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Related Concept Videos

Properties of Fourier Transform I01:21

Properties of Fourier Transform I

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The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
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Properties of Fourier Transform II01:24

Properties of Fourier Transform II

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The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
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Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

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Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

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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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Time and frequency -Domain Interpretation of Phase-lead Control01:24

Time and frequency -Domain Interpretation of Phase-lead Control

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Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
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Updated: Jun 28, 2025

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
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Feedback Beamforming in the Time Domain.

Zvi Aharon Herscovici1, Israel Cohen1

  • 1Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel.

Sensors (Basel, Switzerland)
|April 13, 2024
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Summary
This summary is machine-generated.

This study introduces a novel time-domain feedback beamformer for accurate real-time source localization, enhancing automation and AI by improving direction of arrival and signal range estimation even with low signal-to-noise ratios.

Keywords:
beamformingsource localizationtime domain processinguniform linear array

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Area of Science:

  • Signal Processing
  • Acoustics
  • Artificial Intelligence

Background:

  • Real-time source localization is essential for advanced automation and AI applications.
  • Low signal-to-noise ratio (SNR) and processing time constraints challenge localization accuracy.
  • Existing methods may not meet the demanding requirements of high-end systems.

Purpose of the Study:

  • To propose a new time-domain feedback-based beamformer architecture for real-time source localization.
  • To estimate the direction of arrival (DOA) and signal range of reflective sources.
  • To enhance localization precision through an integrated feedback mechanism.

Main Methods:

  • Development of a novel time-domain feedback-based beamformer architecture.
  • Estimation of direction of arrival (DOA) and signal range for reflective sources.
  • Comparative analysis of the proposed method against conventional time-domain techniques.

Main Results:

  • The proposed architecture effectively addresses real-time processing demands.
  • The feedback mechanism significantly refines localization precision.
  • Performance evaluation using peak-to-sidelobe ratio, mainlobe width, and directivity factor showed improved beamformer effectiveness.

Conclusions:

  • The developed time-domain feedback beamformer offers a significant improvement in localization accuracy for real-time applications.
  • This approach enhances the performance of automation and AI systems by overcoming SNR and processing time limitations.
  • The feedback integration represents a key advancement in beamforming for precise source localization.