ECG Signal Denoising Using an Improved Hybrid DWT-ADTF Approach.

PURPOSE: The electrocardiogram signal (ECG) presents a fundamental source of information to consider for the diagnosis of a heart condition. Given its low-frequency features, this signal is quite susceptible to various noise and interference sources. This paper presents an improved hybrid approach to ECG signal denoising based on the DWT and the ADTF methods. METHODS: The proposed improvements consist of integrating an adaptive [Formula: see text] parameter into the ADTF approach, combining a soft thresholding ADTF-based process with the DWT details, along with employing the mean filter to handle the baseline wandering noise. Furthermore, the proposed approach incorporates several denoising measures based on various proposed noise features, which have also been introduced in this approach. Several real noises collected from the Noise Stress Test Database (NSTDB), as well as several synthetic noises at different SNR levels, are proposed to ensure a thorough assessment of the proposed method's performance. RESULTS: The evaluation focuses on the SN Rimp, PRD, and MSE parameters, as well as the SINAD parameter as a diagnostic distortion measurement. Furthermore, a time complexity evaluation is proposed. The proposed approach demonstrated promising results compared to a recent hybridization of the DWT and ADTF methods, as well as recently published ECG signal denoising-based approaches in various real and synthetic noise cases using different statistical evaluation metrics. CONCLUSION: In the vast majority of the study cases, the proposed approach outperforms the compared methods in terms of statistical results for real and synthetic noises. Furthermore, compared to these methods, it provides a fairly low time complexity. This is consistent with the ambition of embedding this approach in low-cost hardware architectures.

Peroxymonosulfate activation by Fe3O4-MnO2/CNT nanohybrid electroactive filter towards ultrafast micropollutants decontamination: Performance and mechanism.

Electrocatalytic peroxymonosulfate (PMS) activation is a promising advanced oxidation process for the degradation of micropollutants. Herein, we developed an electroactive carbon nanotube (CNT) filter functionalized with Fe3O4-MnO2 hybrid (Fe3O4-MnO2/CNT) to activate PMS towards ultrafast degradation of sulfamethoxazole (SMX). SMX was completely degraded via a single-pass through the nanohybrid filter (τ < 2 s). The ultrafast degradation kinetics were maintained across a wide pH range (from 3.0 to 8.0), in complicated matrices (e.g., tap water, lake water, WWTP effluent and pharmaceutical wastewater), and for the degradation of various persistent micropollutants. Compared with a conventional batch reactor, the flow-through operation provides an 9.2-fold higher SMX degradation kinetics by virtue of the convection-enhanced mass transport (1.47 vs. 0.16 min-1). The efficient redox cycle of Fe2+/Fe3+ and Mn2+/Mn4+ facilitate the PMS activation to generate SO4•- under electric field. Meanwhile, the ketonic groups on the CNT provide active sites for the generation of 1O2. Both experimental and theoretical results revealed the superior activity of nanohybrid filter associated with the synergistic effects among Fe, Mn, CNT and electric field. Therefore, the electrocatalytic filter based PMS activation system provides a green strategy for the remediation of micropollutants in a sustainable manner.

An Alignment Method Based on KF-ASMUKF Hybrid Filtering for Ship's SINS under Mooring Conditions.

To solve the problem that the ship's strapdown inertial navigation system (SINS) alignment accuracy decreases with the increase of the nonlinear filtering state dimension under mooring conditions, a method based on Kalman filter (KF) and Adaptive scale mini-skewness single line sampling Unscented Kalman Filter (ASMUKF) hybrid filtering algorithm is proposed in this paper. Three improvements are made as the following: (1) adopt a new sampling strategy. To obtain the ASMUKF filtering algorithm, scale mini-skewness single line sampling is used to replaced the traditional symmetrical sampling method and an adaptive scale factor is adapted into the Unscented Kalman Filter (UKF) to correct the real-time transformation sampling process; (2) the improved ASMUKF algorithm is combined with KF to form KF-ASMUKF hybrid filtering model; (3) the hybrid filtering model is divided into linear and nonlinear parts. The linear filtering part adopts the KF filtering model and the nonlinear filtering part adopts the ASMUKF model. Then, the calculation steps of the hybrid filtering algorithm is designed in this paper. The simulation and experimental results show that the hybrid filtering algorithm proposed has certain advantages over the traditional algorithm, and it can reduce the ship's SINS calculation amount and improve alignment accuracy under mooring conditions.

A new hybrid filter/wrapper algorithm for feature selection in classification.

Feature selection can greatly enhance the performance of a learning algorithm when dealing with a high dimensional data set. The filter method and the wrapper method are the two most commonly approaches. However, these approaches have limitations. The filter method uses independent evaluation to evaluate and select features, which is computationally efficient but less accurate than the wrapper method. The wrapper method uses a predetermined classifier to compute the evaluation, which can afford high accuracy for particular classifiers, but is computationally expensive. In this study, we introduce a new feature selection method that we refer to as the large margin hybrid algorithm for feature selection (LMFS). In this method, we first utilize a new distance-based evaluation function, in which ideally samples from the same class are close together, whereas samples from other classes are far apart, and a weighted bootstrapping search strategy to find a set of candidate feature subsets. Then, we use a specific classifier and cross-validation to select the final feature subset from the candidate feature subsets. Six vibrational spectroscopic data sets and three different classifiers, namely k-nearest neighbors, partial least squares discriminant analysis and least squares support vector machine were used to validate the performance of the LMFS method. The results revealed that LMFS can effectively overcome the over-fitting between the optimal feature subset and a given classifier. Compared with the filter and wrapper methods, the features selected by the LMFS method have better classification performance and model interpretation. Furthermore, LMFS can effectively overcomes the impact of classifier complexity on computational time, and distance-based classifiers were found to be more suitable for selecting the final subset in LMFS.

Application of powdered activated carbon coating to fabrics in a hybrid filter to enhance mercury removal.

Elemental mercury (Hg0) is predominant constituent of flue gas emitted from coal-fired power plants. Adsorption has been considered the best available technology for removal of Hg0 from flue gas. However, adsorbent injection increases the amount of ash generated. In the present study, powdered activated carbon (PAC) was coated on polytetrafluoroethylene/glass fiber filters to increase Hg0 removal while concurrently reducing the amount of ash generated. The optimal PAC coating rate was determined in laboratory experiments to ensure better Hg0 removal with low pressure drop. When PAC of particle size less than 45 µm was used, and the areal density was 50 g/m2, the pressure drop remained under 30 Pa while the Hg0 removal efficiency increased to 15.8% from 4.3%. The Hg0 removal efficiency also increased with decrease in filtration velocity. The optimal PAC coating rate was applied on a hybrid filter (HF), which was combined with a bag filter and an electrostatic precipitator in a single chamber. Originally designed to remove fine particulates matter, it was retrofitted to the flue gas control device for simultaneous Hg0 removal. By employing the PAC coating, the Hg removal efficiency of the HF increased to 79.79% from 66.35%. Also, a temporary reduction in Hg removal was seen but this was resolved following a cleaning cycle in which the dust layer was removed.

Application of accelerometer-based gait recognition to adjuvant clinical gait analysis.

BACKGROUND: Gait recognition is an emerging biometric technology applied to the mobile environment. With built-in accelerometers, wearable devices are used to recognize user identity according to gait periodic pattern, which shows strong stability and uniqueness property. OBJECTIVE: The purpose of this study is to build analyzing models to find the change of gait normal and pathological function based on gait features. METHODS: This work relies on gait recognition methods. In this paper, the performance of different hybrid filter methods is compared by combining four classical filtering methods. The influence of the abnormal pattern of gait cycle is estimated by standard deviation. The effectiveness of feature matching methods is evaluated by six classical distance discrimination function. RESULTS: The results highlight the stability and invariance of gait periodic pattern. For analyzing models, the best recognition rate is 96.67% with the combination of MF hybrid filter and Correlation distance function in the small sample, and minimal time consumption is 0.038 s. The effectiveness of analyzing models is further analyzed for different practical applications. CONCLUSIONS: This study provides evidence for future scientific teams to make decisions on selecting filter methods and discrimination functions which can more efficiently extract gait features and suggest ways to analyze clinical gait pattern.

Hybrid Filtering Approach for Retrieval of MRI Image.

The quality of Magnetic Resonance Images(MRI) are degraded by the various types of noises. In this paper, a Hybrid Multi-resolution filter for denoising the MRI images degraded by the Salt and Pepper noise is proposed and the wavelet transform is used to improve the resolution of the denoised image.. The Hybrid filter consist of three value weighted filter and similarity based filter. In three value weighted filter, a variable local window is applied to find the noisy pixels. By using the noise free pixels in that window, the noisy pixels are reconstructed using three value method. In similarity based filter, a variable local window is applied to reconstruct the noisy pixels. In that window, based on the similarity between the noisy pixel sequence and noise free pixels sequence are used to reconstruct the noisy pixel. At last wavelet transform is used to increase the resolution of the reconstructed image. The experimental results shows that the proposed filter denoises the image and improves the resolution when compared to the existing methods and produces the efficiency of about 98%.

The simultaneous capture of mercury and fine particles by hybrid filter with powder activated carbon injection.

The hybrid filter (HF) was newly designed and operated with powder activated carbon (PAC) injection to capture mercury and fine particulate matter in the coal power plant. With PAC injection in HF operation, the capture efficiency of elemental mercury was clearly enhanced. When the injection rate of PAC increased from 0 to 20 mg/m3, the speciation fraction of elemental mercury significantly decreased from 85.19% to 3.76% at the inlet of the hybrid filter. The speciation fraction of oxidized mercury did not vary greatly, whereas the particulate mercury increased from 1.31% to 94.04%. It was clearly observed that the HF played a role in the capture of mercury and fine PM by leading the conversion of elemental mercury as particulate mercury and the growth of PM via electrode discharge in the HF operation with PAC injection.