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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Afifi, Salma Thakkar, Ishan Pasricha, Sudeep Colorado State University Department of Electrical and Computer Engineering Fort Collins United States University of Kentucky Department of Electrical and Computer Engineering Lexington United States

ISBN: (纸本)9783982674100

The rapid proliferation of deep learning has revolutionized computing hardware, driving innovations to improve computationally expensive multiply-accumulate operations in deep neural networks. Among these innovations are integrated silicon-photonic systems that have emerged as energy-efficient platforms capable of achieving light speed computation and communication, positioning optical neural network (ONN) platforms as a transformative technology for accelerating deep learning models such as convolutional neural networks (CNNs). However, the increasing complexity of optical hardware introduces new vulnerabilities, notably the risk of hardware trojan (HT) attacks. Despite the growing interest in ONN platforms, little attention has been given to how HT-induced threats can compromise performance and security. This paper presents an in-depth analysis of the impact of such attacks on the performance of CNN models accelerated by ONN accelerators. Specifically, we show how HTs can compromise microring resonators (MRs) in a state-of-the-art non-coherent ONN accelerator and reduce classification accuracy across CNN models by up to 7.49% to 80.46% by just targeting 10% of MRs. We then propose techniques to enhance ONN accelerator robustness against these attacks and show how the best techniques can effectively recover the accuracy drops. © 2025 EDAA.

关键词： Convolutional neural networks

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The Character Recognition Method Based on OCR 26

The Character Recognition Method Based on OCR

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26th ACIS International Winter Conference on Software Engineering, Artificial Intelligence, networking and Parallel/Distributed computing, SNPD-Winter 2023

作者： Jianyang, Liu Junrong, Bao Bingjin, Li Zhiang, Feng Su, Zhang Yunnan Province Information Technology Development Center Yunnan China

ISBN: (纸本)9798350345865

The application scenarios of using digital documents for information exchange in modern society are gradually increasing with the rapid development of new technologies. Manually completing the classification and entry of book texts, book lending certificates and other information will also consume a lot of manpower, material resources and time costs. OCR technology based on deep learning processes data efficiently and with high accuracy which can help users better manage and process information. Therefore, this paper proposes an optical character recognition method based on deep neural network, integrating the DB Net model and the CRNN model. The main work is as follows: (1) We investigate and compare different deep learning-based text detection and text recognition algorithms. (2) Based on the features of random text position and directional orientation in text images under natural scenes, the text recognition method of DB Net and CRNN with CTC is proposed. DB Net Model outperforms other text detection models in terms of accuracy and speed and the accuracy of CRNN model can meet the requirements of real business scenarios. (3) We use the dataset to train the algorithm model and expand the method structure combined with the front-end framework. The method meets the task requirements of users to identify images and text accurately and provides a reference value for the study of the same type of methods. © 2023 IEEE.

关键词： optical character recognition

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Model authentication hashing: Identifying pirated neural network models

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Expert Systems with Applications 2025年 283卷

作者： Xiong, Cheng Feng, Guorui Sun, Yunlong Qian, Zhenxing Yao, Heng Qin, Chuan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Communication and Information Engineering Shanghai University Shanghai200444 China Department of Tourism Fudan university Shanghai200433 China Key Laboratory of Culture & Tourism Intelligent Computing of China Ministry of Culture and Tourism Shanghai200433 China School of Computer Science Fudan University Shanghai200433 China

Nowadays, a great number of neural network models have been trained and applied in many fields. At the same time, the emergence of vast network models has already highlighted the security problems of intellectual property protection. High-value pre-trained models of original inventors may be pirated and used by illegal users without authorization. In order to solve this problem, we first give the definition of model differentiation, and then propose an effective robust hashing method based on model parameters for piracy identification in this paper. Through our model hash generation network, the parameters of original model are transformed into a one-way hash sequence with the fixed length, and the robustness and discrimination of the model hashes can be utilized to authenticate model parameters. Experimental results show that the accuracy rates of similar model detection and different model discrimination under different cases are almost all greater than 96.5%, and the comprehensive performance of proposed method is also the best one compared with other 3 SOTA methods. These results mean that the proposed method can be applied for identifying illegal model piracy effectively. © 2025 Elsevier Ltd

关键词： Terrorism

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Research on Fault Warning Technology of optical network Based on Recurrent neural network

Research on Fault Warning Technology of Optical Network Base...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Ganhong Cai Yinfa Zhang Xiaoyun Kuang Lu Zeng College of Information and Communication National University of Defense Technology Wuhan China Team 14 Unit 75842 of PLA Guangzhou China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

With the rapid development of cyber information system construction, optical networks, as a crucial component of the cyber information infrastructure, carry an increasing amount of data information and directly determine the performance of the network information system. The occurrence of faults in optical networks can severely impact various information transmissions; hence, fault warning and analysis are essential to ensure the stable and reliable operation of optical networks. Traditional fault warning and analysis of optical networks mainly rely on expert experience and network management system alarms, which have limitations such as long training cycles for backbone personnel, poor fault tolerance of network management systems, and long cycles for knowledge base construction. These limitations make it difficult to provide early warnings before faults occur in optical transmission networks, and the implementation of intelligent operation and maintenance of optical networks is challenging. Therefore, warning of optical network performance helps to accurately detect hidden dangers and determine fault causes before faults occur in optical transmission networks, and to prepare spare boards, fiber cores, and backbone forces in advance, minimizing repair time and achieving "zero" fault perception for users. This paper proposes an optical network fault warning model based on recurrent neural networks to achieve high-accuracy and intelligent response for optical network fault warnings.

关键词： Training optical fibers Recurrent neural networks optical computing optical fiber networks Predictive models Maintenance engineering

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Study of Various Deep Learning models for COVID-19 Detection Based on Fractional Order optical Flow 14

Study of Various Deep Learning Models for COVID-19 Detection...

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14th International Conference on computing Communication and networking Technologies, ICCCNT 2023

作者： Singh, Bhavana Khan, Muzammil Kumar, Pushpendra Maulana Azad National Institute of Technology Department of Mathematics Bioinformatics & Computer Applications Bhopal462003 India

ISBN: (纸本)9798350335095

The world entered into a new phase in the wake of Covid-19, which is declared as a global threat because countless people were losing their lives worldwide due to this disease every day. Even the best medical facilities were crumbling due to the rise of COVID-19 and lack of funding. However the use of nucleic acid-based testing generates an excessive amount of bio-waste and increases the risk of infection to the healthcare personnel. These tests take a lot of time, which contributes to the high mortality rate. To address these challenges, vision based techniques have been demonstrated to be beneficial in the early and swift diagnosis of COVID-19. Earlier imaging techniques used X-ray or CT-scan images to detect COVID-19. Consequently, these techniques do not filter out non-COVID cases, resulting in longer processing time. In this paper, COVID-19 identification is accomplished through the dynamic characteristics obtained from chest X-ray (CXR) images. These dynamic characteristics are derived as optical flow (OF). Moreover a fractional order-based variational model is used to estimate OF from two consecutive images of lungs. The novelty of this work is to use OF instead of images in COVID-19 detection. Further, the classification of lung dynamic features is performed with a convolutional neural network (CNN) in coordination of various machine learning (ML) classifiers. A detailed comparison is provided to show the significance of the algorithm. © 2023 IEEE.

关键词： COVID-19

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Efficient calculation of optical properties of suspended-core fiber via a machine learning algorithm

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APPLIED OPTICS 2022年第19期61卷 5714-5721页

作者： Yuan, Shuyu Chen, Shengchao Yang, Jianli Yang, Qian Ren, Sufen Wang, Guanjun Yu, Benguo Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Hainan Med Univ Sch Biomed Informat & Engn Haikou 571199 Hainan Peoples R China

Growing nonlinearity demands in mid-infrared applications place more outstanding requirements on fiber structure design. Chalcogenide suspended-core fibers (SCFs) are considered excellent candidates for mid-infrared applications due to their significant advantages in nonlinearity and dispersion management. However, traditional numerical methods for accurate modeling and optimization of SCFs often rely on the performance of computing devices and have many limitations when dealing with complex models. A machine learning algorithm is applied to calculate the optical properties of chalcogenide SCFs, including effective mode area, nonlinear coefficient, and dispersion. The established artificial neural network (ANN) model enables accurate prediction of the above optical properties of As2S3 SCF, for which in the wavelength range of 1.0 to 4.0 mu m, the radius of the fiber core is 0.4 to 0.6 mu m, and width of the cantilever is 0.06 to 0.09 mu m. We demonstrate that this simple ANN model has considerable advantages over the traditional numerical calculation model in computational speed and resource utilization. In summary, the proposed model can quickly provide more accurate optical property predictions, providing a cost-effective solution for precise modeling and optimization of chalcogenide SCFs. (C) 2022 Optica Publishing Group

关键词： Chalcogenide fibers Fiber design Microstructured fibers optical properties Raman fiber lasers Tapered fibers

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Leveraging Lithium Niobate on Insulator Technology for Photonic Analog computing

Leveraging Lithium Niobate on Insulator Technology for Photo...

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Italian Conference on Optics and Photonics (ICOP)

作者： De Marinis, Lorenzo Paolini, Emilio Contestabile, Giampiero Andriolli, Nicola Scuola Super Sant Anna Via Moruzzi 1 I-56124 Pisa Italy Natl Res Council Italy CNR IEIIT Via Caruso 16 I-56122 Pisa Italy

ISBN: (数字)9781665488815

ISBN: (纸本)9781665488815

Machine learning has experienced an unprecedented growth over the last decade with graphics processing units being instrumental to this success, used as accelerators for the computations required during training and inference in artificial neural networks. However, the increasing complexity of the employed neuromorphic models might pose issues in terms of required computing resources and energy to run these tasks. optical solutions, especially exploiting integrated photonics, are promising to effectively run neuromorphic tasks, exploiting high-speed and low-power elements. In this paper, we propose to exploit cascaded low-loss and low-driving-voltage travelling wave Lithium Niobate on Insulator (LNOI) modulators to perform multiply-accumulate operations at high speed and low power consumption. Thanks to the moderate losses, the same input can be split to multiple weight modulators, which increases the energy efficiency. Simulations of computer vision tasks on well known datasets show that the proposed solution, notwithstanding the limitations due to the analog photonic physical layer, achieves a good accuracy with limited degradation with respect to traditional digital electronic implementations.

关键词： Integrated photonics LNOI photonic neural network neuro-inspired photonic computing

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Optimization Strategy of Raman Pump for C+L+S optical Transmissions with ANN

Optimization Strategy of Raman Pump for C+L+S Optical Transm...

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

作者： Rui Wang Jing Zhang Hong Lin Xue Zhao Bo Xu Kun Qiu Key Lab. of Optical Fiber Sensing and Communications University of Electronic Science and Technology of China Chengdu China

We efficiently establish a differential model to estimate the generalized signal-to-noise ratio profiles with artificial neural network models. The optimization of the Raman pump based on this model is developed to ma... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Stimulated emission Artificial neural networks optical computing Lasers and electrooptics Laser modes optical pumping Optimization

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Med-T: A pixel-level position information aware transformer for medical visual task

Med-T: A pixel-level position information aware transformer ...

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2024 International Conference on Future of Medicine and Biological Information Engineering, MBIE 2024

作者： Huang, Jianbo Zhao, Ke Wang, Jing Zhang, Kexin Shenyang Institute of Computing Technology Chinese Academy of Sciences No.16 Nanping East Road Shenyang China College of Information Engineering of Liaoning University of Traditional Chinese Medicine No. 79 Chongshan East Road Shenyang China

ISBN: (纸本)9781510682825

The model of transformer structure occupies a dominant position in the field of multimodal large model. While previous studies have highlighted the potential of Visual Transformer (ViT) models, their reliance on large datasets poses challenges in domains like medicine, where obtaining extensive data can be difficult. In such scenarios, traditional convolutional neural networks (CNNs) often outperform transformer-based models due to their ability to capture pixel-level fine-grained information. In this paper, we proposed soft mask operation and fine-grained information awared visual transformer Med-T, a CNN-Transformer hybrid visual backbone network tailored for visual feature extraction task on limited datasets. Through extensive evaluation across three small datasets, Med-T consistently outperforms alternative approaches, showcasing the efficacy of leveraging the pixel-level position information extraction ability of CNN branch. © 2024 SPIE.

关键词： Medical image processing

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The role of all-optical neural networks

arXiv

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arXiv 2023年

作者： Matuszewski, Michal Prystupiuk, Adam Opala, Andrzej The Institute of Physics Polish Academy of Sciences Aleja Lotników 32/46 WarsawPL-02-668 Poland Institute of Experimental Physics Faculty of Physics University of Warsaw ul. Pasteura 5 WarsawPL-02-093 Poland

In light of recent achievements in optical computing and machine learning, we consider the conditions under which all-optical computing may surpass electronic and optoelectronic computing in terms of energy efficiency and scalability. When considering the performance of a system as a whole, the cost of memory access and data acquisition is likely to be one of the main efficiency bottlenecks not only for electronic, but also for optoelectronic and all-optical devices. However, we predict that all-optical devices will be at an advantage in the case of inference in large neural network models, and the advantage will be particularly large in the case of generative models. We also consider the limitations of all-optical neural networks including footprint, strength of nonlinearity, optical signal degradation, limited precision of computations, and quantum noise. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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