Mr Brian Lithgow currently represents the interests of the DSP group within the ECSE Department at Monash University. Some signal processing topics currently researched by him and colleagues are:

  1. Cochlear implant signal processing.
  2. Neurophysiological signal processing in the auditory system.
  3. Nerve modelling -- this incorporates the VHDL hardware implementation [co-researcher John Zakis] of multiple nerve fibres/neurones -- this "Digital Brain" project if sucessful has the potential to produce a new form of hardware intellegence with an impact comparable with the microprocessor.
  4. Tinnitus Suppresion and Modelling
  5. Vestibular Organ Diagnostics and Modelling.
  6. Mammographic Image processing using wavelets for microcalcification detection.
  7. Digital hearing aid using wavelet feature extraction and neural network classification. Implementation of features on TI TMS320C54.
  8. Advanced optimisation algorithms and applications to digital signal processing algorithms for hearing aids—a wavelet approach.
  9. Diagnostic pattern detection for aid in pathogology classification eg. A wavelet based Auditory Brainstem Response (ABR) analysis technique appears to be the only ABR technique able to detect and localise Superior Olive Complex abnormalities. Implementation on the TI TMS320C6201.

An example of undergraduate project success includes:

  1. National Institution of Engineers (Aust) Electrical College student award 1998 for "Ambient noise removal in cochlear implants using wavelets: An application of the TMS320C50".

        

Dr David Morgan represents the Monash Centre for Biomedical Engineering:

  1. Many enquiries into the action of muscle length and tension sensors are limited by the number of afferent nerve axons that can be identified in a signal recorded from a bundle. As the impulses are so short and the recording time is so long, identification needs to be done in real time to avoid excessive data files. Such identification has traditionally relied on window discriminators, and is limited to two or three axons. DSPs have the potential to do this much better.
  2. Distributed stimulation of muscle promises reduced fatigue in functional neuromuscular stimulation. However, this technique requires a tension signal from the muscle, which will not be available directly in the FNS situation. Likely indirect sources include accelerometers and miniture gyroscopes. Extracting the necessary information from these is likely to be an appropriate use for a DSP.

Dr. Robert Mahony (ANU):

  1. Subspace algorithm developments for digital hearing aids.
  2. Mathematical modelling and algorithm development.

Mr. John Zakis:

  1. Cochlear modelling and imaging.
  2. VHDL hardware implementation of multiple nerve fibres/neurones

Assoc. Prof David Suter:

  1. Image processing: restoration of film
  2. Detection of microcalcifications in mammograms.