In emergency departments and ICUs, a novel noncontact thermometer is urgently required to measure physical temperatures through common clothing to accomplish body temperature precise measurement for critical patients. Hence, a Ku band digital auto gain compensative microwave radiometer is proposed to get a higher theoretical temperature measurement sensitivity than a Dicke radiometer, benefit miniaturization design and reduce attenuation caused by common clothing. Meanwhile, a novel compensation method for receiver calibration is proposed to improve temperature sensitivity under non-ideal conditions, and the revised systematic calibration method is elaborated. Furthermore, in order to invert body physical temperatures through clothing, a microwave thermal radiation transmission model of clothed human body is constructed, and the microwave radiation apparent temperature equation of clothed human body is derived. Importantly, three groups of experiments are set up to confirm the designed radiometer's performance, especially the biological tissue temperature measurement. Results show that: 1) the designed radiometer has high temperature sensitivity and accuracy for unsheltered targets; 2) amplitude attenuation caused by cotton cloth for Ku band microwave is much smaller than that for infrared thermal radiation; 3) the designed radiometer can track physical temperatures of targets (such as water and swine skin tissue) sheltered or covered by cotton cloth relatively accurately. In conclusion, our designed Ku band microwave radiometer is certificated to have outstanding performance in temperature measurement for biological tissue through common clothing, which can be developed into a promising product in medical monitoring.
Nonlinear photonic sources including semiconductor lasers have been recently utilized as ideal computation elements for information processing. They supply energy-efficient way and rich dynamics for classification and recognition tasks. In this work, we propose and numerically study the dynamics of complex photonic systems including high-β laser element with delayed feedback and functional current modulation, and employ nonlinear laser dynamics of near-threshold region for the application in reservoir computing. The results indicate a perfect (100%) recognition accuracy for the pattern recognition task and an accuracy about 98% for the Mackey-Glass chaotic sequences prediction. Therefore, the system shows an improvement of performance with low-power consumption. In particular, the error rate is an order of magnitude smaller than previous works. Furthermore, by changing the DC pump, we are able to modify the number of spontaneous emission photons of the system, which then allows us to explore how the laser noise impacts the performance of the reservoir computing system. Through manipulating these variables, we show a deeper understanding on the proposed system, which is helpful for the practical applications of reservoir computing.