In this paper, a signal-filtering method based on empirical mode decomposition is proposed. The filtering method is a fully data-driven approach. A noisy signal is adaptively decomposed into intrinsic oscillatory components called intrinsic mode functions (IMFs) by means of an algorithm referred to as a sifting process. The basic principle of the method is to make use of partial reconstructions of the signal, with the relevant IMFs corresponding to the most important structures of the signal (low-frequency components). A criterion is proposed to determine the IMF, after which, the energy distribution of the important structures of the signal overcomes that of the noise and that of the high-frequency components of the signal. The method is illustrated on simulated and real data, and the results are compared to well-known filtering methods. The study is limited to signals that were corrupted by additive white Gaussian noise and is conducted on the basis of extended numerical experiments.

A novel detection approach of linear FM (LFM) signals, with single or multiple components, in the time-frequency plane of Teager-Huang (TH) transform is presented. The detection scheme that combines TH transform and Hough transform is referred to as Teager-Huang-Hough (THH) transform. The input signal is mapped into the time-frequency plane by using TH transform followed by the application of Hough transform to recognize time-frequency components. LFM components are detected and their parameters are estimated from peaks and their locations in the Hough space. Advantages of THH transform over Hough transform of Wigner-Ville distribution (WVD) are: 1) cross-terms free detection and estimation, and 2) good time and frequency resolutions. No assumptions are made about the number of components of the LFM signals and their models. THH transform is illustrated on multicomponent LFM signals in free and noisy environments and the results compared with WVD-Hough and pseudo-WVD-Hough transforms.

The genus Pachygrapsus Randall, 1840 is revised with particular attention to the five Southwest Pacific species: P. fakaravensis Rathbun, 1907, P. laevimanus Stimpson, 1858, P. minutus A. MilneEdwards, 1873, P. planifrons De Man, 1888, and P. plicatus (H. Milne Edwards, 1837). Pachygrapsus laevimanus Stimpson, 1858 is redescribed and a neotype is designated because of confusion of this species with P. transversus (Gibbes, 1850). Seven other species were examined: P. corrugatus (von Martens, 1872), P. crassipes Randall, 1840, P. gracilis (de Saussure, 1858), P. loveridgei Chace, 1966, P. marmoratus (Fabricius, 1787), P. maurus (Lucas, 1846), and P. transversus (Gibbes, 1850). The type specimens of P. striatus A. Milne-Edwards, 1873, and P. laevis Borradaile, 1900, were re-examined and found to be junior synonyms of P. plicatus (H. Milne Edwards, 1837), and P. planifrons De Man, 1888, respectively. Pachygrapsus kraussii (Dana, 1852) is considered a junior synonym of P. plicatus H. Milne Edwards, 1837. Pachygrapsus polyodous Stebbing, 1921, is excluded from Pachygrapsus and provisionally placed in Euchirograpsus H. Milne Edwards, 1853. Pachygrapsus propinquus De Man, 1908, is probably a junior synonym of P. minutus A. MilneEdwards, 1873, but its status needs to be confirmed with the collection of new material from the type locality. Pachygrapsus now includes twelve species, thirteen if P. propinquus De Man, 1908, proves to be a separate species. A key to all thirteen species is given.

In this paper a noise filtering method using the empirical mode decomposition (EMD) is proposed. The noisy signal is decomposed into oscillatory components called intrinsic mode functions (IMFs) using a process referred to as sifting. The basic idea of the proposed scheme is the partial re-construction of the signal using the IMFs corresponding to the most important structures of the signal (low frequency modes). A new criterion is proposed to determine the IMF after which the energy distribution of the important structures of the signal overcomes that of the noise and that of the high frequency components of the signal. The method is tested on simulated and real signals.

We extend and generalize the Teager–Kaiser [in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (1993), Vol. 3, p. 149 ] and the higher-order differential energy operators [IEEE Signal Process. Lett. 2, 152 (1995) ] to a large class of operators called higher-order energy operators. We show that for AM-FM signal demodulation, the introduced partial derivative orders have to satisfy certain conditions. These operators are parameterized for local processing of AM-FM signals. The operators are illustrated using synthetic signals and a real signal from light scanning interferometry.