Transversal filters with nonuniform tap spacings

Maeda, N.

doi:10.1109/tcs.1980.1084712

Cited by 13 publications

(4 citation statements)

References 5 publications

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“…This corresponds to designing an FIR filter with noninteger delays. 2 This issue has been considered earlier in the literature [16][17][18], but mainly for frequency-selective filters, not reconstruction of nonuniformly sampled signals. There are mainly two features that differ the signal reconstruction context in this paper from those contexts.…”

Section: Filter Designmentioning

confidence: 99%

“…There are mainly two features that differ the signal reconstruction context in this paper from those contexts. First, there is only one band of interest here, namely, the passband region ωT ∈ [−ω 0 T, ω 0 T], ω 0 T < π, which means that the problems associated with designing frequency selective filters with noninteger delays [16][17][18] are circumvented. In particular, it is difficult to obtain such frequency selective filters with arbitrary passband and stopband regions throughout the whole region ωT ∈ [−π, π].…”

Section: Filter Designmentioning

confidence: 99%

“…Based on (16), one can draw the conclusion that minimizing A n ( jωT) − 1 ∞ is appropriate for narrow band signals. To see this, consider the sinusoidal signal x(n) = X m sin(ωTn + φ).…”

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confidence: 99%

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Reconstruction of Nonuniformly Sampled Bandlimited Signals by Means of Time-Varying Discrete-Time FIR Filters

Johansson

Lowenborg

2006

EURASIP J. Adv. Signal Process.

113

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This paper deals with reconstruction of nonuniformly sampled bandlimited continuous-time signals using time-varying discretetime finite-length impulse response (FIR) filters. The main theme of the paper is to show how a slight oversampling should be utilized for designing the reconstruction filters in a proper manner. Based on a time-frequency function, it is shown that the reconstruction problem can be posed as one that resembles an ordinary filter design problem, both for deterministic signals and random processes. From this fact, an analytic least-square design technique is then derived. Furthermore, for an important special case, corresponding to periodic nonuniform sampling, it is shown that the reconstruction problem alternatively can be posed as a filter bank design problem, thus with requirements on a distortion transfer function and a number of aliasing transfer functions. This eases the design and offers alternative practical design methods as discussed in the paper. Several design examples are included that illustrate the benefits of the proposed design techniques over previously existing techniques.

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Section: Filter Designmentioning

confidence: 99%

Section: Filter Designmentioning

confidence: 99%

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Reconstruction of Nonuniformly Sampled Bandlimited Signals by Means of Time-Varying Discrete-Time FIR Filters

Johansson

Lowenborg

2006

EURASIP J. Adv. Signal Process.

113

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“…Time domain warping is especially useful in processing frequency-modulated signals (Meda, 1980;Brown and Rabiner, 1982;Wulich et al, 1990;Coates and Fitzgerald, 2000). A typical member of this class of signals is of the form of x(t) = A(t)e j2πϕ(t) , where A(t) is the amplitude and ϕ(t) is the phase in Hz.…”

Section: Time-frequency Analysis Of Mono-component Signals By Fractiomentioning

confidence: 99%

Time–frequency component analyser and its application to brain oscillatory activity

Özdemir

Karakaş

Çakmak

et al. 2005

Journal of Neuroscience Methods

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Currently, event-related potential (ERP) signals are analysed in the time domain (ERP technique) or in the frequency domain (Fourier analysis and variants). In techniques of time-domain and frequency-domain analysis (short-time Fourier transform, wavelet transform) assumptions concerning linearity, stationarity, and templates are made about the brain signals. In the time-frequency component analyser (TFCA), the assumption is that the signal has one or more components with non-overlapping supports in the time-frequency plane. In this study, the TFCA technique was applied to ERPs. TFCA determined and extracted the oscillatory components from the signal and, simultaneously, localized them in the time-frequency plane with high resolution and negligible cross-term contamination. The results obtained by means of TFCA were compared with those obtained by means of other commonly used techniques of ERP analysis, such as bilinear time-frequency distributions and wavelet analysis. It is suggested that TFCA may serve as an appropriate tool for capturing the localized ERP components in the time-frequency domain and for studying the intricate, frequency-based dynamics of the human brain.

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