arXiv subjects

Umar Rashid

Publications and source records attributed to Umar Rashid.

3 recordsLinked to original sources

arXiv2026

Photophysical properties of Eu3+ complexes approaching electronic contact to a metal surface

The application of rare-earth complexes in electrically driven light sources poses a series of challenges that require specific optimization of the molecular photophysical properties. Here, we present a report on films of three different Eu3+ complexes characterized in terms of emission spectra and fluorescence decay. We compare molecular complexes in powder form and sublimed films, in films on glass and on a metal surface, and in films of thicknesses down to less than 3 nm (< 3 ML), approaching electrical coupling. Our photoluminescence experiments supported by scanning tunneling microscopy of sub-monolayers indicate that Eu3+(trensal) complexes are less affected by sublimation and more stable on the metal surface than typical beta diketonate complexes, making them promising candidates for electroluminescence devices.

cond-mat.mes-hall↗

arXiv2025

Hybrid CNN-ViT Framework for Motion-Blurred Scene Text Restoration

Motion blur in scene text images severely impairs readability and hinders the reliability of computer vision tasks, including autonomous driving, document digitization, and visual information retrieval. Conventional deblurring approaches are often inadequate in handling spatially varying blur and typically fall short in modeling the long-range dependencies necessary for restoring textual clarity. To overcome these limitations, we introduce a hybrid deep learning framework that combines convolutional neural networks (CNNs) with vision transformers (ViTs), thereby leveraging both local feature extraction and global contextual reasoning. The architecture employs a CNN-based encoder-decoder to preserve structural details, while a transformer module enhances global awareness through self-attention. Training is conducted on a curated dataset derived from TextOCR, where sharp scene-text samples are paired with synthetically blurred versions generated using realistic motion-blur kernels of multiple sizes and orientations. Model optimization is guided by a composite loss that incorporates mean absolute error (MAE), squared error (MSE), perceptual similarity, and structural similarity (SSIM). Quantitative evaluations show that the proposed method attains 32.20 dB in PSNR and 0.934 in SSIM, while remaining lightweight with 2.83 million parameters and an average inference time of 61 ms. These results highlight the effectiveness and computational efficiency of the CNN-ViT hybrid design, establishing its practicality for real-world motion-blurred scene-text restoration.

cs.CV↗

arXiv2023

Extraordinary electrical conductance through amorphous non-conducting polymers under vibrational strong coupling

Achieving electrical conductance in amorphous non-doped polymers is a challenging task. Here, we show that vibrational strong coupling of the aromatic C-H(D) out-of-plane bending modes of polystyrene, deuterated polystyrene, and poly (benzyl methacrylate) to the vacuum electromagnetic field of the cavity enhance the electrical conductivity by at least six orders of magnitude compared to the uncoupled polymers. The conductance is thermally activated at the onset of strong coupling. It becomes temperature and cavity path length independent at the highest coupling strengths, giving rise to the extraordinary electrical conductance in these polymers. The electrical characterizations are performed without external light excitation, demonstrating the role of quantum light in enhancing the long-range coherent transport even in amorphous non-conducting polymers.

cond-mat.mtrl-sci↗