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Quantitative optical diagnostics on macroscopic soot onset for ethylene diffusion flames with ethyl ester addition

OPTICS EXPRESS 2022年第12期30卷 21410-21422页

作者： Zou, Zhiyu Sun, Haowei Chen, Chen Zhao, Xuan Huang, Qin Ying, Yaoyao Li, Tianjiao Xu, Lei Liu, Dong Nanjing Univ Sci & Technol Sch Energy & Power Engn MIIT Key Lab Thermal Control Elect Equipment Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Energy & Power Engn Adv Combust Lab Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch New Energy Jiangyin 214443 Peoples R China

A novel quantitative optical diagnostics method for determining the threshold of soot onset in counterflow diffusion flames was proposed and demonstrated. The method was based on the proportional discrimination of trichromatic luminescence and the nonparametric and unsupervised automatic threshold selection algorithm. The macroscopic soot onset threshold in ethylene diffusion flame with three ethyl esters additions could be precisely determined. It was found that the undesirable soot onset phenomenon for ethylene diffusion flames was significantly suppressed with ethyl ester addition. The method proposed here will be useful as a reference for soot diagnostics in other flames. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： CCD cameras Combustion Diffusion Flames image recognition algorithms Light sources

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Descriptive Models of Information Transformation Processes in image Analysis

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PATTERN recognition AND image ANALYSIS 2021年第3期31卷 402-420页

作者： Gurevich, I. B. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Bldg 244 Vavilov Str Moscow 119333 Russia

This article is devoted to the basic models of descriptive image analysis, which is the leading section of the modern mathematical theory of image analysis and recognition. The fundamental problem that the subject of the article addresses is automated extraction of information from images necessary for making intelligent decisions. Descriptive analysis envisages the implementation of image analysis processes in the image formalization space, the elements of which are various forms (states, phases) of the image representation, which is converted from the original form to one convenient for recognition (i.e., to a model), and data representation conversion models. The image analysis processes are considered to be sequences of transformations implemented in the phase space and providing the construction of phase states of the image, which together form the phase trajectory of image transfer from the original form to the model. The study of the image formalization space leads to formalization of the concepts of representation/image model, as well as to construction of models of the image recognition and analysis processes and formulation of mathematical statements of image recognition and analysis problems. Two types of image analysis models are considered: (1) models reflecting the methodology and mathematical foundations of image recognition and analysis the problem statement, used mathematical and heuristic methods, algorithmic content of the process: (a) a model based on reverse algebraic closure, (b) a model based on the property of equivalence of images of the same class, (c) a model based on multiple image models and multiple classifiers;(2) models characterizing the architecture and structure of the recognition process: (a) a multilevel model for combining algorithms and initial data for image recognition, (b) information structure for generating descriptive algorithmic image recognition schemes. A brief description of the above models is given. A comparative an

关键词： image analysis descriptive image analysis mathematical theory of image analysis automated extraction of information from images image recognition descriptive algorithmic schemes models of image analysis procedures image representations image models image recognition algorithms mathematical structures information structures formal systems

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Local-flexible coupling optical-resolution photoacoustic microscopy with enhanced sensitivity

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OPTICS LETTERS 2022年第14期47卷 3515-3518页

作者： Li, Dongfang Tao, Chao Hu, Zizhong Zhang, Zhengyu Liu, Xiaojun Nanjing Univ Collaborat Innovat Ctr Adv Microstruct Dept Phys MOE Key Lab Modern Acoust Nanjing 210093 Peoples R China Nanjing Med Univ Dept Ophthalmol Nanjing 210029 Peoples R China

An acoustic coupling scheme largely determines the performance of optical-resolution photoacoustic microscopy (OR-PAM), including practicability, sensitivity, and stability. In this study, we propose OR-PAM based on a local-flexible acoustic coupling scheme, which includes a well-designed combiner connecting a set of circulating systems. The combiner integrates an objective lens and an ultrasonic transducer, controls the water level, restricts the flow rate, and drains bubbles. The circulating system provides sustained and steady flowing water. The flowing water constrained in the combiner and the circulating system forms a flexible and stable local contact between the sample and the transducer. Phantom experiments demonstrate that the proposed method can maintain high optical resolution but improve the detection sensitivity by approximately 1.9 times in comparison to dry coupling. In vivo imaging experiments of the mouse eyeground are conducted to examine the practicability of the proposed system in biomedicine. Moreover, in vivo experiments show that OR-PAM based on local-flexible coupling can reveal more details of eyeground microvasculatures, benefiting from its enhanced sensitivity. These merits promise that OR-PAM based on local-flexible coupling may have broad applications in biomedical fields. (C) 2022 Optica Publishing Group

关键词： image processing image recognition algorithms In vivo imaging Laser energy Medical imaging Three dimensional imaging

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Tiny Machine Learning Approach for Grid-Based Monitoring of UAV Tracking and Cyber-Physical Systems in Hydraulic Surveying

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年

作者： Kiran, Ajmeera Ramesh, Janjhyam Venkata Naga Quraishi, Aadam Patni, Jagdish Chandra Keshta, Ismail Byeon, Haewon Raparthi, Mohan Sandhu, Mukta Soni, Mukesh MLR Inst Technol Dept Comp Sci & Engn Hyderabad 500043 Telangana India Graph Era Hill Univ Dept CSE Dehra Dun 248002 India Graph Era Deemed Univ Dept CSE Dehra Dun 248002 Uttarakhand India Intervent Treatment Inst Houston TX 77080 USA Alliance Univ Sch Adv Comp Dept CSE Bengaluru 562102 India AlMaarefa Univ Coll Appl Sci Comp Sci & Informat Syst Dept Riyadh 13713 Saudi Arabia Inje Univ Dept AI & Software Gimhae Si 50834 South Korea Inje Univ Med Big Data Res Ctr Gimhae Si 50834 South Korea Alphabet Life Sci Dallas TX 75063 USA Shri Vishwakarma Skill Univ Skill Dept Comp Sci & Engn Palwal 121102 Haryana India Lovely Profess Univ Div Res & Dev Phagwara 144411 Punjab India Dr DY Patil Vidyapeeth Dr DY Patil Sch Sci & Technol Pune 411033 India

With the advancement in Tiny Machine Learning (ML) technologies, their application in enhancing unmanned aerial vehicles (UAVs) for hydraulic engineering surveying and mapping has become increasingly significant. TinyML's integration offers a leap in processing efficiency and capabilities, particularly in addressing challenges such as UAV search and monitoring due to loss of contact or forced landings. The usage of medical cyber-physical systems in healthcare can revolutionize existing service delivery methods. The study focuses into the spatial grid mapping technique for three-dimensional information, the PTZ camera spatial grid target locking algorithm, and the UAV detection and image correction algorithm. The UAV target is processed using the surveying UAV target tracking method. TinyML techniques are essential for processing and analyzing these photos quickly. Precise UAV identification and tracking are made possible by the combination of image recognition and radar data, which are then processed using TinyML algorithms. This study explores the complexities of algorithms designed specifically for TinyML, such as tracking, UAV detection, grid mapping, and 3D grid space division. Experimental results validate the enhanced capability of this. The results show how well the proposed technique maps and surveys water conservation regions while promptly catching, locking onto, and tracking drones. The algorithm in this study betters than the YOLO, SSD, and RetinaNet algorithms in the recognition and detection of image-oriented surveying and mapping drones.

关键词： Radar tracking Target tracking Autonomous aerial vehicles Radar Tiny machine learning Monitoring Drones Radar imaging Water conservation Target recognition Unmanned aerial vehicles (UAVs) hydraulic engineering surveying grid-based tracking image recognition algorithms

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Microwave photonics interrogation for multiplexing fiber Fabry-Perot sensors

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OPTICS EXPRESS 2021年第11期29卷 16652-16664页

作者： Wu, Nishan Xia, Min Wu, Ying Li, Shiyu Qi, Ruiling Huang, Yuhao Xia, Li Huazhong Univ Sci & Technol Sch Opt & Elect Informat Wuhan 430074 Peoples R China

A microwave photonics interrogation system for multiplexing fiber Fabry-Perot (FP) sensors is demonstrated in this paper. Different from previous FP demodulation schemes, this system aims at quasi-distributed sensing networks composed of FP sensors with a short effective cavity length less than 1 mm. With the help of a dispersion element, the superimposed reflected spectrum from FP sensors based on a hollow core fiber (HCF) can be converted into separate response passbands in the frequency domain simultaneously, whose center frequency will shift linearly with the variations of environment. The experimental results exhibit high linearity and interrogation ability for both the all-FP multiplexing system and hybrid multiplexing system. A strain interrogation sensitivity of 0.938 kHz/mu epsilon and temperature sensitivity of -0.699 MHz/degrees C have been realized, corresponding to a FP cavity length demodulation sensitivity of 1.563 MHz/mu m. Furthermore, numerical studies about the impacts of the HCF-FP spectrum envelope on the RF response passband, as well as the theoretical minimum detectable cavity length and multiplexing capacity of the system, are also carried out. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Fiber optic sensors Hollow core fibers image recognition algorithms Microwave photonic filters Microwave photonics Optical frequency domain reflectometry

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Ghost imaging of blurred object based on deep-learning

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APPLIED OPTICS 2021年第13期60卷 3732-3739页

作者： Zhang, Zijin Wang, Chunfang Gong, Wenlin Zhang, Dawei Univ Shanghai Sci & Technol Dept Phys Shanghai 200093 Peoples R China Chinese Acad Sci Key Lab Quantum Opt Shanghai Inst Opt & Fine Mech Shanghai 201800 Peoples R China Chinese Acad Sci Ctr Cold Atom Phys CAS Shanghai Inst Opt & Fine Mech Shanghai 201800 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

In this paper, a new, to the best of our knowledge, neural network combining a new residual neural network (ResNetV2), the residual dense block (RDB), and eHoloNet is proposed to reconstruct a blurred object. With the theory of ghost imaging, only the bucket signal that passes through the blurred object is necessary for reconstruction. The training sets are ENMNIST, which is used for simulation, and the blurred object is designed by Airy convolution. To test the generalization of the neural network, we use multi-slit as the testing sets. Both simulated and experimental results show that the trained neural network is superior in a generalized reconstruction of the blurred object. In addition, the limitation of the reconstruction is also explained in this work. (C) 2021 Optical Society of America

关键词： Computational imaging Ghost imaging image recognition algorithms Medical image processing Neural networks Optical imaging

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Self-adaptive metasurface platform based on computer vision

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OPTICS LETTERS 2021年第15期46卷 3520-3523页

作者： Yu, Qian Zheng, Yi Ning Gu, Ze Liu, Jia Liang, Yu Chen Li, Lin Zhou Zhang, Xin Ge Jiang, Wei Xiang Southeast Univ Sch Informat Sci & Engn State Key Lab Millimeter Waves Nanjing 210096 Peoples R China

Programmable metasurfaces allow real-time electromagnetic (EM) manipulation in a digital manner, showing great potential to construct advanced multifunctional EM devices. However, the current programmable metasurfaces typically need human participation to achieve various EM functions. In this Letter, we propose, design, and construct a self-adaptive metasurface platform that can achieve beam control automatically based on image recognition. Such a platform is composed of a metasurface with 36 x 36 active units, a single-board computer, and two independent cameras that can detect the position of the objects. The single-board computer, Raspberry Pi, is used to calculate the information of the objects and generate the coding sequences to control the digital metasurface based on a low complexity binocular localization algorithm. Such a smart metasurface platform can generate different beams according to the location of the receiver and can be used as intelligent antennas in future communications and radars. (C) 2021 Optical Society of America

关键词： image processing image recognition image recognition algorithms Machine vision Negative refraction Object detection

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Descriptive image Analysis: Part IV. Information Structure for Generating Descriptive Algorithmic Schemes for image recognition

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PATTERN recognition AND image ANALYSIS 2020年第4期30卷 638-654页

作者： Gurevich, I. B. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Bldg 244 Vavilov Str Moscow 119333 Russia

This article is the fourth in a series of publications devoted to the state of the art and prospects for developing Descriptive image Analysis, one of the leading and intensively developing fields of modern mathematical image analysis theory. The fundamental problem discussed in the article is to automate information extraction from images necessary for making intelligent decisions. This study is devoted to regularizing the generation of descriptive algorithmic image analysis and recognition schemes. The main result is the definition of a new mathematical structure with the following functional capabilities: 1) solution of an image recognition problem in a given formulation, with given initial data and a scenario that determines the sequence of application of information processing procedures and their iterative loops;2) construction of descriptive algorithmic schemes for solving a problem with given initial data in the absence of a given scenario;in the case of a successful solution, the fixation of the sequence of procedures and information processing loops that yielded its solution governs the corresponding descriptive algorithmic schemes and scenarios that can be further used to solve the corresponding class of image recognition problems;3) comparative analysis and optimization of methods for solving image recognition problems via their realization as descriptive algorithmic schemes and scenarios allowed by the structure. The introduced structure is a tool for representing and implementing information processing while solving an image recognition problem for arbitrary formulations, scenarios, models, and solution methods;it can also emulate any descriptive algorithmic scheme and combinations thereof, which are used and generated when solving an image recognition problem. The introduced structure is interpreted as a fundamental model for generating and emulating image recognition procedures. A type characteristic of the introduced information structure for genera

关键词： image analysis descriptive image analysis mathematical theory of image analysis automated information extraction from images image recognition descriptive algorithmic schemes models of image analysis procedures image representations image models image recognition algorithms mathematical structures information structures formal systems

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Hyperspectral face recognition based on sparse spectral attention deep neural networks

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OPTICS EXPRESS 2020年第24期28卷 36286-36303页

作者： Xie, Zhihua Li, Yi Niu, Jieyi Shi, Ling Wang, Zhipeng Lu, Guoyu Jiangxi Sci & Technol Normal Univ Key Lab Opt Elect & Commun Nanchang 330029 Jiangxi Peoples R China Rochester Inst Technol Chester Carlson Ctr Imaging Sci Rochester NY 14623 USA

Inspired by the robust capability and outstanding performance of convolutional neural networks (CNN) in image classification tasks, CNN-based hyperspectral face recognition methods are worthy of further exploration. However, hyperspectral imaging poses new challenges including high data dimensionality and interference between bands on spectral dimension. High data dimensionality can result in high computational costs. Moreover, not all bands are equally informative and discriminative. The usage of a useless spectral band may even introduce noises and weaken the performance. For the sake of solving those problems, we proposed a novel CNN framework, which adopted a channel-wise attention mechanism and Lasso algorithm to select the optimal spectral bands. The framework is termed as the sparse spectral channel-wise attention-based network (SSCANet) where the SSCA-block focuses on the inter-band channel relationship. Different from other methods which usually select the useful bands manually or in a greedy fashion, SSCA-block can adaptively recalibrate spectral bands by selectively emphasizing informative bands and suppressing less useful ones. Especially, a Lasso constraint strategy can zero out the bands during the training of the network, which can boost the training process by making the weights of bands sparser. Finally, we evaluate the performance of the proposed method in comparison of other state-of-the-art hyperspectral face recognition algorithms on three public datasets HK-PolyU, CMU, and UWA. The experimental results demonstrate that SSCANet based method outperforms the state-of-the-art methods for face recognition on the benchmark. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Face recognition Hyperspectral imaging image recognition algorithms Light intensity Neural networks Spectral imaging

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Feedforward Optimisation of Optical Trapping Potentials for Ultracold Atoms

Feedforward Optimisation of Optical Trapping Potentials for ...

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Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR)

作者： Gauthier, Guillaume Bell, Thomas A. Baker, Mark Rubinsztein-Dunlop, Halina Neely, Tyler W. Univ Queensland ARC Ctr Excellence Engn Quantum Syst Brisbane Qld 4072 Australia

Microscopic control of BECs is realised through an atom -density based optimisation of optical potentials. These techniques make progress on the long-standing goal of producing smooth and arbitrary potentials for cold... 详细信息

ISBN: (纸本)9780646825045

关键词： Cold atoms Digital micromirror devices image recognition algorithms Optical potentials Point spread function Spatial light modulators

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