Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm.

Purpose: Coronary microvascular dysfunction (CMD) is emerging as an important cause of myocardial ischemia, but there is a lack of a non-invasive method for reliable early detection of CMD.

Aim: To develop an electrocardiogram (ECG)-based machine learning algorithm for CMD detection that will lay the groundwork for patient-specific non-invasive early detection of CMD.

Methods: Vectorcardiography (VCG) was calculated from each 10-second ECG of CMD patients and healthy controls.

A Novel ECG Signal Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition.

ECG signal processing is an important basis for the prevention and diagnosis of cardiovascular diseases; however, the signal is susceptible to noise interference mixed with equipment, environmental influences, and transmission processes. In this paper, an efficient denoising method based on the variational modal decomposition (VMD) algorithm combined with and optimized by the sparrow search algorithm (SSA) and singular value decomposition (SVD) algorithm, named VMD-SSA-SVD, is proposed for the first time and applied to the noise reduction of ECG signals. SSA is used to find the optimal combination of parameters of VMD [K,α], VMD-SSA decomposes the signal to obtain finite modal components, and the components containing baseline drift are eliminated by the mean value criterion.

Novel Detection of Atmospheric Turbulence Profile Using Mie-Scattering Lidar Based on Non-Kolmogorov Turbulence Theory.

Turbulence can cause effects such as light intensity fluctuations and phase fluctuations when a laser is transmitted in the atmosphere, which has serious impacts on a number of optical engineering application effects and on climate improvement. Therefore, accurately obtaining real-time turbulence intensity information using lidar-active remote sensing technology is of great significance. In this paper, based on residual turbulent scintillation theory, a Mie-scattering lidar method was developed to detect atmospheric turbulence intensity.

Novel Simulation and Analysis of Mie-Scattering Lidar for Detecting Atmospheric Turbulence Based on Non-Kolmogorov Turbulence Power Spectrum Model.

The Mie-scattering lidar can detect atmospheric turbulence intensity by using the return signals of Gaussian beams at different heights. The power spectrum method and Zernike polynomial method are used to simulate the non-Kolmogorov turbulent phase plate, respectively, and the power spectrum method with faster running speed is selected for the subsequent simulation. In order to verify the possibility of detecting atmospheric turbulence by the Mie-scattering lidar, some numerical simulations are carried out.

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram.

Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles.

Simulation and Analysis of Mie-Scattering Lidar-Measuring Atmospheric Turbulence Profile.

Based on the residual turbulent scintillation theory, the Mie-scattering lidar can measure the intensity of atmospheric turbulence by detecting the light intensity scintillation index of the laser return signal. In order to evaluate and optimize the reliability of the Mie-scattering lidar system for detecting atmospheric turbulence, the appropriate parameters of the Mie-scattering lidar system are selected and optimized using the residual turbulent scintillation theory. Then, the Fourier transform method is employed to perform the numerical simulation of the phase screen of the laser light intensity transformation on the vertical transmission path of atmospheric turbulence.

Monitoring and predicting Fusarium wilt disease in cucumbers based on quantitative analysis of kinetic imaging of chlorophyll fluorescence.

Cucumber (Cucumis sativus L.) is a widely cultivated and economically profitable crop. However, Fusarium wilt disease can seriously affect cucumber yields, as it is difficult to prevent and eliminate.

Six-channel multi-wavelength polarization Raman lidar for aerosol and water vapor profiling.

Aerosols and water vapor are important atmospheric components, and have significant effects on both atmospheric energy conversion and climate formation. They play the important roles in balancing the radiation budget between the atmosphere and Earth, while water vapor also directly affects rainfall and other weather processes. To further research atmospheric aerosol optical properties and water vapor content, an all-time six-channel multi-wavelength polarization Raman lidar has been developed at Beifang University of Nationalities.

[Research on Modeling Method for Chlorophyll Content Fine Measurement Based on Neural Network].

Aiming at SPAD values of living plant leaf chlorophyll content affected easily by the blade thickness, water content, etc, a fine retrieval method of chlorophyll content based on multiple parameters of neural network model is presented. The SPAD values and water index (WI) of leaves were obtained by the leaf transmittance under the irradiation of light central wavelength in 650 nm, 940 nm, 1450 nm respectively. Meanwhile, the corresponding blade thickness is got by micrometer and the chlorophyll content is measured by spectrophotometric method.

Simultaneous reduction in noise and cross-contamination artifacts for dual-energy X-ray CT.

Purpose: Dual-energy CT imaging tends to suffer from much lower signal-to-noise ratio than single-energy CT. In this paper, we propose an improved anticorrelated noise reduction (ACNR) method without causing cross-contamination artifacts.

Methods: The proposed algorithm diffuses both basis material density images (e.

[Lidar observations of atmospheric aerosol optical properties over Yinchuan area].

A compact Mie scattering lidar system has been developed to measure the optical properties and temporal-spatial distribution of atmospheric aerosol particles and some continuous experiments were carried out over Yinchuan area (38 degrees 29'N, 106 degrees 06'E) from 1 to 10 April in 2009 for the first time. The laser located at wavelength of 532 nm was selected as the light source and the Fernald method was used to retrieve the extinction coefficient. The aerosol extinction coefficient profiles and temporal-spatial variation properties of aerosol relative density were obtained and analyzed within the whole day at one hour interval, and also an obvious sand-dust-weather process over Yinchuan area was observed and analyzed.