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.

Stretchable ultrasonic transducer arrays for three-dimensional imaging on complex surfaces.

Abstract: Ultrasonic imaging has been implemented as a powerful tool for noninvasive subsurface inspections of both structural and biological media. Current ultrasound probes are rigid and bulky and cannot readily image through nonplanar three-dimensional (3D) surfaces. However, imaging through these complicated surfaces is vital because stress concentrations at geometrical discontinuities render these surfaces highly prone to defects. This study reports a stretchable ultrasound probe that can conform to and detect nonplanar complex surfaces. The probe consists of a 10 × 10 array of piezoelectric transducers that exploit an "island-bridge" layout with multilayer electrodes, encapsulated by thin and compliant silicone elastomers. The stretchable probe shows excellent electromechanical coupling, minimal cross-talk, and more than 50% stretchability. Its performance is demonstrated by reconstructing defects in 3D space with high spatial resolution through flat, concave, and convex surfaces. The results hold great implications for applications of ultrasound that require imaging through complex surfaces.

Feature selection and parameter optimization for support vector machines

Abstract: Research highlights? A genetic algorithm with feature chromosomes (GAFC) is proposed. ? The asymptotic behaviors of support vector machines (SVM) are fused with GA. ? The GAFC has not only the search ability of GA, but also has the search ability of feature chromosomes. ? The GAFC obtained good performances by optimizing feature subset and parameters of SVM simultaneously. Support vector machines (SVM) are an emerging data classification technique with many diverse applications. The feature subset selection, along with the parameter setting in the SVM training procedure significantly influences the classification accuracy. In this paper, the asymptotic behaviors of support vector machines are fused with genetic algorithm (GA) and the feature chromosomes are generated, which thereby directs the search of genetic algorithm to the straight line of optimal generalization error in the superparameter space. On this basis, a new approach based on genetic algorithm with feature chromosomes, termed GA with feature chromosomes, is proposed to simultaneously optimize the feature subset and the parameters for SVM.To evaluate the proposed approach, the experiment adopts several real world datasets from the UCI database and from the Benchmark database. Compared with the GA without feature chromosomes, the grid search, and other approaches, the proposed approach not only has higher classification accuracy and smaller feature subsets, but also has fewer processing time.

Recent Progress in Stimulus-Responsive Two-Dimensional Metal-Organic Frameworks

Abstract: Two-dimensional (2D) metal–organic frameworks (MOFs) play an essential role in response to external stimuli by changing their physical or chemical properties. This Review mainly aims at the recent ...

Range-Angle Dependent Transmit Beampattern Synthesis for Linear Frequency Diverse Arrays

Abstract: Phased-array is widely used in communication and radar systems, but the beam steering is fixed in an angle for all the ranges. In this paper, we propose a range-angle dependent beampattern synthesis scheme for linear frequency diverse array (FDA) using the discrete spheroidal sequence, with an aim to focus the transmit energy in a desired two-dimensional spatial section. Different from conventional phased-arrays, FDA employs a small frequency increment, compared to the carrier frequency across the array elements. The range-angle dependent beampattern synthesis method allows the FDA to transmit energy over a desired range or angle sector. This provides a potential to suppress range-dependent clutter and interference, which is not accessible for conventional phased-arrays. The system performance of the proposed FDA is evaluated by the output signal-to-interference-plus-noise ratio (SINR). The effectiveness is verified by comprehensive numerical simulation results.