University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Millimeter-Wave Substrate Integrated Waveguide Long Slot Leaky-Wave Antennas and Two-Dimensional Multibeam Applications

Abstract: Two types of substrate integrated waveguide (SIW) long slot leaky-wave antennas with controllable sidelobe level are proposed and demonstrated in this paper. The first prototype is able to achieve an excellent sidelobe level of -27.7ndB by properly meandering a long slot etched on the broadside of a straight SIW section from the centerline toward the sidewall then back. But it is known that an asymmetrically curved slot would worsen the cross-polar level. To overcome this drawback, a modified leaky-wave antenna is proposed, which has a straight long slot etched on the broadside of a meandering SIW section. It yields an outstanding sidelobe level of -29.3 dB and also improves the cross-polar level by more than 11 dB at 35 GHz. Experimental results agree well with simulations, thus validating our design. Then, a two-dimensional (2-D) multibeam antenna is developed by combining such 14 leaky-wave antennas with an SIW beamforming network (BFN). It has features of scanning both in elevation orientation by varying frequency and in cross-plane direction by using the BFN. Excited at ports 1-10 of such a 2-D multibeam antenna at 35 GHz, angular region of 86.6° in azimuth can effectively be covered by 3 dB beam-width of ten pencil beams. Varying frequency from 33 GHz to 37 GHz, the angular region of 37.5° and 38.9° in elevation can be covered by 3 dB beam-width of those continuous scanning beams excited at ports 6 and 8 respectively.

Identification of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Multivariate Predictors

Abstract: Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of major interest in AD research. A large number of potential predictors have been proposed, with most investigations tending to examine one or a set of related predictors. In this study, we simultaneously examined multiple features from different modalities of data, including structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid (CSF) biomarkers and neuropsychological and functional measures (NMs), to explore an optimal set of predictors of conversion from MCI to AD in an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. After FreeSurfer-derived MRI feature extraction, CSF and NM feature collection, feature selection was employed to choose optimal subsets of features from each modality. Support vector machine (SVM) classifiers were then trained on normal control (NC) and AD participants. Testing was conducted on MCIc (MCI individuals who have converted to AD within 24 months) and MCInc (MCI individuals who have not converted to AD within 24 months) groups. Classification results demonstrated that NMs outperformed CSF and MRI features. The combination of selected NM, MRI and CSF features attained an accuracy of 67.13%, a sensitivity of 96.43%, a specificity of 48.28%, and an AUC (area under curve) of 0.796. Analysis of the predictive values of MCIc who converted at different follow-up evaluations showed that the predictive values were significantly different between individuals who converted within 12 months and after 12 months. This study establishes meaningful multivariate predictors composed of selected NM, MRI and CSF measures which may be useful and practical for clinical diagnosis.

An overview of lead-free piezoelectric materials and devices

Abstract: Piezoelectric materials and devices have drawn extensive attention for energy harvesting due to their excellent electromechanical conversion properties. With increasing concerns about environmental problems in traditional lead-based piezoelectric materials, it is imperative to develop lead-free piezoelectric alternatives. This paper is intended to give a brief review on the most recent advances in both inorganic (with an emphasis on piezoelectric ceramics and ZnO nanostructures) and organic (i.e. polyvinylidene difluoride (PVDF) and its copolymers and their composites, and biopolymers) lead-free piezoelectric materials. State-of-the-art piezoelectric devices, namely, nanogenerators, sensors, and transducers, are also introduced with detailed examples. Finally, the challenges and perspectives of lead-free piezoelectric materials and devices are given.

Simplified Relay Selection and Power Allocation in Cooperative Cognitive Radio Systems

Abstract: In this paper, we investigate joint relay selection and power allocation to maximize system throughput with limited interference to licensed (primary) users in cognitive radio (CR) systems. As these two problems are coupled together, we first develop an optimal approach based on the dual method and then propose a suboptimal approach to reduce complexity while maintaining reasonable performance. From our simulation results, the proposed approaches can increase the system throughput by over 50%.