Naeem Khan received the B. Sc. degree in Electrical Engineering from UET Peshawar, Pakistan in 2002. He then pursued M. Sc. in Electrical Engineering with specialization in Power Engineering in 2007. He achieved PhD scholarship from HEC to United Kingdom, University of Leicester, in 2007. After completing PhD program, Dr. Naeem returned to Pakistan and joined parent department, UET Bannu Campus as an Assistant Professor in 2011.
His research interest includes Linear & nonlinear system control, Linear parameter varying system and handling interrupted system in the process of state estimation. He has published numerous journal and conference papers in reputed journals and conferences like Automatica, IEEE, CDC and ECE. In addition, one book and several chapters have been written after PhD degree by Dr. Khan. In 2016, established Control systems lab at Bannu where state-of-the-art research is taking place on burning research topics like Remote Health monitoring especially Heart rate and respiration rate through various techniques such as Fourier Transform, Wavelet Transform and Hilbert Transform under contingent situation. Besides, various new modes of Kalman filters have been proposed that deals robotics and UAV.  Based on the satisfactory performance, Dr. Naeem Khan has been promoted to Professor post in 2022 and Head of the department since 2015.

Title: Remotely Health Monitoring (Vital Signs) through various Transform mechanisms

Abstract:
Remotely monitoring of health is an interesting research topic and profession. Modern countries have adopted this mechanism since a couple of decades. After the Covid-19, its need and important has been realized almost all over the world. Signal sent through radar, striking an affected person or patient, reflected back, and received through an antenna is subjected to analyzed. Based on various features including health, age and level of disease, various vital signs such as heart rate (HR), respiration rate (RR) etc. are diagnosed. The routine method utilized by the researchers is Fourier Transform (FT) or Fast Fourier Transform (FFT). But it is unable to analyze the data from non-stationary patient – which is a more realistic scenario. For that reason, advance tools like Wavelet transform and Hilbert transform is advised. Another big challenge to be addressed is noise available in the data, for which, Modified and robust Kalman filter (RKF) is proposed. Since, the monitoring is continues and usually for a long period, it possess huge data. It sometimes, leads to congestion of buffer zone and droplet of data, which in term of patient monitoring is very crucial. For that the whole mechanism is modified based on nature of data like AR model, ARMA model etc. Overall, a very promising area is addressed in this research which is society oriented.