Usman Shahzad^1,2,*, Ishfaq Ahmad¹, Ibrahim Mufrah Almanjahie^3,4, Nadia H.Al –Noor⁵

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3411-3430, 2021, DOI:10.32604/cmc.2021.017046

Abstract Variance is one of the most vital measures of dispersion widely employed in practical aspects. A commonly used approach for variance estimation is the traditional method of moments that is strongly influenced by the presence of extreme values, and thus its results cannot be relied on. Finding momentum from Koyuncu’s recent work, the present paper focuses first on proposing two classes of variance estimators based on linear moments (L-moments), and then employing them with auxiliary data under double stratified sampling to introduce a new class of calibration variance estimators using important properties of L-moments (L-location, L-cv, L-variance). Three populations are… More >

Usman Shahzad^1,2,*, Ishfaq Ahmad¹, Ibrahim Mufrah Almanjahie^3,4, Nadia H. Al Noor⁵, Muhammad Hanif²

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3013-3028, 2021, DOI:10.32604/cmc.2021.014101

Abstract Variance is one of the most important measures of descriptive statistics and commonly used for statistical analysis. The traditional second-order central moment based variance estimation is a widely utilized methodology. However, traditional variance estimator is highly affected in the presence of extreme values. So this paper initially, proposes two classes of calibration estimators based on an adaptation of the estimators recently proposed by Koyuncu and then presents a new class of L-Moments based calibration variance estimators utilizing L-Moments characteristics (L-location, L-scale, L-CV) and auxiliary information. It is demonstrated that the proposed L-Moments based calibration variance estimators are more efficient than… More >

Mingxu Sun^1,#,*, Yinghang Jiang^2,3,#, Qi Liu^3,4,*, Xiaodong Liu⁴

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 67-83, 2019, DOI:10.32604/cmc.2019.06079

Abstract A Functional Electrical stimulation (FES) therapy is a common rehabilitation intervention after stroke, and finite state machine (FSM) has proven to be an effective and intuitive FES control method. The FSM uses the data information generated by the accelerometer to robustly trigger state transitions. In the medical field, it is necessary to obtain highly safe and accurate acceleration data. In order to ensure the accuracy of the acceleration sensor data without affecting the accuracy of the motion analysis, we need to perform acceleration big data calibration. In this context, we propose a method for robustly calculating the auto-calibration gain using… More >