The conventional assessment of human semen, especially sperm movement characteristics, is a highly subjective assessment, with considerable intra- and inter-technician variability. Computer-assisted sperm analysis (CASA) systems provide a rapid and automated assessment of the parameters of sperm motion, together with improved standardization and quality control. However, it should be noted that the measurement of the sperm head motion by CASA is sensitive to the technique of experimentation. While conventional CASA systems use digital microscopes with phase-contrast accessories that make the sperm's head appear brighter and sharper than the other parts, in this research, a regular light microscope was used with a digital camera directly attached to its eyepiece. One of the drawbacks of this method is that the images lack proper contrast and sharpness. To remedy this, we have proposed an algorithm for sperm tracking that is insensitive to image acquisition conditions. This tracking algorithm was used after the background and extra particles were successfully removed through a two-step enhancement algorithm. Additionally, in this research, a template matching method was used for finding the sperm's path. Upon examination, it was proven that our tracking algorithm worked well with different image acquisition conditions. This paper explains how this method reduces error probability in finding and tracking sperm in various frames.
Hot-wire spirometer is a kind of constant temperature anemometer (CTA). The working principle of CTA, used for the measurement of fluid velocity and flow turbulence, is based on convective heat transfer from a hot-wire sensor to a fluid being measured. The calibration curve of a CTA is nonlinear and cannot be easily extrapolated beyond its calibration range. Therefore, a method for extrapolation of CTA calibration curve will be of great practical application. In this paper, a novel approach based on the conventional neural network and self-organizing map (SOM) method has been proposed to extrapolate CTA calibration curve for measurement of velocity in the range 0.7-30 m/seconds. Results show that, using this approach for the extrapolation of the CTA calibration curve beyond its upper limit, the standard deviation is about –0.5%, which is acceptable in most cases. Moreover, this approach for the extrapolation of the CTA calibration curve below its lower limit produces standard deviation of about 4.5%, which is acceptable in spirometry applications. Finally, the standard deviation on the whole measurement range (0.7-30 m/s) is about 1.5%.
Fuzzy controllers are being used in various control schemes. The aim of this study is to adjust the hemodialysis machine parameters by utilizing a fuzzy logic controller (FLC) so that patient's hemodynamic condition remains stable during hemodialysis treatment. For this purpose, a comprehensive mathematical model of the arterial pressure response during hemodialysis, including hemodynamic, osmotic, and regulatory phenomena has been used. The multi-input multi-output (MIMO) fuzzy logic controller receives three parameters from the model (heart rate, arterial blood pressure, and relative blood volume) as input. According to the changes in the controller input values and its rule base, the outputs change so that the patient's hemodynamic condition remains stable. The results of the simulations illustrate that applying the controller can improve the stability of a patient's hemodynamic condition during hemodialysis treatment and it also decreases the treatment time. Furthermore, by using fuzzy logic, there is no need to have prior knowledge about the system under control and the FLC is compatible with different patients.
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