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Fourier Analysis

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Gravitational Wave-Signal Recognition Model Based on Fourier Transform and Convolutional Neural Network.

Computational intelligence and neuroscience
The recent detection of gravitational waves is a remarkable milestone in the history of astrophysics. With the further development of gravitational wave detection technology, traditional filter-matching methods no longer meet the needs of signal reco...

PySio: A New Python Toolbox for Physiological Signal Visualization and Feature Analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In physiological signal analysis, identifying meaningful relationships and inherent patterns in signals can provide valuable information regarding subjects' physiological state and changes. Although MATLAB has been widely used in signal processing an...

Dynamic intelligent measurement of multiple chirped signals of different types based on the optical computing STFT and the YOLOv3 neural network.

Optics letters
We propose a simultaneous measurement system for multiple signals of different types which combines the optical computing short-time Fourier transform (STFT) and You Only Look Once (YOLOv3) neural network. Through the system, the analytical expressio...

Fourier ptychographic microscopy with untrained deep neural network priors.

Optics express
We propose a physics-assisted deep neural network scheme in Fourier ptychographic microscopy (FPM) using untrained deep neural network priors (FPMUP) to achieve a high-resolution image reconstruction from multiple low-resolution images. Unlike the tr...

Empirical Mode Decomposition-Based Feature Extraction for Environmental Sound Classification.

Sensors (Basel, Switzerland)
In environment sound classification, log Mel band energies (MBEs) are considered as the most successful and commonly used features for classification. The underlying algorithm, fast Fourier transform (FFT), is valid under certain restrictions. In thi...

Classification Approach for Attention Assessment via Singular Spectrum Analysis Based on Single-Channel Electroencephalograms.

Sensors (Basel, Switzerland)
Attention refers to the human psychological ability to focus on doing an activity. The attention assessment plays an important role in diagnosing attention deficit hyperactivity disorder (ADHD). In this paper, the attention assessment is performed vi...

Exploration of Effective Time-Velocity Distribution for Doppler-Radar-Based Personal Gait Identification Using Deep Learning.

Sensors (Basel, Switzerland)
Personal identification based on radar gait measurement is an important application of biometric technology because it enables remote and continuous identification of people, irrespective of the lighting conditions and subjects' outfits. This study e...

Phonocardiogram transfer learning-based CatBoost model for diastolic dysfunction identification using multiple domain-specific deep feature fusion.

Computers in biology and medicine
Left ventricular diastolic dyfunction detection is particularly important in cardiac function screening. This paper proposed a phonocardiogram (PCG) transfer learning-based CatBoost model to detect diastolic dysfunction noninvasively. The Short-Time ...

Recurrent neural networks for time domain modelling of FTIR spectra: application to brain tumour detection.

The Analyst
Attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy alongside machine learning (ML) techniques is an emerging approach for the early detection of brain cancer in clinical practice. A crucial step in the acquisition of an...

A Robust Deep Learning Framework Based on Spectrograms for Heart Sound Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Heart sound analysis plays an important role in early detecting heart disease. However, manual detection requires doctors with extensive clinical experience, which increases uncertainty for the task, especially in medically underdeveloped areas. This...