Jove
Visualize
联系我们

相关概念视频

Upsampling01:22

Upsampling

238
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...
238
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

203
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...
203

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

GATESynergy: Integrating Molecular Global-Local Aggregator and Hierarchical Gene-Gated Encoder for Drug Synergy Prediction.

Interdisciplinary sciences, computational life sciences·2026
Same author

DeepDPM: A Deep Learning Method for MoRFs Prediction Based on Wavelet Transform and Dynamic Convolutional Attention Mechanism.

Journal of chemical information and modeling·2026
Same author

stMixer for Scalable Mosaic Integration and Label Transfer in Spatial Histology and Multi-Omics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

An SE(3)-equivariant and dynamic multi-modal engine advancing from PTM site prediction to network understanding.

Communications chemistry·2026
Same author

A pre-trained language model-based cross-modal fusion framework for predicting miRNA-drug resistance and sensitivity associations.

PLoS computational biology·2026
Same author

Attention-Guided Multiview Deep Learning Framework Uncovers miRNA-Drug Associations for Therapeutic Discovery.

Journal of chemical information and modeling·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jul 5, 2025

Three-Dimensional Imaging of Aortic Tissues in Atherosclerosis
09:55

Three-Dimensional Imaging of Aortic Tissues in Atherosclerosis

Published on: October 25, 2024

931

基于二维可配置管道的现场可编程网关数组的图像直方图等级加速方法.

Yan Wang1, Peirui Liu1, Dalin Li1,2

  • 1Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
概括

这项研究介绍了一种基于FPGA的新方法,用于加快图形平衡 (HE) 图像处理. 二维管道架构显著提高了实时AI应用程序的率.

关键词:
现场可编程门阵列 (FPGA)阶层状态的状态机器.基因组图均等化 基因图均等化两个维的管道管道.

更多相关视频

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.7K
Visualizing Cellular Gibberellin Levels Using the nlsGPS1 Förster Resonance Energy Transfer (FRET) Biosensor
08:53

Visualizing Cellular Gibberellin Levels Using the nlsGPS1 Förster Resonance Energy Transfer (FRET) Biosensor

Published on: January 12, 2019

10.8K

相关实验视频

Last Updated: Jul 5, 2025

Three-Dimensional Imaging of Aortic Tissues in Atherosclerosis
09:55

Three-Dimensional Imaging of Aortic Tissues in Atherosclerosis

Published on: October 25, 2024

931
Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.7K
Visualizing Cellular Gibberellin Levels Using the nlsGPS1 Förster Resonance Energy Transfer (FRET) Biosensor
08:53

Visualizing Cellular Gibberellin Levels Using the nlsGPS1 Förster Resonance Energy Transfer (FRET) Biosensor

Published on: January 12, 2019

10.8K

科学领域:

  • 计算机工程 计算机工程
  • 图像处理 图像处理
  • 人工智能的人工智能

背景情况:

  • 由于工业检测和自动驾驶等新的AI应用,实时图像处理需求正在增加.
  • 立体图平衡 (HE) 对图像质量至关重要,但现有的加速度方法在高率下扎.
  • 对于复杂的,高速的场景,CPU和嵌入式系统的当前HE加速需要改进.

研究的目的:

  • 开发一种高效的硬件加速度方法,用于FPGAs上的直方图平衡 (HE).
  • 提高高等教育的率和性能,以满足需求的实时人工智能应用.
  • 为FPGAs上的HE提出一种新的二维可配置管道架构.

主要方法:

  • 设计了一个二维可配置管道架构,以优化FPGAs上的HE并行性.
  • 通过实现并行累积直方图计算和同时进行多输入处理,使HE算法适应硬件.
  • 优化了计算单元内的管道和关键路径,以实现更高的操作频率.

主要成果:

  • 在VCU118测试板上达到891 MHz的最大频率.
  • 在1080p图像中达到每秒1899的率.
  • 与CPU实现相比,演示了高达22.6倍的加速.

结论:

  • 提出的基于FPGA的HE加速方法有效地满足了实时AI的高率和低功耗需求.
  • 两维可配置管道架构为图像预处理提供了显著的性能提升.
  • 这种方法可以实现更复杂,更苛刻的AI应用程序,需要快速的图像分析.