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関連する概念動画

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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多次元光学コンピューティング

Zhetao Jia, Hector Rubio, Lilian Neim

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    この要約は機械生成です。

    研究者は,波長分割マルチプレキシング (WDM) を超える帯域幅を高めるために,モード分割マルチプレキシング (MDM) を使用した新しい光学コンピューティング戦略を開発しました. この新しいアプローチは ディープニューラルネットワークの ハードウェア機能を強化します

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    科学分野:

    • 光学コンピューティング
    • 光学について
    • 統合光学

    背景:

    • ディープニューラルネットワークの成長には 先進的なハードウェア・コンピューティング・プラットフォームが必要です
    • 光学コンピューティング,特に波長分割マルチプレキシング (WDM) は,計算帯域幅を増加させますが,統合と容量の課題に直面しています.
    • 既存のWDMアーキテクチャは,チャンネル容量強化のための新しいソリューションを必要とします.

    研究 の 目的:

    • 光学コンピューティングにおける新しい自由度としてモード分割マルチプレキシング (MDM) を導入する.
    • 多次元アーキテクチャを提案し,WDMをMDMで拡張し,コンピューティングの帯域幅を向上させる.
    • マイクロリング共振器のプラットフォームでMDMベースの光学コンピューティングの実現可能性を実証する.

    主な方法:

    • モード分割マルチプレキシング (MDM) と波長分割マルチプレキシング (WDM) を組み合わせた新しい光学コンピューティングアーキテクチャを提案した.
    • 設計され,実験的に検証された主要フォトニックコンポーネント:マルチモードビームスプリッター,高級モードのための熱光学チューナー,マルチモード波導体の曲線.
    • 鋳造工場のプロセスを使って マトリックスマルチプレキシングシステムを製造した.

    主要な成果:

    • 提案されたMDM-WDM光学コンピューティングアーキテクチャの重要なコンポーネントを成功裏に実証しました.
    • 製造されたシステムは,MDMと組み合わせたMDM-WDMコンピューティングパラダイムの両方に動作します.
    • マイクロリング共振器プラットフォームは,強化された光学計算のためのMDMの統合を可能にします.

    結論:

    • モード分割マルチプレキシング (MDM) は,光学コンピューティングの帯域幅を大幅に増加させる新しい経路を提供します.
    • 提案された多次元MDM-WDMアーキテクチャは,次世代コンピューティングのためのチャネル容量を効果的に強化します.
    • 実験的な検証は,ディープニューラルネットワークのハードウェアのためのMDMベースの光学コンピューティングの実行可能性を確認しています.