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Related Concept Videos

Aliasing01:18

Aliasing

271
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...
271
Sampling Theorem01:15

Sampling Theorem

833
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.
833
Bandpass Sampling01:17

Bandpass Sampling

279
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....
279
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

134
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,...
134
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

400
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...
400
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

388
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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Related Experiment Video

Updated: Oct 2, 2025

Quasi-light Storage for Optical Data Packets
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PESA: Probabilistic Efficient Storage Algorithm for Time-Domain Spectrum Measurements.

Mohamad Omar Al Kalaa1, Madelene Ghanem2, Hazem H Refai2

  • 1M. O. Al Kalaa and S. J. Seidman are with the Center for Devices and Radiological Health (CDRH), U.S. Food and Drug Administration, Silver Spring, MD 20993.

IEEE Transactions on Instrumentation and Measurement
|February 25, 2022
PubMed
Summary
This summary is machine-generated.

A new Probabilistic Efficient Storage Algorithm (PESA) dramatically cuts storage needs for radio frequency spectrum surveys. This method achieves 99% storage reduction while accurately capturing wireless activity, making spectrum analysis more accessible.

Keywords:
Cognitive RadioGaussian mixture modelInternet of Things (IoT)Spectrum surveyWi-FiWireless coexistence

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Wireless communication is integral to modern life, powering devices from medical equipment to IoT nodes.
  • Accurate radio frequency spectrum utilization measurements are crucial for understanding wireless patterns.
  • The high storage demands of fast-sampling spectrum surveys pose significant cost and accessibility challenges.

Purpose of the Study:

  • To propose a novel algorithm for efficient data storage in high-accuracy, time-domain spectrum surveys.
  • To address the storage volume limitations hindering fast sample acquisition rates for detecting sporadic spectrum occupancy.
  • To enable cost-effective and accessible spectrum utilization measurements.

Main Methods:

  • Development of a Probabilistic Efficient Storage Algorithm (PESA).
  • Utilizing Gaussian Mixture Models (GMM) to represent dynamic range bins.
  • Establishing activity/inactivity windows and recording indicators to the best-fitting GMM component.

Main Results:

  • Achieved approximately 99% reduction in storage volume.
  • Maintained accurate estimation of channel utilization and activity/inactivity periods.
  • Demonstrated a practical implementation surveying Wi-Fi in a healthcare setting, reducing 25 billion samples to 96.28 MB.

Conclusions:

  • PESA significantly overcomes storage limitations in spectrum surveys.
  • The algorithm enables high-accuracy measurements at fast acquisition rates.
  • PESA facilitates practical, large-scale spectrum monitoring with reduced data footprint.