In this paper, a new method for estimation of flat fading is proposed. First, the channel is modeled by a noisy autoregressive (AR) model and then Zheng method is used to estimate the AR model parameters. After the model is determined, the channel is estimated using Kalman filter. Using simulations the performance of the proposed method is evaluated and compared with the other existing methods in terms of estimation accuracy and bit error rate (BER). Simulation results show that the proposed method outperforms the other existing methods.

Stochastic differential equations(SDEs), arise from physical systems that possess inherent noise and certainty. We derive a SDE for electrical circuits. In this paper, we will explore the close relationship between the SDE and autoregressive(AR) model. We will solve SDE related to RC circuit with using of AR(1) model (Markov process) and however with Euler-Maruyama(EM) method. Then, we will compare this solutions. Numerical simulations in MATLAB are obtained.

Hypernasality is a frequently occurring resonance disorder in children with cleft palate. Generally an operation is necessary to reduce the hypernasality and therefore an assessment of hypernasality is imperative to quantify the effect of the surgery and design the speech therapy sessions which are crucial after surgery. In this paper, a new quantitative method is proposed to estimate hypernasality. The proposed method used the fact that an Autoregressive (AR) model for vocal tract system of a patient with hypernasal speech is not accurate; because of the zeros appear in the frequency response of vocal tract system. Therefore in our method hypernasality was estimated by a quantity calculated from comparing the distance between the sequences of cepstrum coefficients extracted from AR model and Autoregressive Moving Average (ARMA) model. K-means and Bayes theorem were utilized to classify the utterances of subjects by means of proposed index. We achieved the accuracy up to 81.12% on utterances and 97.14% on subjects. Since the proposed method needs only computer processing of speech data, compare to other clinical methods it is provides a simple evaluation of hypernasality.

Hypernasality is a frequently occurring resonance disorder in children with cleft palate. Generally an operation is necessary to reduce the hypernasality and therefore an assessment of hypernasality is imperative to quantify the effect of the surgery and design the speech therapy sessions which are crucial after surgery. In this study, a new quantitative method is proposed to estimate hypernasality. The proposed method used the fact that an Autoregressive (AR) model for vocal tract system of a patient with hypernasal speech is not accurate; because of the zeros appear in the frequency response of vocal tract system due to existence of extra channel between oral and nasal cavity of these patients. Therefore in our method hypernasality was estimated by a quantity calculated from comparing the distance between the sequences of cepstrum coefficients extracted from AR model and Autoregressive Moving Average (ARMA) model. K-means and Bayes theorem were utilized for finding a threshold value for proposed index to classify the utterances of subjects. We achieved the balanced accuracy up to 82.18% on utterances and 97.72% on subjects. Since the proposed method needs only computer processing of speech data, compare to other clinical methods it is provides a simple evaluation of hypernasality.

To obtain reliable model for auto correlated and time series data, robust approach should be considered because outliers and contaminations can have bad effect on parameter estimation of these models. Since most finance data are auto correlated and they are affected by the previous data, they can be modeled by time series regression models. In this paper, the autoregressive (AR) model is investigated and novel robust procedure based on filtered S-estimator is proposed to estimate the parameters of AR model. This model is used to obtain robust forecasting procedure. We present 148 data gathered from a firm which are related to profit as a numerical example and show the efficiency of the proposed estimation approach. The robust model can forecast more accurate than classical model in presence of outlier.

Nowadays time series analysis is an important challenge in engineering problems. In this paper, we proposed the Comprehensive Learning Polynomial Autoregressive Model (CLPAR) predict linear and nonlinear time series. The presented model is based on the autoregressive (AR) model but developed in a polynomial aspect to make it more robust and accurate. This model predicts future values by learning the weights of the weighted sum of the polynomial combination of previous data. The learning process for the hyperparameters and properties of the model in the training phase is performed by the metaheuristic optimization method. Using this model, we can predict nonlinear time series as well as linear time series. The intended method was implemented on eight standard stationary and non-stationary large-scale real-world datasets. This method outperforms the state-of-the-art methods that use deep learning in seven time series and has better results compared to all other methods in six datasets. Experimental results show the advantage of the model accuracy over other compared methods on the various prediction tasks based on root mean square error (RMSE).

It is often needed to label electroencephalogram(EEG) signals by segments of similar characteristics that are particularly meaningful to clinicians and for assessment by neurophysiologists. Within each segment, the signals are considered statistically stationary, usually with similar characteristics such as amplitude and/or frequency. In order to detect the segment boundaries of a signal, we propose an improved method using time-varying autoregressive (TVAR) model, integral, basic generalized likelihood ratio (GLR) and new particle swarm optimization (NPSO) which is a powerful intelligent optimizer. Since autoregressive (AR) model for the GLR method is valid for only stationary signals, the TVAR as a valuable and powerful tool for non-stationary signals is suggested. Moreover, to improve the performance of the basic GLR and increase the speed of that, we propose to use moving steps formore than one sample for successive windows in the basic GLR method. The purpose of using NPSO is finding two parameters used in this approach. By using synthetic and real EEG data, the proposed method is compared with the conventional ones, i.e. the GLR and wavelet GLR (WGLR). The simulation results indicate the absolute advantages of the proposed method.

Control charts act as the most important tool for monitoring process parameters. The assumption of independence that underpins the implementation of the charts is violated when process observations are correlated.The effect of this can lead to the malfunctioning of the usual control charts by causing a large number of false alarms or slowing the detection ability of the chart to unstable situations. In this paper, we investigated the performance of the Mixed EWMA-CUSUM and Mixed CUSUM-EWMA charts for the efficient monitoring of autocorrelated data. The charts are applied on the residuals obtained from fitting an autoregressive (AR) model to the autocorrelated observations. The performance of these charts is compared with residual Shewhart, EWMA, CUSUM, combined Shewhart-CUSUM and combined Shewhart-EWMA charts. Performance criteria such as average run length (ARL) and extra quadratic loss (EQL) are used for the evaluation and comparison of the charts. Illustrative examples are presented to demonstrate the application of the charts on serially correlated observations.

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K‑nearest neighbor (KNN) classifier using EEG signals during emotional audio‑visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg’s method) based on Levinson‑Durbin’s recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10‑15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively.

The applications of time series modeling and statistical similarity methods to structural health monitoring (SHM) provide promising and capable approaches to structural damage detection. The main aim of this article is to propose an efficient univariate similarity method named as Kullback similarity (KS) for identifying the location of damage and estimating the level of damage severity. An improved feature extraction technique based on autoregressive (AR) model is presented to extract independent residuals of the AR model as damage-sensitive features. This technique emphasizes to choose a sufficient order such that the model residuals be independent. The proposed univariate similarity approach is a new application of the well-known KS method that attempts to measure a difference between two randomly distributed variables. The major contribution of the proposed KS method is that it only requires one measurement of undamaged and damaged conditions to compute the similarity between them. For the process of damage localization, the sensor location associated with the largest KS quantity is identified as the damaged area. In the damage level estimation, it is necessary to compare at least two different damaged conditions and find the maximum KS value in these conditions as the highest level of damage severity. The performance and capability of the improved and proposed methods is successfully verified by an experimental laboratory frame belonging to the Los Alamos National Laboratory. Results show that the methods are powerful and reliable tools for identifying the location of damage and estimating the level of damage severity.