• Kernel design in Cohen's class improves cross-term suppression and resolution for multicomponent signals, unifying exponential, reduced-interference, and signal-dependent approaches with foundational Wigner analysis to guide kernel choice across time-frequency maps. Exponential kernel (1989) [1], RID (1992) [6], optimal kernel (1993) [7], Wigner-analytic foundations (1988) [8], and Radon-based kernel work (1993) [9].

• Tomographic and Radon-transform based time-frequency representations enable reconstruction-based maps of signal concentration, aiding separation of multicomponent contents through backprojection and projections of the ambiguity/Wigner domain. Key works: Tomographic time-frequency analysis (1994) [11], Radon transformation of TFDs (1994) [10], and Radon-kernel approaches (1993) [9].

• Data-adaptive and signal-dependent TFDs tailor the distribution kernel to the signal to maximize concentration and minimize orientation sensitivity in time-frequency space. This includes radially-Gaussian kernels (1991) [3], high-resolution data-adaptive TFD (1990) [16], and signal-dependent optimal kernel design (1993) [7].

• Biomedical signal processing applications leverage time-frequency methods for ECG pattern recognition, HRV analysis, and adaptive signal handling: Syntactic ECG recognition (1990) [15], long-term HRV analysis (1988) [20], adaptive quantile estimation for biological signals (1993) [12], adaptive ECG compression (1988) [18], adaptive QRS detection (1988) [19].