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Full-Text Articles in Engineering
Substituent Effects On The Solubility And Electronic Properties Of The Cyanine Dye Cy5: Density Functional And Time-Dependent Density Functional Theory Calculations, Austin Biaggne, William B. Knowlton, Bernard Yurke, Jeunghoon Lee, Lan Li
Substituent Effects On The Solubility And Electronic Properties Of The Cyanine Dye Cy5: Density Functional And Time-Dependent Density Functional Theory Calculations, Austin Biaggne, William B. Knowlton, Bernard Yurke, Jeunghoon Lee, Lan Li
Materials Science and Engineering Faculty Publications and Presentations
The aggregation ability and exciton dynamics of dyes are largely affected by properties of the dye monomers. To facilitate aggregation and improve excitonic function, dyes can be engineered with substituents to exhibit optimal key properties, such as hydrophobicity, static dipole moment differences, and transition dipole moments. To determine how electron donating (D) and electron withdrawing (W) substituents impact the solvation, static dipole moments, and transition dipole moments of the pentamethine indocyanine dye Cy5, density functional theory (DFT) and time-dependent (TD-) DFT calculations were performed. The inclusion of substituents had large effects on the solvation energy of Cy5, with pairs of …
Deep Learning For Compressive Sar Imaging With Train-Test Discrepancy, Morgan R. Mccamey
Deep Learning For Compressive Sar Imaging With Train-Test Discrepancy, Morgan R. Mccamey
Browse all Theses and Dissertations
We consider the problem of compressive synthetic aperture radar (SAR) imaging with the goal of reconstructing SAR imagery in the presence of under sampled phase history. While this problem is typically considered in compressive sensing (CS) literature, we consider a variety of deep learning approaches where a deep neural network (DNN) is trained to form SAR imagery from limited data. At the cost of computationally intensive offline training, on-line test-time DNN-SAR has demonstrated orders of magnitude faster reconstruction than standard CS algorithms. A limitation of the DNN approach is that any change to the operating conditions necessitates a costly retraining …