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arXiv 2024年

作者： Wu, Jiong Zhou, Shuang Lin, Li Wang, Xin Tan, Wenxue School of Computer and Electrical Engineering Hunan University of Arts and Science Hunan China Furong College Hunan University of Arts and Science Hunan China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

Diffeomorphic image registration is a fundamental step in medical image analysis, owing to its capability to ensure the invertibility of transformations and preservation of topology. Currently, unsupervised learning-based registration techniques primarily extract features at the image level, potentially limiting their efficacy. This paper proposes a novel unsupervised learning-based fully convolutional network (FCN) framework for fast diffeomorphic image registration, emphasizing feature acquisition at the image patch level. Furthermore, a novel differential operator is introduced and integrated into the FCN architecture for parameter learning. Experiments are conducted on three distinct T1-weighted magnetic resonance imaging (T1w MRI) datasets. Comparative analyses with three state-of-the-art diffeomorphic image registration approaches including a typical conventional registration algorithm and two representative unsupervised learning-based methods, reveal that the proposed method exhibits superior performance in both registration accuracy and topology preservation. Copyright © 2024, The Authors. All rights reserved.

关键词： Unsupervised learning

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Depth from Coupled Optical Differentiation

arXiv

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arXiv 2024年

作者： Luo, Junjie Liu, Yuxuan Alexander, Emma Guo, Qi Elmore Family School of Electrical and Computer Engineering Purdue University 501 Northwestern Ave West LafayetteIN47906 United States Department of Computer Science McCormick School of Engineering and Applied Science Northwestern University 2233 Tech Drive EvanstonIL60208 United States

We propose depth from coupled optical differentiation, a low-computation passive-lighting 3D sensing mechanism. It is based on our discovery that per-pixel object distance can be rigorously determined by a coupled pair of optical derivatives of a defocused image using a simple, closed-form relationship. Unlike previous depth-from-defocus (DfD) methods that leverage spatial derivatives of the image to estimate scene depths, the proposed mechanism’s use of only optical derivatives makes it significantly more robust to noise. Furthermore, unlike many previous DfD algorithms with requirements on aperture code, this relationship is proved to be universal to a broad range of aperture codes. We build the first 3D sensor based on depth from coupled optical differentiation. Its optical assembly includes a deformable lens and a motorized iris, which enables dynamic adjustments to the optical power and aperture radius. The sensor captures two pairs of images: one pair with a differential change of optical power and the other with a differential change of aperture scale. From the four images, a depth and confidence map can be generated with only 36 floating point operations per output pixel (FLOPOP), more than ten times lower than the previous lowest passive-lighting depth sensing solution to our knowledge. Additionally, the depth map generated by the proposed sensor demonstrates more than twice the working range of previous DfD methods while using significantly lower *** Codes 68U10 © 2024, CC BY-NC-ND.

关键词： Pixels

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Angularly Stable, Single-Layer Absorptive Metasurface for X-Band Applications

Angularly Stable, Single-Layer Absorptive Metasurface for X-...

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Microwave, Antennas & Circuits (ICMAC), International Conference on

作者： Muhammad Danial Shafi Ahsaan Gul Hassan Harun Ildeş Inam Ullah Nosherwan Shoaib Symeon Nikolaou School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical and Electronics Engineering Hacettepe University Ankara Turkey Frederick Research Center Nicosia Cyprus Electrical Engineering Department Frederick University Nicosia Cyprus

ISBN: (数字)9798331518424

ISBN: (纸本)9798331518431

This work presents the design of a single-layer absorptive metasurface capable of achieving broadband absorption across the X -band over a wide range of incidence angles. The design introduces novelty through its unit cell configuration, angular stability, enhanced fractional bandwidth, and absorption without the use of an air gap. Both measured and simulated results are in strong agreement, underscoring the potential of the proposed metasurface for radar cross-section (RCS) reduction applications. Overall, the absorption ratio (AR) exceeds 80% for incidence angles up to 50°, highlighting its effectiveness for practical applications.

关键词： Electric potential Radar cross-sections Absorption Circuits Bandwidth Metasurfaces Stability analysis Broadband communication Circuit stability Electromagnetics

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Towards cross-modal causal structure and representation learning 2

Towards cross-modal causal structure and representation lear...

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2nd Machine Learning for Health Symposium, ML4H 2022

作者： Mao, Haiyi Liu, Hongfu Xiaotian Dou, Jason Benos, Panayiotis V. Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh United States Michtom School of Computer Science Brandeis University United States Department of Electrical and Computer Engineering University of Pittsburgh United States Department of Epidemiology University of Florida United States

Does the SARS-CoV-2 virus cause patients' chest X-Rays ground-glass opacities? Does an IDH-mutation cause differences in patients' MRI images? Conventional causal discovery algorithms, although well developed to uncover the cause-effect relationships on structured data, cannot elucidate causal relations between unstructured images and structured scalar variables due to the complexity of the former. In this paper, we consider causal discovery between images and structured (scalar) variables. Specifically, we derive low dimensional image representations to analyze with structured variables. We propose a two-module amortized variational algorithm named Cross-Modal Variational Causal representation and structure Learning (CMCL). CMCL jointly learns identifiable representations given a set of independent structured variables and causal relations via formulating latent representations and structured variables into a direct acyclic graph. Moreover, we further enforce counterfactual invariance/variance onto representations. We demonstrate that CMCL outperforms other related methods on synthetic datasets and validate causal relations on semi-synthetic datasets by visualization. © 2022 P.N. Argaw, E. Healey & I.S. Kohane.

关键词： Coronavirus

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Optimizing Spectrum Prediction in Cognitive Radio: Genetic Algorithm-Enhanced Neural Networks and Radial Basis Functions 16

Optimizing Spectrum Prediction in Cognitive Radio: Genetic A...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Ansari, Sam Kaissar, Antanios Khater, Tarek Alnajjar, Khawla A. Mahmoud, Soliman Hussain, Abir University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates University of Khorfakkan Khor fakkan United Arab Emirates Liverpool John Moores University School of Computer Science and Mathematics Faculty of Engineering LiverpoolL3 3AF United Kingdom

ISBN: (纸本)9798350381344

Throughout recent years, the field of wireless communication has experienced exponential growth. This expansion has been propelled by the continual innovation of diverse wireless standards and the evolution of high-speed applications, resulting in a mounting scarcity of spectrum and an intensified demand for bandwidth. Regrettably, existing studies substantiate an inefficient utilization of available frequency bands. Channel bandwidth and effective spectrum utilization persist as formidable challenges in the realm of wireless communication. Addressing these challenges, cognitive radio stands as a pivotal solution, enabling the efficient sharing of available spectrum among primary/secondary or licensed/unlicensed users. The successful implementation of cognitive radio relies significantly on accurate spectrum sensing and prediction to avert interference or collisions among users. This work introduces a neural network-based model augmented and fine-tuned by a genetic algorithm, exemplifying state-of-the-art effectiveness in spectrum prediction. To expand the horizon, this paper investigates a novel approach based on the radial basis function network, further enriching the exploration. The proposed model demonstrates exceptional performance as validated through rigorous MATLAB simulations. The comparative analysis of these simulations serves as a robust benchmark, illuminating the superior efficacy and practicality of the model in real-world scenarios. © 2023 IEEE.

关键词： Genetic algorithms

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Graph-based deep learning models in the prediction of early-stage Alzheimers

Graph-based deep learning models in the prediction of early-...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Bishal Thapaliya Zundong Wu Ram Sapkota Bhaskar Ray Pranav Suresh Santosh Ghimire Vince Calhoun Jingyu Liu Department of Computer Science Georgia State University Atlanta USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Applied Sciences and Chemical Engineering Tribhuvan University Nepal

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease Neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications.

关键词： Deep learning Neuroimaging Analytical models Biological system modeling Predictive models Functional magnetic resonance imaging Transformers Brain modeling Alzheimer's disease engineering in medicine and biology

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Reconfigurable Intelligent Surface Aided Integer Forcing MIMO

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IEEE Transactions on Wireless Communications 2025年

作者： Chae, Sung Ho Jeon, Sang-Woon Kwangwoon University Department of Electronic Engineering Seoul01897 Korea Republic of Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of

This paper studies a cooperative relaying communication scheme that employs a single passive reconfigurable intelligent surface (RIS), utilizing integer forcing (IF) as a multiple-input multiple-output (MIMO) technique. In the case of IF-based transceivers, the transmitter sends independently encoded data streams using the same lattice code, and the receiver decodes integer-linear combinations of codewords instead of decoding each codeword separately. Although the flexible decoding provided by IF improves achievable rates compared to conventional separate decoding, the integer-linear combinations observed at the IF-based receiver must remain unchanged throughout the codeword’s duration, even in the presence of channel variations, to enable the decoding of summed codewords. Motivated by this fact, we introduce a novel strategy tailored for IF that involves adjusting the reflection matrix of the RIS to reduce fluctuations in the resulting end-to-end channel between the transmitter and receiver throughout codeword transmission, which we refer to as channel stabilization. Furthermore, we develop a novel IF-based transceiver scheme called successive cancellation IF (SC-IF), which effectively integrates successive IF (S-IF) sum decoding with minimum mean square error–successive interference cancellation (MMSE–SIC) individual decoding within a unified MIMO framework to achieve improved performance. Simulation results demonstrate that when a large number of reflective elements are employed in the RIS, the proposed channel stabilization scheme significantly outperforms benchmark schemes that aim to individually optimize the reflection matrix for each sub-block, and the proposed SC-IF can achieve a rate comparable to the theoretical upper bound represented by the joint maximal likelihood (ML) receiver. © 2002-2012 IEEE.

关键词： Decoding

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Intent-Guided Trajectory Prediction for E-Scooter Riders and Bicyclists

Intent-Guided Trajectory Prediction for E-Scooter Riders and...

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International Conference on Intelligent Transportation

作者： Zhengming Zhang Zhengming Ding Yaobin Chen Stanley Chien Lingxi Li Rini Sheroy Joshua Domeyer Renran Tian School of Industrial Engineering Purdue University USA Department of Computer Science Tulane University USA School of Electrical and Computer Engineering Purdue University USA Toyota Collaborative Safety Research Center (CSRC) USA Department of Industrial and Systems Engineering North Carolina State University USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The rapidly increasing popularity of micromobility devices, such as e-scooters and bicycles, in urban environments underscores the critical need for advanced trajectory prediction models to enhance road safety and facilitate seamless integration into intelligent transportation systems. This study introduces a novel approach to predicting the movements of bicyclists and e-scooter riders by leveraging a unique dataset that combines bird's-eye and egocentric views, collected from diverse urban settings across the United States. Employing state-of-the-art deep learning techniques, our model significantly outperforms traditional linear and polynomial regression models in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE), demonstrating superior accuracy in forecasting the trajectories of vulnerable road users. Furthermore, our approach incorporates cognitive annotations to predict crossing intentions, enriching the model's predictive capabilities. The findings highlight the potential of our method to contribute to the development of proactive safety measures and collision avoidance systems, ultimately fostering safer urban mobility landscapes for all road users.

关键词： Deep learning Accuracy Urban areas Predictive models Traffic control Birds Trajectory Forecasting Vehicle dynamics Micromobility

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Tensor Extended Kalman Filter and its Application to Traffic Prediction

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 13813-13829页

作者： Chang, Shih Yu Wu, Hsiao-Chun Kao, Yi-Chih San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Taoyuan32003 Taiwan National Yang Ming Chiao Tung University Information Technology Service Center Hsinchu30010 Taiwan

Traffic prediction is a very important mechanism in intelligent transportation systems for applications including routing planning and traffic control. In order to infer multifarious traffic information, one/two-relational traffic data in the vector/matrix form need to be expanded to multi-relational traffic data in an arbitrary tensor form. However, none of the existing approaches is capable of performing traffic prediction by characterizing and tracking the inherent nonlinear dynamics which are often encountered in realistic time-series analysis. Although the extended Kalman filter (EKF) has been proven to be quite promising in inferring nonlinear dynamics from time series, but the current EKF approach still suffers, unfortunately, from a serious drawback that state variables have to be represented in vector form. In fact, the characteristics of multi-relational states in practice can never been manifested accurately in practice and the performance of an EKF would be greatly restricted thereby. In this work, we introduce a new tensor extended Kalman filter (TEKF) approach to accommodate arbitrary input, output, and state variables all in arbitrary tensor forms. We also propose a new tensor-based expectation-maximization (EM) algorithm to estimate the nonlinear state-transition and observation-model mappings. The computational and memory complexities of the proposed TEKF approach are also studied in this paper. Finally, numerical experiments are conducted to evaluate the traffic prediction performance of the proposed new TEKF approach over the simulated and realworld traffic datasets in comparison with three other existing deep-learning prediction methods. © 2000-2011 IEEE.

关键词： Tensors

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A study on obtaining accurate height information of red blood cells in digital holographic microscopy (DHM)

A study on obtaining accurate height information of red bloo...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Kosei Nakamura Hyun-Woo Kim Myungjin Cho Min-Chul Lee Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyoung National University Anseong Republic of Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Digital holographic microscopy (DHM) is used in the fields of microbiological research and disease diagnosis. Especially in the medical field, it is required to obtain the precise shape information of red blood cells and living cells for disease diagnosis. To obtain the highly accurate three-dimensional profile of an object, it is necessary to set parameters accurately on the system and perform the analysis. In DHM, it is especially important to set the refractive index of the medium surrounding the object accurately. However, the refractive index varies depending on the temperature and the humidity of the experimental environment, as well as on the sample preparation method, and it is difficult to derive the appropriate refractive index. This is a problem in the medical field, which requires the accurate three-dimensional information. Therefore, in this paper, we propose a method to derive the optimum refractive index and to obtain the accurate height information of red blood cells by conducting experiments using two types of lasers with different wavelengths and comparing the height information of red blood cells obtained with each laser. By using the proposed method, the optimu m refractive index of the surrounding medium and the accurate height information of the red blood cells are obtained.

关键词： Temperature dependence Accuracy Red blood cells Shape Microscopy Lasers Refractive index Process control Medical diagnosis Medical diagnostic imaging

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