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Acceleration Vectors

In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h due...
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Imaging Biological Samples with Optical Microscopy01:18

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Updated: Jun 3, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

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Published on: March 20, 2017

ConvShareViT: A Vision Transformer-Like Architecture for Free-Space Optical Accelerators.

Riad Ibadulla, Thomas M Chen, Constantino Carlos Reyes-Aldasoro

    IEEE Transactions on Neural Networks and Learning Systems
    |June 1, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces convolutional shared vision transformers (ConvShareViT), a novel deep learning model for optical systems. ConvShareViT achieves comparable attention scores to standard vision transformers (ViTs) and offers significantly faster inference speeds.

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    High-speed Particle Image Velocimetry Near Surfaces
    11:59

    High-speed Particle Image Velocimetry Near Surfaces

    Published on: June 24, 2013

    Area of Science:

    • Computer Science
    • Optical Engineering
    • Artificial Intelligence

    Background:

    • Vision Transformer (ViT) architectures are powerful for image recognition but computationally intensive.
    • Adapting deep learning models to free-space optical systems presents unique challenges.
    • Existing methods may not fully leverage the potential of optical computing for AI tasks.

    Purpose of the Study:

    • To introduce a novel deep learning architecture, convolutional shared vision transformers (ConvShareViT), tailored for 4f free-space optical systems.
    • To investigate the effectiveness of replacing linear layers in ViT with depthwise convolutional layers with shared weights.
    • To evaluate the attention learning capabilities and inference speed of ConvShareViT compared to standard ViTs.

    Main Methods:

    • Developed ConvShareViT by replacing Multi-Head Self-Attention (MHSA) and Multilayer Perceptron (MLP) linear layers with depthwise convolutional layers.
    • Employed shared weights across input channels in the convolutional layers.
    • Conducted 12 experiments to systematically analyze the attention mechanism's effectiveness with different configurations, including valid-padded and same-padded convolutions.
    • Compared quantitative attention scores and theoretical inference speeds against standard ViTs.

    Main Results:

    • ConvShareViT configurations utilizing valid-padded shared convolutions successfully learned attention mechanisms.
    • Attention scores achieved by ConvShareViT were comparable to those of standard ViTs.
    • Same-padded convolutions demonstrated limitations in attention learning, behaving more like traditional Convolutional Neural Networks (CNNs).
    • ConvShareViT theoretically offers up to 3.04x faster inference compared to GPU-based systems.

    Conclusions:

    • ConvShareViT is a viable adaptation of the ViT architecture for optical deep learning applications.
    • Shared convolutional layers, particularly with valid padding, can effectively implement attention mechanisms in optical systems.
    • The proposed architecture presents a significant speed advantage, making it promising for future optical AI hardware.