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Synergizing the low-phase noise characteristics of a dielectric resonator oscillator and a bichromatic Brillouin laser oscillator

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Optics Letters 2025年第3期50卷 988-991页

作者： Hu, Jiahao Xue, Siyi Xiao, Yanlan Wu, Chenlin Wen, Huashun Zhou, Heng Key Lab of Optical Fiber Sensing and Communication Networks School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Chengdu Simon Electronic Technology Co. Ltd Chengdu China Institute of Intelligent Photonics Nankai University Tianjin300071 China

We introduce a method that synergistically combines the distinct phase noise profiles of a dielectric resonator oscillator (DRO) and bichromatic Brillouin laser oscillator (BBLO), for high spectral purity microwave generation. Typically, free-running DROs exhibit high-phase noise at low-frequency offsets but ultra-low-phase noise at high offsets. In contrast, BBLOs based on photonic devices demonstrate very low-phase noise at low offsets, yet they experience increased phase noise at higher offsets due to photodetector shot noise. By locking the DRO to a frequency-matching BBLO, our experiment combines the merits of both oscillators and generates X-band microwave with exceptional phase noise across the entire frequency offset range, reaching −93 dBc/Hz at 100 Hz offset and −171 dBc/Hz at 10 MHz offset. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Photonic devices

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Maize leaf disease multiclass classification and recognition for sustainable agriculture using multi preceptive deep learning model

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Discover Sustainability 2025年第1期6卷 1-18页

作者： Gautam, Vinay Quraishi, Aadam AlGhamdi, Azzah Kaur, Gaganpreet Alghayadh, Faisal Byeon, Haewon Bhatt, Mohammed Wasim CUIET Chitkara University Punjab Rajpura 140401 India M.D. Research Intervention Treatment Institute Texas Houston United States Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh Saudi Arabia Convergence Department Korea University of Technology and Education Cheonan South Korea Model Institute of Engineering and Technology J&K Jammu India

Agriculture sector is faced with perennial challenges that threaten both its productivity and sustainability. Among the greatest threats to cereal crops is disease, especially on cereal grains such as maize. Maize is an important grain that has grown globally, yet it often falls prey to maize leaf disease, a destructive and prevalent disorder. The consequences of these diseases go beyond individual farmers;reduced yields destabilize supply chains, market stability, and global efforts towards creating sustainable food systems. The prevalence of leaf diseases adversely affects crop productivity, which directly impacts the objective of sustainable agriculture. In order to address this problem, technology, more specifically artificial intelligence, has been a game-changer. Adopting cutting-edge research for maize disease detection not only raises diagnostic accuracy but also supports sustainable agricultural practices. This approach encourages effective input use, supports food security, minimizes environmental degradation, and provides farmers with accurate tools, all of which contribute to long-term agricultural resilience and sustainability. However, the incorporation of AI in farming is confronted by a number of challenges. Numerous hindrances hinder precise detection and categorization of maize leaf diseases via artificial intelligence methods. In tackling the limitations, the current study introduces an AI-based approach. It applied Multi-scaled Xception pre-trained models to extract deep features from images. The models were fine-tuned with varying weights for advancing the feature extraction so as to enhance the likelihood of correct visual classification. In addition to its strong accuracy, the research provides a formal and strict mathematical formulation, and different optimization methods further establish the effectiveness of the model. Additionally, the examination of fusion operators helps to improve the interpretability of the model. The expected model w

关键词： CCA Deep learning Feature extraction Feature Fusion Xception

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Photoluminescence enhancement of carbon quantum dots via the synergistic effect of thiol-ene cross-linking and gold nanoparticles 2

Photoluminescence enhancement of carbon quantum dots via the...

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2nd International Academic Conference on Optics and Photonics, IACOP 2024

作者： Wang, Jialiang Li, Mengmeng Feng, Ming Ren, Linjiao Zhang, Pei Jiang, Liying Henan Key Lab of Information-based Electrical Appliances College of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou450000 China School of Electronics and Information Academy for Quantum Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

The co-doping of the CQDs and gold nanoparticles (Au NPs) in the non-stoichiometric thiol-ene (OSTE) polymer was prepared through "click" chemistry reaction. It is interesting to find that the excitation-independent behavior of the CQDs is converted into an excitation-dependent characteristic after polymerization with the OSTE, likely due to changes in the CQDs’ electronic structure. Meanwhile, the PL intensity is also enhanced, being approximately 3 times stronger than that of CQDs in toluene solution. In addition, the PL intensity of the Au-CQDs/OSTE composite reaches its maximum as the concentration of Au NPs is 2.5×10-6 M, which is about 1.87-fold and 1.35-fold stronger than that of CQDs in toluene solution and CQDs/OSTE composite, respectively. We demonstrate that the cross-linking effect of the OSTE polymer, along with the localized surface plasmon resonance (LSPR) effect of the Au NPs, are responsible for the increased PL intensity of the CQDs. © 2025 SPIE.

关键词： Crosslinking

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MUNE: A Multi-Level User-News Embedding Model for Personalised News Recommendations 24

MUNE: A Multi-Level User-News Embedding Model for Personalis...

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8th International Conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Long, Haixiang Luo, Xudong School of Computer Science and Engineering Guangxi Key Lab of Multi-Source Information Mining & Security Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education Guangxi Normal University Guangxi Guilin China

ISBN: (纸本)9798400718182

Personalised news recommendation systems are critical for delivering relevant content, but effectively capturing and modelling users’ diverse and evolving interests is challenging. In this paper, we introduce a Multi-level User-News Embedding (MUNE) model for personalised news recommendations. MUNE uses pre-trained BERT embeddings to generate contextual representations, enabling dynamic, multi-level interaction modelling between users and candidate news. It incorporates a Dynamic Multi-Level Interaction Attention (DMIA) module for capturing complex interactions and a Dynamic Similarity Unit (DSU) for enhancing feature fusion using cosine similarity and non-linear transformations. Evaluated on large-scale datasets (MIND and EB-NeRD), MUNE significantly outperforms state-of-the-art baselines in key metrics, with ablation studies validating the importance of DMIA and DSU in improving user interest modelling and recommendation quality. MUNE’s integration of multi-level features makes it highly effective in capturing user behaviours and understanding news content. © 2024 Copyright held by the owner/author(s).

关键词： Linear transformations

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SPRGAN: Streamlined Progressive Refinement for Adversarial Point Cloud Video Upsampling

SPRGAN: Streamlined Progressive Refinement for Adversarial P...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Zhiyong Liu, Ruyu Wang, Chaochao Zhang, Xianchao Zhang, Jianhua Liu, Xiufeng School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Information Science and Technology Hangzhou Normal University Hangzhou China Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Zhejiang China Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark

ISBN: (纸本)9798350368741

Getting dense, uniform, time-series point cloud data is critical for effective rendering. However, due to the limited computational power of edge devices, existing methods cannot achieve real-time results, which affects the visual quality of the consumer experience. To effectively address this issue, this paper presents a self-supervised adversarial upsampling method for point cloud video streams called SPR-GAN. In the generator, we design the Temporal Iterative Graph module to learn local features for each frame and captures long-range spatial information using three iterations of graph convolution operations. Then the Contextual Temporal Fusion module is developed to merge information between different frames, synthesizing temporal information and enriching the dynamic feature representation of the point cloud. Meanwhile, in the discriminator, we introduce the Efficient Shape module. Through dynamic graph convolution operations and stacked learning, it significantly improves the resolution efficiency of global shape information in point clouds. The final experiments show that the proposed method exhibits high practicality and superiority. The model achieves a good result on both the D-FAUST and DeformingThing4D-Animals datasets. © 2025 IEEE.

关键词： point cloud sequence Point cloud upsampling self-supervised temporal aggregation

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Toward Drought Modeling in South Asia: Machine Learning Approaches, Challenges, and Opportunities

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IEEE Access 2025年 13卷 87654-87671页

作者： Mana Saleh Al Reshan Muhammad Owais Raza Naeem Ahmed Mahoto Adel Rajab Asadullah Shaikh Mohamed A. Elmagzoub Khairan D. Rajab Department of Information Systems College of Computer Science and Information Systems Najran University Najran Saudi Arabia Emerging Technologies Research Laboratory (ETRL) College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Computer Engineering Istanbul Sabahattin Zaim University İstanbul Türkiye Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Network and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools, which offer accurate and reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction, Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1) The selection of the region for this study, for this study South Asia(SA) is selected as region of interest (ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, and advances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This review encapsulates the indicator and metrics (Complex Machine learning and deep learning models) for drought modeling which are most relevant to the SA region. This study sum up as most common challenges in drought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack of explainable and interpretable models, and unavailability of data for model uncertainty analysis. This study proposes that these problems can be solved with modern machine learning techniques such as explainable machine learning and federa

关键词： Droughts Machine learning Asia Deep learning Indexes Water resources Remote sensing Predictive models Computer science Climate change Environmental monitoring Economics

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Mental Status Identification for Infertile Patients Using Artificial Intelligence Models 14th

Mental Status Identification for Infertile Patients Using Ar...

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14th International Conference on Frontier Computing, FC 2024

作者： Hsu, Hao-Siang Wang, Jong-Yi Chiou, Hai-Chi Wu, Fu-Hsing Lin, Chuen-Horng Chan, Yung‑Kuan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Health Services Administration China Medical University Taichung406040 Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung404336 Taiwan Department of Management Information Systems National Chung-Hsing University Taichung402202 Taiwan

ISBN: (纸本)9789819623945

Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in providing preventive measures accordingly. Rare studies focused on the mental status identification using artificial intelligence (AI) models for infertile people. This study aimed to precisely identify the mental status (anxiety and depression) of infertile patients using three AI models, the XGBoost, decision tree, and stacking models. The dataset, retrieved from a structured questionnaire survey in 2011, which included 400 infertility outpatients seeking reproductive treatments at three teaching hospitals in Taiwan was adopted for training and testing AI models. Aforementioned AI models were designed and reached identification performances of accuracy, precision, recall and area under ROC curve (AUC) equaling 0.75–0.82, 0.70–0.80, 0.64–0.72, and 0.64–0.72, respectively. The XGBoost model (accuracy = 0.82, precision = 0.80, recall = 0.72, and AUC = 0.72) outperformed or equaled the decision tree and stacking models in terms of accuracy, precision, recall, and AUC values. Designed AI models for mental status identification can be integrated into the clinical decision support system (CDSS) to provide the anxiety and depression group automatically. Accordingly, the worsening of infertility-related anxiety and depression can be prevented. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： artificial intelligence infertile patients Mental status identification stacking model XGBoost

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Multi-User Pilot Pattern Optimization for Channel Extrapolation in 5G NR systems

Multi-User Pilot Pattern Optimization for Channel Extrapolat...

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IEEE Conference on Wireless Communications and Networking

作者： Yubo Wan An Liu Tony Q. S. Quek College of Information Science and Electronic Engineering Zhejiang University Information Systems Technology and Design Pillar Singapore University of Technology and Design

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Pilot pattern optimization in orthogonal frequency division multiplexing (OFDM) systems has been widely investigated due to its positive impact on channel estimation. In this paper, we consider the problem of multi-user pilot pattern optimization in OFDM systems. In particular, the goal is to enhance channel extrapolation performance for 5G NR systems by optimizing multi-user pilot patterns in frequency-domain. We formulate a novel pilot pattern optimization problem with the objective of minimizing the maximum integrated side-lobe level (ISL) among all users, subject to a statistical resolution limit (SRL) constraint. Unlike existing literature that only utilizes ISL for controlling side-lobe levels of the ambiguity function, we also leverage ISL to mitigate multi-user interference in code-domain. Additionally, the introduced SRL constraint ensures sufficient delay resolution of the system to resolve multipath, thereby improving channel extrapolation performance. Then, we employ the estimation of distribution algorithm (EDA) to solve the formulated problem in an offline manner. Simulation results demonstrate that the optimized pilot pattern yields significant performance gains in channel extrapolation over the conventional pilot patterns.

关键词： Extrapolation 5G mobile communication OFDM Simulation Frequency-domain analysis Estimation Interference Performance gain Delays Optimization

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A glance over the past decade: road scene parsing towards safe and comfortable autonomous driving

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Autonomous Intelligent systems 2025年第1期5卷 1-15页

作者： Fan, Rui Li, Jiahang Li, Jiaqi Wang, Jiale Long, Ziwei Jia, Ning Liu, Yanan Wang, Wenshuo Bocus, Mohammud J. Vityazev, Sergey Chen, Xieyuanli Xiao, Junhao Andreev, Stepan Lu, Huimin Dvorkovich, Alexander College of Electronics & Information Engineering Shanghai Institute of Intelligent Science and Technology Shanghai Research Institute for Intelligent Autonomous Systems the State Key Laboratory of Intelligent Autonomous Systems and the Frontiers Science Center for Intelligent Autonomous Systems Tongji University 4800 Caoan Rd Shanghai201804 China College of Electronics & Information Engineering Tongji University 4800 Caoan Rd Shanghai201804 China School of Microelectronics Shanghai University 20 Chengzhong Rd Shanghai201800 China School of Mechanical Engineering Beijing Institute of Technology 5 Zhongguancun South Street Beijing100081 China Department of Electrical and Electronic Engineering University of Bristol Woodland Road England BristolBS8 1UB United Kingdom Faculty of Radio Engineering and Telecommunications Ryazan State Radio Engineering University Ulitsa Gagarina 59/1 Ryazan Oblast Ryazan390005 Russia College of Intelligence Science and Technology National University of Defense Technology 109 Deya Road Changsha410073 China Microwave Photonics Department Telecommunications Center Moscow Institute of Physics and Technology Institutsky Lane 9 Moscow Region Dolgoprudny141701 Russia Multimedia Technology and Telecom Department Telecommunications Center Moscow Institute of Physics and Technology Institutsky Lane 9 Moscow Region Dolgoprudny141701 Russia

Road scene parsing is a crucial capability for self-driving vehicles and intelligent road inspection systems. Recent research has increasingly focused on enhancing driving safety and comfort by improving the detection of both drivable areas and road defects. This article reviews state-of-the-art networks developed over the past decade for both general-purpose semantic segmentation and specialized road scene parsing tasks. It also includes extensive experimental comparisons of these networks across five public datasets. Additionally, we explore the key challenges and emerging trends in the field, aiming to guide researchers toward developing next-generation models for more effective and reliable road scene parsing. © The Author(s) 2025.

关键词： Semantics

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Integration of a Hybrid Model with XAI for Stock Price Forecasting: A Dashboard-Driven Approach

Integration of a Hybrid Model with XAI for Stock Price Forec...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Jagannathan J Veera Venkata Shivamani Krishna Goditi School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

This paper on Integration of a Hybrid Model with XAI for Stock price Forecasting focuses on improving the accuracy and transparency of stock price predictions through a combined model of Long Short-Term Memory (LSTM) and MultiProphet, with an emphasis on explainable artificial intelligence (XAI). LSTM which is a deep learning model, is good at identifying patterns in time-series data. while MultiProphet which is an enhanced version of Facebook's Prophet model, effectively handles seasonal variations and anomalies in financial data. By integrating both models, the hybrid approach delivers more reliable stock forecasting. In addition, this project integrates XAI techniques like SHAP and LIME to offer greater insight into how the model generates predictions, which is crucial for informed financial decision-making. Dynamic dashboard provides an intuitive interface for users to track stock price trends, view forecasts, and access explanations of the model's predictions in real-time. This combination of improved prediction accuracy and model transparency enables better decision-making by stakeholders, who can base their actions on clear and interpretable information. Overall, the project represents a significant advancement in making advanced AI models more accessible and actionable in practical, user-oriented financial applications.

关键词： Analytical models Technological innovation Accuracy Explainable AI Decision making Predictive models Market research Data models Forecasting Long short term memory

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