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njit-etd2003-081 - New Jersey Institute of Technology

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45<br />

distributions [24]. Time-frequency distributions in the hyperbolic class are covariant to<br />

scales and hyperbolic time shifts. The hyperbolic class intersects with the affine class<br />

but not with Cohen's class. Baraniuk [30] has recently presented a fundamental result<br />

concerning the existence <strong>of</strong> quadratic time-frequency distributions covariant to arbitrary<br />

operators. One troublesome aspect <strong>of</strong> quadratic distributions is that they always contain<br />

"cross terms". The cross terms are undesirable for two reasons. First, a true distribution<br />

function should be non-negative and cross terms can have negative values. Second, the<br />

cross terms do not usually represent any energy present in the signal.<br />

It is <strong>of</strong>ten desired that a time-frequency distribution be positive valued, and<br />

Cohen has created a general class <strong>of</strong> time-frequency distributions that are always<br />

positive valued [21]. Since this class is very general, it intersects with every class<br />

(quadratic and non-quadratic) <strong>of</strong> time-frequency distributions. The main difficulty with<br />

this class is that it is not clear how to construct time-frequency distributions in the class.<br />

Loughlin et. al. [32] and also Sang et. al. [33] have created positive time-frequency<br />

distributions by iterating quadratic time-frequency distributions. There has also been<br />

recent work in constructing distributions <strong>of</strong> quantities other than time and frequency [28-<br />

32]. The most common distributions outside <strong>of</strong> time and frequency are those <strong>of</strong> time<br />

and scale. The concept <strong>of</strong> scale is closely related to the concept <strong>of</strong> frequency and some<br />

time-frequency distributions have analogous time-scale distributions. A summary <strong>of</strong> the<br />

above methods for computing the distribution <strong>of</strong> a signal is presented in Figure 3.1

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