Biography: 

Prof. Jinhui Song received his B.S. degree from Department of Physics at Nankai University, China in 1998; his M.S. degree in School of Physics at Georgia Institute of Technology, U.S. in 2003; and his Ph.D. degree in School of Materials Science and Engineering from Georgia Institute of Technology, U.S. in 2008. He carried out his Post-Doctoral Fellowship in Georgia Institute of Technology from 2008-2011. Then, Prof. Song worked as a tenure track assistant professor in Department of Metallurgical and Materials Engineering, the University of Alabama, U.S. from 2011-2015. Starting from 2016, Prof. Song joined School of Mechanical Engineering, Dalian University of Technology, China as professor and the director of Institute of Vibration and Sensor Engineering. Prof. Song’s research interests include studying mechanical, electrical, piezoelectrical, photoelectrical properties of nanomaterials; and fabricating piezoelectrical nanogenerators, energy cell, photoelectric nanosensors and other nanodevices. Till to Aug. 2024, Prof. Song has authored or co-authored more than 110 journal articles, 5 US patents, and 10+ China Patens. 

Title: Exploring the application of photoelectric nanoscience and nanotechnology

Abstract: 

Due to the quantum effects caused by nanoscale confinement, the fundamental properties of nanoscale materials exhibit peculiar characteristics that are completely different from macroscopic bulk materials. With the development of semiconductor electronics, present integrated circuits is in the nanoscale. Systematically studying the fundamental properties of semiconductor nanostructures and exploring new devices based on nanostructures will greatly enhance the basic understanding of nanomaterials, while also vigorously promoting the development of semiconductor optoelectronic technology. Based on fundamental scientific research and guided by major applications, our research group has systematically characterized the performance of typical semiconductor nanostructures and explored their applications in various fields such as optoelectronic sensing, luminescence, and medical engineering integration.

Biography: 

Yang Yue received the B.S. and M.S. degrees in electrical engineering and optics from Nankai University, China, in 2004 and 2007, respectively. He received the Ph.D. degree in electrical engineering from the University of Southern California, USA, in 2012. He is a Professor with the School of Information and Communications Engineering, Xi'an Jiaotong University, China. Dr. Yue’s current research interest is intelligent photonics, including optical communications, optical perception, and optical chip. He has published over 260 journal papers (including Science) and conference proceedings with >10,000 citations, six edited books, two book chapters, >60 issued or pending patents, >200 invited presentations (including 1 tutorial, >30 plenary and >50 keynote talks). Dr. Yue is a Fellow of SPIE, a Senior Member of IEEE and Optica. He is an Associate Editor for IEEE Access and Frontiers in Physics, Editor Board Member for four other scientific journals, Guest Editor for >10 journal special issues. He also served as Chair or Committee Member for >100 international conferences, Reviewer for >70 prestigious journals.

Title: Efficient Detection of Multi-Opening Objects via Gaussian Beam Probing and Machine Learning

Abstract: Objects and structures with intricate patterns are extensively employed in numerous applications, driving the demand for precise identification of features in objects containing multiple openings. Conventional optical sensing methods often rely on analyzing the intensity or phase information of light beams deviating from the original incident beam. However, these approaches are frequently hindered by noise and distortions introduced during data acquisition, which can compromise the accuracy of the detected features. In this work, we propose a novel approach for multi-opening object detection using a probe Gaussian beam combined with machine learning (ML) algorithms. By processing the truncated beam captured as intensity images, our method eliminates the need for phase information and replaces traditional orbital angular momentum (OAM) spectrum analysis. Our research focuses on multi-opening objects with 14 types of openings, achieving a highly accurate detection results in both wedge angle and direction angle identification. Experimental results demonstrate that the proposed CNN-based method achieves nearly 100% accuracy for most multi-opening objects. Even with extremely subtle changes, a 1° resolution can be achieved with an accuracy exceeding 88%. This work showcases the potential of combining Gaussian beam probing with ML for robust and efficient multi-opening object detection in practical applications. Additionally, we also find MobileNet (MN), a lightweight model, outperforms VGG in computational efficiency while maintaining high accuracy.

Biography: