Spectral correction method based on improved flat-top convoluted window for parameter estimation of power harmonic

Točna procjena harmonijskog parametra je važan zadatak u obradi signala elektroenergetskog sustava. Predlaže se nova vrsta flat-top prozora koja se generira vlastitim konvolucijama brzo padajućeg flat-top prozora (FDMS-FT) u domenu vremena. Proučavaju se značajke bočnog i glavnog režnja novog prozora. Nadalje, kako bi se poboljšala glatkoća glavnog režnja novog prozora, optimiziraju se koeficijenti matičnog prozora. Predstavlja se izmjenjivi algoritam ispravke razlike faze duljine prozora baziran na novom prozoru kako bi se procijenio strujni harmonični parametar. Kako bi se provjerila učinkovitost i točnost prikazane metode, provedeno je nekoliko računalnih simulacija i praktičnih eksperimenata s višefrekvencijskim strujnim signalima. Rezultati pokazuju da predložena metoda može učinkovito smanjiti opseg računanja i daje visok parametar točnosti procjene harmonijske snage.Accurate estimation of harmonic parameter is an important task in signal processing of power system. A new class of flat-top windows is proposed, which is generated by self-convolutions of the fast-decaying minimum-sidelobe flat-top (FDMS-FT) window in the time-domain. The mainlobe and sidelobe features of the new window are studied. In addition, to improve the flatness of mainlobe of the new window, the coefficients of its parent window are optimized. A window-length changeable discrete phase difference correction algorithm based on the new window is presented to estimate power harmonic parameter. In order to inspect the efficiency and accuracy of the presented method, several computer simulations and practical experiments were conducted with power multi-frequency signals. Results show that the proposed method can reduce the computation load efficiently and gives a high parameter estimation accuracy of power harmonic

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data