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Objectives The purpose of the current study was to determine the upper threshold number of cases for which pertussis infection would reach an outbreak level nationally in Iran. Methods Data on suspected cases of pertussis from the 25th February 2012 to the 23rd March 2018 from the Center for Disease Control and Prevention in Iran was used. The national upper threshold level was estimated using the exponentially weighted moving average (EWMA) method and the Poisson regression method. Results In total, 2,577 (33.6%) and 1,714 (22.3%) cases were reported in the Spring and Summer respectively. There were 1,417 (18.5%) and 1,971 (25.6%) cases reported in the Autumn and Winter, respectively. The overall upper threshold using the EWMA and the Poisson regression methods, was estimated as a daily occurrence of 8 (7.55) and 7.50 (4.48–11.06) suspected cases per 10,000,000 people, respectively. The daily seasonal thresholds estimated by the EWMA and the Poisson regression methods were 10, 7, 6, 8 cases and 10, 7, 7, 9 cases for the Spring, Summer, Autumn, and Winter, respectively. Conclusion The overall and seasonal estimated thresholds by the 2 methods were similar. Therefore, the estimated thresholds of 6–10 cases in a day, per 10,000,000 people could be used to detect pertussis outbreaks and epidemics by health policymakers.

Objective: To define the level of alarm threshold for pertussis aberrations and to detect the aberrations of the reported suspected cases of pertussis from the Mazandaran province in the north of Iran. Methods: The included cases were composed of the suspected pertussis patients who came from Mazandaran province and registered in the Center for Disease Control and Prevention from 20 March 2012 to 20 March 2018. A discrete wavelet transform- based method was used to detect the aberrations. All analyses were performed using MATLAB Software version 2018a and Excel 2010. Results: A total of 1 162 cases were recruited in the study, including 545 (46.90%) males and 617 (53.10%) females, with median age of 1.47 (0.22-9.56) years. The median age of males was 1.18 (0.21-8.24) years, while that of females was 1.82 (0.21-10.75) years. Concerning the level of the alarm threshold, it was 1.28 case/d when k=2, while it was 1.34 case/d when k=3. The total detected aberration days were 123 d and 57 d by considering k=2 and 3, respectively. The most defined alarm threshold was related to spring (>2 cases/d) and summer (>1 case/d), respectively. Conclusions: The sensitivity of the surveillance system is subjected to a different time. Thus, determining the level of alarm threshold periodically using different methods is recommended.

Stereophonic acoustic echo cancellers are being used in high quality audio communication systems to reduce the echoes that arise from coupling between the loudspeakers and microphones. These echo cancellers have problems more severe than their monophonic counterparts. The most important problem is the mismatch between the impulse responses of adaptive filters and those of the acoustic paths of the receiving section and therefore a severe divergence of filters in the event of abrupt changes of the transmission section acoustic paths. The main reason for such mismatching is the two-channel nature of the system and strong cross-correlation between these two channels. In this paper, with inspiration from the lattice predictor structure, we present a novel approach for reduction of the interchannel cross-correlation of the input vectors in the wavelet domain that leads to faster reduction of the misalignment error.

We have shown that duct modeling using the generalized RBF neural network (DM_RBF), which has the capability of modeling the nonlinear behavior, can suppress a variable-frequency narrow band noise of a duct more efficiently than an FX-LMS algorithm. In our method (DM_RBF), at first the duct is identified using a generalized RBF network, after that N stage of time delay of the input signal to the N generalized RBF network is applied, then a linear combiner at their outputs makes an online identification of the nonlinear system. The weights of linear combiner are updated by the normalized LMS algorithm. We have showed that the proposed method is more than three times faster in comparison with the FX-LMS algorithm with 30% lower error. Also the DM_RBF method will converge in changing the input frequency, while it makes the FX-LMS cause divergence.