Heat exchangers with unique specifications are administered in the food industry, which has expanded its sphere of influence even to the automotive industry due to this feature. It has been used for convenient maintenance and much easier cleaning. In this study, two different nanomaterials, such as Cu-based nanoparticles and an organic nanoparticle of Chloro-difluoromethane (R22), were used as nanofluids to enhance the efficiency of heat transfer in a turbulator. It is simulated by computational fluid dynamics software (Ansys-Fluent) to evaluate the Nusselt number versus Reynolds number for different variables. These variables are diameter ratio, torsion pitch ratio, and two different nanofluids through the shell tube heat exchanger. It is evident that for higher diameter ratios, the Nusselt number has been increased significantly in higher Reynolds numbers as the heat transfer has been increased in turbulators. For organic fluids (R22), the Nusselt number has been increased significantly in higher Reynolds numbers as the heat transfer has been increased in turbulators due to the proximity of heat transfer charges. At higher torsion pitch ratios, the Nusselt number has been increased significantly in the higher Reynolds number as the heat transfer has been increased in turbulators, especially in higher velocities and pipe turbulence torsions.

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Use of Organic and Copper-Based Nanoparticles on the Turbulator Installment in a Shell Tube Heat Exchanger: A CFD-Based Simulation Approach by Using Nanofluids

The electrocardiogram (ECG) signal can easily be affected by various types of noises while being recorded, which decreases the accuracy of subsequent diagnosis. Therefore, the efficient denoising of ECG signals has become an important research topic. In the paper, we proposed an efficient ECG denoising approach based on empirical mode decomposition (EMD), sample entropy, and improved threshold function. This method can better remove the noise of ECG signals and provide better diagnosis service for the computer-based automatic medical system. The proposed work includes three stages of analysis: (1) EMD is used to decompose the signal into finite intrinsic mode functions (IMFs), and according to the sample entropy of each order of IMF following EMD, the order of IMFs denoised is determined; (2) the new threshold function is adopted to denoise these IMFs after the order of IMFs denoised is determined; and (3) the signal is reconstructed and smoothed. The proposed method solves the shortcoming of discarding the first-order IMF directly in traditional EMD denoising and proposes a new threshold denoising function to improve the traditional soft and hard threshold functions. We further conduct simulation experiments of ECG signals from the MIT-BIH database, in which three types of noise are simulated: white Gaussian noise, electromyogram (EMG), and power line interference. The experimental results show that the proposed method is robust to a variety of noise types. Moreover, we analyze the effectiveness of the proposed method under different input SNR with reference to improving SNR ( ) and mean square error ( ), then compare the denoising algorithm proposed in this paper with previous ECG signal denoising techniques. The results demonstrate that the proposed method has a higher and a lower . Qualitative and quantitative studies demonstrate that the proposed algorithm is a good ECG signal denoising method.

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An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function

In addition to helping develop products that aid the disabled, brain-computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain-computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning.

Design and analysis of improved high-speed adaptive filter architectures for ECG signal denoising

Hybrid organic–inorganic halide perovskites (HOIPs) have recently represented a material breakthrough for optoelectronic applications. Obviously, studying the interactions between the central organic cation and the Pb-X inorganic octahedral could provide a better understanding of HOIPs. In this work, we used a first-principles theoretical study to investigate the effect of different orientations of central formamidinium cation (FA+) on the electronic and optical properties of FAPbBr3 hybrid perovskite. In order to do this, the band structure (with and without spin–orbit coupling (SOC)), density of states (DOS), partial density of states (PDOS), electron density, distortion index, bond angle variance, dielectric function, and absorption spectra were computed. The findings revealed that a change in the orientation of FA+ caused some disorders in the distribution of interactions, resulting in the formation of some specific energy levels in the structure. The interactions between the inorganic and organic parts in different directions create a distortion index in the bonds of the inorganic octahedral, thus leading to a change in the volume of PbBr6. This is the main reason for the variations observed in the electronic and optical properties of FAPbBr3. The obtained results can be helpful in solar-cell applications.

Influence of Different Rotations of Organic Formamidinium Molecule on Electronic and Optical Properties of FAPbBr3 Perovskite

Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods.

An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter

There are many image resizing techniques, which include scaling, scale-and-stretch, seam carving, and so on. They have their own advantages and are suitable for different application scenarios. Therefore, a universal detection of tampering by image resizing is more practical. By preliminary experiments, we found that no matter which image resizing technique is adopted, it will destroy local texture and spatial correlations among adjacent pixels to some extent. Due to the excellent performance of local Tchebichef moments (LTM) in texture classification, we are motivated to present a detection method of tampering by image resizing using LTM in this paper. The tampered images are obtained by removing the pixels from original images using image resizing (scaling, scale-and-stretch and seam carving). Firstly, the residual is obtained by image pre-processing. Then, the histogram features of LTM are extracted from the residual. Finally, an error-correcting output code strategy is adopted by ensemble learning, which turns a multi-class classification problem into binary classification sub-problems. Experimental results show that the proposed approach can obtain an acceptable detection accuracies for the three content-aware image re-targeting techniques.

Detection of Tampering by Image Resizing Using Local Tchebichef Moments

The invention discloses an electrocardiosignal denoising algorithm based on wavelet energy. The electrocardiosignal denoising algorithm comprises the steps that a signal is subjected to equalization processing, and a tolerance value is determined; the maximum number of decomposition layers is calculated, and the signal after equalization processing is subjected to wavelet decomposition; a detail order change point which needs to be denoised is judged according to detail coefficient energy of each layer after wavelet decomposition; detail coefficients of all layers before the detail order change point are subjected to threshold denoising, and the signal is subjected to wavelet reconstruction according to the detail coefficient of the layer where the detail order change point is located, thedetail coefficient of the layer behind the detail order change point, an approximate coefficient of the last layer and the detail coefficients of all the layers after threshold denoising. By means ofthe method, the wavelet coefficient which needs to be subjected to threshold denoising is selected according to the wavelet energy, the other wavelet coefficients remain unchanged, the calculation amount is significantly reduced, the denoising efficiency is improved, the denoising effect is excellent, and the signal-to-noise ratio is high.

Shanshan Wang

Papers

An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter

An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function

Detection of Tampering by Image Resizing Using Local Tchebichef Moments

Electrocardiosignal denoising algorithm based on wavelet energy