Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Computer science. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

AlGaN/GaN MOS-HEMT With $\hbox{HfO}_{2}$ Dielectric and $\hbox{Al}_{2}\hbox{O}_{3}$ Interfacial Passivation Layer Grown by Atomic Layer Deposition

Abstract: We have developed a novel AlGaN/GaN metal-oxide-semiconductor high-electron mobility transistor using a stack gate HfO2/Al2O3 structure grown by atomic layer deposition. The stack gate consists of a thin HfO2 (30-A) gate dielectric and a thin Al2O3 (20- A) interfacial passivation layer (IPL). For the 50-A stack gate, no measurable C-V hysteresis and a smaller threshold voltage shift were observed, indicating that a high-quality interface can be achieved using a Al2O3 IPL on an AlGaN substrate. Good surface passivation effects of the Al2O3 IPL have also been confirmed by pulsed gate measurements. Devices with 1- mum gate lengths exhibit a cutoff frequency (fT) of 12 GHz and a maximum frequency of oscillation (f MAX) of 34 GHz, as well as a maximum drain current of 800 mA/mm and a peak transconductance of 150 mS/mm, whereas the gate leakage current is at least six orders of magnitude lower than that of the reference high-electron mobility transistors at a positive gate bias.

Suppression of Mainbeam Deceptive Jammer With FDA-MIMO Radar

Abstract: Suppression of radar-to-radar jammers, especially the mainbeam jammers, has been an urgent demand in vehicular sensing systems with the expected increased number of vehicles equipped with radar systems. This paper deals with the suppression of mainbeam deceptive jammers with frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radar, utilizing its extra degrees-of-freedom (DOFS) in the range domain. At the modelling stage, false targets, which lag several pulses behind the true target, are considered as a typical form of mainbeam jammers. To this end the data-independent beamforming is performed to suppress false targets by nulling at the equivalent transmit beampattern with an appropriate frequency increment. However, the suppression performance degrades in the presence of transmit spatial frequency mismatch, which could be induced by quantization errors, angle estimation errors and frequency increment errors. To solve this problem, a preset broadened nulling beamformer (PBN-BF) is proposed by placing artificial interferences with appropriate powers around the nulls of the equivalent transmit beampattern. In such a way, effective suppression of deceptive jammer can be guaranteed owing to the broadened notches. At the analysis stage, numerical results in a scenario with multiple unmanned aerial vehicles (UAVs) are provided to illustrate the effectiveness of the devised data-independent BF, and the signal-to-interference-plus-noise ratio is improved compared with the conventional data-independent BF.

Learning to Rank for Blind Image Quality Assessment

Abstract: Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, subjective quality scores are imprecise, biased, and inconsistent, and it is challenging to obtain a large-scale database, or to extend existing databases, because of the inconvenience of collecting images, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as the quality of image ${\boldsymbol {I}}_{\boldsymbol {a}}$ is better than that of image ${\boldsymbol {I}}_{\boldsymbol {b}}$ for training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at a very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves a performance comparable with that of state-of-the-art BIQA algorithms. Moreover, the proposed method can be easily extended to new distortion categories.