In this paper the phase of two Duffing-Vanderpol oscillators with different initial conditions are synchronized using an alternative force added to the slave system. The master and slave systems are solved using Adomian’s decomposition methodphas and the solutions and their phase differences are plotted to show the effectiveness of the method.

Background: The successful prediction of epileptic seizures will significantly improve the living conditions of patients with refractory epilepsy. A proper warning impending seizure system should be resulted not only in high accuracy and low false-positive alarms but also in suitable prediction time. Methods: In this research, the mean phase coherence index used as a reliable indicator for identifying the preictal period of the 14-patient Freiburg EEG dataset. In order to predict the seizures on-line, an adaptive Neuro-fuzzy model named ENFM (evolving neuro-fuzzy model) was used to classify the extracted features. The ENFM trained by a new class labeling method based on the temporal properties of a prediction characterized by two time intervals, seizure prediction horizon (SPH), and seizure occurrence period (SOP), which subsequently applied in the evaluation method. It is evident that an increase in the duration of the SPH can be more useful for the subject in preventing the irreparable consequences of the seizure, and provides adequate time to deal with the seizure. Also, a reduction in duration of the SOP can reduce the patient’ s stress in the SOP interval. In this study, the optimal SOP and SPH obtained for each patient using Mamdani fuzzy inference system considering sensitivity, false-positive rate (FPR), and the two mentioned points, which generally ignored in most studies. Results: The results showed that last seizure, as well as 14-hour interictal period of each patient, were predicted on-line without false negative alarms: the average yielding of sensitivity by 100%, the average FPR by 0. 13 per hour and the average prediction time by 30 minutes. Conclusion: Based on the obtained results, such a data-labeling method for ENFM showed promising seizure prediction for online machine learning using epileptic seizure data. Apart from that, the proposed fuzzy system can consider as an evaluation method for comparing the results of studies.

In this paper, we address the problem of frame synchronization and Carrier Frequency Offset (CFO) estimation for Continuous Phase Modulation (CPM) in frame-based transmission. We present an efficient algorithm to estimate the start of signal and CFO simultaneously. The state of art methods for frame synchronization mostly adopt the AWGN channel model and suffer from realworld multipath and frequency-selective channels to estimate the start of signal. By using spectral analysis in the estimation procedure over a specific length of the received signal, we propose a method which is robust to multipath channels. When the start of signal is detected, the CFO estimation is performed on the preamble part of the received frame in the frequency domain. Simulation results show that the proposed algorithm detects the start of signal at signal-to-noise ratios (SNRs) below 0dB with a probability not exceeding 5 2 10  . We show that our algorithm outperforms the pervious methods in the frame synchronization even with short preamble lengths.

One of the critical components for the efficient operation of single-phase grid-connected converters is the synchronization unit. This paper presents a fast and adaptive phase-locked loop (PLL) structure that ameliorates the dynamic response of the estimated frequency and amplitude for grid-connected single-phase power systems. The second-order generalized integrator (SOGI) with a novel frequency-locked loop (FLL) is utilized which contains a DC offset rejection loop. The proposed method not only eliminates the transient response of the estimated frequency which is produced by FLL in grid voltage phase angle jumps, but also improves the PLL dynamic characteristics. The whole system has been simulated in MATLAB Simulink environment where a very small settling time for the estimated frequency of the FLL has been achieved. Therefore, it will improve the whole dynamic parameters of the system. Based on the simulation results, the settling time for the estimated frequency and amplitude are 22 ms and 10 ms, respectively.

Background and Aims: The present study was conducted to evaluate the intra-rater reliability of synchronization software so as to measure the Tibialis Anterior Muscle (TAM) structural parameters and force during contraction in healthy-nonathletes using ultrasonography and dynamometery in a synchronized way. This software was used in order to increase the ability of recording of pictures and data from ultrasonography and dynamometry synchronizingly. Materials and Methods: In the present cross-sectional study, the reliability of measuring the TAM structural parameters and force with the synchronization software was evaluated on 15 healthy nonathletes, aged 18-35 years, with BMI (23± 1/8). An Ultrasonography B mode and a Dynamometer were used during three states of contractions: maximum voluntary contraction, electrical stimulation, and the combination of both in healthy non-athlete individuals. The Pennation Angle (PA), Fascicle Length (FL), and Muscle Thickness (MT) and Force (F) of TAM were measured in three states of contraction and then the measurements were repeated one hour later in order to determine intra-rater reliability. Interclass Correlation Coefficient (ICC), Standard Error of Measurement (SEM), and Smallest Detectable Difference (SDD) were used to evaluate the relative and absolute reliability, respectively. Results: The synchronization software was found to be highly reliable in measuring the TAM structures and force in three states of contraction (MVC), (ES), and (MVC+ES) in healthy-non athletes. The ICCs for muscle thickness of TAM were 80%, 98%, 93%, respectively. For PA, the ICCs were 88%, 77%, and 75% respectively and ICCs for FL force in the three states of contraction (MVC), (ES) and (MVC+ES) were 95%, 97%, and 91%, respectively. Also, the ICCs for F in three states of contraction (MVC), (ES), and (MVC+ES) were 82%, 97%, and 91%, respectively. Conclusion: Ultrasonographic measurement and dynamometry of TAM using synchronization software has proved to be reliable for measuring the structural parameters of TAM and force in healthy-non athletes in three states of contraction in a synchronized manner.

This paper presents a new method for studying brain functional connectivity using multichannel scalp EEG signals. The proposed method uses the values of pair-wise phase synchrony between different EEG channels as a measure to quantify the strength of the connection between different parts of the brain. Using these values, the resulted brain networks are visualized by the use of graph theory. The method is then deployed to explore brain functional connectivity in newborn EEG signals in the presence of burst and suppression patterns. The results show that the brain networks are sparser in the presence of burst patterns compared to suppression patterns. They also show that the links in brain networks representing suppression patterns have greater strength. To validate the proposed method, the graphs describing burst and suppression patterns are classified. The results show that the brain networks for burst and suppression patterns are statistically different. The findings of this study show that the proposed method can be used to study brain functional connectivity in the presence of other abnor-malities.