A New Method for Detecting Sleep Apnea

Sleep apnea is a sleep disorder that can induce hypertension, coronary heart disease, stroke and other diseases, so the detection of sleep apnea is clinically important for the prevention of these diseases. In order to improve the detection performance and verify which physiological signals are better for sleep apnea detection, this paper uses multi-channel signal superposition and channel summation to improve the content of valid information in the original signal. Thirty features are analyzed by Relief feature selection algorithm. Finally, 15 features were used to build a classification model and support vector machine (SVM) was used for classification. The experimental results showed that the highest accuracy of 96.24% was achieved when electrocardiogram (X2) and electroencephalogram (C3-A2) channels were used for channel summation.

Fuzzy Control Based Stage Phobia Analysis System for Vocal Performers

This paper integrates wavelet sound wave analysis with a fuzzy control method to develop a stage phobia analysis system for vocal performers in order to enhance the psychological efficiency of vocal performers and reduce the effect of stage phobia on vocal performance. To achieve howling signal filtering, the frequency sub-band with howling is reversed and then superimposed with the original signal in audio processing. Furthermore, this paper incorporates the actual requirements for processing the vocal audio spectrum and builds the corresponding practical modules. Furthermore, this paper integrates the research needs of vocal performers’ stage phobia to create system function modules, and investigates the psychological activities of vocal performers using the fuzzy control system, discovers the factors that influence stage performances, and improves the psychological output of vocal performers. Finally, this paper proposes experiments to test and evaluate the system’s results. The research findings indicate that the system described in this paper has a significant impact.

Adaptive Block-Based Compressed Video Sensing Based on Saliency Detection and Side Information

The setting of the measurement number for each block is very important for a block-based compressed sensing system. However, in practical applications, we only have the initial measurement results of the original signal on the sampling side instead of the original signal itself, therefore, we cannot directly allocate the appropriate measurement number for each block without the sparsity of the original signal. To solve this problem, we propose an adaptive block-based compressed video sensing scheme based on saliency detection and side information. According to the Johnson–Lindenstrauss lemma, we can use the initial measurement results to perform saliency detection and then obtain the saliency value for each block. Meanwhile, a side information frame which is an estimate of the current frame is generated on the reconstruction side by the proposed probability fusion model, and the significant coefficient proportion of each block is estimated through the side information frame. Both the saliency value and significant coefficient proportion can reflect the sparsity of the block. Finally, these two estimates of block sparsity are fused, so that we can simultaneously use intra-frame and inter-frame correlation for block sparsity estimation. Then the measurement number of each block can be allocated according to the fusion sparsity. Besides, we propose a global recovery model based on weighting, which can reduce the block effect of reconstructed frames. The experimental results show that, compared with existing schemes, the proposed scheme can achieve a significant improvement in peak signal-to-noise ratio (PSNR) at the same sampling rate.

Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG Clustering

The nonlinear and nonstationary characteristics of vibration signal in mechanical equipment make fault identification difficult. To tackle this problem, this paper proposes a novel fault identification method based on improved variational mode decomposition (IVMD), multiscale permutation entropy (MPE), and adaptive GG clustering. Firstly, the original vibration signal is decomposed into a set of mode components adaptively by IVMD, and the mode components that are highly correlated with the original signal are selected to reconstruct the original signal. After that, the MPE values of the reconstructed signal are calculated as feature vectors which can differentiate machinery conditions. Finally, low-dimensional sensitive features obtained by principal component analysis (PCA) are fed into the adaptive GG clustering algorithm to perform fault identification. In this method, the residual energy ratio is used to find the optimal parameter K of the VMD and the PBMF function is incorporated into the GG to determine the number of clusters adaptively. Two bearing datasets are used to validate the performance of the proposed method. The results show that the proposed method can effectively identify different fault types.

Image Denoising Using Multiwavelet Transform with Different Filters and Rules

<p class="0abstract">Image denoising is a technique for removing unwanted signals called the noise, which coupling with the original signal when transmitting them; to remove the noise from the original signal, many denoising methods are used. In this paper, the Multiwavelet Transform (MWT) is used to denoise the corrupted image by Choosing the HH coefficient for processing based on two different filters Tri-State Median filter and Switching Median filter. With each filter, various rules are used, such as Normal Shrink, Sure Shrink, Visu Shrink, and Bivariate Shrink. The proposed algorithm is applied Salt&amp; pepper noise with different levels for grayscale test images. The quality of the denoised image is evaluated by using Peak Signal to Noise Ratio (PSNR). Depend on the value of PSNR that explained in the result section; we conclude that the (Tri-State Median filter) is better than (Switching Median filter) in denoising image quality, according to the results of applying rules the result of the Shrinking rule for each filter shows that the best result using first the Bivariate Shrink.</p>

Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

To eliminate the noise from the signals received by MEMS vector hydrophone, a joint algorithm is proposed in this paper based on wavelet threshold (WT) denoising, variational mode decomposition (VMD) optimized by a hybrid algorithm of Multiverse Optimizer (MVO) and Particle Swarm Optimization (PSO), and correlation coefficient (CC) judgment to perform the signal denoising of MEMS vector hydrophone, named as MVO-PSO-VMD-CC-WT, whose fitness function is the root mean square error (RMSE) and whose individual is the parameters of VMD. For every individual, the original signal is decomposed by VMD into pure components, noisy components, and noise components in terms of CC judgment, where the pure components are directly retained, the noisy components are denoised by WT denoising, and the noise components are discarded, and then, the denoised noisy components and the pure components are reconstructed to be the denoised signal of the original signal. Then, the obtained optimal individual is utilized to perform the signal denoising by MVO-PSO-VMD-CC-WT by the use of the above repeated signal processing. Two simulated experimental results show that the MVO-PSO-VMD-CC-WT algorithm which has the highest signal-to-noise ratio and the least RMSE is superior to the other compared algorithms. And the proposed MVO-PSO-VMD-CC-WT algorithm is effectively applied to perform the signal denoising of the actual lake experiments. Therefore, the proposed MVO-PSO-VMD-CC-WT is suitable for the signal denoising and can be applied into the actual experiments in signal processing.

Remote Evaluation System of Tennis Batting Action Standard Based on Acceleration Sensor

This paper studies the remote evaluation system of tennis batting action standard based on acceleration sensor, which aims to help improve the standard degree and technical level of tennis batting action. The system includes a data acquisition module to collect original signal data of tennis batting action by the acceleration sensor signal acquisition device in the bracelet and upload to the personal computer (PC) for storage, data preprocessing module to smooth original signal data and extract the key time and frequency domain features as the evaluation basis, and remote evaluation module to assess tennis batting action standard. We applied our system to five tennis trainees from the experimental university, and the results show that the batting action standard level of student c and student e is lower. Student c is weak mainly in the best position of the hitting point and the timing of the lead shot, while student e mainly shows poor performance in the timing of movement and the stability of the overall center of gravity. Compared with the proposed system or device, our system has a short real-time delay under the concurrent use of different types of users indicating stable and high real-time evaluation performance. More importantly, our system strictly protects the user’s privacy when uploading the user’s data remotely. In short, the evaluation results obtained by our system can be used as a scientific basis to improve the tennis batting action standard.

New Lossless Compression Algorithms for Transport Images

The article considers the relevance of the development of lossless image compression and transmission algorithms and their application for creating transport video surveillance systems. A brief overview of lossless transport image compression methods is provided. We propose a method for compressing transport plots based on the pyramid-recursive method of splitting the source image into polygons of various shapes and sizes. We consider two new algorithms for implementing the proposed method that are fundamentally different from each other: with a transition to the spectral region and without a transition to the spectral region of the original signal to ensure lossless compression. The results of testing various well-known lossless compression algorithms are analyzed: series length, Huffman, and arithmetic encoding, and compared with the proposed algorithms. It is shown that the proposed algorithms are more efficient in terms of compression ratio (2—3 times) compared to the known ones, while the computational complexity increases approximately by more than 3-4 times.

Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

Synchronization of Acoustic Signals for Steganographic Transmission

Steganography is a technique that makes it possible to hide additional information (payload) in the original signal (cover work). This paper focuses on hiding information in a speech signal. One of the major problems with steganographic systems is ensuring synchronization. The paper presents four new and effective mechanisms that allow achievement of synchronization on the receiving side. Three of the developed methods of synchronization operate directly on the acoustic signal, while the fourth method works in the higher layer, analyzing the structure of the decoded steganographic data stream. The results of the research concerning both the evaluation of signal quality and the effectiveness of synchronization are presented. The signal quality was assessed based on both objective and subjective methods. The conducted research confirmed the effectiveness of the developed methods of synchronization during the transmission of steganographic data in the VHF radio link and in the VoIP channel.