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Related Concept Videos

Aliasing01:18

Aliasing

812
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...
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Bandpass Sampling01:17

Bandpass Sampling

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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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Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
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NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

2.0K
A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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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...
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High Frequency Sampling of TTL Pulses on a Raspberry Pi for Diffuse Correlation Spectroscopy Applications.

Matthew Tivnan1,2, Rajan Gurjar3,4, David E Wolf5,6

  • 1Radiation Monitoring Devices Inc., 44 Hunt Street, Watertown, MA 02472, USA. tivnan.m@husky.neu.edu.

Sensors (Basel, Switzerland)
|August 15, 2015
PubMed
Summary
This summary is machine-generated.

This study demonstrates a cost-effective method for Diffuse Correlation Spectroscopy (DCS) blood flow measurements using a Raspberry Pi. The minicomputer successfully acquires and processes data, offering a promising alternative to expensive hardware for biomedical applications.

Keywords:
Raspberry Piblood flowcoherent scatteringlaser speckleoptical spectroscopysoftware autocorrelation

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Area of Science:

  • Biomedical Optics
  • Optical Instrumentation
  • Physiological Monitoring

Background:

  • Diffuse Correlation Spectroscopy (DCS) is a key optical technique for non-invasive tissue blood flow measurement.
  • Current DCS instrumentation relies on expensive dedicated hardware for signal acquisition and autocorrelation.
  • High cost limits the widespread adoption of DCS in biomedical applications.

Purpose of the Study:

  • To investigate the feasibility of using a Raspberry Pi minicomputer for DCS signal acquisition and processing.
  • To assess the performance, stability, and accuracy of a Raspberry Pi-based DCS system compared to commercial hardware.
  • To explore a lower-cost instrumentation approach for DCS to enhance its accessibility.

Main Methods:

  • A Raspberry Pi minicomputer was utilized for high-fidelity acquisition and storage of rapidly varying time-series signals.
  • Numerical processing of the Raspberry Pi-acquired data was performed to compute intensity autocorrelations.
  • DCS measurements using the Raspberry Pi system were experimentally compared against a commercial hardware autocorrelation board.

Main Results:

  • The Raspberry Pi successfully acquired and stored time-series signals with high fidelity.
  • Numerical processing yielded intensity autocorrelations suitable for DCS applications.
  • Experimental comparisons demonstrated comparable stability, performance, and accuracy to commercial DCS hardware.

Conclusions:

  • A Raspberry Pi can serve as a viable, low-cost alternative for DCS signal acquisition and processing.
  • This approach has the potential to significantly reduce the instrumentation cost of DCS systems.
  • Wider implementation of DCS in biomedical research and clinical settings may be facilitated by this cost reduction.