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Related Concept Videos

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

251
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.
In the...
251
Upsampling01:22

Upsampling

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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...
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Properties of DTFT I01:24

Properties of DTFT I

412
In signal processing, Discrete-Time Fourier Transforms (DTFTs) play a critical role in analyzing discrete-time signals in the frequency domain. Various properties of the DTFTs such as linearity, time-shifting, frequency-shifting, time reversal, conjugation, and time scaling help understand and manipulate these signals for different applications.
The linearity property of DTFTs is fundamental. If two discrete-time signals are multiplied by constants a and b respectively, and then combined to...
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Properties of DTFT II01:24

Properties of DTFT II

202
In the study of discrete-time signal processing, understanding the properties of the Discrete-Time Fourier Transform (DTFT) is crucial for analyzing and manipulating signals in the frequency domain. Several properties, including frequency differentiation, convolution, accumulation, and Parseval's relation, offer powerful tools for signal analysis.
The frequency differentiation property is illustrated by considering a DTFT pair and differentiating both sides with respect to ω.
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Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

275
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
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Systematization and Comparison of the Binary Successive Approximation Variants.

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A New Successive Time Balancing Time-to-Digital Conversion Method.

Konrad Jurasz1, Dariusz Kościelnik1, Jakub Szyduczyński1

  • 1Department of Electronics, AGH University of Science and Technology, 30-059 Krakow, Poland.

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Summary

This study introduces a novel self-clocked time-to-digital converter using a successive approximation algorithm. This clockless method simplifies circuits and directly measures long time intervals, ideal for low-power biomedical applications.

Keywords:
analog-to-digital conversionasynchronous time-to-digital conversionclockless circuitself-clocked methodsuccessive approximation

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Biomedical Engineering

Background:

  • Traditional time-to-digital converters often rely on complex clocking mechanisms.
  • Low-power solutions are crucial for portable and implantable biomedical devices.
  • Accurate measurement of long time intervals is essential for specific signal processing applications.

Purpose of the Study:

  • To present a novel self-clocked time-to-digital conversion (TDC) method.
  • To demonstrate a TDC that operates without a reference clock.
  • To enable direct conversion of measured time intervals for simplified processing.

Main Methods:

  • Implementation of a binary successive approximation (SA) algorithm for TDC.
  • Design of a fully clockless circuit architecture.
  • Focus on direct conversion of time intervals, avoiding intermediate clocking stages.

Main Results:

  • Achieved self-clocked operation, eliminating the need for an external reference clock.
  • Demonstrated direct conversion of time intervals, simplifying the measurement process.
  • The proposed method is suitable for measuring time intervals in the hundreds of microseconds range.

Conclusions:

  • The developed self-clocked TDC offers a potentially low-power solution due to its clockless nature.
  • The simplified circuit design reduces component count and complexity.
  • The method's suitability for long time intervals makes it applicable to biomedical signal processing.