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Related Concept Videos

Design Example01:23

Design Example

The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Even and Odd Signals01:17

Even and Odd Signals

An even signal, whether in continuous-time or discrete-time, is defined by its symmetry with its time-reversed version. Mathematically, this is represented as
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the others.
Aliasing01:18

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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An algorithm for separating multilevel random telegraph signal from 1/f noise.

G Giusi1, F Crupi, C Pace

  • 1DEIS, University of Calabria, Via Pietro Bucci 42C, 87030 Arcavacata di Rende (CS), Italy.

The Review of Scientific Instruments
|March 5, 2008
PubMed
Summary

This study introduces a new algorithm to separate random telegraph signals (RTSs) from 1/f noise. The method effectively identifies signal jumps, even with multiple dominant RTSs and varying corner frequencies.

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Area of Science:

  • Physics
  • Electrical Engineering
  • Signal Processing

Background:

  • 1/f noise is a common interference in electronic devices.
  • Random Telegraph Signals (RTSs) are frequently observed in nanoscale devices.
  • Separating RTSs from 1/f noise is crucial for accurate device characterization.

Purpose of the Study:

  • To develop a robust time-domain algorithm for separating multi-level random telegraph signals (RTSs) from 1/f noise.
  • To overcome limitations of methods relying on fixed signal level ranges.
  • To enable accurate extraction of the 1/f noise component in complex signal environments.

Main Methods:

  • A novel algorithm based on efficient recognition of jumps between RTS levels.
  • Time-domain signal analysis without pre-defined signal value ranges for RTS levels.
  • Validation using synthesized two-level and four-level signals.

Main Results:

  • Successful separation of dominant two- and multi-level RTSs from 1/f noise.
  • Effective extraction of the 1/f noise component even with multiple RTSs.
  • Demonstrated robustness across different signal complexities and corner frequencies.

Conclusions:

  • The proposed algorithm offers a robust solution for RTS and 1/f noise separation.
  • The jump-recognition approach provides superior performance compared to fixed-range methods.
  • This technique is valuable for analyzing signals from electronic devices affected by noise.