#### Filter Results:

- Full text PDF available (2)

#### Publication Year

1992

1998

- This year (0)
- Last 5 years (0)
- Last 10 years (0)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Ramachandra G. Shenoy, Thomas W. Parks
- IEEE Trans. Signal Processing
- 1994

We describe the Weyl correspondence and its properties, showing how it gives a “window-independent” definition of time-frequency concentration for use in models in signal detection. The definition of concentration is justified by showing that it gives reasonable answers in certain intuitive cases. The Weyl correspondence expresses a linear transformation as… (More)

- Ramachandra G. Shenoy, Daniel Burnside, Thomas W. Parks
- IEEE Trans. Signal Processing
- 1994

- Michael S. Richman, Thomas W. Parks, Ramachandra G. Shenoy
- IEEE Trans. Signal Processing
- 1998

A formulation of a discrete-time, discrete-frequency Wigner distribution for analysis of discrete-time, periodic signals is given using an approach involving group representation theory. This approach is motivated by a well-known connection between group theory and the continuous Wigner distribution. The advantage of this approach is that the resulting… (More)

- Ramachandra G. Shenoy, Thomas W. Parks
- Signal Processing
- 1995

- Ramachandra G. Shenoy
- ICASSP
- 1994

- Ramachandra G. Shenoy
- ISCAS
- 1994

- Ramachandra G. Shenoy, Thomas W. Parks
- IEEE Trans. Signal Processing
- 1992

- Michael S. Richman, Thomas W. Parks, Ramachandra G. Shenoy
- ICASSP
- 1995

A discrete-time, discrete-frequency Wigner distribution is derived using a group-theoretic approach. It is based upon a study of the Heisenberg group generated by the integers mod N , which represents the group of discrete-time and discrete-frequency shifts. The resulting Wigner distribution satis es several desired properties. An example demonstrates that… (More)

- Sean A. Ramprashad, Thomas W. Parks, Ramachandra G. Shenoy
- IEEE Trans. Signal Processing
- 1996

A new signal model-cone classes-is presented. These models include classical models such as subspaces but are more general and potentially more useful than some existing signal models. Examples of cone classes include time-frequency concentrated classes and subspaces with bounded mismatch. The maximum likelihood detector for a cone class of signals in the… (More)

- Beth A. Weisburn, Thomas W. Parks, Ramachandra G. Shenoy
- ICASSP
- 1994