Tensor Dictionary Learning with an Enhanced Sparsity Constraint for Sparse-View Spectral CT Reconstruction

Spectral computed tomography (CT) can divide collected photons into multi-energy channels and gain multi-channel projections synchronously by using photon-counting detectors. However, reconstructed images usually contain severe noise due to the limited number of photons in the corresponding energy channel. Tensor dictionary learning (TDL)-based methods have achieved better performance, but usually lose image edge information and details, especially from an under-sampling dataset. To address this problem, this paper proposes a method termed TDL with an enhanced sparsity constraint for spectral CT reconstruction. The proposed algorithm inherits the superiority of TDL by exploring the correlation of spectral CT images. Moreover, the method designs a regularization using the L0-norm of the image gradient to constrain images and the difference between images and a prior image in each energy channel simultaneously, further improving the ability to preserve edge information and subtle image details. The split-Bregman algorithm has been applied to address the proposed objective minimization model. Several numerical simulations and realistic preclinical mice are studied to assess the effectiveness of the proposed algorithm. The results demonstrate that the proposed method improves the quality of spectral CT images in terms of noise elimination, edge preservation, and image detail recovery compared to the several existing better methods.

Investigating the Effect of Noise Elimination on LSTM Models for Financial Markets Prediction Using Kalman Filter and Wavelet Transform

Predicting financial markets is of particular importance for investors who intend to make the most profit. Analysing reasonable and precise strategies for predicting financial markets has a long history. Deep learning techniques include analyses and predictions that can assist scientists in discovering unknown patterns of data. In this project, application of noise elimination techniques such as Wavelet transform and Kalman filter in combination of deep learning methods were discussed for predicting financial time series. The results show employing noise elimination techniques such as Wavelet transform and Kalman filter, have considerable effect on performance of LSTM neural network in extracting hidden patterns in the financial time series and can precisely predict future actions in these markets.

Neuroprotective Effect of Monosialotetrahexosylganglioside (GM1) on Patients with Parkinson’s Disease Anesthetized by Ketamine under Denoising Algorithm-Based Ultrasound Image Diagnosis

This study was to investigate the value of ultrasound technology based on the bilateral filtering noise elimination algorithm in evaluating the neuroprotective effect of monosialoganglioside in ketamine-anesthetized Parkinson’s disease patients. The research subjects were 75 patients with Parkinson’s disease admitted to the hospital. The patients were randomly divided into three groups according to different treatment methods: A (GM1 + ketamine anesthesia group), B (conventional treatment + ketamine anesthesia group), and C (GM1 + nonketamine anesthesia group), with 25 patients in each group. Twenty-five healthy people with similar general data in the three groups (groups A, B, and C) were also selected as the control group (group D). All patients underwent ultrasonography, and ultrasound images were processed using the bilateral filter noise elimination. Structural similarity (SSIM), mean absolute error (MAE), and peak signal to noise ratio (PSNR) were used to evaluate the treatment effect. Plasma phospholipids, the third part of the PD unified score scale, Montreal cognitive assessment scale, and other indicators were analyzed and compared among the four groups. The bilateral filtering image noise was effectively suppressed, and the edge details were kept well. Some of the weak edges and texture information in the image were eliminated, the visual effect was ideal, and the accuracy of the edges of the picture remained good. The serotonin lipid level in group A was greatly lower than the serum phospholipid level in group B after GM1 treatment (6.55 VS 7.84, P < 0.05 ). Compared with that before treatment, the serotonin lipid level of group A patients decreased after the treatment, and the difference was considerable (7.46 VS 6.55, P < 0.05 ). In short, GM1 had a protective effect on the nerves of patients with Parkinson’s disease anesthetized by ketamine.

Calibration of X-Band Radar for Extreme Events in a Spatially Complex Precipitation Region in North Peru: Machine Learning vs. Empirical Approach

Cost-efficient single-polarized X-band radars are a feasible alternative due to their high sensitivity and resolution, which makes them well suited for complex precipitation patterns. The first horizontal scanning weather radar in Peru was installed in Piura in 2019, after the devastating impact of the 2017 coastal El Niño. To obtain a calibrated rain rate from radar reflectivity, we employ a modified empirical approach and draw a direct comparison to a well-established machine learning technique used for radar QPE. For both methods, preprocessing steps are required, such as clutter and noise elimination, atmospheric, geometric, and precipitation-induced attenuation correction, and hardware variations. For the new empirical approach, the corrected reflectivity is related to rain gauge observations, and a spatially and temporally variable parameter set is iteratively determined. The machine learning approach uses a set of features mainly derived from the radar data. The random forest (RF) algorithm employed here learns from the features and builds decision trees to obtain quantitative precipitation estimates for each bin of detected reflectivity. Both methods capture the spatial variability of rainfall quite well. Validating the empirical approach, it performed better with an overall linear regression slope of 0.65 and r of 0.82. The RF approach had limitations with the quantitative representation (slope = 0.44 and r = 0.65), but it more closely matches the reflectivity distribution, and it is independent of real-time rain-gauge data. Possibly, a weighted mean of both approaches can be used operationally on a daily basis.

Channelized active noise elimination (CANE) for suppressing quantization noise in bitstream modulated transmitter (BMT)

Bitstream modulated transmitters may offer improved power efficiency and linearity simultaneously in RF power amplifiers. Several modulation techniques including envelop delta-sigma modulation and envelope pulse width modulation have been applied. The out-of-band quantization noise associated with these modulations may be rejected by a high-quality factor output filter, yet the in-band quantization noise needs to be further suppressed to meet the requirement of the emission mask. The proposed channelized active noise elimination technique can offer additional quantization noise suppression through software control without involving a passive filter. The essential concept is based on combining the outputs of multiple channels of Pas that have digitally controlled delays to form a FIR filter in analogue domain. A two-channel and a four-channel GaN power amplifiers are built to demonstrate this noise suppression concept and power combiners based on T-junction with quarter wavelength transmission line are proposed to retain the high power efficiency of the transmitters.

A Novel Method for Laser Peak Detection with Subpixel Accuracy for the Rail Corrugation Measurement

In this article, a new wavelet-based laser peak detection algorithm is proposed having subpixel accuracy. The algorithm provides an accurate and rapid measurement platform for the rail surface corrugation with no need to any image noise elimination. The proposed rail Corrugation Measurement System (CMS) is based on the laser triangulation principle, and the accuracy of such system is mainly affected by the laser peak detection in the captured image. The intensity of each row or column of the image is taken as a 1-D discrete signal. Intensity distribution of a laser stripe in this signal follows a Gaussian pattern contaminated by the white noise. Against usual peak detection algorithms with need to prenoise-filtering process, the proposed method based on the wavelet transform is able to perform these tasks efficiently and robustly. Present wavelet-based methods for the peak detection are at pixel level, but for achieving high accuracy subpixel detection is proposed. Experiments show that the capability of the proposed method for laser peak detection is more accurate and faster than the filter-based methods, especially for low S/N ratios. Also, this technique can be utilized for any application in laser peak detection with subpixel accuracy. A prototype system based on the proposed method for the rail corrugation measurement has been designed and manufactured. Results of the rail corrugation measurement guarantee capability of the proposed methodology for accurate measurement of the rail corrugation and its potential for industrial application.

Image mixed gaussian and impulse noise elimination based on sparse representation model

<p>A modified mixed Gaussian plus impulse image denoising algorithm based on weighted encoding with image sparsity and nonlocal self-similarity priors regularization is proposed in this paper. The encoding weights and the priors imposed on the images are incorporated into a variational framework to treat more complex mixed noise distribution. Such noise is characterized by heavy tails caused by impulse noise which needs to be eliminated through proper weighting of encoding residual. The outliers caused by the impulse noise has a significant effect on the encoding weights. Hence a more accurate residual encoding error initialization plays the important role in overall denoising performance, especially at high impulse noise rates. In this paper, outliers free initialization image, and an easier to implement a parameter-free procedure for updating encoding weights have been proposed. Experimental results demonstrate the capability of the proposed strategy to recover images highly corrupted by mixed Gaussian plus impulse noise as compared with the state of art denoising algorithm. The achieved results motivate us to implement the proposed algorithm in practice.</p>

Research on the Fault Diagnosis Method of Mine Fan Based on Sound Signal Analysis

The underground local fan and auxiliary fan also play a vital role in the underground air quality, compared with the system fan. However, the number of underground local fans and auxiliary fans is large and widely distributed, which is disadvantageous to adopt the same method of online monitoring and fault diagnosis method as the system fan. In order to find a new fault diagnosis method, which is cost-effective and reliable, this paper proposes a fault diagnosis method based on sound signal. It analyzes the source of fan noise and studies the overall scheme of mine fan fault diagnosis expert system based on sound signal. The fault expert system consists of four parts: signal acquisition and noise elimination, feature extraction, state recognition, and fault diagnosis. Its principle is briefly introduced. The denoising method of wavelet is adopted in this paper. Wavelet packet is used to extract the characteristics of sound signal, and the energy size and energy proportion of each frequency component are used as the basis of knowledge acquisition and reasoning. Through the analysis of the measured signals of the fan in the normal operating state, the feature vectors were extracted as the basis for the discrimination of the normal state after noise elimination. At the same time, the audio processing software was used to simulate the sound signals in three fault states. Then, the feature vector of the fault state is extracted, which is obviously different from that of the fan in the normal operation. As the basis of fault state analysis of the expert system, it lays the foundation for the realization of the expert system of mine fan equipment running state diagnosis.