Estimation of the observation standard deviation error formula thanks to the a posteriori diagnosis

<p><em>Within the UERRA project, a daily precipitation reanalysis at a 5,5km resolution has been realized from 1961 to 2015. The reanalysis was obtained by the MESCAN analysis system which combines an a priori estimate of the atmosphere – called background – and observations using an optimum interpolation (OI) scheme. Such method requires the specification of observations and background errors. In general, constant standard deviation errors are used but more errors are made when high precipitation are observed. Then, to take this effect into account and to avoid a model over-estimation in case of light precipitation, a variable formula of the observation standard deviation error was purposed with a small value for null precipitation and greater values when precipitation are higher, following a linear equation.</em></p><p><em> Desroziers et al proposed a method to determine observations and background errors called a posteriori diagnosis. To use this iterative method, the analysis has to be ran several times until it converged. In this study, the a posteriori diagnosis is used per precipitation class to determine the observation standard deviation error formula. MESCAN was tested using the French operational model AROME at 1,3km resolution and the atmopsheric UERRA analysis downscaled to 5,5km background and combined to the French observational network over the 2016-2018 period. The observation standard deviation error formula obtained by the a posteriori diagnosis is then used in the MESCAN analysis system to produce precipitation analysis over the 2016-2018 period. Results are compared to UERRA precipitation reanalysis over independant observations by comparing bias, RMSE and scores per precipitation class.</em></p>

Non-parametric dynamical estimation of blood flow rate, pressure difference and viscosity for a miniaturized blood pump

Blood pumps are becoming increasingly important for medical devices. They are used to assist and control the blood flow and blood pressure in the patient’s body. To accurately control blood pumps, information about important hydrodynamic parameters such as blood flow rate, pressure difference and viscosity is needed. These parameters are difficult to measure online. Therefore, an accurate estimation of these parameters is crucial for the effective operation of implantable blood pumps. In this study, in vitro tests with bovine blood were conducted to collect data about the non-linear dependency of blood flow rate, flow resistance (pressure difference) and whole blood viscosity on motor current and rotation speed of a prototype blood pump. Gaussian process regression models are then used to model the non-linear mappings from motor current and rotation speed to the hydrodynamic variables of interest. The performance of the estimation is evaluated for all three variables and shows very high accuracy. For blood flow rate – correlation coefficient ([Formula: see text] = 1, root mean squared error ([Formula: see text]) = 0.31 ml min−1, maximal error ([Formula: see text]) = 9.31 ml min−1; for pressure [Formula: see text] = 1, [Formula: see text] = 0.09 mmHg, [Formula: see text] = 8.34 mmHg; and for viscosity [Formula: see text] = 1,[Formula: see text] = 0.09 mPa.s, [Formula: see text] = 0.31 mPa⋅s. The current findings suggest that this method can be employed for highly accurate online estimation of essential hydrodynamic parameters for implantable blood pumps.

An investigation to the design of high-precision wind sensing device based on piezoelectric array

A new wind sensing device based on bimorphs array is designed for apperceiving wind direction and velocity, whose structure is an L-shaped cantilevers array. A coupling model of the array is proposed to predict the response voltage stimulated by wind. By extracting the response voltage ([Formula: see text]) on each bimorph cantilever, the values of wind direction ([Formula: see text]) and velocity ([Formula: see text]) can be calculated in terms of the geometric relation. Further, the relations among wind errors, array amounts ( m) and array radius ( r) are acquired, and m & r can be utilized to decreasing angle error [Formula: see text] and velocity error [Formula: see text] of wind. The calculated and experimental results show that, increasing m initially yields decreasing these two errors, then it begins to increase the errors when m is larger than 6. The minimal wind angle and velocity errors of the array are 0.14° and 0.34%, respectively, at actual wind of 13.4 m/s, m = 6 and r = 20 mm. Meanwhile, increasing r can decrease [Formula: see text] and [Formula: see text], Besides, for calculating 20 random incidence wind angles, respectively, the range of wind velocity error is from 0.34% to 0.87%, the range of wind angle error is from 0.12°to 0.38°, and the response time is 10 ms.

A PHONOCARDIOGRAM-BASED NOISE-ROBUST REAL-TIME HEART RATE MONITORING ALGORITHM FOR OUTPATIENTS DURING NORMAL ACTIVITIES

For outpatients who need continuous monitoring of heart rate (HR) variation, it is important that HR can be monitored during normal activities such as speaking and walking. In this study, a noise-robust real-time HR monitoring algorithm based on phonocardiogram (PCG) signals is proposed. PCG signals were recorded using an electronic stethoscope; electrocardiogram (ECG) signals were recorded simultaneously with HR references. The proposed algorithm consisted of pre-processing, peak/nonpeak classification, voice noise processing, walking noise processing, and HR calculation. The performance of the algorithm was evaluated using PCG/ECG signals from 11 healthy participants. For comparison, the absolute errors between manually extracted ECG-based HR values and automatically calculated PCG-based HR values were calculated for the proposed algorithm and the comparison algorithm in two different test protocols. Experimental results showed that the average absolute errors of the proposed algorithm were 72.03%, 22.92%, and 36.39% of the values of the comparison algorithm for resting-state, speaking-state, and walking-state data, respectively, in protocol-1. In protocol-2, the average absolute error was 36.99% of that of the comparison algorithm. A total of 1102 cases in protocol-1 and 783 in protocol-2 had an absolute error [Formula: see text] beats per minute (BPM) using the comparison algorithm and an absolute error [Formula: see text] BPM using the proposed algorithm. On the basis of these results, we anticipate that the proposed algorithm can potentially improve the performance of continuous real-time HR monitoring during activities of normal life, thereby improving the safety of outpatients with cardiovascular diseases.

Error Compensation Techniques for Fixed-Width Array Multiplier Design — A Technical Survey

This paper provides a comprehensive review of various error compensation techniques for fixed-width multiplier design along with its applications. In this paper, we have studied different error compensation circuits and their complexities in the fixed-width multipliers. Further, we present the experimental results of error metrics, including normalized maximum absolute error [Formula: see text], normalized mean error [Formula: see text] and normalized mean-square error [Formula: see text] to evaluate the accuracy of fixed-width multipliers. This survey is intended to serve as a suitable guideline and reference for future work in fixed-width multiplier design and its related research.