In this work, we explore the possibility of using hybrid graphene/GaN phototransistors to get high responsivity, high speed, and large photosensitive area. The responsivity of our hybrid graphene/GaN phototransistors with a relatively large 15.2 mm² active area reaches 361 mA/W at 10 V and the response time is ~5 ms, much better performance than traditional GaN photodetectors. This is because graphene acts as the carrier transport channel with a high mobility and greatly increases the charge collection efficiency. Our results should shed more light on the role of graphene in hybrid phototransistors and open a feasible pathway to develop large area ultraviolet photodetectors with high responsivity and high speed.

We report preparation of large area quasi-one-dimensional (1D) monolayer graphene superlattices on a prototypical high-index surface Cu(410)-O and characterization by Raman spectroscopy, Auger electron spectroscopy, low-energy electron diffraction, scanning tunneling microscopy, and scanning tunneling spectroscopy. The periodically stepped substrate gives a 1D modulation to graphene, forming a superlattice of the same superperiodicity. Consequently, the moiré pattern is also quasi-1D, with a different periodicity. Scanning tunneling spectroscopy measurements revealed new Dirac points formed at the superlattice Brillouin zone boundary as predicted by theories.

Controlling the self-assembly of surface-adsorbed molecules into nanostructures requires understanding physical mechanisms that act across multiple length and time scales. By combining scanning tunneling microscopy with hierarchical ab initio and statistical mechanical modeling of 1,4-substituted benzenediamine (BDA) molecules adsorbed on a gold (111) surface, we demonstrate that apparently simple nanostructures are selected by a subtle competition of thermodynamics and dynamics. Of the collection of possible BDA nanostructures mechanically stabilized by hydrogen bonding, the interplay of intermolecular forces, surface modulation, and assembly dynamics select at low temperature a particular subset: low free energy oriented linear chains of monomers and high free energy branched chains.

We have designed an all-dielectric device based on permittivity-asymmetric rectangular blocks on meta-surfaces, yielding multiple Fano resonances with high sensitivity and high figure of merit (FOM) in the near-infrared regime. By introducing different materials to break the permittivity-symmetry, three sharp Fano peaks are generated with high Q-factors arising from the interference between the sub-radiant modes and the magnetic dipole resonance modes. Combining the field distributions and the multipole decomposition in Cartesian coordinates, the resonance modes are analyzed to be magnetic quadrupoles (MQs) and magnetic dipoles (MDs). Furthermore, the dependence on materials and geometric parameters has been studied. The maximal sensitivity, FOM and Q-factor reach 394nm/RIU, 4925 and 14437, respectively. This proposed structure provides a good alternative to geometry-asymmetric meta-surface structures and may be used for multichannel sensing, nonlinear optical devices, and laser.

The structure of a document contains rich information such as logical relations in context, hierarchy, affiliation, dependence, and applicability. It will greatly affect the accuracy of document information processing, particularly of legal documents and business contracts. Therefore, intelligent document structural analysis is important to information extraction and data mining. However, unlike the well-studied field of text semantic analysis, current work in document structural analysis is still scarce. In this paper, we propose an intelligent document structural analysis framework through data pre-processing, feature engineering, and structural classification with a dynamic sample weighting algorithm. As a typical application, we collect more than 11,000 insurance document content samples and carry out the machine learning experiments to check the efficiency of our framework. Meanwhile, to address the sample imbalance problem in the hierarchy classification task, a dynamic sample weighting algorithm is incorporated into our Dynamic Weighting Structural Analysis (DWSA) framework, in which the weights of different category tags according to the structural levels are iterated dynamically in training. Our results show that the DWSA has significantly improved the comprehensive accuracy and the classification F1-score of each category. The comprehensive accuracy is as high as 94.68% (3.36% absolute improvement) and the Macro F1-score is 88.29% (5.1% absolute improvement).

This paper proposes a high-performance embedded system for offline Urdu handwritten signature verification. Though many signature datasets are publicly available in languages such as English, Latin, Chinese, Persian, Arabic, Hindi, and Bengali, no Urdu handwritten datasets were available in the literature. So, in this work, an Urdu handwritten signature dataset is created. The proposed embedded system is then used to distinguish genuine and forged signatures based on various features, such as length, pattern, and edges. The system consists of five steps: data acquisition, pre-processing, feature extraction, signature registration, and signature verification. A majority voting (MV) algorithm is used for improved performance and accuracy of the proposed embedded system. In feature extraction, an improved sinusoidal signal multiplied by a Gaussian function at a specific frequency and orientation is used as a 2D Gabor filter. The proposed framework is tested and compared with existing handwritten signature verification methods. Our test results show accuracies of 66.8% for ensemble, 86.34% for k-nearest neighbor (KNN), 93.31% for support vector machine (SVM), and 95.05% for convolutional neural network (CNN). After applying the majority voting algorithm, the overall accuracy can be improved to 95.13%, with a false acceptance rate (FAR) of 0.2% and a false rejection rate (FRR) of 41.29% on private dataset. To test the generalization ability of the proposed model, we also test it on a public dataset of English handwritten signatures and achieve an overall accuracy of 97.46%.