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Ternary wavelets and their applications to signal compression

Ghulam Mustafa, Falai Chen, Zhangjin Huang (2004)

International Journal of Applied Mathematics and Computer Science

We introduce ternary wavelets, based on an interpolating 4-point C^2 ternary stationary subdivision scheme, for compressing fractal-like signals. These wavelets are tightly squeezed and therefore they are more suitable for compressing fractal-like signals. The error in compressing fractal-like signals by ternary wavelets is at most half of that given by four-point wavelets (Wei and Chen, 2002). However, for compressing regular signals we further classify ternary wavelets into 'odd ternary' and 'even...

Theoretical analysis for ℓ 1 - ℓ 2 minimization with partial support information

Haifeng Li, Leiyan Guo (2025)

Applications of Mathematics

We investigate the recovery of k -sparse signals using the ℓ 1 - ℓ 2 minimization model with prior support set information. The prior support set information, which is believed to contain the indices of nonzero signal elements, significantly enhances the performance of compressive recovery by improving accuracy, efficiency, reducing complexity, expanding applicability, and enhancing robustness. We assume k -sparse signals 𝐱 with the prior support T which is composed of g true indices and b wrong indices,...

Time-frequency analysis of Sjöstrand's class.

Karlheinz Gröchenig (2006)

Revista Matemática Iberoamericana

We investigate the properties an exotic symbol class of pseudodifferential operators, Sjöstrand's class, with methods of time-frequency analysis (phase space analysis). Compared to the classical treatment, the time-frequency approach leads to striklingly simple proofs of Sjöstrand's fundamental results and to far-reaching generalizations.

Transformée en paquets d'ondelettes des signaux stationnaires: comportement asymptotique des densités spectrales.

Loïc Hervé (1996)

Revista Matemática Iberoamericana

We consider quadrature mirror filters, and the associated wavelet packet transform. Let X = {Xn}n∈Z be a stationary signal which has a continuous spectral density f. We prove that the 2n signals obtained from X by n iterations of the transform converge to white noises when n → +∞. If f is holderian, the convergence rate is exponential.

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