Abstract:The tracking Mean Square Error (MSE) of the least squares (LS) Multiple Input Multiple Output (MIMO) channel estimation in rician flat fading channel is derived in closed-form, which is then verified by Monte Carlo simulation. The channel accomodates applications where the elevation angle between the transmitter and the receiver dictates the doppler shift of the Line of Sight (LOS) component, as encountered in communications from satellite or High Altitude Platform (HAP) to a moving mobile. A higher tracking loss is revealed as the mobile travels to a lower elevation angle.

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In the industrial era 4.0, domestic baby incubator producers are facing the challenge of free trade of foreign products that will compete in innovation with the application of IoT technology. One of the opportunities that arise consciously or unconsciously at the NICU (Neonatal Intensive Care Unit) unit in Hospitals, in general, is that there are no facilities for parents to monitor the baby’s condition inside the incubator directly. The purpose of this research project is to build a prototype of an internet-based baby incubator monitoring system based on things equipped with various sensors that will send data to the server in real-time and mobile apps for monitoring facilities for parents, including the voice of the baby. This study focused on how to develop baby incubators that can listen to baby’s crying, capture the voice, and interpret it using artificial intelligence. More than 40 (forty) voice datasets were used and successfully classified into five possible terms of the baby’s condition: burping, sleepy, hungry, uncomfortable, and pain by energy signal and spectrum analysis. The benefit of this research is as a driver of innovation for baby incubator products that will support the national medical devices manufacturing company.

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