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Optical Fiber Alignment Aid with image processing on FPGA-based System

IETE JOURNAL OF RESEARCH 2023年

作者： Latha, P. Sumitra, V. Reddy, T. Snehitha Debbarma, Sunita Nath, Vijay Kannagi, V. RMK Coll Engn & Technol Thiruvallur 601206 Tamil Nadu India Birla Inst Technol Mesra Ranch Ranchi 835 215 Jharkhand India Tripura Inst Technol Elect & Commun Engn Dept Agartala Tripura India

This paper introduces an innovative and efficient solution for achieving precise optical fiber alignment in fiber optic splicing applications. By harnessing the capabilities of FPGA-based real-time image processing, the system surpasses the required frame rate of 20 frames per second, ensuring swift and accurate alignment. With a verified resolution of approximately 1 pixel per mu m, the system offers high precision suitable for various applications. Through advanced image processing techniques, the system calculates precise correction values for each frame, resulting in fast and accurate alignment of fiber cores and cladding. This unique feature enhances performance and adaptability, leading to substantial reduction in splice wastage. By integrating motorized alignment stages and guided machine vision, the system provides a robust and automated approach to fiber optic alignment. The FPGA-driven stepper motors enable precise and controlled fiber movements with micrometer-level step resolutions. The calculated correction factors effectively guide the alignment process, with deviations from actual results impressively low - a maximum of 12 mu m in the Y correction factor and 7 mu m in the X correction factor. This reliability makes the system a crucial component in any optical fiber splicing machine. Furthermore, the paper emphasizes the ability to handle specialty fiber alignment contributes significantly to cost-effective manufacturing of fiber optic components and modules. By ensuring precise and optimal fiber alignment, the system mitigates signal loss, leading to improved performance and reduced production costs.

关键词： Calibration CMOS Edge detection FPGA Optical fibers Misalignment

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Hydrogel-Scalable Nanoslide for Switchable Optical Spatial-Frequency processing

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LASER & PHOTONICS REVIEWS 2023年第4期17卷

作者： Dai, Chenjie Li, Zhe Sh, Yangyang Wan, Shuai Hu, Wanlin Li, Zhongyang Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Wuhan Inst Quantum Technol Wuhan 430206 Peoples R China Wuhan Univ Sch Microelect Wuhan 430072 Peoples R China Wuhan Univ Suzhou Inst Suzhou 215123 Peoples R China

Spatial frequency processing is an essential technology for extracting morphological information from an optical image. Although various Fourier-based flat optical elements have been proposed as spatial-frequency filters to realize image processing, their transfer functions are statically fixed once fabricated, thus limiting the versatile, dynamic functionalities and practical applications. Here, a novel practical tuning strategy is demonstrated to realize switchable spatial-frequency processing for edge-enhanced and bright-field imaging by employing a tunable hydrogel-scalable nanoslide. By utilizing multilayered metallic and hydrogel stacks construction, the nanoslide directly manipulates the optical spatial frequency in the wavevector domain and exhibits opposite image processing at different wavelength channels due to the cavity-induced wavelength-sensitivity. More intriguingly, via controlling the ambient humidity, the angular-dependent optical response of the nanoslide can be effectively tuned for dynamic edge-enhanced imaging due to the hydrogel's inflation from moisture. In addition, the nanoslide is readily fabricated at a large scale and integrated into compact imaging systems, such as a biomicroscope. With the advantages of a high numerical aperture approximate to 0.8, polarization-insensitive, microscopy-compatible, and facile architecture, the proposed hydrogel-based nanoslide can find potential applications in machine vision, real-time image processing, biological imaging, and analog computing.

关键词： humidity-responsive hydrogel-scalable nanoslides spatial-frequency processing switchable wavevector manipulation

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A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

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IEEE ACCESS 2022年 10卷 65376-65400页

作者： DE DONATO, L. O. R. E. N. Z. O. FLAMMINI, F. R. A. N. C. E. S. C. O. MARRONE, S. T. E. F. A. N. O. MAZZARIELLO, C. L. A. U. D. I. O. NARDONE, R. O. B. E. R. T. O. SANSONE, C. A. R. L. O. VITTORINI, V. A. L. E. R. I. A. Univ Naples Federico II Dept Elect Engn & Informat Technol I-80125 Naples Italy Linnaeus Univ Dept Comp Sci & Media Technol S-35195 Vaxjo Sweden Hitachi Rail STS Digital & Data Driven Innovat Unit I-80147 Naples Italy Univ Naples Parthenope Dept Engn I-80143 Naples Italy

Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions.

关键词： Rail transportation Maintenance engineering Rails Sensors Inspection Task analysis Focusing Computer vision machine learning fault detection inspection CNN smart railways

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Enhancing Algorithmic Robustness through Noise-Induced Dataset Augmentation for Fusion Fuel Profile Inversion

Enhancing Algorithmic Robustness through Noise-Induced Datas...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Ke Xu Jun Zhao Guoliang Xiao XingZhong Xiong Artificial Intelligence Key Laboratory of Sichuan Province Sichuan University of Science and Engineering Yinbin China Southwestern Institute of Physics Chengdu China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In the field of nuclear fusion fueling, the distribution of fuel beam parameters is of great significance for optimizing injection techniques and for in-depth study of the interaction between particles and plasma. To obtain the beam velocity distribution of the supersonic molecular fueling beam, a U-net model has been applied in our previous research, successfully capturing the mapping relationship between density distribution and velocity distribution, and effectively predicts velocity distributions across a wide range of parameters. To effectively apply this model to the inversion of experimental data, it is imperative to rigorously investigate its noise resistance capabilities. This paper focuses on enhancing algorithmic robustness through noise-induced dataset augmentation. The results suggest that under 20% noise interference, the model can still achieve stable prediction of the velocity distribution of the supersonic molecular beam, providing strong support for subsequent research.

关键词： Resistance Particle beams Gaussian noise Molecular beam applications Predictive models Fusion reactors Feature extraction Prediction algorithms Robustness Fuels

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Research on Evaluation Method of Aerial image Segmentation Algorithm 7

Research on Evaluation Method of Aerial Image Segmentation A...

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7th International Conference on Signal and image processing, ICSIP 2022

作者： Wang, Heng Zhuang, Chuangang Zhao, Jiamei Shi, Rui Jiang, Hai Yuan, Yanrong Guo, Xinyi Xue, Zhichao China Academy of Launch Vehicle Technology Science and Technology on Space Physics Laboratory Beijing100076 China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Aerial image segmentation algorithms are used in many scenarios such as traffic warning, road assistance, terrain mapping and military reconnaissance. It has been widely applied in both military and civilian applications. A lot of researches on target detection and recognition technology in aerial images have been carried out. In addition, technologies such as computer vision and machine learning have been introduced. Compared with the research on image segmentation algorithms, there are relatively few studies on evaluation method segmentation algorithms. In view of the above situation, the evaluation method of aerial image segmentation is established, and the performance of comprehensive evaluation of the medhod for image segmentation algorithms is verified. Specifically, it includes similarity index, misidentification similarity index and distance index. Selecting image data sources with different degrees of complexity are selected in order to verify the effectiveness of the above image segmentation evaluation method. The results show that the evaluation indicators prove that the algorithms can achieve effective target segmentation to a certain extent. The segmentation accuracy rate is above 95%. In view of the accuracy and effectiveness of the aerial image segmentation algorithm, it is expected to be further applied in the field of aerial images. © 2022 IEEE.

关键词： image segmentation

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From Templates to Transformers: A survey of Multimodal image Captioning Decoders

From Templates to Transformers: A survey of Multimodal Image...

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Computer, Electronics & Electrical Engineering & their applications (IC2E3), International Conference on

作者： Himanshu Sharma Devanand Padha Department of Computer Science and IT Central University of Jammu Rahya-Suchani Jammu India

Automated text-to-image (image synthesis) and image-to-text (image captioning) generation are two of the most challenging and cutting-edge fields of study in Computer vision (CV) in conjunction with Natural Language processing (NLP). The image-to-text synthesis, also known as image Captioning (IC), has numerous applications in visual assistance, machine vision, remote security, healthcare, and remote sensing, among others. Since their origin, the IC frameworks have been constructed utilizing a two-subcomponent pipeline consisting of the visual feature extraction and natural language modeling subcomponents. The recent surge of interest in the applications of sequential deep learning models in machine translation has resulted in the development of efficient language modeling architectures. Deep sequential modeling architectures, such as Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU), tackle the intricacies of the multi-modular space far better than existing machine learning-based translation approaches. The tremendous growth of deep language modeling architectures in IC necessitates a comprehensive review of its literature. In this survey, we conduct an exhaustive and analytical analysis of the language modeling architectures used in IC frameworks for caption generation, along with their training corpus datasets. We also identify a list of open research issues and potential research areas for future work.

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Generating Caption From images Using Flickr image Dataset

Generating Caption From Images Using Flickr Image Dataset

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International Conference on Computing and Networking Technology (ICCNT)

作者： Piyush Sharma Kiranpreet Kaur Aaryan Gandotra Pranay Patial Radhika Sehaj Gaba Department of CSE Chandigarh University Mohali India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In an age where the Internet is dominated by visual content, the generation of animated captions has become a must. It has always been an interesting study for researchers in the Department of Artificial Intelligence. Enabling the machine to describe images with the same skillful accuracy as the human has important applications in various fields such as robotic vision, manufacturing, and beyond This project integrates recurrent neural networks with is a topic of contextual parallelism dedicated to extracting features from images Natural Language processing Computer vision and integrating them seamlessly, this research provides insights a it goes further on this interdisciplinary topic. Additionally, annotations for the sample images are created and performed a comparative analysis of different feature extraction and encoder patterns to determine which model provided the highest accuracy and delivered the desired results.

关键词： Visualization Accuracy Recurrent neural networks image databases Computational modeling Parallel processing Feature extraction Web sites Multimedia communication Object recognition

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machine Learning Algorithms for Signal and image processing

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2023年

作者： edited by Deepika Ghai Lovely Professional University IN Suman Lata Tripathi Lovely Professional University IN Sobhit Saxena Lovely Professional University IN Manash Chanda Meghnad Saha Institute of Technology IN Mamoun Alazab Charles Darwin University AS. Ghai Deepika editor. Tripathi Suman Lata editor. Saxena Sobhit editor. Chanda Manash editor. Alazab Mamoun 1980-editor.

ISBN: (数字)9781119861843;9781119861836

ISBN: (纸本)9781119861829

machine Learning Algorithms for Signal and image processing Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing machine Learning Algorithms for Signal and image processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as:

Speech recognition, image reconstruction, object classification and detection, and text processing
Healthcare monitoring, biomedical systems, and green energy
How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time
Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection

Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.

关键词： Signal processing.image processing.machine learning

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Feature extraction and multi-period classification of sea mushroom based on image information

Feature extraction and multi-period classification of sea mu...

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2022 International Conference on Artificial Intelligence, Information processing and Cloud Computing, AIIPCC 2022

作者： Zhong, Yang Du, Wanhe Yang, Shuzhen Yu, Tao Zheng, Xueping School of Mechatronic Engineering and Automation Shanghai University Shanghai China College of Engineering Shanghai Polytechnic University Shanghai China Shanghai Rongmei Agricultural Technology Co LTD Shanghai China

ISBN: (纸本)9781665462877

The phenotype of edible fungus basically relies on visual observation and empirical judgment at present, and there is still a lack of reports on the phenotypic techniques and applications for cultivation of seafood mushroom. Accurate characterization of different growth stages in the reproductive culture step of sea mushroom and rapid judgment of its growth stage is the foundation for its industrial cultivation to intelligent cultivation. In this work, a method of multi-period growth feature extraction and period classification of sea mushroom based on machine vision technology is proposed. According to the characteristics of the image itself, the method extracts 28 features of color, shape, and texture from the acquired data set. Subsequently, the KNN (K-nearest Neighbor) is utilized to classify the images represented by the selected features. The results indicated that the multi-period classification accuracy of the growth period of the mushroom image based on the comprehensive representation of the three types of features was 85%, which could better distinguish the growth period through the image, and made guidelines for the regulation of the cultivation environment. © 2022 IEEE.

关键词： Classification (of information)

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1st International Conference on Artificial Intelligence and its applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st International Conference on Artificial Intelligence and its applications, ICAIA 2023

ISBN: (纸本)9783031843938

The proceedings contain 37 papers. The special focus in this conference is on Artificial Intelligence and its applications. The topics include: The Hybrid Cardiac Risk Assessment and Prediction Model Using Convolutional Neural Networks;deep Learning applications for Malaria Detection and Diagnosis: A Review;Environmental Considerations in the Ethics of AI Adoption in Healthcare: Striving for Sustainable and Responsible Practices;harnessing the Power of Cognitive Computing: Assessing Point-of-Care Decision Support Tools in Oncology Practice;Critical Analysis in Use of AI in Health Care Management;classification and Prediction of Spinal Tuberculosis Disease Using Optimization of Convolution Neural Network Using Spatial and Temporal Constraints;artificial Intelligence for Remote Healthcare in Underserved Areas: Enhancing Access and Quality of Healthcare Delivery;machine Learning Based Skin Cancer Detection and Recognitions Techniques in IoT Environment;validation of a Chronic Kidney Disease Prediction System Using machine Learning Techniques;real-Time Feedback Detection Using Emotion Detection and Facial Recognition;revolutionizing vision Tasks: Unlocking Potential Through Patch-Based Approaches;automated Knee Implant Identification from 2D Templates Using image processing and Artificial Intelligence – An Experimental Approach;crop Analysis and Classification Based on Phenotype Using Ensemble Learning;Classification and Identification with Health Benefit Assessment and Nutrient Profile of Brewed Tea Utilizing Computer vision with ML and DL and Sensory Approaches;enhancing Industrial Automation Flexibility Through Neural Network-Empowered machine vision applications.

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