Quantum Estimation
SCSAIn order to improve the biomedical signal quality, we designed different algorithms based on a quantum method called Semi-Classical Signal Analysis (SCSA) [1]. This method has the ability to decompose the input signal/image into a set of components based on the Eigen-spectrum of the Schrödinger operator.
We targeted different biomedical applications:
- MRI image enhancement using adaptive algorithm based on image segmentation
- MRS residual water suppression using the squared eigenfunctions of the Schrödinger operator
- Complex MRS signal denoising using SCSA-based soft thresholding.
This work was under the supervision of professor Meriem laleg (KAUST).
Useful references
[1] https://emang-kaust.github.io/index.html
[2] https://cemse.kaust.edu.sa/emang/quantum-based-signal-processing
Abderrazak Chahid
Data Scientist
My research interests include feature extraction, computer vision, real-time implementation of smart decision making systems.
Publications
Residual Water Suppression Using the Squared Eigenfunctions of the Schrodinger Operator
We developed a post-processing water suppression technique based on the squared eigenfunctions of the Schrodinger operator.
Quantum epileptic Spikes Detection
New Feature extraction method based on the Quantization-based Semi-Classical Signal Analysis designed explicitly for epileptic spikes using Magnetoencephalography (MEG) signals.
