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Abstract-In this paper, we study precise localization using Angle of Arrival (AOA) estimations by smart-antenna equipped beacons in Ad-Hoc networks. The node to be localized sends a signal to its surrounding beacons. The beacons estimate the signal directions with high resolution AOA methods and feed them back to the node for position calculation. In other words, position calculation is not required at beacons. When AOA estimates from three or more beacons are received, ambiguity occurs. Three resolution methods, namely (a) simple averaging (b) Minimax and (c) Precision-weighted averaging are proposed and compared. As estimation bias is heavily dependent on antenna orientations the center-facing approach is found to give better performance in a square field.

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Aim To translate the instrument for measuring different types of cognitive load (MDT‐CL) into Chinese and assess the reliability and validity of the Chinese version of the MDT‐CL. Background The MDT‐CL is needed for hospital administrators to identify which nursing staff are prone to high cognitive load and to provide tailored interventions for specific types of cognitive load. Methods The MDT‐CL was translated into Chinese using forward and back translation, cultural adaptation and pilot tested. The reliability and validity of the instrument were assessed with intensive care unit (ICU) nurses in three tertiary hospitals in China. Results A total of 222 ICU nurses were recruited. The scale‐content validity index of the Chinese version of the MDT‐CL was 0.966. Confirmatory factor analysis indicated that all the goodness‐of‐fit indicators were acceptable. Cronbach's α coefficient was 0.818. Test–retest reliability was 0.785. Conclusions The Chinese version of the MDT‐CL is a valid and reliable instrument for evaluating the cognitive load of ICU nurses in China. Implications for nursing management The validated Chinese version of the MDT‐CL is a feasible, quantitative tool for evaluating different types of cognitive load in busy clinical practice, suggesting significant clinical application value.