Vital Sign Monitor Device Equipped with a Telegram Notifications Based on Internet of Thing Platform

Vital Sign Monitor is a tool used to diagnose a patient who needs intensive care to know the condition of the patient. Parameters used in monitoring the patient's condition include body temperature and respiration. The contribution of this research designed a vital sign monitoring tool with IoT-based notifications so that remote monitoring can be done by utilizing web Thinger.io, LCD, RGB LEDs as a display of the results of the study and notify telegrams if it becomes abnormal to the patient's condition. Therefore, in order to produce accurate data in the process of data retrieval, a relaxed position of the patient is required and the stability of the wi-fi network so that monitoring is not hampered. The study used the DS18B20 digital temperature sensor placed on the axilla and the piezoelectric sensor placed on the abdomen of the patient. The results of the study were obtained by taking data on patients. The resulting temperature value will be compared to the thermometer, which produces the highest error value of 0.56%, which is still possible because the tolerance limit is 1oC. and for the collection of respiration values that have been compared to the patient monitor obtained the highest error value of 6.2%, which is still feasible because the tolerance limit is 10%. In this study, there is often a crash library between the temperature sensor and other sensors, so for further research, recommend to replacing the temperature sensor

A Digital Improvement—Trimming a Digital Temperature Sensor with EEPROM Reprogrammable Fuses

An EEPROM (electrically erasable programmable read-only memory) reprogrammable fuse for trimming a digital temperature sensor is designed in a 0.18-µm CMOS EEPROM. The fuse uses EEPROM memory cells, which allow multiple programming cycles by modifying the stored data on the digital trim codes applied to the thermal sensor. By reprogramming the fuse, the temperature sensor can be adjusted with an increased trim variation in order to achieve higher accuracy. Experimental results for the trimmed digital sensor showed a +1.5/−1.0 ℃ inaccuracy in the temperature range of −20 to 125 ℃ for 25 trimmed DTS samples at 1.8 V by one-point calibration. Furthermore, an average mean of 0.40 ℃ and a standard deviation of 0.70 ℃ temperature error were obtained in the same temperature range for power supply voltages from 1.7 to 1.9 V. Thus, the digital sensor exhibits similar performances for the entire power supply range of 1.7 to 3.6 V.

Developing New Thermal Protection Method for AC Electric Motors

Monitoring the thermal state of windings of electrical machines is a backbone for protection from unacceptable overheating. A large number of different methods and systems aim to solve this problem. This article discusses the main known methods of thermal protection of electric motors and provides their comparative analysis. This paper shows that the most promising methods are those based on control of the current active resistance of the stator winding, as its value uniquely depends on temperature. It is demonstrated that the known methods have a number of disadvantages. A new phase method for thermal protection of AC motors is proposed. The method is based on the fact that a temperature-induced change in the active and reactive components of the winding impedance causes a corresponding change in the angle between the vectors of phase voltages and currents. This allows for thermal protection by controlling the change in this angle. This article provides tabular analytical substantiation of the proposed method, which is based on the direct measurements of voltage and current and the subsequent algorithmic calculation of physical values functionally related to the sought angle. The authors develop a structural block diagram of a device that implements the proposed thermal protection method. All relevant experimental studies were carried out. In this case, a small-sized electronic thermometer with a remote digital temperature sensor connected to the USB port of a personal computer was used as a temperature meter. The results obtained confirm the functional capability and efficiency of the proposed technical solution.

Improving the Accuracy of Low-Cost Sensor Measurements for Freezer Automation

In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor’s accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method’s outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area—resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).