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Fabrication of lithium niobate metasurfaces via a combination of FIB and ICP-RIE

Chinese Optics Letters 2022年第11期20卷 95-99页

作者： Chunyan Jin Wei Wu Lei Cao Bofeng Gao Jiaxin Chen Wei Cai Mengxin Ren Jingjun Xu Key Laboratory of Weak-Light Nonlinear Photonics Ministry of EducationSchool of Physics and TEDA Applied Physics InstituteNankai UniversityTianjin 300071China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300308China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China

Lithium niobate(LN)metasurfaces have emerged as a new platform for manipulating electromagnetic ***,we report a fabrication technique for LN nano-grating metasurfaces by combining focused ion beam(FIB)milling with inductively coupled plasma reactive ion etching(ICP-RIE).Steep sidewalls with angles larger than 80°are *** quasi-bound states in the continuum are observed from our *** measured transmission spectra show good agreement with the numerical simulations,confirming the high quality of the fabricated *** technique can be applied to fabricate the LN metasurfaces with sharp resonances for various applications in optical communications,on-chip photonics,laser physics,sensing,and so on.

关键词： lithium niobate metasurfaces fabrication FIB ICP-RIE

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Infrared Target Detection Based on the Fusion of Mask R-CNN and Image Enhancement Network

Infrared Target Detection Based on the Fusion of Mask R-CNN ...

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Chinese Automation Congress (CAC)

作者： Fang Liu Song Guan Kuaikuai Yu Hua Gong College of Science Shengyang Ligong University Shenyang China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjing China

In this paper, we focus on the problem of the infrared image detection. Aiming at the problem of the low contrast of the infrared images, an adaptive gamma transformation algorithm (AGT) is established to intelligently improve the contrast of the infrared images. Aiming at the problem of the weak edge information of the infrared images, an improved Sobel operator edge enhancement algorithm (ISOEE) is designed to enrich the edge features of the infrared images. In order to solve the problem that the traditional infrared image detection algorithm relies on expert experience, the fusion infrared images detection model AIE-Mask R-CNN based on Mask R-CNN, AGT algorithm and ISOEE algorithm is proposed. This fusion model can realize accurate and intelligent detection for the infrared images. The experimental results show that the fusion model AIE-Mask R-CNN improves the detection accuracy of the infrared images.

关键词： Adaptation models Automation Image edge detection Object detection Detection algorithms Image enhancement

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Accelerating High-resolution Image Stitching for the Dual Camera System based on GPU

Accelerating High-resolution Image Stitching for the Dual Ca...

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Chinese Automation Congress (CAC)

作者： Jingsi Wang Dong Liu Ran Zheng Yongcai Hu Wu Gao Chen Zhao School of Computer Science Northwestern Polytechnical University Xi’an China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China

Image stitching plays an important role in the field of computer vision and image processing. Although some sophisticated stitching methods have been proposed, they are usually time-consuming. Because many application scenarios, such as video surveillance, need quick image stitching to provide a wide field of view, accelerating the image stitching is critical. In recent years, the neural network is used to stitching images, but only relatively small size images can be processed; meanwhile, time-consuming image stitching is also unavoidable. In this paper, to quickly stitch 1920×1080 images generated by the dual camera system, an image stitching algorithm is proposed. We divided the algorithm depending on its timeliness on different steps, and its time-consuming steps are parallelized on Graphics Processing Unit (GPU) platforms to be accelerated. We tested different hierarchies for organization of threads and access of memory on compute unified device architecture (CUDA) programming, and the final experiment results showed that our algorithm could reach a speedup ratio up to 119x on the Desktop with 3080ti.

关键词： Image registration Neural networks Merging Graphics processing units Organizations Programming Cameras

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Integrating Parafac and Pso Fitting Model: A New View into Fluorescence Spectroscopic Analysis for Adulterant Quantification of Lubricant Oil

SSRN

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SSRN 2024年

作者： Zhang, Lujun Cai, Nuo Wang, Yan Fan, Xinmin Gao, Juanjuan Gao, Jin Li, Sensen Lan, Christopher Q. Xiaodong, Huang Wang, Chunyan Department of Physics and Electronic Information Weifang University Weifang261061 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China Department of Chemical & Biological Engineering University of Ottawa OttawaONK1N 6N5 Canada

The adulteration of high-quality engine lubricants with inferior, dubious alternatives or reclaimed oils presents a significant challenge, necessitating the development of precise and rapid detection methodologies. This study introduces a novel analytical approach of excitation-emission matrix fluorescence (EEMF) spectroscopy paired with parallel factor analysis (PARAFAC) for the quantification of adulterants within adulterated lubricant samples. A fitting model is introduced to establish a correlation between the PARAFAC component scores of pure adulterants and those of adulterated samples. Furthermore, particle swarm optimization (PSO) is employed to refine the fitting parameters within this model, enabling the determination of individual adulterant' relative quantities in adulterated samples. This methodology not only broadens the application of EEMF combined with PARAFAC for adulteration quantification but also shows potential as a universal quantitative analysis tool for diverse complex fluorophobic mixtures. © 2024, The Authors. All rights reserved.

关键词： Fluorescence

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A Dual-Band Metagrating for Diffraction control: Design and Experimental Demonstration

A Dual-Band Metagrating for Diffraction Control: Design and ...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Shan Xuan Kai Feng Jianjia Yi School of Information and Communications Engineering Xi’an Jiaotong University Xi’an China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China State Key Laboratory of Millimeter Waves Southeast University Nanjing China

The metagratings have attracted extensive attention over the recent years as a special class of periodic metamaterials for their near-perfect electromagnetic wave manipulation through a simple and sparse array structure. In this paper, we propose a dual-band metagrating design for wide-angle reflection. The proposed structure with dual-band response can perform as one period at low frequency band and equal as two periods at high frequency band, thus realizing beam manipulation at two frequency bands by periodic conversion. The design procedure is fast, accurate and easy to implement combined with analytical design and local periodic approximation. To verify the performance of the proposed design, a dual-band printed circuit board metagrating that performs perfect anomalous reflection at 6.16 GHz and 20 GHz is designed and demonstrated.

关键词： Diffraction Printed circuits Dual band Switches Frequency conversion Microwave frequencies Reflection

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Design Compact YOLO based Network for Small Target Detection on Infrared Image

Design Compact YOLO based Network for Small Target Detection...

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Chinese Automation Congress (CAC)

作者： Shuang Liu Zhicheng Liu Yuhai Li Wancheng Liu Chenyang Ge Longjun Liu Institute of Artificial Intelligence and Robotics (IAIR) Xi’an Jiaotong University Xi’an China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China

most YOLO object detection neural networks prefer to focus on traditional RGB image, but previous studies rarely consider special YOLO network with compact architecture for infrared image. In this paper, we analyze original YOLO network architecture, and we propose a compact YOLO based network by using different blocks from previous work for small target detection on infrared image. We use small target detection layer, GhostConv convolution, Focus structure, Focal EIOU Loss and soft NMS modules to improve original YOLO network structure, which improves the accuracy and speed of target detection for infrared image. The experimental results show that the accuracy of target detection of original model can be effectively improved by adding the small target detection layer, the Focus structure and the BI-FPN structure. By replacing the GhostConv convolution, the calculation speed is significantly improved. At last, after the model is quantized and deployed with NCNN framework, the object detection speed can reached to 35ms per infrared image.

关键词： Automation Convolution Neural networks Object detection Network architecture

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Design an Efficient DNN Inference Framework with PS-PL Synergies in FPGA for Edge Computing

Design an Efficient DNN Inference Framework with PS-PL Syner...

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Chinese Automation Congress (CAC)

作者： Wancheng Liu Yuhai Li Yining Yang Jinyu Zhu Longjun Liu Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China Institute of Artificial Intelligence and Robotics (IAIR) Xi’an Jiaotong University Xi’an China

in recent years, Deep Neural Network (DNN) based methods have achieved great success for changing inflexible machine to intelligent and live system. Internet of Things (IoT) applications equipped with DNN in domains such as self-driving, speech processing and video surveillance particular challenges. Since DNNs are memory-/computing- intensive applications, FPGAs are customized to accelerate DNN on edge system due to their low latency and high energy efficiency. However, embedded CPU is usually essential for IoT system and single accelerator of FPGA is hard to gain maximized performance of the whole IoT system. Therefore, we design an efficient DNN inference framework with programming system (PS, e.g., ARM CPU) and programming logic (PL, e.g. FPGA) synergies for intelligent IoT system. Compared with the common IoT system only separately leverages FPGA for DNN accelerating and ARM for other tasks, our framework can make ARM and FPGA jointly accelerate DNN inference from the beginning. Our PS-PL co-design approach can take full advantage of the merits of ARM (control operations) and FPGA (parallel computing operations) for various DNN model accelerating. We implemented our co-design framework on Zynq SoCs. The experimental results show that our framework can achieve maximal 205.9 GOPs/s DNN inference accelerating under 6.2W while it would not affect other CPU tasks running on IoT system.

关键词： Program processors Computational modeling Neural networks Programming Parallel processing Video surveillance Internet of Things

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FPGA-based Acceleration of Structured Light Depth Estimation

FPGA-based Acceleration of Structured Light Depth Estimation

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Chinese Automation Congress (CAC)

作者： Shijie Wang Xiaodong Deng Wancheng Liu Yuhai Li Shitao Chen Longjun Liu Institute of Artificial Intelligence and Robotics (IAIR) Xi’an Jiaotong University Xi’an China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China

Stereo depth estimation is widely applied in computer vision and intelligent robotic systems. For real-time mobile stereo vision applications, how to efficiently design and implement stereo matching on resource-limited hardware platforms is still a grand challenge. In this paper, we propose and implement a low-power, high-resolution stereo matching hardware architecture on field-programmable gate array (FPGA) with the input of structured light camera. In order to obtain high-quality point cloud data, we present an image preprocessing module and an improved cost function for stereo matching algorithm. Furthermore, we leverage hardware description language to implement our stereo matching computing architecture and all algorithm models are evaluated on the Xilinx ZCU102 development board, which only takes about 61147 LUTs and 786 BRAMs. Compared with high-performance CPUs, the computing speed is significantly improved, 1.84 times faster than Intel i7-10700@2.90GHz, and the power consumption is only 3.915W.

关键词： Costs Power demand Robot vision systems Estimation Computer architecture Hardware Real-time systems

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Ground-to-air Defense Firepower Assignment Decision Based on Non-cooperative Game

Ground-to-air Defense Firepower Assignment Decision Based on...

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2021 China Automation Congress, CAC 2021

作者： Gong, Hua Cui, Mengya Zhang, Yong Sun, Wenjuan School of Science Shenyang Ligong University Shenyang110159 China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

ISBN: (纸本)9781665426473

The key problem of ground-to-air defense is how to make accurate and effective firepower assignment decision for attacking targets. A multi-objective optimization model of ground-to-air defense firepower assignment is established with the aim of maximizing the damage probability and minimizing the weapon consumption, and non-cooperative game theory is used to solve the problem. A non-cooperative game model is established by mapping the two objectives to players, and weapon assignment schemes to strategy profiles. An improved tabu genetic algorithm based on Nash equilibrium is designed to get the assignment scheme. Experimental results show that the proposed method has better convergence and stability than the improved genetic algorithm based on tabu search for multi-objective optimization model. © 2021 IEEE

关键词： Genetic algorithms

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Dynamic Threat Assessment of Air multi-target Based on DBN-TOPSIS Method

Dynamic Threat Assessment of Air multi-target Based on DBN-T...

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2021 China Automation Congress, CAC 2021

作者： Gong, Hua Yu, Xiaoye Zhang, Yong Liu, Fang School of Science Shenyang Ligong University Shenyang110159 China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

ISBN: (纸本)9781665426473

In the traditional air target threat assessment, only quantitative threat degree is considered, and the target threat range assessment is missing. Aiming at the problem, a comprehensive dynamic threat assessment method is designed in this paper. This comprehensive method considers two terms of the target threat: threat degree and threat range. In terms of threat degree assessment, a dynamic Bayesian network (DBN) and a technique for order preference by similarity to an ideal solution (TOPSIS) are fused. The multi-target fusion assessment method DBN-TOPSIS is proposed. This method maps the probability of multi-target threat level obtained by dynamic Bayesian network to TOPSIS evaluation matrix. This strategy solves the problem of multi-target threat ranking. In terms of threat range assessment, a grey prediction threat range assessment method GM(1,1) is obtained. This method solves the problem of small sample threat range assessment. The experimental results show that the multi-target fusion threat assessment method is reasonable, and the target threat range can directly reflect the change of target threat degree in airspace. © 2021 IEEE

关键词： Bayesian networks

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