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Sampling Continuous Time Signal

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Related Experiment Video

Updated: Jul 17, 2026

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

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Published on: May 10, 2019

Lomb-Wech periodogram for non-uniform sampling.

Tran Thong¹, James McNames, Mateo Aboy

¹Dept. of Biomedical Eng., OGI/Oregon Health & Sci. Univ., Beaverton, OR, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 3, 2007

Summary

The Lomb-Scargle transform can now evaluate shorter, windowed, and averaged non-uniformly sampled signals. This enhancement provides a Welch-like periodogram for improved analysis of unevenly spaced data.

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Area of Science:

Signal Processing
Time Series Analysis
Astronomy

Background:

The Lomb-Scargle transform enables direct evaluation of non-uniformly sampled signals without interpolation.
Current limitations restrict its use to single transform evaluations due to implicit normalization.

Purpose of the Study:

To enhance the Lomb-Scargle transform for evaluating shorter segments of non-uniformly sampled data.
To enable the application of windowing and averaging techniques to non-uniform time series.
To adapt the transform for a Welch-like periodogram analysis.

Main Methods:

De-normalization of the transform by a factor of 2(sigma)/sup 2//N.
Utilizing records of equal time duration.
Multiplying by windows sampled at corresponding non-uniform time instances.

Main Results:

The enhanced transform allows for the evaluation of shorter transforms.
Windowing and averaging of overlapped records are now feasible.
The method produces a Welch-like periodogram for non-uniform sampling.

Conclusions:

The proposed enhancements significantly extend the applicability of the Lomb-Scargle transform.
The modified transform provides a robust method for analyzing non-uniformly sampled data, similar to Welch's method for uniform data.
This advancement is crucial for fields dealing with irregularly sampled time series data.