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関連する概念動画

Vision01:24

Vision

60.3K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
60.3K
Mechanism of Breathing I: Inspiration01:30

Mechanism of Breathing I: Inspiration

3.3K
Introduction to Inspiration: The Respiratory System in Action
The respiratory system, an essential network for breathing, comprises the conducting and respiratory zones, each playing a crucial role in the overall process of respiration. Let us explore the detailed mechanism of inspiration, or inhalation, which is the first phase of the respiratory cycle.
Pathway of Air during Inspiration
During inspiration, air enters our body through the nose or mouth and moves through the conducting zone,...
3.3K
Color Vision01:24

Color Vision

1.5K
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
1.5K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

2.1K
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.
2.1K
Computed Tomography01:10

Computed Tomography

8.9K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
8.9K
Design Example: Traverse Angle Computations01:25

Design Example: Traverse Angle Computations

345
Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
345

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関連する実験動画

Updated: Feb 14, 2026

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
09:29

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

Published on: February 11, 2014

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オオカミのビジョンにインスパイアされた近接センサーコンピューティングです.

Zishen Zhao1,2, Yixin Cao1,3, Shuaiwei Huang1

  • 1School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China.

Nature communications
|February 12, 2026
PubMed
まとめ
この要約は機械生成です。

オオカミの視力からインスピレーションを得て,この研究では,低光下での機械視力のために新しいバイモダルシナプストランジスタを導入しています. この装置は,光子不足の条件下でも,効率的で高度に適応する受動的標的検出を可能にします.

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科学分野:

  • ニューロモルフィックエンジニアリング
  • マテリアルサイエンス 材料科学
  • 人工知能 (AI) とは,人工知能 (AI) のことです.

背景:

  • パッシブターゲット検出は,弱光条件下での機械視力にとって不可欠であり,監視や誘導などのアプリケーションを可能にします.
  • 既存の技術は,光子不足の環境における感度と適応に苦労しています.
  • オオカミのような生物学的視力システムを模倣することは,パフォーマンスを向上させる有望なアプローチを提供します.

研究 の 目的:

  • オオカミの視力にインスパイアされたバイモダルシナプストランジスタを開発する.
  • パラレルフォトンの知覚と電気的可塑性のエミュレーションのための光電子解離を実現するために.
  • ニューロモルフィックビジョンセンサーのためのエネルギー効率の良い低光画像処理を可能にします.

主な方法:

  • バイモダルシナプストランジスタを,光電子解離機構で製造.
  • 低光強度感知におけるデバイスの性能の特徴.
  • 人工ニューラルネットワークのアプリケーションのためのシナプス重量調節の実証.
  • 画像処理のための適応コントラスト強化のテスト.

主要な成果:

  • この装置は,約331. 1の高いアクティブ適応指数を達成しました.
  • 0.146 nW cm-2の低い光の強度を成功裏に検出しました.
  • 循環的に安定したシナプス重量調節 (強化および抑制) が実証されました.
  • 3つの人工ニューラルレベルに重量を配置することが可能なことは,特定の光の強度範囲で検証されました.

結論:

  • 開発されたオオカミのビジョンにインスパイアされたデバイスは,高度なニューロモルフィックビジョンセンサーのためのハードウェア基盤を提供します.
  • 低光下でも効率的で効果的な画像処理を可能にします.
  • この技術は,高感度と適応を要求するアプリケーションで,困難な視覚環境で重要な可能性を秘めています.