Speckle Engineering through Singular Value Decomposition of the Transmission Matrix
نویسندگان
چکیده
Speckle patterns are ubiquitous in optics and have multiple applications for which the control of their spatial correlations is essential. Here, we report on a method to engineer speckle behind scattering medium through singular value decomposition transmission matrix. We not only demonstrate over grain size shape but also realize with nonlocal correlations. Moreover, show that reach our extends along axial dimension, allowing volumetric engineering layers.
منابع مشابه
پیشنهاد روش جدیدی برای محاسبه polynomial singular value decomposition ) psvd )
در این پایان نامه به معرفی روشهای مختلف محاسبه psvd می پردازیم. بخشی از این روشها به بررسی روشهای مختلف محاسبه psvd در مقالات مطالعه شده می پردازد که می توان به محاسبهpsvd با استفاده از الگوریتمهای pqrd و pevd و sbr2 و محاسبه psvd براساس تکنیک kogbetliantz و روش پارامتریک برای محاسبه psvd اشاره نمود. بخش بعدی نیز به بررسی روشهای مستقیم پیشنهادی محاسبه psvd برای ماتریسهای 2×2و2× n و n×2 و 3× n و...
15 صفحه اولClustered Sub-Matrix Singular Value Decomposition
This paper presents an alternative algorithm based on the singular value decomposition (SVD) that creates vector representation for linguistic units with reduced dimensionality. The work was motivated by an application aimed to represent text segments for further processing in a multi-document summarization system. The algorithm tries to compensate for SVD’s bias towards dominant-topic document...
متن کاملGeneralized essential matrix: Properties of the singular value decomposition
When considering non-central imaging devices, the computation of the relative pose requires the estimation of the rotation and translation that transform the 3D lines from one coordinate system to the second. In most of the state-ofthe-art methods, this transformation is estimated by the computing a 6× 6 matrix, known as the Generalized Essential Matrix. To allow a better understanding of this ...
متن کاملThe Singular Value Decomposition
Carlo Tomasi Any m n matrix of rank r transforms the unit sphere in Rn into an r-dimensional hyperellipsoid in Rm. For instance, the rank-2 matrix A = 1 p2 264 p3 p3 3 3 1 1 375 (1) transforms the unit circle on the plane into an ellipse embedded in three-dimensional space. Figure 1 shows the map y = Ax : Two diametrically opposite points on the unit circle are mapped into the two endpoints of ...
متن کاملSingular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD)
The singular value decomposition (SVD) is a generalization of the eigen-decomposition which can be used to analyze rectangular matrices (the eigen-decomposition is definedonly for squaredmatrices). By analogy with the eigen-decomposition, which decomposes a matrix into two simple matrices, the main idea of the SVD is to decompose a rectangular matrix into three simple matrices: Two orthogonal m...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Physical Review Letters
سال: 2021
ISSN: ['1079-7114', '0031-9007', '1092-0145']
DOI: https://doi.org/10.1103/physrevlett.127.093903