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Related Concept Videos

Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Blind Procedures02:07

Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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...

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Related Experiment Video

Updated: May 23, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Blind deconvolution for robust signal estimation and approximate source localization.

Shima H Abadi1, Daniel Rouseff, David R Dowling

  • 1Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA. shimah@umich.edu

The Journal of the Acoustical Society of America
|April 17, 2012
PubMed
Summary
This summary is machine-generated.

Synthetic time reversal (STR) improves underwater acoustic signal estimation and source localization. This technique effectively reconstructs signals and pinpoints sound sources even in complex, unknown environments.

Related Experiment Videos

Last Updated: May 23, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Area of Science:

  • Acoustics
  • Signal Processing
  • Underwater Acoustics

Background:

  • Multipath propagation in unknown environments poses challenges for signal deconvolution.
  • Synthetic time reversal (STR) offers a blind deconvolution approach using ray or mode features.

Purpose of the Study:

  • To enhance ray-based STR signal estimation.
  • To utilize STR for approximate source localization in underwater settings.

Main Methods:

  • Simulations and underwater experiments were conducted.
  • Chirp signals were used with varying array elements (2-32) and SNRs (-5 to 30 dB).
  • Ray-based STR impulse response was applied to source localization.

Main Results:

  • High SNR STR signal estimates achieved ~90% correlation with as few as four array elements.
  • Similar performance was obtained at nearly 0 dB SNR with 32 array elements.
  • STR-based source localization provided less ambiguous results than conventional matched field processing.

Conclusions:

  • Ray-based STR is effective for improving underwater acoustic signal estimation.
  • STR facilitates approximate source localization with improved clarity over traditional methods.