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Related Concept Videos

Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Sampling Theorem01:15

Sampling Theorem

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.
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.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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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Video-rate Scanning Confocal Microscopy and Microendoscopy
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Video-rate Scanning Confocal Microscopy and Microendoscopy

Published on: October 20, 2011

Optical oversampled analog-to-digital conversion.

B L Shoop, J W Goodman

    Applied Optics
    |August 25, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel optical analog-to-digital (A/D) converter using oversampling and interpolative coding. It achieves high resolution and speed, surpassing current electronic and optical converter capabilities.

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    Optical Trapping of Nanoparticles
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    Published on: January 15, 2013

    Area of Science:

    • Photonics and Optoelectronics
    • Signal Processing
    • Solid-State Devices

    Background:

    • Traditional analog-to-digital converters (ADCs) face limitations in speed and resolution.
    • Optical processing offers potential for high-speed data conversion.
    • Oversampling and interpolative coding are established high-resolution techniques.

    Purpose of the Study:

    • To develop a novel optical analog-to-digital (A/D) conversion approach.
    • To present both interferometric and noninterferometric architectures for this optical A/D conversion.
    • To combine the benefits of high resolution and high-speed optical processing.

    Main Methods:

    • Utilizing oversampling and interpolative coding techniques for A/D conversion.
    • Implementing architectures with multiple quantum well self-electro-optic effect devices and photodetectors.
    • Employing common optical components for simplicity and scalability.

    Main Results:

    • Demonstrated optical converters capable of operating at sampling rates up to 15 Gbits/s.
    • Achieved scalable resolutions of 16 bits at 117 MHz and 8 bits at 938 MHz.
    • Presented a method that extends resolution and conversion rates beyond current technologies.

    Conclusions:

    • The proposed optical A/D converters offer a significant advancement in conversion technology.
    • The combination of optical processing with oversampling provides a pathway to higher performance.
    • The developed architectures are simple and scalable, paving the way for practical applications.