson system and its extensions
Signal Processing
Selection of significant independent components for ECG beat classification
Expert Systems with Applications: An International Journal
A source adaptive independent component analysis algorithm through solving the estimating equation
Expert Systems with Applications: An International Journal
Fetal heart rate monitoring based on independent component analysis
Computers in Biology and Medicine
IEEE Transactions on Signal Processing
Spatio–Temporal FastICA Algorithms for the Blind Separation of Convolutive Mixtures
IEEE Transactions on Audio, Speech, and Language Processing
Hi-index | 12.05 |
In this paper, we apply the blind source separation model to the scope of extracting information from a workpiece about the process that made it. Given any manufactured workpiece, we may think about it as the carrier of the information built in the process that made it. Using recent inspection technologies such as stylus profiler, we are able to generate signals from a workpiece. We analyze these signals using independent component analysis (ICA) in its various formulations. In doing this, we develop a convolutive version of ICA to overcome technical and metrological problems arisen. By using this convolutive modification of ICA we are able to demix the recorded signal and to recover the technological fingerprint over it. Simulations on NIST benchmarks are included, as well as a case study on a turned workpiece.