Vehicle audio signal estimation of AR model and power spectrum estimation based on artificial neural network

2013 ◽  
Author(s):  
Hongfeng Wei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Spectrum ◽  
Audio Signal ◽  
Ar Model ◽  
Spectrum Estimation ◽  
Signal Estimation ◽  
Power Spectrum Estimation ◽  
Artificial Neural

scholarly journals Pengaruh Latar Belakang Warna pada Objek Gambar terhadap Hasil Ekstraksi Sinyal EEG

2017 ◽  
Vol 7 (2) ◽  
pp. 161 ◽  
Author(s):  
Catur Atmaji ◽  
Zandy Yudha Perwira
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training System ◽  
Training Data ◽  
Spectrum Estimation ◽  
Quality Classification ◽  
Eeg Data ◽  
Two Systems ◽  
Artificial Neural ◽  
Color Background

In this study, observation on the differences in features quality of EEG records as a result of training on subjects has been made. The features of EEG records were extracted using two different methods, the root mean square which is acquired from the range between 0.5 and 5 seconds and the average of power spectrum estimation from the frequency range between 20 and 40Hz. All of the data consists of a 4-channel recording and produce good quality classification on artificial neural network, with each of which generates training data accuracy over 90%. However, different results are occured when the trained system is tested on other test data. The test results show that the two systems which are trained using training data with object with color background produce higher accuracy than the other two systems which are trained using training data with object without background color, 63.98% and 60.22% compared to 59.68% and 56.45% accuracy respectively. From the use of the features on the artificial neural network classification system, it can be concluded that the training system using EEG data records derived from the visualization of object with color background produces better features than the visualization of object without color background.

The Power Spectrum Estimation of the AR Model Based on Motor Imagery EEG

2013 ◽  
Vol 706-708 ◽  
pp. 1923-1927 ◽  
Author(s):  
Li Zhao ◽  
Yang He
Keyword(s):  
Power Spectrum ◽  
Motor Imagery ◽  
Ar Model ◽  
Spectrum Estimation ◽  
Power Spectrum Estimation ◽  
Estimation Algorithms ◽  
Model Based

This paper uses three common AR model power spectrum estimation algorithms which are the Yule-Walker method, the burg method and the improved covariance method. Taking Matlab as a tool, the corresponding algorithms are used to carry out the power spectrum estimation of motor imagery EEG, the relationships and distinctions between the spectrum charts are compared in order to find the relatively appropriate algorithm for analyzing the EEG, which aims at providing a theoretical guidance for processing the motor imagery EEG and laying a foundation for further research.

Abstract P365: Blood Pressure Evaluation From Ppg Signal Analysis and Artificial Neural Network

Hypertension ◽  
2017 ◽  
Vol 70 (suppl_1) ◽  
Author(s):  
Francesco Lamonaca ◽  
Vitaliano Spagnuolo ◽  
Serena De Prisco ◽  
Domenico L Carnì ◽  
Domenico Grimaldi
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Artificial Neural Network ◽  
Audio Signal ◽  
Hardware Interface ◽  
Validation Procedure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Time Domain ◽  
And Storage

The analysis of the PPG signal in the time domain for the evaluation of the blood pressure (BP) is proposed. Some features extracted from the PPG signal are used to train an Artificial Neural Network (ANN) to determine the function that fit the target systolic and diastolic BP. The data related to the PPG signals and BP used in the analysis are provided by the Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC II) database. The pre-analysis of the signal to remove inconsistent data is also proposed. A set of 1750 valid pulse is considered. The 80% of the input samples is used for the training of the network. Instead, the 10% of the input data are used for the validation of the network and 10% for final test of this last. The results show as the error for both the systolic and diastolic BP evaluation is included in the range of ±3 mmHg. Tab.1 shows the results for 20 PPG pulses randomly selected analyzed together with the systolic and diastolic blood pressure furnished by MIMC and evaluated by the trained ANN. Tab.1 experimental results comparing MIMIC and the ANN results. Moreover, a suitable hardware to validate the ANN with the sphygmomanometer is designed and realized. This hardware allows clinicians to collect data according to the requirements of the validation procedure. With the sphygmomanometer the systolic and diastolic values are referred to two different PPG pulses. As a consequence, it is proposed a new hardware interface allowing the synchronized acquisition and storage of the PPG signal and clinician voice. For the validation, the clinician: (i) evaluates the BP on both the arms and assesses that no significant differences occur; (ii) plugs the PPG sensor on the finger of one arm; (iii) starts the recording of both the PPG signal and the audio signal; (iv) evaluates the BP on the other arm with sphygmomanometer and says the systolic and diastolic values when detected. Through suitable post processing algorithm, the Systolic and Diastolic values are associated to the corresponding PPG Pulses. Following this procedure, the dataset to further validate the ANN according the standard is obtained. Once the ANN is validated it will be implemented on smartphone to have always in the pocket a reliable measurement system for Blood Pressure, oximetry and heart rate.

scholarly journals Analysing 21cm signal with artificial neural network

2017 ◽  
Vol 12 (S333) ◽  
pp. 39-42
Author(s):  
Hayato Shimabukuro ◽  
Benoit Semelin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Spectrum ◽  
Machine Learning Techniques ◽  
Inversion Method ◽  
Observation Data ◽  
The Neural Network ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractThe 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

AR Model Power Spectrum Estimation and MATLAB Simulation

2014 ◽  
Vol 971-973 ◽  
pp. 1561-1564 ◽  
Author(s):  
Min Ji ◽  
Jing Feng He ◽  
You Qiang Lai
Keyword(s):  
Parameter Estimation ◽  
Power Spectrum ◽  
Frequency Domain ◽  
Ar Model ◽  
Model Parameters ◽  
Spectrum Estimation ◽  
Actual Situation ◽  
Matlab Simulation ◽  
Power Spectrum Estimation ◽  
Advantages And Disadvantages

the power spectrum estimation researches various characteristics of signals in the frequency domain. The purpose is that signals are recognized and extract .Because these useful signals are submerged in noise .The article introduces estimation in the classic power spectrum and modern power spectrum. It is important that algorithm of AR model parameters are introduced in the parameter estimation of several typical. It discusses the advantages and disadvantages of various algorithms, and with the help of MATLAB platform, the various algorithms of power spectrum are simulated, in order to undertake choosing according to the actual situation.

Daily Load Forecasting Based on Combination Forecasting Techniques

2011 ◽  
Vol 201-203 ◽  
pp. 2685-2689
Author(s):  
Chong Gao ◽  
Hai Jie Ma ◽  
Pei Na Gao
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Ar Model ◽  
Neural Network Training ◽  
Network Training ◽  
Artificial Neural ◽  
Daily Load ◽  
Forecasting Techniques

To improve the accuracy of load forecasting is the focus of the load forecasting. As the daily load by various environmental factors and periodical, this makes the load time series of changes occurring during non-stationary random process. The key of improving the accuracy of artificial neural network training is to select effective training sample. This paper based on the time series forecasting techniques’ random time series autocorrelation function to select the neural network training samples. The method of modeling is more objective. By example, the comparison with autoregressive (AR) Model predictions and BP Artificial Neural Network (ANN) predicted results through error analysis and confirmed the proposed scheme good performance.

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