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Related Concept Videos

Sampling Theorem01:15

Sampling Theorem

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.
Sampling Continuous Time Signal01:11

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.
In the...
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
Rectangular and Triangular Pulse Function01:19

Rectangular and Triangular Pulse Function

The unit rectangular pulse function is mathematically represented by a rectangular function centered at the origin with a height of one unit. This function is defined by two parameters: T, which specifies the center location of the pulse along the time axis, and τ, which determines the pulse duration.
For example, consider a rectangular pulse with a 5V amplitude, a 3-second duration, and centered at t=2 seconds. This pulse can be expressed using the rectangular function, written as,
Upsampling01:22

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...
NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.

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Measurement of Coherence Decay in GaMnAs Using Femtosecond Four-wave Mixing
15:58

Measurement of Coherence Decay in GaMnAs Using Femtosecond Four-wave Mixing

Published on: December 3, 2013

Algorithm for precision subsample timing between Gaussian-like pulses.

R A Lerche1, B P Golick, J P Holder

  • 1Lawrence Livermore National Laboratory, P.O. Box 808, Livermore, California 94551, USA. lerche1@llnl.gov

The Review of Scientific Instruments
|November 2, 2010
PubMed
Summary
This summary is machine-generated.

A new midpoint-timing algorithm achieves 1 ps precision for cross-timing measurements using moderately priced oscilloscopes. This method enhances accuracy for National Ignition Facility (NIF) experiments by optimizing pulse width relative to sample spacing.

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Area of Science:

  • High-energy-density physics
  • Experimental diagnostics
  • Signal processing

Background:

  • National Ignition Facility (NIF) experiments require precise cross-timing between diagnostic instruments.
  • Available oscilloscopes offer 6 GHz bandwidths at 20-25 Gsamples/s, with 40 ps sample spacing.
  • Existing methods may not meet the stringent timing accuracy requirements (<30 ps).

Purpose of the Study:

  • To present a simple midpoint-timing algorithm for precise cross-timing measurements.
  • To demonstrate the algorithm's effectiveness for Gaussian-like pulses, such as NIF timing fiducials.
  • To determine optimal conditions for achieving high timing precision.

Main Methods:

  • Development and simulation of a midpoint-timing algorithm for analyzing pulse data.
  • Analysis of Gaussian-like pulses, specifically the 100-ps-wide NIF timing fiducial.
  • Experimental validation of the algorithm's performance and precision.

Main Results:

  • The midpoint-timing algorithm achieves single-event cross-timing precision of 1 ps.
  • Optimal pulse width for the algorithm is approximately 2.5 times the oscilloscope's sample spacing.
  • Experimental measurements confirm the algorithm's capability under specific conditions.

Conclusions:

  • The midpoint-timing algorithm offers a viable solution for achieving sub-30 ps cross-timing accuracy in NIF experiments.
  • Precise timing can be obtained with moderately priced oscilloscopes by applying this analysis technique.
  • Understanding the relationship between pulse width and sample spacing is crucial for optimal timing performance.