Results 221 to 230 of about 1,040,227 (270)
Advanced Functional Materials, EarlyView.The combination of formamidinium thiocyanate and 1,3‐propane diammonium iodide for bulk and top‐surface passivation, and a ternary fullerene blend to improve energy band alignment, suppresses energy losses in wide‐bandgap FAPbBr3 perovskite solar cells.
Laura Bellini +9 more
wiley +1 more source
Microsphere Autolithography—A Scalable Approach for Arbitrary Patterning of Dielectric Spheres
Advanced Functional Materials, EarlyView.MicroSphere Autolithography (µSAL) enables scalable fabrication of patchy particles with customizable surface motifs. Focusing light through dielectric microspheres creates well defined, tunable patches via a conformal poly(dopamine) photoresist. Nearly arbitrary surface patterns can be achieved, with the resolution set by the index contrast between ...
Elliott D. Kunkel +3 more
wiley +1 more source
Advanced Functional Materials, EarlyView.Aggregates of diketopyrrolopyrrole dipolar and quadrupolar systems exhibit panchromatic absorption spectra extending in the entire visible range. Upon aggregation in water and in the solid state, their fluorescence is almost completely quenched and their singlet excited state lifetime is significantly shortened.
Roberto Cantoni +10 more
wiley +1 more source
Unusual Swelling Behavior of Hydrogels Modified with Spiropyran as Appendage or Crosslinker
Advanced Functional Materials, EarlyView.Not so innocent after all—spiropyran crosslinkers in methylenebisacrylamide‐crosslinked poly(acrylamide‐co‐acrylic acid) hydrogels increase crosslinking density, but also, counterintuitively, increase swelling. Charge complexation, cooperative chemo‐mechanical effects, and aggregation may explain these observations.
Michael M. Lerch +7 more
wiley +1 more source
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2020
In Chapter 3, we learned that certain types of matrices, which are referred to as positive semidefinite matrices, can be expressed in the following form: $$\displaystyle A= V \varDelta V^T $$ Here, V is a d × d matrix with orthonormal columns, and Δ is a d × d diagonal matrix with nonnegative eigenvalues of A. The orthogonal matrix V can also be
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In Chapter 3, we learned that certain types of matrices, which are referred to as positive semidefinite matrices, can be expressed in the following form: $$\displaystyle A= V \varDelta V^T $$ Here, V is a d × d matrix with orthonormal columns, and Δ is a d × d diagonal matrix with nonnegative eigenvalues of A. The orthogonal matrix V can also be
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SIAM Journal on Control and Optimization, 2009
We give a necessary and sufficient condition for the existence of a matrix $F$ such that the singular values of $A+BF$ are the prescribed values. We also give a simple algorithm to compute $F$ if the prescribed values satisfy the condition.
Clyde F. Martin, Xiaochang A. Wang
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We give a necessary and sufficient condition for the existence of a matrix $F$ such that the singular values of $A+BF$ are the prescribed values. We also give a simple algorithm to compute $F$ if the prescribed values satisfy the condition.
Clyde F. Martin, Xiaochang A. Wang
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2018
In Chap. 4 we learned how to diagonalize a square matrix using the Eigen decomposition. Eigen decomposition has many uses, but it has a limitation: it can only be applied to a square matrix. In this chapter, we will learn how to extend the decomposition to a rectangular matrix using a related method known as a Singular Value Decomposition (SVD ...
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In Chap. 4 we learned how to diagonalize a square matrix using the Eigen decomposition. Eigen decomposition has many uses, but it has a limitation: it can only be applied to a square matrix. In this chapter, we will learn how to extend the decomposition to a rectangular matrix using a related method known as a Singular Value Decomposition (SVD ...
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Nonlinear singular value decomposition
2017Linear functions are widely used and well-understood. For example, to solve f(x) = 0 or f(x) = λx, with linear f, we can rely on matrix decompositions (singular value decomposition (SVD), eigenvalue decomposition (EVD), etc.). On the other hand, having nonlinear multivariate vector functions (multiple input-multiple output static nonlinearities), it is
Ishteva, Mariya Kamenova +1 more
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1994
Many numerical methods used in application areas such as signal processing, estimation, and control are based on the singular value decomposition (SVD) of matrices. The SVD is widely used in least squares estimation, systems approximations, and numerical linear algebra.
Uwe Helmke, John B. Moore
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Many numerical methods used in application areas such as signal processing, estimation, and control are based on the singular value decomposition (SVD) of matrices. The SVD is widely used in least squares estimation, systems approximations, and numerical linear algebra.
Uwe Helmke, John B. Moore
openaire +1 more source