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检索条件"任意字段=Conference on Optical Modeling and Performance Predictions V"

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Experiments supporting the Momentum Exchange Theory description for photon diffraction 12

Experiments supporting the Momentum Exchange Theory descript...

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conference on optical modeling and performance predictions XII

作者： Mobley, Michael J. Grand Canyon Univ Off Res & Innovat Phoenix AZ 85017 USA Arizona State Univ Biodesign Inst Tempe AZ 85287 USA

ISBN: (纸本)9781510654150;9781510654143

Mobley (2018) presented Momentum Exchange Theory (MET) as an alternative picture for photon diffraction, providing comparable predictions to Classical optical Wave and Boundary Diffraction Wave Theories. MET explains diffraction scattering in terms of momentum transfer probabilities determined by two primary factors: 1) the momentum transfer states of the scattering lattice (aperture);and 2) the distance and location of momentum transfer between the lattice and the scattered photon path. For example, MET was illustrated examining straight edge diffraction where the Fresnel pattern is obtained assuming local maxima in the probability of perpendicular momentum exchange approximated by Delta p = +/-(2j+1)h/4x, where x is the distance between the photon's path and the edge, (j = 0, 1, 2, 3...). This study explores diffraction experiments that might discriminate between the predictions of MET and alternative wave theories. This reports preliminary results with novel observations supporting descriptions based upon MET. Experiments examine diffraction of a narrow laser beam by thin aluminum ribbons with a variety of configurations: e.g. diffraction by the edge of an Al ribbon and single-slit diffraction between Al ribbons. Additional fringes are observed in diffraction patterns with a dependence related to Delta p = +/-(2j+1)h/2L where L is the width of the Al ribbon. Photon scattering exhibits similarities in dependence to multiple-slit diffraction but where the photons only pass through a single slit. Observations suggest the photon scattering probabilities are dependent on the electromagnetic field geometry that determines momentum exchange at the aperture as predicted by MET.

关键词： optical diffraction edge diffraction Fresnel diffraction Fourier optics single-slit diffraction double-slit diffraction wave-particle duality Uncertainty Principle

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Scalable Coherent optical Crossbar Architecture using PCM for AI Acceleration

Scalable Coherent Optical Crossbar Architecture using PCM fo...

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Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Sturm, Dan Moazeni, Sajjad Univ Washington Elect & Comp Engn Seattle WA 98195 USA

ISBN: (纸本)9798350396249

optical computing has recently been proposed as a new compute paradigm to meet the demands of future AI/ML workloads in datacenters and supercomputers. However, proposed implementations so far suffer from lack of scalability, large footprints and high power consumption, and incomplete system-level architectures inhibit integration within existing datacenter systems for real-world applications. In this work, we present a truly scalable optical AI accelerator based on a crossbar architecture. We have considered all major roadblocks and address them in this design. Weights will be stored on-chip using phase change material (PCM) that can be monolithically integrated in silicon photonic processes. All electro-optical components and circuit blocks are modeled based on measured performance metrics in a 45nm monolithic silicon photonic process, which can be co-packaged with advanced CPU/GPUs and HBM memories. We also present a system-level modeling and analysis of our chip's performance for the Resnet-50v1.5, considering all critical parameters, including memory size, array size, photonic losses, and energy consumption of peripheral electronics. Both on-chip SRAM and off-chip DRAM energy overheads have been considered in this modeling. We additionally address how using a dual-core crossbar design can eliminate programming time overhead at practical SRAM block sizes and batch sizes. Our results show that a 128x128 proposed architecture can achieve inference per second (IPS) similar to Nvidia A100 GPU at 15.4x lower power and 7.24x lower area.

关键词： optical Neural Networks AI Accelerator Crossbar Phase Change Material System-level Optimization

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A Beam Scanning Array for Enhancing v2X Security 2

A Beam Scanning Array for Enhancing V2X Security

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2nd International conference on Applied Statistics, Computational Mathematics, and Software Engineering, ASCMSE 2023

作者： Liu, Shuhan Wu, DaKui Shanghai University Shanghai200072 China

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

A new kind of Rotman lens array which used for security enhancement of v2X communication was proposed in this paper, this new kind of array is realized by adding circular gaps on the original designed lens, the return loss was effectively reduced, achieving good results. To validate the performance of this new array, a 3D model was constructed and simulated with CST, the simulation results show the good performance of the array, the simulated beam scanning range is from -30 to 30, and the gain is up to 8dBi. © 2023 SPIE.

关键词： 3D modeling

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CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and optical Flow

CroCo v2: Improved Cross-view Completion Pre-training for St...

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IEEE/CvF International conference on Computer vision (ICCv)

作者： Weinzaepfel, Philippe Lucas, Thomas Leroy, vincent Cabon, Yohann Arora, vaibhav Bregier, Romain Csurka, Gabriela Antsfeld, Leonid Chidlovskii, Boris Revaud, Jerome NAVER LABS Europe Meylan France

ISBN: (纸本)9798350307184

Despite impressive performance for high-level downstream tasks, self-supervised pre-training methods have not yet fully delivered on dense geometric vision tasks such as stereo matching or optical flow. The application of self-supervised concepts, such as instance discrimination or masked image modeling, to geometric tasks is an active area of research. In this work, we build on the recent cross-view completion framework, a variation of masked image modeling that leverages a second view from the same scene which makes it well suited for binocular downstream tasks. The applicability of this concept has so far been limited in at least two ways: (a) by the difficulty of collecting realworld image pairs - in practice only synthetic data have been used - and (b) by the lack of generalization of vanilla transformers to dense downstream tasks for which relative position is more meaningful than absolute position. We explore three avenues of improvement. First, we introduce a method to collect suitable real-world image pairs at large scale. Second, we experiment with relative positional embeddings and show that they enable vision transformers to perform substantially better. Third, we scale up vision transformer based cross-completion architectures, which is made possible by the use of large amounts of data. With these improvements, we show for the first time that state-of-the-art results on stereo matching and optical flow can be reached without using any classical task-specific techniques like correlation volume, iterative estimation, image warping or multi-scale reasoning, thus paving the way towards universal vision models.

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Improving Factuality in Large Language Models via Decoding-Time Hallucinatory and Truthful Comparators 39

Improving Factuality in Large Language Models via Decoding-T...

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39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Yang, Dingkang Xiao, Dongling Wei, Jinjie Li, Mingcheng Chen, Zhaoyu Li, Ke Zhang, Lihua Academy for Engineering and Technology Fudan University China ByteDance China Tencent Youtu Lab China

ISBN: (纸本)157735897X

Despite their remarkable capabilities, Large Language Models (LLMs) are prone to generate responses that contradict verifiable facts, i.e., unfaithful hallucination content. Existing efforts generally focus on optimizing model parameters or editing semantic representations, which compromise the internal factual knowledge of target LLMs. In addition, hallucinations typically exhibit multifaceted patterns in downstream tasks, limiting the model’s holistic performance across tasks. In this paper, we propose a Comparator-driven Decoding-Time (CDT) framework to alleviate the response hallucination. Firstly, we construct hallucinatory and truthful comparators with multi-task fine-tuning samples. In this case, we present an instruction prototype-guided mixture of experts strategy to enhance the ability of the corresponding comparators to capture different hallucination or truthfulness patterns in distinct task instructions. CDT constrains next-token predictions to factuality-robust distributions by contrasting the logit differences between the target LLMs and these comparators. Systematic experiments on multiple downstream tasks show that our framework can significantly improve the model performance and response factuality. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Comparators (optical)

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Accurate Data-driven Fiber Channel modeling Based on BiLSTM and Conditional GAN

Accurate Data-driven Fiber Channel Modeling Based on BiLSTM ...

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2023 Opto-Electronics and Communications conference, OECC 2023

作者： Liu, Jiaming Huang, Xiatao Ren, Xuecheng Li, Jinjiang Zhang, Jing Zhang, Qianwu University of Electronic Science and Technology of China School of Communication and Information Engineering Chengdu China Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai China

ISBN: (纸本)9781665462136

In this paper, we compare the performance of two data-driven fiber channel modeling methods based on Bi-LSTM and CGAN waveform modeling schemes. The results show that the two methods can learn the accurate transfer function of the fiber channel, with only 0.6% average MSE compared with SSFM. The running times are less than 0.2 seconds and 0.25 seconds for 200-km transmission, versus 400 seconds using the SSFM under the same hardware conditions, which shows the reduction in complexity of the fiber channel modeling. © 2023 IEEE.

关键词： Generative adversarial networks

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Artificial Neural Network and Its Benefit in modeling and Efficient Yield Analysis of Antennas 11

Artificial Neural Network and Its Benefit in Modeling and Ef...

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11th International conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Kouhalvandi, Lida Matekovits, Ladislau Ozoguz, Serdar Dogus University Department of Electrical and Electronics Engineering Istanbul Turkey Politecnico di Torino Department of Electronics and Telecommunications Turin Italy Politehnica University Timisoara Department of Measurements and Optical Electronics Timisoara Romania Istituto di Elettronica e di Ingegneria dell' lnformazione e delle Telecomunicazioni National Research Council Turin Italy Istanbul Technical University Department of Electronics and Communication Engineering Istanbul Turkey

ISBN: (纸本)9798350362541

This paper presents yield-aware antenna sizing and modelling through artificial neural network (ANN) leading to an accurate and efficient synthesis process. In the proposed methodology, the suitable amount of data is provided and then Quicksort algorithm is employed for categorizing the most/least effectiveness of each parameter. Finally, the ANN is trained and used for optimizing the design parameters leading to find optimal output specifications. The performance is named as yield analysis, and it accelerates in predicting the effects of variations in the antenna's dimension and it leads to find the optimal design parameters in a shortest time without any further simulation effort. The developed process results in accurate yield predictions of antenna dimensions with reduced time. To validate the efficiency of the proposed method, three antennas from the recently published literature are considered, and then our presented method is employed for optimizing them and quantify the computational time in modelling. The simulation results demonstrate that using the proposed method, there is approximately 25% speed-up in modelling, sizing, and optimizing antennas. © 2024 IEEE.

关键词： Neural network models

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performance Prediction of a Range of Diverse Solid Oxide Fuel Cells using Deep Learning and Principal Component Analysis 4

Performance Prediction of a Range of Diverse Solid Oxide Fue...

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4th modeling, Estimation, and Control conference (MECC)

作者： Salehi, Zeynab Tofigh, Mohamadali vafaeenezhad, Sajad Kharazmi, Ali Smith, Daniel J. Koch, Charles Robert Shahbakhti, Mahdi Univ Alberta Dept Mech Engn 116 St & 85 Ave Edmonton AB T6G 1H9 Canada Cummins Inc Res & Technol Dept R&T 1900 McKinley Ave Columbus IN 47201 USA

Solid oxide fuel cell (SOFC) is a common fuel cell type that has high efficiency. SOFC is a complex non-linear system, subject to aging and manufacturing variation, which makes performance prediction difficult. To estimate the SOFC performance data-driven methods allow a trade-off between computation cost and accuracy. Eight different SOFC tubular cells with different properties are fabricated and experimentally tested in 18 different operating conditions. A deep neural network (DNN) is used to predict the output voltage of the cells. The input features of this network are the cell physical properties determined by scanning electron microscope (SEM) analysis and the operating parameters. As a first step, all measurable features are provided to principal component analysis (PCA) for feature selection resulting in a 50% reduction in features, resulting in a corresponding 50% reduction in the training time of the DNN. This trained DNN is able to capture the non-linear voltage drop of concentration polarization in the current density-voltage (J-v) curves. The prediction performance of the network is evaluated using three performance metrics of coefficient of determination (R-2), root mean square error (RMSE), and mean absolute percentage error (MAPE) with satisfactory accuracy for both the training and test datasets. For all predictions, R-2 is 0.99, MAPE is less than 1%, and RMSE is 0.0001 on the test dataset. During the training process, both the validation loss and training loss approach zero indicating that the trained model is not overfitted. The DNN model can be useful for design and operation optimization purposes. Copyright (c) 2024 The Authors.

关键词： Solid Oxide Fuel Cell Design and Operation Optimization Deep learning Principal Component Analysis Data-driven modeling

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Steel Surface Defect Detection Based on YOLO v8 4

Steel Surface Defect Detection Based on YOLO V8

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4th International conference on Computer vision and Pattern Analysis, ICCPA 2024

作者： Li, Minzheng Dong, Hao Shang Hai Dian Ji University 300 Shui Hua Road Shang Hai201306 China

ISBN: (纸本)9781510682528

The detection of surface defects in steel is a crucial step in ensuring the quality of the steel. Traditional defect detection methods suffer from low accuracy. The complex shapes of steel surface defects and generally small target areas significantly affect the accuracy of steel surface defect detection. To address these issues, this paper proposes an improved steel surface defect detection algorithm based on a target detection model. This is achieved by incorporating a lightweight backbone network, a Prior Attention Mechanism module (CPCA), and redesigning the Neck module to enhance the accuracy of steel defect detection. Finally, the proposed defect detection algorithm is validated using the publicly available steel surface defect detection dataset NEU-DET. Experimental results show that the network model proposed in this paper has good detection accuracy, with an average precision of 79.1%, which is a 5% improvement over the original algorithm. © 2024 SPIE.

关键词： Defect detection Feature extraction Convolution Detection and tracking algorithms Feature fusion Object detection performance modeling Target detection Data modeling

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Multimodal-capable, multiphoton miniaturized microscope for mice brain imaging 12

Multimodal-capable, multiphoton miniaturized microscope for ...

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conference on optical modeling and performance predictions XII

作者： Sahraeibelverdi, T. Shirazi, A. Lee, M. Li, H. Wang, T. Kwon, S. Oldham, K. Univ Michigan Dept Mech Engn 2350 Hayward St Ann Arbor MI 48109 USA Univ Michigan Div Integrat Syst & Design 1075 Beal Ave Ann Arbor MI 48109 USA Univ Michigan Dept Internal Med 109 Zina Pitcher Ann Arbor MI 48109 USA Univ Michigan Dept Mol Cellular & Dev Biol 1105 N Univ Ave Ann Arbor MI 48109 USA

ISBN: (纸本)9781510654150;9781510654143

A multiphoton, multimodal miniaturized microscope for mice brain imaging is developed. The optical design to provide a compact and lightweight probe uses small lenses, of less than 4mm diameter, selected for performance using ray-tracing software. The microscope is designed to maintain diffraction-limited resolution for imaging reporters GCAMP6 and eYFP or performing near-infra-red reflection confocal microscopy. A Mai-Tai femtosecond laser is used to provide a 920nm pulse laser, an Insight X3 laser to provide a 960nm pulse laser, and a separate 785nm continuous wave (CW) laser to support NIR reflection with optical sectioning capability with a high signal- to-noise ratio. The miniaturized system was tested as a benchtop prototype using a reflection target, green fluorescent protein, and enhanced yellow fluorescent protein. The resolution for all three wavelengths is less than 2 mu m. A customized parametric resonance electrostatic MEMS scanner provided the beam scanning of 1.8mm in diameter, resulting in a 350 mu m by 350 mu m field of view with a numerical aperture of 0.42, at a working distance of about 350 mu m. A 4 mu m core single-mode fiber and a photodiode collect the reflection beam in the confocal mode. Two dichroic mirrors out of the probe are used to combine all three beams toward the probe. Two dichroic mirrors are used for two emission wavelength separations.

关键词： Multiphoton microscopy Brain imaging Optic MEMS optical imaging Biomedical imaging

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