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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Lensless Fluorescent Microscopy on a Chip
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Lossless data compression for improving the performance of a GPU-based beamformer.

U-Wai Lok1, Gang-Wei Fan1, Pai-Chi Li2

  • 1Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.

Ultrasonic Imaging
|August 21, 2014
PubMed
Summary
This summary is machine-generated.

A new lossless compression algorithm speeds up graphics processing unit (GPU) beamforming by reducing data transfer times. This method enhances real-time processing without compromising image quality, improving data transfer efficiency.

Keywords:
GPU parallel programmingbeamformercompressionparallel decoder

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Area of Science:

  • * Digital Signal Processing
  • * Computer Engineering

Background:

  • * Graphics Processing Units (GPUs) offer parallel computation for dynamic receive beamforming.
  • * Real-time GPU beamforming demands high data rates for radio-frequency (RF) data transfer.
  • * Existing compression methods like JPEG can increase decoding time, degrading performance.

Purpose of the Study:

  • * To propose and implement a lossless compression-decompression algorithm for GPU-based beamforming.
  • * To reduce data transfer requirements between hardware, CPU, and GPU memory.
  • * To improve the overall performance of real-time GPU beamformers without image quality loss.

Main Methods:

  • * Developed a parallel lossless compression-decompression algorithm.
  • * Implemented the algorithm with a focus on low hardware resource usage and latency in Field-Programmable Gate Arrays (FPGAs).
  • * Evaluated performance through simulations and CPU-to-GPU data transfer time analysis.

Main Results:

  • * Achieved a compression ratio of approximately 1.7.
  • * The lossless compression encoder requires minimal hardware resources and has reasonable latency.
  • * CPU-to-GPU data transfer time was reduced threefold with the parallel decoding process compared to uncompressed data.

Conclusions:

  • * The proposed lossless compression with parallel decoding effectively mitigates transmission bandwidth requirements.
  • * This approach significantly reduces data transfer times for GPU beamforming systems.
  • * Real-time performance is enhanced without sacrificing image quality.