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Related Concept Videos

Upsampling01:22

Upsampling

349
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...
349
Aliasing01:18

Aliasing

278
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.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
278
Downsampling01:20

Downsampling

294
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.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
294
Sampling Theorem01:15

Sampling Theorem

839
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.
839
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

405
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...
405
Bandpass Sampling01:17

Bandpass Sampling

280
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....
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Enabling Fine Sample Rate Settings in DSOs with Time-Interleaved ADCs.

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This study introduces a novel digital storage oscilloscope time-base enabling fine sample rate selection for improved memory usage and reduced post-processing. The new design ensures stable performance with jitter independent of the chosen sample rate.

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Instrumentation

Background:

  • Digital storage oscilloscopes (DSOs) have limited sample rate selections, often restricted to integer submultiples of the maximum rate.
  • This limitation simplifies data transfer and ensures stable jitter performance but hinders optimal memory utilization and necessitates post-processing.
  • Existing architectures with single analog-to-digital converters differ from those employing time-interleaved converters.

Purpose of the Study:

  • To propose a new time-base for digital storage oscilloscopes that allows for fine frequency resolution in sample rate selection.
  • To address the specific challenges posed by oscilloscopes utilizing time-interleaved converters.
  • To enhance memory resource utilization and minimize post-processing requirements in oscilloscope applications.

Main Methods:

  • Development of a novel time-base architecture for digital storage oscilloscopes.
  • Implementation of fine frequency resolution for sample rate selection, particularly rational submultiples of the maximum rate.
  • Focus on oscilloscopes with time-interleaved analog-to-digital converters.

Main Results:

  • The proposed time-base enables sample rate selection with very fine frequency resolution, up to 200 GHz and beyond.
  • Jitter performance remains independent of the selected sample rate, ensuring consistent stability.
  • Improved efficiency in memory resource usage and potential reduction in post-processing needs.

Conclusions:

  • The novel time-base significantly enhances the flexibility and efficiency of digital storage oscilloscopes.
  • It provides a solution for optimizing sample rate selection in instruments with time-interleaved converters.
  • This advancement leads to better performance and usability in demanding signal acquisition applications.