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IEEE Transactions on Intelligent Vehicles 2024年 1-10页

作者： Selvaraj, Palanisamy Sakthivel, Ramalingam Kwon, Oh-Min Sakthivel, Rathinasamy School of Electrical Engineering Chungbuk National University Cheongju South Korea Department of Applied Mathematics Bharathiar University Coimbatore India

This paper focuses on the design and implementation of a sampled-data controller for connected autonomous vehicle platoons operating in a predecessor-follower configuration. Due to the cost and reliability concerns associated with velocity and acceleration sensors, this study involves the development of an event-based sampled-data controller that relies solely on position measurements. Considering the limitations of velocity and acceleration sensors, a memory-based sampled-data controller is proposed that utilizes current and preceding position data information to approximate velocity and acceleration. To conserve communication resources, the controller incorporates a dynamic event-driven communication mechanism. In particular, event-driven communication thresholds are adaptively adjusted based on platooning errors between vehicles. This enhances resource utilization while maintaining control performance. In addition, determining the maximum allowable sampling period and event-triggered constraint parameters is crucial for reliable control performance. This is achieved by formulating and solving stability criteria for the closed-loop platoon error system using Lyapunov stability theory and the linear matrix inequality framework. Finally, comprehensive numerical simulations demonstrate the effectiveness of the proposed event-triggered control algorithm under the influence of some key factors, including triggering instants and unknown nonlinearity effects. IEEE

关键词： Controllers

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Video Background Music Recommendation Based on Multi-Level Fusion Features

Video Background Music Recommendation Based on Multi-Level F...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Zhao, Xin Li, Xiaobing Tie, Yun Hu, Ziqing Qi, Lin School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Central Conservatory of Music Department of Music Artificial Intelligence Beijing China University of Notre Dame Department of Applied and Computational Mathematics and Statistics South Bend on Lake MI United States

ISBN: (纸本)9798350313154

People resonate more with music when exposed to visual information, and music enhances their perception of video content. Cross-modal recommendation techniques can be used to suggest appropriate background music for a given video. However, there is not a simple correspondence between the different modal data. Therefore, to explore the association between the two modalities of video and music, we propose MFF-VBMR, a video background music recommendation model based on multi-level fusion features. The model uses the cross-modal information of static, dynamic and emotional content of video and music to realize the task of matching and recommending suitable background music for a given video. Experimental results show that the proposed model outperforms other existing models in terms of performance and achieves satisfactory results for video background music recommendation. © 2023 IEEE.

关键词： Convolutional neural networks

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Quantum Wrapper Networking: Investigating the Noise Impact of Classical Headers on the Quantum Payload

Quantum Wrapper Networking: Investigating the Noise Impact o...

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2024 Frontiers in Optics, FiO 2024

作者： Gül, Gamze On, Mehmet Berkay Tan, Shannon G. Proietti, Roberto Kanter, Gregory S. Yoo, S.J. Ben Kumar, Prem Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy

We study the impact in time and frequency domains of classical headers on quantum payloads in quantum wrapper networking. We identify and characterize in-fiber scattering processes that produce noise photons degrading...

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Reconfiguration and Optimal Positioning of Multiple-Point Capacitors in a High-Voltage Distribution Network Using the NSGAII

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Open Journal of applied Sciences 2025年第2期15卷 501-516页

作者： Arouna Oloulade Richard Gilles Agbokpanzo Maurel Richy Aza-Gnandji Hassane Ousseyni Ibrahim Moussa Gonda Eméric Tokoudagba Juliano Sétondji François-Xavier Fifatin Adolphe Moukengue Imano Abomey-Calavi Polytechnic School Electrical Engineering Department (EPAC) Electronics Telecommunications and Applied Computing Laboratory (LETIA) University of Abomey-Calavi Cotonou Benin Electrical Engineering Department National University of Science Technology Engineering and Mathematics Lokossa Benin Laboratory of Rural Engineering National University of Agriculture Porto-Novo Benin Polytech Maradi Electrical Engineering Department University Dan Dicko of Dankoulodo of Maradi Maradi Niger University Institute of Technology Electrical Engineering Department University of Douala Douala Cameroun

The distribution networks sometimes suffer from excessive losses and voltage violations in densely populated areas. The aim of the present study is to improve the performance of a distribution network by successively applying mono-capacitor positioning, multiple positioning and reconfiguration processes using GA-based algorithms implemented in a Matlab environment. From the diagnostic study of this network, it was observed that a minimum voltage of 0.90 pu induces a voltage deviation of 5.26%, followed by active and reactive losses of 425.08 kW and 435.09 kVAR, respectively. Single placement with the NSGAII resulted in the placement of a 3000 kVAR capacitor at node 128, which proved to be the invariably neuralgic point. Multiple placements resulted in a 21.55% reduction in losses and a 0.74% regression in voltage profile performance. After topology optimization, the loss profile improved by 65.08% and the voltage profile improved by 1.05%. Genetic algorithms are efficient and effective tools for improving the performance of distribution networks, whose degradation is often dynamic due to the natural variability of loads.

关键词： Reconfiguration Capacitor Bank NSGA II Dynamic Network Degradation Distribution Network Reliability

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Aperiodicity and disorder as systematic spectral tuning mechanisms for plasmonic nanostructures

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Optics Express 2025年第11期33卷 23227-23243页

作者： Ozaktas, Ekin Gunes Chintapalli, Sreyas O’Reilly, Eliza Thon, Susanna M. Department of Electrical Engineering Ginzton Laboratory Stanford University United States Department of Electrical and Computer Engineering Johns Hopkins University United States Department of Physics and Astronomy Johns Hopkins University United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Department of Materials Science and Engineering Johns Hopkins University United States

We demonstrate how aperiodicity and disorder can be used as quantifiable mechanisms for tuning the spectral response of plasmonic nanostructure arrays. We tune the extinction spectra of these arrays using deterministically aperiodic (quasicrystal), perturbed lattice (Bernoulli point process, frozen phonon disorder, long-range frozen phonon disorder), negatively correlated (Strauss point process), and positively correlated (Log Gaussian Cox point process) assemblies. We quantify this tuning by considering the local variance of the extinction spectra, demonstrating two orders of magnitude of tunability. Our structures have potential applications in plasmonic or waveguide-based optoelectronic devices such as photovoltaics and photosensing, where spectral tuning is critical to performance. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Phototubes

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Farsi Handwritten Text Recognition via a Lightweight Attention-Driven Sequence Recognition Network

Farsi Handwritten Text Recognition via a Lightweight Attenti...

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Iranian Conference on Intelligent Systems (ICIS)

作者： Ali Afkari-Fahandari Fatemeh Asadi-Zeydabadi Elham Shabaninia Hossein Nezamabadi-pour Department of Electrical Engineering Shahid Bahonar University of Kerman Kerman Iran Department of Applied Mathematics Graduate University of Advanced Technology Kerman Iran

ISBN: (数字)9798331507565

ISBN: (纸本)9798331507572

In today's digital age, there's a growing demand for a dependable system that can recognize handwritten text. This demand comes from various industries as well as individual users, highlighting the importance of a system that can quickly convert, store, and enhance the accessibility of handwritten documents. Encoder-decoder models have proven their effectiveness in various sequence learning tasks, such as machine translation, image captioning, action recognition, and optical character recognition. However, these models have encountered difficulties in achieving competitive results in the specific context of Farsi handwritten text recognition. This paper introduces an innovative lightweight attention-driven encoder-decoder model designed to address the challenges associated with Farsi handwritten text recognition. To evaluate the proposed model, two well-known Farsi handwritten text datasets, Sadri and Iranshahr, are utilized. Additionally, to potentially enhance recognition accuracy, two different convolutional backbones-ResNet34 and MobileNetV3-are explored. Through experimental analysis, our best-performing model surpasses existing CNN-based and CRNN-based methods, achieving recognition accuracies of 94.79% on the Iranshahr dataset and 98.99% on the Sadri dataset. Additionally, our model offers faster inference speeds and a lower computational burden compared to recent Transformer-based approaches. These results demonstrate the effectiveness of our attention-driven encoder-decoder model in addressing the challenges of lightweight Farsi handwritten text recognition.

关键词： Handwriting recognition Analytical models Visualization Accuracy Text recognition Computational modeling Transformers Optical network units Character recognition Context modeling

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Detecting Persian Signatures in Realistic Images using the YOLO Algorithm

Detecting Persian Signatures in Realistic Images using the Y...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Shiva Jafarian Elham Shabaninia Hossein Nezamabadi-Pour Department of Electrical Engineering Shahid Bahonar University of Kerman Kerman Iran Department of Applied Mathematics Graduate University of Advanced Technology Kerman Iran

While crucial for document forensics and security, detecting Persian signatures in real-world scenarios poses a considerable challenge due to the distinctive features of Persian signatures—characterized by complex combinations of shapes and letters instead of conventional names—along with issues like noise, cluttered backgrounds, and more. Furthermore, the scarcity of annotated datasets complicates signature detection in natural settings. This paper addresses these challenges by introducing a new dataset for Persian documents, merging publicly available signatures with diverse backgrounds to create semi-realistic images reflecting real-world conditions. Additionally, the proposed method leverages the YOLOv5 architecture for signature detection. The experimental findings illustrate that, under various lighting conditions, background complexities, and signature distortions, this approach effectively and accurately detects Persian signatures.

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CVIT: CONTINUOUS VISION TRANSFORMER FOR OPERATOR LEARNING

arXiv

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arXiv 2024年

作者： Wang, Sifan Seidman, Jacob H. Sankaran, Shyam Wang, Hanwen Pappas, George J. Perdikaris, Paris Institution for Foundation of Data Science Yale University United States Graduate Program in Applied Mathematics and Computational Science University of Pennsylvania United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania United States Department of Electrical and Systems Engineering University of Pennsylvania United States

Operator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various physical domains. Here we introduce the Continuous Vision Transformer (CViT), a novel neural operator architecture that leverages advances in computer vision to address challenges in learning complex physical systems. CViT combines a vision transformer encoder, a novel grid-based coordinate embedding, and a query-wise cross-attention mechanism to effectively capture multi-scale dependencies. This design allows for flexible output representations and consistent evaluation at arbitrary resolutions. We demonstrate CViT’s effectiveness across a diverse range of partial differential equation (PDE) systems, including fluid dynamics, climate modeling, and reaction-diffusion processes. Our comprehensive experiments show that CViT achieves state-of-the-art performance on multiple benchmarks, often surpassing larger foundation models, even without extensive pretraining and roll-out fine-tuning. Taken together, CViT exhibits robust handling of discontinuous solutions, multi-scale features, and intricate spatio-temporal dynamics. Our contributions can be viewed as a significant step towards adapting advanced computer vision architectures for building more flexible and accurate machine learning models in the physical sciences. All data and code are publicly available at https://***/PredictiveIntelligenceLab/cvit. © 2024, CC BY-NC-SA.

关键词： Computer vision

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Enhancing Farsi Text Recognition via Iteratively Using a Language Model

Enhancing Farsi Text Recognition via Iteratively Using a Lan...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Ali Afkari-Fahandari Fatemeh Asadi-Zeydabadi Elham Shabaninia Hossein Nezamabadi-Pour Department of Electrical Engineering Shahid Bahonar University of Kerman Kerman Iran Department of Applied Mathematics Graduate University of Advanced Technology Kerman Iran

In the present era, there is a growing demand for a reliable Farsi text recognition system, that addresses the needs of both industrial and individual users. The process of text recognition is typically accompanied by a subsequent post-processing step, which aims to tailor the text to specific language constraints. However, the iterative use of post-correction offers valuable insights for enhancing the precision of the recognized text. Meanwhile, building upon the recent successes of Transformer-based approaches in natural language processing and computer vision, we propose a Farsi text recognition model. This model seamlessly integrates a decoupled convolutional Transformer-based vision model for initial predictions and a Transformer-based language model for refining the obtained predictions. Evaluation of well-known Farsi OCR datasets, including Iranshahr, Sadri, and Shotor, demonstrates the model’s superior performance, surpassing existing literature with recognition accuracies of 97.59%, 99.25%, and 99.97% on Iranshahr, Sadri, and Shotor datasets, respectively.

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Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems

arXiv

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

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