Continuous flow left ventricular assist devices (LVADs) are commonly used as bridge-to-transplantation or destination therapy for heart failure patients. However, non-optimal pumping speeds can reduce the efficacy of circulatory support or cause dangerous ventricular arrhythmias. Optimal flow control for continuous flow LVADs has not been defined and calls for an implantable pressure sensor integrated with the LVAD for real-time feedback control of pump speed based on ventricular pressure. A MEMS pressure sensor prototype is designed, fabricated and seamlessly integrated with LVAD to enable real-time control, optimize its performance and reduce its risks. The pressure sensing mechanism is based on Fabry-Pérot interferometer principle. A biocompatible parylene diaphragm with a silicon mirror at the center is fabricated directly on the inlet shell of the LVAD to sense pressure changes. The sensitivity, range and response time of the pressure sensor are measured and validated to meet the requirements of LVAD pressure sensing.
We propose an optical spectrometer using a hybrid grating-Fresnel (G-Fresnel) diffractive optical element. Theoretical simulation shows that a spectral resolution of approximately 1 nm can be potentially achieved with a millimeter-sized G-Fresnel. A proof-of-concept G-Fresnel-based spectrometer with subnanometer spectral resolution is experimentally demonstrated. The proposed method provides a promising new way for realizing compact optical spectrometers.
The aim of non-intrusive appliance load monitoring (NIALM) is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to ON-OFF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodnessof-fit (GOF) methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-OFF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The Kmeans clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and Kmeans clustering. The results of the algorithm implementation were discussed and ideas on future work were also proposed.
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