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関連する概念動画

Influence of Earth's Curvature and Atmospheric Refraction on Leveling01:26

Influence of Earth's Curvature and Atmospheric Refraction on Leveling

1.0K
During leveling, the Earth's curvature and atmospheric refraction introduce deviations in the line of sight from a true horizontal reference. When the line of sight is leveled, it remains perpendicular to the plumb line only at a single point. Beyond this, it deviates due to the Earth’s curvature, represented by the correction C. For a sight distance D, the deviation can be derived using the relationship:This relationship shows that the deviation increases quadratically with distance. Over a...
1.0K
Distance Corrections01:15

Distance Corrections

308
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
308
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

512
A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
512
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

2.2K
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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Focusing of Light in the Eye01:16

Focusing of Light in the Eye

6.5K
Light rays enter the eye through the cornea, a transparent dome-shaped tissue that is the eye's outermost layer. The cornea bends or refracts, light rays traveling to the pupil. The shape of the cornea determines how much of the light is bent and whether the image will be focused correctly on the retina at the back of the eye. Once the light has passed through both refraction layers, it converges into a single focal point onto a small area. This is where photoreceptors start transforming...
6.5K
Differential Leveling01:12

Differential Leveling

768
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
768

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Updated: Feb 21, 2026

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
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シンプルで効率的な偏差補正フレームワークは,多高さレンズなしの画像処理に適しています.

Fang Feng, Hui Zhao, Xun Xue

    Optics express
    |February 20, 2026
    PubMed
    まとめ
    この要約は機械生成です。

    この研究は,適応的光学カリブレーションと定量的な相回収を使用するコンピューティングのレンズレスイメージングフレームワークを導入します. 調整誤差を自動的に修正し,コストと複雑性を削減することで,堅牢で高品質なイメージングを実現します.

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    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
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    関連する実験動画

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    科学分野:

    • オプティクスは光学です.
    • コンピューティング・イマージング (Computational Imaging) とは
    • バイオメディカルエンジニアリング

    背景:

    • レンズレスイメージングは,高通量イメージングを提供していますが,軸と半径の位移のエラーに敏感です.
    • 従来の方法は,高価な精密装置と複雑な光学アラインメントを必要とし,アクセシビリティと速度を制限します.

    研究 の 目的:

    • 費用対効果が高く,迅速なレンズレスイメージングフレームワークを開発する.
    • 適応的光学カリブレーションと定量的な相回収を統合することにより,堅牢で高品質の複合幅画像を可能にします.
    • 精密な位置付け装置や複雑な調整手順の必要性を排除するためです.

    主な方法:

    • 適応的光学校正と定量的な相回収を統合したコンピューティングフレームワークを開発しました.
    • 自動並べ替えのためのサブdiffraction パターンの座標の繰り返し最適化を実装.
    • 構造的類似度指数最小化が利用され,分割区間戦略と軸移動誤差校正のためのストキャスティックグラデント下降が利用されました.

    主要な成果:

    • サブピクセル半径位置精度と1.05%未満の軸距離補正誤差を達成しました.
    • 複数のパラメータの偏差を組み合わせた様々な標本で,堅牢な画像再構築を実証しました.
    • 高品質のコンプレックスアンプリチュードイメージングは,機動的な位置付けが厳しい極端な条件下でも維持されています.

    結論:

    • 提案されたコンピューティングフレームワークは,レンズレス画像システム設計を大幅に簡素化し,ハードウェアコストを削減します.
    • アダプティブ光学カリブレーションと定量的なフェーズリトリーチャーは,要求の高い物理的アライナメントと高精度デバイスの必要性を排除します.
    • 多種多様なアプリケーションのための高通量,費用対効果,および堅牢なレンズレス画像を可能にします.