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Remote and contactless heart rate detection is still an open research issue of great clinical importance. Available approaches lack the necessary accuracy and reliability for acceptance by medical experts. In this study, we propose a new method for remote heart rate extraction using the Microsoft Kinect TM v2.0 image sensor. The proposed approach relies on signal processing and machine learning methods in order to create a model for accurate estimation of the heart rate via RGB and infrared face videos. Electrocardiography (ECG) recordings and RGB and infrared face videos, captured using the Kinect TM v2.0 image sensor, were acquired from 17 subjects and used to create a machine learning model for remote heart rate detection. Experimental evaluation through supervised regression experiments showed that the proposed approach achieved a mean absolute error of 6.972 bpm, demonstrating the capabilities of the underlying technology.

Modern communication networks uses optical fibre extensively. The transport networks are upgrading its capacity continuously to provide the bandwidth requirement of the customer requirements. To provide such an increase in bandwidth, the transmission networks are moving from Amplitude Shift Keying modulation methods to Phase Shift Keying methods. In phase shift keying systems, data reception and regeneration required phase synchronization. This requires original optical carrier phase information. In this paper, we report a model for optical carrier recovery for optical synchronization of a Binary Phase Shift Keying input by exploiting Four Wave Mixing in Highly Non-Linear Fibers. The noise influence from the signal laser for the recovered carrier was analyzed.

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