scholarly journals Noise Reduction for MEMS Gyroscope Signal: A Novel Method Combining ACMP with Adaptive Multiscale SG Filter Based on AMA

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4382 ◽  
Author(s):  
He ◽  
Sun ◽  
Wang
Keyword(s):  
Moving Average ◽  
Window Size ◽  
Superior Performance ◽  
Discrete Wavelet ◽  
Stopping Criterion ◽  
Mems Gyroscope ◽  
Smoothing Methods ◽  
Mode Decomposition ◽  
Novel Method ◽  
Micro Electromechanical System

In this paper, a novel hybrid method combining adaptive chirp mode pursuit (ACMP) with an adaptive multiscale Savitzky–Golay filter (AMSGF) based on adaptive moving average (AMA) is proposed for offline denoising micro-electromechanical system (MEMS) gyroscope signal. The denoising scheme includes preliminary denoising and further denoising. At the preliminary denoising stage, the original gyroscope signal is decomposed into signal modes one by one using ACMP with modified stopping criterion based on mutual information. Useful information is extracted while most noise is discarded in the residue at this stage. Then, AMSGF is proposed to further denoise the signal modes. Sample variance based on AMA is used to adjust the window size of AMSGF adaptively. Practical MEMS gyroscope signal denoising results under different motion conditions show the superior performance of the proposed method over empirical mode decomposition (EMD)-based denoising, discrete wavelet threshold denoising, and variational mode decomposition (VMD)-based denoising. Moreover, AMSGF is proven to gain a better denoising effect than some other common smoothing methods.

scholarly journals Denoising of Speech Signal using Empirical Mode Decomposition and Kalman Filter

2020 ◽  
Vol 9 (8) ◽  
pp. 232-237
Keyword(s):  
Kalman Filter ◽  
Speech Recognition ◽  
Empirical Mode Decomposition ◽  
Mean Squared Error ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Mode Decomposition ◽  
Speech Features ◽  
Speech Denoising ◽  
Original Speech

Speech denoising is the process of removing the noise from the noise corrupted speech. The applications of speech denoising are used in speech enhancement, speech recognition and many more. In this work, a new approach is proposed to de-noise the speech which is corrupted from different noises, Empirical mode decomposition and the Kalman filter (EMD-KF) is used for speech denoising in the proposed work. The clean speech is corrupted by the noise with the different SNR’s, and further Empirical mode decomposition (EMD) is applied to the noise corrupted speech later the obtained resultant speech is passed through the Kalman filter (KF) which gives the denoised speech. The result shows that the mean squared error (MSE) values of EMD-KF are extremely less when compared to other methods like discrete wavelet transform (wavelet families like Daubechies and Symlet), empirical mode decomposition (EMD) and moving average filter followed by empirical mode decomposition (MA-EMD). As an application the proposed algorithm is used in the feature extraction for speech recognition. Mel frequency cepstral coefficient (MFCC) is performed on both the original speech and the denoised speech and found majority of the denoised speech features are similar to the original speech features and few denoised speech features are nearby to the original speech features.

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

Hybrid approach combining EMD, ARIMA and monte carlo for multi-step ahead medical tourism forecasting

2021 ◽  
pp. 1-17
Author(s):  
Nuzhat Fatema ◽  
H Malik ◽  
Mutia Sobihah Binti Abd Halim
Keyword(s):  
Monte Carlo ◽  
Hybrid Model ◽  
Medical Tourism ◽  
Moving Average ◽  
Hybrid Approach ◽  
Mode Decomposition ◽  
Hybrid Forecasting ◽  
Input Variables ◽  
Intelligent Approach

This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA) and Monte Carlo simulation (MCS) methods for multi-step ahead medical tourism (MT) forecasting using explanatory input variables based on two decade real-time recorded database. In the proposed hybrid model, these variables are 1st extracted then medical tourism is forecasted to perform the long term as well as the short term goal and planning in the nation. The multi-step ahead medical tourism is forecasted recursively, by utilizing the 1st forecasted value as the input variable to generate the next forecasting value and this procedure is continued till third step ahead forecasted value. The proposed approach firstly tested and validated by using international tourism arrival (ITA) dataset then proposed approach is implemented for forecasting of medical tourism arrival in nation. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using Monte Carlo method and the results are compared. Obtained results shows that the proposed hybrid forecasting approach for medical tourism has outperformance characteristics.

scholarly journals Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2381
Author(s):  
Jaewon Lee ◽  
Hyeonjeong Lee ◽  
Miyoung Shin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Accidents ◽  
Physiological Signals ◽  
Superior Performance ◽  
Short Term ◽  
Input Signals ◽  
Skin Response ◽  
Novel Method ◽  
Fully Connected

Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).

scholarly journals Research on Novel Denoising Method of Variational Mode Decomposition in MEMS Gyroscope

2021 ◽  
Vol 21 (1) ◽  
pp. 19-24
Author(s):  
Xiaolei Wang ◽  
Huiliang Cao ◽  
Yuzhao Jiao ◽  
Taishan Lou ◽  
Guoqiang Ding ◽  
...  
Keyword(s):  
Resonant Frequency ◽  
Reduction Method ◽  
Noise Signal ◽  
Sampling Frequency ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Mems Gyroscope ◽  
Frequency Level ◽  
Mode Decomposition ◽  
Four Levels

Abstract The noise signal in the gyroscope is divided into four levels: sampling frequency level, device bandwidth frequency level, resonant frequency level, and carrier frequency level. In this paper, the signal in the dual-mass MEMS gyroscope is analyzed. Based on the variational mode decomposition (VMD) algorithm, a novel dual-mass MEMS gyroscope noise reduction method is proposed. The VMD method with different four-level center frequencies is used to process the original output signal of the MEMS gyroscope, and the results are analyzed by the Allan analysis of variance, which shows that the ARW of the gyroscope is increased from 1.998*10−1°/√h to 1.552*10−4°/√h, BS increased from 2.5261°/h to 0.0093°/h.

scholarly journals Empirical Mode Decomposition in Discrete Time Signals Denoising

2019 ◽  
Vol 16 (1) ◽  
pp. 10-13 ◽  
Author(s):  
Zoltán Germán-Salló
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Discrete Wavelet ◽  
Intrinsic Mode Functions ◽  
Nonlinear Signal Processing ◽  
Mode Decomposition ◽  
Time Signals ◽  
Nonlinear Signal ◽  
Filtering Procedure

Abstract This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

scholarly journals Penggunaan metode algoritma wagner within dalam upaya pengendalian persediaan scrap besi di PT XYZ

2020 ◽  
Vol 5 (1) ◽  
pp. 56-73
Author(s):  
Somadi ◽  
Syah Rajendra Hari Septa ◽  
Nila Dahlia Juita
Keyword(s):  
Control Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Lot Size ◽  
Total Size ◽  
Total Cost ◽  
Smoothing Methods ◽  
Before And After ◽  
Iron Scrap ◽  
Total Inventory

The research objective is to determine the total size of the lot of iron scrap orders, and the total cost of the company's inventory before and after carrying out the method of controlling iron scrap inventory using the Wagner Within Algorithm method. Demand forecasting uses the Single Moving Averge, Weight Moving Average, and Exponential Smoothing methods. Based on the results of the study, the total lot size of iron scrap material orders is smaller than the size of previous lot orders without using the inventory control method, which is 15,362 tons per year. Total inventory of Rp. 105,076,125,840 and the total cost is more optimal when compared with the total cost of inventory with the company system that is Rp. 109,734,165,840 so that the company can save costs by Rp. 4,658,040,000.

scholarly journals Universal statistical steganalytic method

2017 ◽  
Vol 68 (2) ◽  
pp. 117-124
Author(s):  
Martin Broda ◽  
Vladimír Hajduk ◽  
Dušan Levický
Keyword(s):  
Wavelet Transformation ◽  
Ensemble Classifier ◽  
Secret Message ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Discrete Cosine Transformation ◽  
Static Images ◽  
Novel Method ◽  
Statistical Steganalysis ◽  
Popular Image

Abstract Novel image steganalytic method used to detection of secret message in static images is introduced in this paper. This method is based on statistical steganalysis (SS), where statistical vector is composed by 285 statistical features (parameters) extracted from DCT (Discrete Cosine Transformation) domain and 46 features extracted mainly from DWT (Discrete Wavelet Transformation) domain. Classification process was realized by Ensemble classifier that was helpful in reduction of computational and time complexity. Proposed steganalytic method was verified by detection of popular image steganographic methods. Novel method was also compared with existing steganalytic methods by overall detection accuracy of a secret message.

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