TeilSamples

TeilSamples is a class of sampling schemes used to acquire signals by partitioning the time axis into segments and applying different sampling rates within each segment. The approach aims to balance data fidelity with resource constraints by concentrating sampling effort where the signal exhibits greater variation and reducing it in smoother intervals. In reconstruction, the samples from all segments are combined to recover the original signal.

The name blends the German word Teil, meaning part or portion, with Samples to emphasize the segmented nature of the technique. It emerged in the context of multirate and sparse-signal processing research and has been applied in scenarios where traditional uniform sampling would be costly in power, bandwidth, or storage.

In practical implementations, the time axis is divided into segments. Each segment may use a distinct sampling rate or pattern (for example, dense sampling during expected high-variation periods and sparse sampling during quieter periods). Reconstruction uses models of the signal, such as sparsity, piecewise smoothness, or prior segment boundaries, and solves an optimization problem to infer values that best match the observed samples while satisfying the chosen model.

Applications include biomedical monitoring, environmental sensor networks, industrial process control, and audio or multimedia applications where long-term observation is paired with limited resources.

Advantages include reduced data rates and lower power consumption, as well as greater flexibility to adapt to changing signal activity. Limitations involve increased computational complexity for reconstruction, sensitivity to model assumptions, and the need for precise timing or segment boundary information to avoid artifacts at segment transitions.

Related topics include nonuniform sampling, compressed sensing, multirate signal processing, and adaptive sampling.