A Novel Approach for the Reduction of 50Hz Noise in Electrocardiogram Using Variational Mode Decomposition

2017 ◽  
Vol 12 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Suchetha Manikandan ◽  
Kumaravel Natesan
Keyword(s):  
Variational Mode Decomposition ◽  
Novel Approach ◽  
Mode Decomposition

scholarly journals A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2949
Author(s):  
Changpeng Li ◽  
Tianhao Peng ◽  
Yanmin Zhu
Keyword(s):  
Signal Processing ◽  
Acoustic Signal ◽  
Clustering Algorithm ◽  
Pso Algorithm ◽  
Variational Mode Decomposition ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Drum Shearer ◽  
Best Parameter ◽  
And Cluster Analysis

During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.

scholarly journals Variational Mode Decomposition Based Retinal Area Detection and Merging Of Superpixels in SLO Image

2020 ◽  
Vol 15 ◽  
Author(s):  
Suchetha.M ◽  
Deepika.V
Keyword(s):  
Fuzzy Inference ◽  
Retinal Area ◽  
Variational Mode Decomposition ◽  
Inference System ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Regional Gradient ◽  
Artificial Neural Network Ann ◽  
Processing Techniques

: Scanning Laser Ophthalmoscope (SLO) image can be used to detect retinal diseases. However identifying retinal area is a major task as retinal artefacts such as eyelashes and eyelids are also captured. Major part of retina can be viewed if detection is done with the help of images of SLO. In this paper our novel technique helps in detecting the true retinal area based on image processing techniques. To the SLO image two dimensional Variational Mode Decomposition (VMD) is applied. As a result of this different modes are obtained. Mode-1 is choosed because it has high frequency. Then mode1 is pre-processed using median filtering. After this preprocessed mode1 image is grouped into pixels based on regional size and compactness called superpixels. Superpixels are generated to reduce complexity. Superpixel merging is done next to Superpixel generation. It is done to reduce further difficulty and to enhance the speed. From the merged superpixels feature generation is performed using Regional, Gradient and textural features. It is done to eliminate artefacts and to detect the retinal area. Also feature selection will reduce the processing time and increase the speed. A classifier is constructed using Adaptive Network Fuzzy Inference System (ANFIS)for classification of features and its performance is compared with Artificial Neural Network(ANN). By this novel approach we got an classification accuracy of 98.5%.

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

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