With conventional stethoscopes, the auscultation results may vary from one doctor to another due to a decline in his/her hearing ability with age or his/her different professional training, and the problematic cardiopulmonary sound cannot be recorded for analysis. In this paper, to resolve the above-mentioned issues, an electronic stethoscope was developed consisting of a traditional stethoscope with a condenser microphone embedded in the head to collect cardiopulmonary sounds and an AI-based classifier for cardiopulmonary sounds was proposed. Different deployments of the microphone in the stethoscope head with amplification and filter circuits were explored and analyzed using fast Fourier transform (FFT) to evaluate the effects of noise reduction. After testing, the microphone placed in the stethoscope head surrounded by cork is found to have better noise reduction. For classifying normal (healthy) and abnormal (pathological) cardiopulmonary sounds, each sample of cardiopulmonary sound is first segmented into several small frames and then a principal component analysis is performed on each small frame. The difference signal is obtained by subtracting PCA from the original signal. MFCC (Mel-frequency cepstral coefficients) and statistics are used for feature extraction based on the difference signal, and ensemble learning is used as the classifier. The final results are determined by voting based on the classification results of each small frame. After the testing, two distinct classifiers, one for heart sounds and one for lung sounds, are proposed. The best voting for heart sounds falls at 5–45% and the best voting for lung sounds falls at 5–65%. The best accuracy of 86.9%, sensitivity of 81.9%, specificity of 91.8%, and F1 score of 86.1% are obtained for heart sounds using 2 s frame segmentation with a 20% overlap, whereas the best accuracy of 73.3%, sensitivity of 66.7%, specificity of 80%, and F1 score of 71.5% are yielded for lung sounds using 5 s frame segmentation with a 50% overlap.

The "long-distance home health care service" has become one of the key emerging businesses in Taiwan. In this paper, a mobile health management system is presented which is the first one integrating a wearable ring-type pulse monitoring sensor with a smart phone. All physiological measurements are transmitted to the smart phone through Bluetooth. The user can monitor his/her own pulse and temperature from the smart phone. Then these data are transmitted to a remote server through the mobile communication of the smart phone, such as HSDPA, Wi-Fi, WiMax, GPRS, etc. The build-in GPS further provides the position information of the monitored person. The remote server not only collects physiological measurements but also tracks the position of the monitored person in real time.

Effective approaches dealing with four important issues in a direct nonlinear interior point algorithm (DNIPA) for optimal power flows (OPF) are presented. The first issue, initialisation, is important for the robustness of DNIPA; the second issue, decoupling, is important for OPF applications. The other two issues are particularly important to OPF performance in energy management systems. The approaches enhance the performance of DNIPA for OPF problems in terms of iteration counts and CPU times. Extensive numerical tests are performed on two different sizes of real-life systems: 244- and 2423-bus systems. Based on these results, the initialisation approach improves PCPDIPA by 24% in term of CPU times. The decoupling approach enhances PCPDIPA (with initialisation) by 27%. The hot start and warm start solve the problems about 2.0 and 2.7 times, respectively, faster than PCPDIPA with initialisation, and 2.5 and 3.3 times, respectively, faster than PCPDIPA without using initialisation.

This paper presents a healthcare information and medical resource management platform utilizing wearable devices, physiological sensors, and an indoor positioning system (IPS). This platform provides medical healthcare information management based on the physiological information collected by wearable devices and Bluetooth data collectors. The Internet of Things (IoT) is constructed for this medical care purpose. The collected data are classified and used to monitor the status of patients in real time with a Secure MQTT mechanism. The measured physiological signals are also used for developing an IPS. When the patient is out of the safety zone, the IPS will send an alert message instantly by pushing the server to remind the caretaker, easing the caretaker’s burden and offering extra protection for the patient. The presented system also provides medical resource management with the help of IPS. The medical equipment and devices can be tracked by IPS to tackle some equipment rental problems, such as lost and found. A platform for the medical staff work coordination information exchange and transmission is also developed to expedite the maintenance of medical equipment, providing the shared medical information to healthcare and management staff in a timely and transparent manner. The presented system in this paper will finally reduce the loading of medical staff during the COVID-19 pandemic period.