Fractional synchrosqueezing transform for enhanced multicomponent signal separation.

The precise separation of multicomponent signals encounters numerous challenges due to the complexity of signals and widespread interference. Synchrosqueezing Transform (SST) is one of the important technologies for improving the accurate separation of multicomponent signals, but it faces challenges in terms of the difficulty and effectiveness of squeezing. This paper introduces a multicomponent signal separation method based on innovative Fractional Synchrosqueezing Transform (FrSST).

Sparse Component Analysis (SCA) Based on Adaptive Time-Frequency Thresholding for Underdetermined Blind Source Separation (UBSS).

Blind source separation (BSS) recovers source signals from observations without knowing the mixing process or source signals. Underdetermined blind source separation (UBSS) occurs when there are fewer mixes than source signals. Sparse component analysis (SCA) is a general UBSS solution that benefits from sparse source signals which consists of (1) mixing matrix estimation and (2) source recovery estimation.

Electrocardiogram Biometrics Using Transformer's Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification.

The existing electrocardiogram (ECG) biometrics do not perform well when ECG changes after the enrollment phase because the feature extraction is not able to relate ECG collected during enrollment and ECG collected during classification. In this research, we propose the sequence pair feature extractor, inspired by Bidirectional Encoder Representations from Transformers (BERT)'s sentence pair task, to obtain a dynamic representation of a pair of ECGs. We also propose using the self-attention mechanism of the transformer to draw an inter-identity relationship when performing ECG identification tasks.

A CNN based Handwritten Numeral Recognition Model for Four Arithmetic Operations.

The pandemic of Covid-19 has caused a shift of paradigm of education, from face-to-face to e-learning. E-learning leads to an escalation in digitalization of handwritten documents because it requires submission of homework and assignments through online. To help teachers in checking digitalized handwritten homework, this paper proposes an automatic checking system based on a convolutional neural network (CNN) for handwritten numeral recognition.

A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier.

Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation.