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红外与激光工程 2025年第3期54卷 93-113,I0001页

作者：杜金帅邓寅曹世阳陆泽营李杰韩卓周晋民王棵桂丽丽徐坤北京邮电大学信息光子学与光通信全国重点实验室北京100876

随着人工智能(AI)的快速发展,其高能耗、低解释性和灵活性不足等问题日益凸显。生物神经网络(Biological neural networks,BNN)因其基于真实生物神经元的动态可塑性和自适应性,成为突破AI瓶颈的重要研究方向。离体培养的BNN通过精确的... 详细信息

随着人工智能(AI)的快速发展,其高能耗、低解释性和灵活性不足等问题日益凸显。生物神经网络(Biological neural networks,BNN)因其基于真实生物神经元的动态可塑性和自适应性,成为突破AI瓶颈的重要研究方向。离体培养的BNN通过精确的培养技术和先进的调控手段,为理解生物大脑的信息处理机制以及开发新型智能系统提供了实验平台。文中系统地综述了离体BNN的培养方法及功能优化技术,包括二维和三维培养方式,以及模块化网络构建与连接调控方法。详细探讨了电学刺激、光学刺激和化学刺激等信号输入技术,以及细胞内外动作电位记录、钙荧光成像等信号输出技术。此外,还分析了BNN在静态任务(如语音识别)和实时交互任务(如神经机器人控制)中的应用实例,展示了其在动态学习、复杂模式识别和实时任务适应中的潜力。最后,总结了当前基于BNN的生物智能计算研究的主要挑战,提出了未来研究的方向,希望为未来生物智能计算的研究与发展提供一定启示。

关键词：生物神经网络离体培养双向通信技术生物智能计算

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Improved support vector machine and deep neural network-based rural education platform for data analysis and network education enhancement 5

Improved support vector machine and deep neural network-base...

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5th International Conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Yang, Jiaxue International Education College Guizhou Normal University Guizhou Guiyang55000 China

ISBN: (纸本)9781510691667

This paper presents the design and implementation of a rural education platform utilizing Improved Support Vector Machine (ISVM) and Deep neural network (DNN) methodologies within a cloud computing framework. The platform addresses critical challenges in rural education, including limited access to resources, inadequate infrastructure, and a shortage of qualified educators. By integrating ISVM and DNN, the platform effectively analyzes and predicts the needs of students and teachers, facilitating personalized learning resources and teaching support. Performance evaluations demonstrate that the ISVM-DNN model outperforms traditional SVM and DNN-based SVM models in accuracy, precision, recall, and F1-score, showcasing its superior capability in managing complex, non-linear data. Furthermore, dynamic resource scheduling ensures the platform's scalability and robust performance under high concurrent user loads. User feedback confirms the platform's user-friendly interface and its positive impact on student engagement and teaching efficiency. This study contributes to educational technology by offering a scalable solution that promotes educational equity and quality in rural regions. © 2025 SPIE.

关键词： Deep neural networks

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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

4th International Conference on Advanced Network Technologie...

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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

ISBN: (纸本)9783031837951

The proceedings contain 109 papers. The special focus in this conference is on Advanced network Technologies and Intelligent computing. The topics include: Exploratory Study on Different Transformer models;visionLingua: Empowering Multimodal Understanding with Cutting-Edge Language models;human Action Recognition Using Mediapipe Holistic Keypoints: A Deep Learning Approach;machine Learning for Waste-to-Energy: Optimization and Predictive Analytics;enhanced Traffic Forecasting for Urban Planning Using DeepWalk Embeddings and Spatio-Temporal Graph Ordinary Differential Equation Model;fault Detection in Semiconductor Manufacturing Using Advanced Machine Learning Techniques with Interpretability;fine-Tuning RetinaNet for Few-Shot Fruit Detection;Smart Street: AI-Powered Traffic Flow Enhancement with Adaptive Signal Control;a Hybrid Machine Learning Approach for Accurate Milk Quality Classification;Designing VoiceGPT Chatterbox Concierge: A Personal AI Assistant for Daily Services Using Supervised Machine Learning Based Speech Recognition Techniques;improved Classification Techniques Using Convolutional neural networks for Rice Plant Disease Detection;Enhancing Customer Segmentation and Behavior Prediction Through Machine Learning and Medium Gaussian SVM;Grapevine SDLC Model for Real-Time Fake News Classification;Detecting AI-Generated Text Using Fine-Tuned Transformers: A Study on Academic Integrity;MoReco: AI Model Recommendation and Optimization for B5G networks;file Prefetching Accuracy Enhancement Using Deep Learning Approaches;Mamba Based Conditional GAN for optical Cloud Removal of Satellite Images;a Hybrid Quantum Machine Learning Model for Multi-class Classifier;adaptive Resource Management in Massive Machine-Type Communications Using Reinforcement Learning;Advanced Machine Learning and Features Based Data Analytics of Electricity Consumption in USA and India;cross-Domain Adversarial Attacks and Robust Defense Mechanisms for Multimodal neural networks;deep Featu

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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

4th International Conference on Advanced Network Technologie...

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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

ISBN: (纸本)9783031837890

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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

4th International Conference on Advanced Network Technologie...

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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

ISBN: (纸本)9783031837821

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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

4th International Conference on Advanced Network Technologie...

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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

ISBN: (纸本)9783031837920

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Improved all photonics diffraction neural network based on multi-channel integrated optical fibers

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iScience 2025年第6期28卷

作者： Zhu, Jiakang An, Qichang Yang, Fei Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun 130033 China University of Chinese Academy of Sciences Beijing 100039 China

AI's exponentially growing computational demands conflict with slow hardware advances. The high-power consumption and long training times of large-scale models call for alternative solutions. optical computing-based traditional optical networks and diffractive deep neural network (D²NN) still face deployment challenges and reliance on electronic networks. To address these issues, we replace the free-space interlayer propagation in conventional optical networks with fiber-based propagation. This preserves the advantages of traditional optical networks while providing additional benefits such as ease of deployment, reduced dependence on electronic networks, and enhanced robustness. Experimental results demonstrate that this untrained structure exhibits strong nonlinear mapping capabilities across different configurations, yielding distinct outputs for three input targets, especially at 1550 nm. Furthermore, the influence of environment and noise is around 1% in target recognition. Leveraging inherent spectral discrimination, this architecture enables multidimensional target identification with important implications for complex target classification and multidimensional sensing. © 2025 The Authors

关键词： Applied computing Optics Physics

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Classifying spectra of emission-line regions with neural networks An application to integral field spectroscopic data of M33

arXiv

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arXiv 2025年

作者： Bracci, Caterina Belfiore, Francesco Ginolfi, Michele Feltre, Anna Mannucci, Filippo Marconi, Alessandro Cresci, Giovanni Bertola, Elena Bombini, Alessandro Ceci, Matteo Marconcini, Cosimo Moreschini, Bianca Scialpi, Martina Tozzi, Giulia Ulivi, Lorenzo Venturi, Giacomo Dipartimento di Fisica e Astronomia Università di Firenze Via G. Sansone 1 Sesto F.no FirenzeI-50019 Italy INAF - Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 FlorenceI-50125 Italy Via Bruno Rossi 1 FI Sesto Fiorentino50019 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data & Quantum Computing Via Magnanelli 2 BO Casalecchio di Reno40033 Italy University of Trento Via Sommarive 14 TrentoI-38123 Italy Gießenbachstraße 1 Garching85748 Germany Scuola Normale Superiore Piazza dei Cavalieri 7 PisaI-56126 Italy

Emission-line regions are key to understanding the properties and evolution of galaxies, as they trace the exchange of matter and energy between stars and the interstellar medium (ISM). In nearby galaxies, individual nebulae can be resolved from the background, and classified through their optical spectral properties. Traditionally, the classification of these emission-line regions in H ii regions, planetary nebulae (PNe), supernova remnants (SNR), and diffuse ionised gas (DIG) relies on criteria based on single or multiple emission-line ratios. However, these methods face limitations due to the rigidity of the classification boundaries, the narrow scope of information they are based upon, and the inability to take line-of-sight superpositions of emission-line regions into account. In this work, we explore the use of artificial neural networks to classify emission-line regions using their optical spectra. Our training set consists of simulated optical spectra, obtained from photoionisation and shock models, and processed to match observations obtained with the MUSE integral field spectrograph at the ESO/VLT. We evaluate the performance of the network on simulated spectra exploring a range of signal-to-noise (S/N) levels, different values for dust extinction, and the superposition of different nebulae along the line of sight. At infinite S/N the network achieves perfect predictive performance, while, as the S/N decreases, the classification accuracy declines, reaching an average of ∼ 80% at S/N(Hα)=20. We then apply our model to real spectra from MUSE observations of the Local Group galaxy M33. The network provides a robust classification of individual spaxels, even at low S/N, identifying H ii regions and PNe and distinguishing them from SNRs and diffuse ionized gas, while identifying overlapping nebulae. Moreover, we compare the network’s classification with traditional diagnostics, finding a satisfactory level of agreement between the two approaches. We identify t

关键词： Supernovae

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Hausa Handwriting Character Recognition Using CNN and Tesseract

Hausa Handwriting Character Recognition Using CNN and Tesser...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Muhammad Khamis Dauda Umar Musa Hassanin Al-Barhamtoshy Department of Information Technology Faculty of Computing and Information Technology Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

The Hausa language, spoken by over 64 million people, is a vital part of West Africa's cultural and social fabric. Despite its importance, handwritten Hausa script recognition has little concern due to its tonal nature, unique characters, and limited datasets. This study tackles these challenges by creating a dataset and evaluating two models commonly used in handwritten recognition: a Convolutional neural network (CNN) and Tesseract optical Character Recognition (OCR). The custom CNN, trained on data from 30 volunteers with varied writing styles, achieved an accuracy of 96%, with strong precision, recall, and F1-score metrics. In contrast, Tesseract OCR struggled, particularly with Hausa-specific characters like Ɓ, Ƙ, Ƴ, and ɗ, achieving lowest accuracy for these classes. Although it had non-zero recognition in some characters, its performance was inconsistent, indicating difficulties in generalization. This work provides a scalable solution for handwritten Hausa character recognition, contributing to research on low-resource African languages and paving the way for better access to Hausa handwritten literature and integration into natural language processing applications.

关键词： Handwriting recognition Accuracy Biological system modeling optical character recognition Writing Natural language processing Fabrics Character recognition Convolutional neural networks Cultural differences

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UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking neural networks

arXiv

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arXiv 2025年

作者： Qian, Yuanbin Ye, Shuhan Wang, Chong Cai, Xiaojie Qian, Jiangbo Wu, Jiafei Faculty of Electrical Engineering and Computer Science Ningbo University China Merchants’ Guild Economics and Cultural Intelligent Computing Laboratory Ningbo University China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

Video anomaly detection plays a significant role in intelligent surveillance systems. To enhance model’s anomaly recognition ability, previous works have typically involved RGB, optical flow, and text features. Recently, dynamic vision sensors (DVS) have emerged as a promising technology, which capture visual information as discrete events with a very high dynamic range and temporal resolution. It reduces data redundancy and enhances the capture capacity of moving objects compared to conventional camera. To introduce this rich dynamic information into the surveillance field, we created the first DVS video anomaly detection benchmark, namely UCF-Crime-DVS. To fully utilize this new data modality, a multi-scale spiking fusion network (MSF) is designed based on spiking neural networks (SNNs). This work explores the potential application of dynamic information from event data in video anomaly detection. Our experiments demonstrate the effectiveness of our framework on UCF-Crime-DVS and its superior performance compared to other models, establishing a new baseline for SNN-based weakly supervised video anomaly detection. © 2025, CC BY-NC-SA.

关键词： optical flows

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