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Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

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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Aliasing

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

Bandpass Sampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

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Basic Send-on-Delta Sampling for Signal Tracking-Error Reduction.

Miguel Diaz-Cacho¹, Emma Delgado², Antonio Barreiro³

¹School of Computer Engineering, Campus de Ourense, Edificio Politecnico, 32004 Ourense, Spain. mcacho@uvigo.es.

Sensors (Basel, Switzerland)

|February 18, 2017

Summary

This summary is machine-generated.

This study introduces a dynamic threshold algorithm for Send-on-Delta (SoD) sampling to minimize signal tracking error. The method effectively reduces error while maintaining desired data transmission rates.

Keywords:

Send-on-Delta deadband networks tracking error

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Area of Science:

Control Systems Engineering
Signal Processing
Data Transmission

Background:

Send-on-Delta (SoD) sampling is crucial for efficient data transmission.
Minimizing signal tracking error is a key challenge in real-time systems.
Optimizing transmission rates while maintaining accuracy is essential.

Purpose of the Study:

To dynamically select an optimal threshold for Send-on-Delta (SoD) sampling.
To reduce signal tracking error under a given transmission rate.
To propose a real-time algorithm for calculating the amplitude threshold (delta value).

Main Methods:

Formulation of an error-reduction principle.
Development of a real-time algorithm for dynamic threshold calculation.
Implementation of the algorithm within a Send-on-Delta driver.

Main Results:

The proposed algorithm effectively calculates the delta value for desired mean transmission rates.
Testing with second-order control system signals demonstrated significant tracking-error reduction.
Comparison with periodic transmission showed superior performance in error reduction and throughput maintenance.

Conclusions:

The dynamic threshold algorithm offers a significant improvement over traditional methods.
This approach enhances signal tracking accuracy in data transmission systems.
The method provides a robust solution for balancing error reduction and transmission efficiency.