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Upsampling01:22

Upsampling

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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...
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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...
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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.
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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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FPGA-based systolic deconvolution architecture for upsampling.

Alex Noel Joseph Raj1, Lianhong Cai1, Wei Li1

  • 1Department of Electronic Engineering, Shantou University, Shantou City, Guangdong Province, China.

Peerj. Computer Science
|May 31, 2022
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Summary
This summary is machine-generated.

This study introduces an efficient deconvolution accelerator for image upsampling. The novel architecture reduces computations and enhances resource efficiency, achieving high performance and accuracy.

Keywords:
Deep learningFPGATransposed convolutionUpsample

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Area of Science:

  • Digital Signal Processing
  • Hardware Acceleration
  • Image Processing

Background:

  • Upsampling is crucial for enhancing image resolution in various applications.
  • Existing methods often suffer from computational redundancy and resource inefficiency.

Purpose of the Study:

  • To propose a novel deconvolution accelerator for efficient image upsampling.
  • To improve resource efficiency by eliminating zero-insertion and padding.

Main Methods:

  • A systolic, pipelined deconvolution accelerator architecture is designed.
  • The accelerator convolves an n x n input with a k x k kernel to produce a 2n x 2n output.
  • Implementation on a Xilinx XC7Z020 platform.

Main Results:

  • Achieved 3.641 giga operations per second (GOPS) for upsampling 32x32 to 256x256.
  • Demonstrated high resource efficiency of 0.135 GOPS/DSP at 200 MHz.
  • Obtained a peak signal-to-noise ratio (SNR) of ~80 dB, comparable to double precision.

Conclusions:

  • The proposed deconvolution accelerator offers significant performance and resource efficiency gains.
  • The architecture effectively reduces redundant computations for high-quality image upsampling.
  • The results indicate its suitability for real-time image processing applications.