Conference Papers
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Item Performance analysis of a novel piezo actuated valveless micropump for biomedical application(American Institute of Physics Inc. subs@aip.org, 2020) Mohith, M.; Upadhya, A.R.; Karanth P, N.K.; Kulkarni, S.M.Micropumps constitute an integral part of microfluidic systems for delivery and control of a precise volume of fluids in a specific direction. Micropumps find extensive utilization in biomedical applications such as drug delivery, biological fluid transmission, organic analysis, and other applications such as electronic cooling, chemical analysis, spacecraft, etc. In the present work, design, fabrication, and testing of a novel valveless micropump with amplified piezo actuator have been presented explicitly for biomedical fluid transmission. In the current work, design, manufacturing, and experimental study of a novel micropump with amplified piezo actuator have been presented explicitly for biomedical fluid transmission. The designed prototype of the micropump has a distinctive feature of the disposable chamber, which allows the pump chamber to be disposed off upon use, thus overcoming the problem of contamination. The micropump employed low-cost polymethylmethacrylate (PMMA) as the structural material with silicone rubber diaphragm. The proposed prototype of the micropump was capable of delivering 5.771 ml/min of water for a sinusoidal input voltage of 150 V at 5 Hz. © 2020 Author(s).Item Diagnostic classification of undifferentiated fevers using artificial neural network(American Institute of Physics Inc. subs@aip.org, 2020) Vasudeva, S.T.; Rao, S.S.; Karanth P, N.K.; Mahabala, C.; Dakappa, P.H.; Prasad, K.Accurate diagnosis of undifferentiated fever case at the earliest is a challenging effort, which needs extensive diagnostic tests. Prediction of undifferentiated fever cases at an early stage will help in diagnosing the disease in comparatively lesser time and more effectively. The aim of the present study was to apply Artificial Intelligence (AI) algorithm using temperature information for the prediction of major categories of diseases among undifferentiated fever cases. This was an observational study carried out in tertiary care hospital. Total of 103 patients were involved in the study and 24-hour continuous temperature recording was done. Analysis was done using Artificial Neural Network (ANN) model based on the temperature data of each patients and its statistical parameters. Temperature datasets were labeled with the help of experienced physicians. Levenberg Marquardt error back-propagation algorithm was used to train the network. A good relation was found between the target data set and output data set, purely based on the observed 24 hr continuous tympanic temperature of the patients. An accuracy of 98.1% was obtained from ANN prediction model. The study concluded that a single noninvasive temperature parameter is sufficient to predict the major categories of diseases using ANN algorithms, from the undifferentiated fever cases. © 2020 Author(s).