Robust detector design based on Stochastic Resonance

The stochastic resonance (SR) is a phenomenon discovered recently in some nonlinear systems where addition of a certain amount of noise can, somewhat paradoxically, enhance its performance. It has found applications in biological sensory systems [1] such as visual, auditory systems, and tactile system as well as engineering applications such as ac-driven Schmitt triggers, and bistable ring lasers. The bistable systems (BS) are nonlinear systems [4] that are widely used as SR systems. The BS systems based detectors (henceforth referred to as BS-SR detectors) are constructed with a BS followed by an inner detector. The inner detector can be a matched filter (MF), a coherent detector or other types. It is also possible to directly apply the input to the inner detector and to make a decision. Therefore, the BS is considered as a preprocessor of the inner detector. Despite several achievements by researchers in the design of BS-SR detectors, there are still many unsolved issues and difficulties. The focus of this project is to address some of these issues.

Two potential applications of SR will be considered in this project. First, the SR detector will be used in digital watermarking in the DCT domain. The watermark information is considered as weak signal embedded in the DCT coefficients considered as noise. The statistics of the noise is difficult to estimate. The BS-detector provides a robust solution and the detection performance is expected to be improved significantly. Secondly, a SR detector will be used to detect features in MRI brain images. The MRI images do not have sharp features, and are noisy. It is difficult to detect small features (e.g., lesions or tumors) in an MRI image. We would investigate the use of SR detector in improving the feature detection performance.

The student will have a role both in theoretical development and experimental evaluation of the BS-SR detector, including the following three major tasks: The student will evaluate various measures of the BS performance for signal transmission, the student will investigate the relationship between the BS performance measure (MI or cross-correlation) and the BS system parameters, for given input signal and noise characteristics, the student will evaluate the efficacy of the developed performance measure by applying the SR detector in digital watermarking applications.

Faculty Supervisor:

Dr. Mrinal Mandal

Student:

Shaileshh BV

Partner:

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

University of Alberta

Program: