Signal processing for T-ray bio-sensor

Pulsed terahertz (T-ray) imaging systems represent an extremely promising method of obtaining sub-millimetre spectroscopic measurements for a wide range of applications. This paper investigates a number of techniques for optimally processing terahertz data. Specifically we consider wavelet de-noising and Wiener deconvolution algorithms. A goal of this research is the design and implementation of a high speed, compact and portable T-ray imaging system. This system will draw heavily on MOEMS technology. A significant challenge in the development of such a system is the development of efficient software algorithms to perform signal recognition and imaging operations in real time. This paper takes the example application of a smart bio-sensor for surface tumours and investigates the signal processing techniques amenable to the tasks of efficiently de-convolving the system response, de-noising and extracting the salient features from the terahertz response waveform.