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arXiv 2024年

作者： Patra, Subhajit Panda, Sonali Parida, Bikram Keshari Arya, Mahima Jacobs, Kurt Bondar, Denys I. Sen, Abhijit Department of Electrical Engineering Indian Institute of Technology Delhi India Department of Physics Indian Institute of Technology Dhanbad India Artificial Intelligence and Image Processing Lab. Sun Moon University Korea Republic of School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore641112 India United States DEVCOM Army Research Laboratory AdelphiMD20783 United States Department of Physics University of Massachusetts at Boston BostonMA02125 United States Department of Physics and Engineering Physics Tulane University New OrleansLA70118 United States

Physics-informed neural networks have proven to be a powerful tool for solving differential equations, leveraging the principles of physics to inform the learning process. However, traditional deep neural networks often face challenges in achieving high accuracy without incurring significant computational costs. In this work, we implement the Physics-Informed Kolmogorov-Arnold Neural Networks (PIKAN) through efficient-KAN and WAV-KAN, which utilize the Kolmogorov-Arnold representation theorem. PIKAN demonstrates superior performance compared to conventional deep neural networks, achieving the same level of accuracy with fewer layers and reduced computational overhead. We explore both B-spline and wavelet-based implementations of PIKAN and benchmark their performance across various ordinary and partial differential equations using unsupervised (data-free) and supervised (data-driven) techniques. For certain differential equations, the data-free approach suffices to find accurate solutions, while in more complex scenarios, the data-driven method enhances the PIKAN’s ability to converge to the correct solution. We validate our results against numerical solutions and achieve 99% accuracy in most scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Towards Analytically Computable Quality Classes for MCG Sensor Systems

Towards Analytically Computable Quality Classes for MCG Sens...

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作者： Engelhardt, Erik Zaman, Adrian Elzenheimer, Eric Frey, Norbert Schmidt, Gerhard Digital Signal Processing and System Theory Institute of Electrical Engineering and Information Technology Faculty of Engineering Kiel University Kaiserstr. 2 Kiel24143 Germany Medical Department Iii Specialised in Cardiology and Angiology University Medical Center Schleswig-Holstein Arnold-Heller-Str. 3/House K3 Kiel24105 Germany Internal Medicine III: Heart Vascular and Lung University Medical Center Heidelberg Im Neuenheimer Feld 410 Heidelberg69120 Germany

While metrics such as SNR (signal-to-noise ratio) and ASC (application-specific capacity) can be used to describe signal quality quantitatively, they do not allow qualitative assessment. If only the SNR of a signal is given, it is not clear for which applications it is suitable. The subjective evaluation of signals by application experts, on the other hand, can provide such a qualitative assessment, but it is not reproducible. In this study, we investigate the relationship between these easily computable metrics such as SNR and ASC and the subjective evaluation of cardiologists using MCG signals as an example. To do this, we define four quality classes and generate a collection of noisy prototype signals. The cardiologists' assessments are then compared with the SNR and ASC of each signal. Due to the small amount of data, we only use a linear regression to predict quality classes based on common quantitative metrics. The achieved coefficients of determination are RSNR 2 = 0.612 and RASC 2 = 0.593. In perspective, it is therefore plausible to analytically calculate the quality class of a MCG sensor system from the system characteristics with the support of a larger cohort of cardiologists. This will allow sensor system manufacturers to optimize their systems more efficiently for specific applications, enabling faster innovation loops at lower cost. For users of sensor systems, it will simplify the selection of the most suitable system for their application. © 2022 The Author(s), published by De Gruyter.

关键词： signal to noise ratio

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Movable-Antenna-Array-Enabled Communications with CoMP Reception

arXiv

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arXiv 2023年

作者： Hu, Guojie Wu, Qingqing Ouyang, Jian Xu, Kui Cai, Yunlong Al-Dhahir, Naofal The College of Communication Engineering Rocket Force University of Engineering Xi'An710025 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications Nanjing210003 China The College of Communications Engineering The Army of Engineering University Nanjing210007 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States

We consider the movable-antenna (MA) array-enabled wireless communication with coordinate multi-point (CoMP) reception, where multiple destinations adopt the maximal ratio combination technique to jointly decode the common message sent from the transmitter equipped with the MA array. Our goal is to maximize the effective received signal-to-noise ratio, by jointly optimizing the transmit beamforming and the positions of the MA array. Although the formulated problem is highly non-convex, we reveal that it is fundamental to maximize the principal eigenvalue of a hermite channel matrix which is a function of the positions of the MA array. The corresponding sub-problem is still non-convex, for which we develop a computationally efficient algorithm. Afterwards, the optimal transmit beamforming is determined with a closed-form solution. In addition, the theoretical performance upper bound is analyzed. Since the MA array brings an additional spatial degree of freedom by flexibly adjusting all antennas' positions, it achieves significant performance gain compared to competitive benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Blip-Up Blip-Down Circular EPI (BUDA-cEPI) for Distortion-Free dMRI with Rapid Unrolled Deep Learning Reconstruction

arXiv

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arXiv 2023年

作者： Yarach, Uten Chatnuntawech, Itthi Liao, Congyu Teerapittayanon, Surat Iyer, Siddharth Srinivasan Kim, Tae Hyung Haldar, Justin Cho, Jaejin Bilgic, Berkin Hu, Yuxin Hargreaves, Brian Setsompop, Kawin Radiologic Technology Department Associated Medical Sciences Chiang Mai University Chiang Mai Thailand National Nanotechnology Center National Science and Technology Development Agency Pathum Thani Thailand Department of Radiology Stanford University StanfordCA United States Department of Electrical Engineering Stanford University StanfordCA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Computer Engineering Hongik University Seoul Korea Republic of Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School BostonMA United States Department of Bioengineering Stanford University StanfordCA United States Packard Electrical Engineering Building 350 Jane Stanford Way StanfordCA94305 United States

Purpose: We implemented the blip-up, blip-down circular echo planar imaging (BUDA-cEPI) sequence with readout and phase partial Fourier to reduced off-resonance effect and T2* blurring. BUDA-cEPI reconstruction with S-based low-rank modeling of local k-space neighborhoods (S-LORAKS) is shown to be effective at reconstructing the highly under-sampled BUDA-cEPI data, but it is computationally intensive. Thus, we developed an ML-based reconstruction technique termed "BUDA-cEPI RUN-UP" to enable fast reconstruction. Methods: BUDA-cEPI RUN-UP – a model-based framework that incorporates off-resonance and eddy current effects was unrolled through an artificial neural network with only six gradient updates. The unrolled network alternates between data consistency (i.e., forward BUDA-cEPI and its adjoint) and regularization steps where U-Net plays a role as the regularizer. To handle the partial Fourier effect, the virtual coil concept was also incorporated into the reconstruction to effectively take advantage of the smooth phase prior, and trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS. Results: BUDA-cEPI with S-LORAKS reconstruction enabled the management of off-resonance, partial Fourier, and residual aliasing artifacts. However, the reconstruction time is approximately 225 seconds per slice, which may not be practical in a clinical setting. In contrast, the proposed BUDA-cEPI RUN-UP yielded similar results to BUDA-cEPI with S-LORAKS, with less than a 5% normalized root mean square error detected, while the reconstruction time is approximately 3 seconds. Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by ~88x when compared to the state-of-the-art technique, while preserving imaging details as demonstrated through DTI application. © 2023, CC BY.

关键词： Magnetic resonance imaging

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L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging

arXiv

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arXiv 2023年

作者： Phan, Huy Lorenzen, Kristian P. Heremans, Elisabeth Chén, Oliver Y. Tran, Minh C. Koch, Philipp Mertins, Alfred Baumert, Mathias Mikkelsen, Kaare B. De Vos, Maarten Amazon Alexa CambridgeMA02142 United States The Department of Electrical and Computer Engineering Aarhus University Aarhus8200 Denmark The Department of Electrical Engineering The Department of Development and Regeneration KU Leuven Leuven3001 Belgium Lausanne Switzerland University of Lausanne Lausanne Switzerland Nuffield Department of Clinical Neurosciences University of Oxford OxfordOX3 9DU United Kingdom The Institute for Signal Processing University of Lübeck Lübeck23562 Germany Lübeck23562 Germany School of Electrical and Electronic Engineering The University of Adelaide AdelaideSA5005 Australia

Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sleep staging models has remained untouched. In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose. We thus introduce a method for efficient long sequence modelling and propose a new deep learning model, L-SeqSleepNet, which takes into account whole-cycle sleep information for sleep staging. Evaluating L-SeqSleepNet on four distinct databases of various sizes, we demonstrate state-of-the-art performance obtained by the model over three different EEG setups, including scalp EEG in conventional Polysomnography (PSG), in-ear EEG, and around-the-ear EEG (cEEGrid), even with a single EEG channel input. Our analyses also show that L-SeqSleepNet is able to alleviate the predominance of N2 sleep (the major class in terms of classification) to bring down errors in other sleep stages. Moreover the network becomes much more robust, meaning that for all subjects where the baseline method had exceptionally poor performance, their performance are improved significantly. Finally, the computation time only grows at a sub-linear rate when the sequence length increases. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Research on Coordinated Coverage of Non-Lambertian Optical Wireless Communications 7

Research on Coordinated Coverage of Non-Lambertian Optical W...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Ding, Jupeng Chih-Lin, I. Wang, Jintao Yang, Hui Wang, Lili Xinjiang University Key Laboratory of Signal Detection and Processing In Xinjiang Uygur Autonomous Region School of Information Science and Engineering Ürümqi China China Mobile Research Institute Beijing China Tsinghua University Beijing National Research Center For Information Science and Technology Department of Electronic Engineering Beijing China Ludong University School of Information and Electrical Engineering Yantai China

ISBN: (纸本)9781665409506

To a large extent, the performance of wireless optical communication system is affected by the light source radiation characteristics. Previous works are limited to rotationally symmetric Lambertian beam configurations, which fail to support target location oriented coordinated coverage. Different from the existing research, this paper proposes a coordinated coverage enhancement scheme based on asymmetric non-Lambertian optical beam configuration. Without loss of generality, this paper presents numerical analysis of coordinated and non-coordinated coverage performance in typical medium-sized indoor application scenario. Compared with non-coordinated coverage, the numerical results illustrate that the proposed coordinated coverage scheme could introduce about 4.08 dB SNR gain without transmission power increase. Therefore, non-Lambertian beam cooperation should be viewed as one efficient wireless optical communication technology. © 2021 IEEE.

关键词： Light sources

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Can We Distinguish Genuine Minutiae from Imposter Minutiae

Can We Distinguish Genuine Minutiae from Imposter Minutiae

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International Conference on electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Krisada Phromsuthirak Vutipong Areekul Kasetsart Signal & Image Processing Laboratory Department of Electrical Engineering Faculty of Engineering Kasetsart University Bangkok Thailand

ISBN: (数字)9781728164861

ISBN: (纸本)9781728164878

we propose a novel method to differentiate genuine minutiae from impostor minutiae of an enhanced latent fingerprint. Because poor-quality latent fingerprints are deposited on uncontrolled backgrounds with noises, the imposter minutiae are incorrectly detected by a conventional fingerprint enhancement process. As a result, these imposter minutiae decrease identification performance. We introduce a histogram of the spectrum difference between the original latent fingerprint and its enhanced image. This new feature is suitable for distinguishing between genuineness and imposter. The minutiae set from the NIST SD27 latent fingerprint database is used as ground truth to evaluate the performance of the proposed method and the existing quality assessment methods. The experimental results show that the proposed method outperforms the current methods in terms of precision and F-measure.

关键词： Fingerprint recognition NIST Histograms Databases image matching Frequency-domain analysis image processing

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Enhanced Gaussian Process Regression for Diagnosing Wind Energy Conversion Systems

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IFAC-PapersOnLine 2022年第6期55卷 673-678页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

Fault detection and diagnosis techniques are increasingly important to ensure robust and resource efficient operation of Wind Energy Conversion (WEC) systems. In this context, this paper presents a Reduced Enhanced Gaussian Process Regression (REGPR)-based Random Forest (RF) technique (REGPR-RF) to identify and diagnose faults occurring in a nonlinear WEC systems. The proposed technique uses REGPR technique for features extraction and selection from raw sensor data. Then, these selected features are fed to RF classifier to reliably detect and classify faults. The use of REGPR to learn features avoid the dimension problems and improves the classification performance significantly with a small number of training data. The results obtained by REGPR-RF are compared to those obtained with other conventional classifiers (Support Vector Machines (SVM), Naive Bayes (NB), ...). The results show that the developed REGPR-RF technique achieve higher accuracy (99.99%) with small data sets.

关键词： Gaussian Process Regression (GPR) Noise-signal-Dependence Random Forest (RF) Feature Extraction Selection Fault Detection Diagnosis (FDD) Wind Energy Conversion (WEC) Systems

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Efficient sentence embedding via semantic subspace analysis

arXiv

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arXiv 2020年

作者： Wang, Bin Chen, Fenxiao Wang, Yuncheng Jay Kuo, C.-C. Signal and Image Processing Institute Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States

A novel sentence embedding method built upon semantic subspace analysis, called semantic subspace sentence embedding (S3E), is proposed in this work. Given the fact that word embeddings can capture semantic relationship while semantically similar words tend to form semantic groups in a high-dimensional embedding space, we develop a sentence representation scheme by analyzing semantic subspaces of its constituent words. Specifically, we construct a sentence model from two aspects. First, we represent words that lie in the same semantic group using the intra-group descriptor. Second, we characterize the interaction between multiple semantic groups with the inter-group descriptor. The proposed S3E method is evaluated on both textual similarity tasks and supervised tasks. Experimental results show that it offers comparable or better performance than the state-of-the-art. The complexity of our S3E method is also much lower than other parameterized models. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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Incoherent Digital Holography using Spiral Rotating Point Spread Functions Created by Double-helix Beams

Incoherent Digital Holography using Spiral Rotating Point Sp...

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Digital Holography and Three-Dimensional Imaging, DH 2022

作者： Dubey, Nitin Anand, Vijayakumar Khonina, Svetlana Kumar, Ravi Reddy, Andra Naresh Kumar Rosen, Joseph School of Electrical and Computer Engineering Ben-Gurion University of the Negev P.O. Box 653 Beer-Sheva8410501 Israel Institute of Physics University of Tartu Tartu50411 Estonia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn MelbourneVIC3122 Australia Samara National Research University Samara443086 Russia Image Processing Systems Institute Branch of the Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences Samara443001 Russia Hee Photonic Labs RigaLV-1002 Latvia Department of Physics of Complex Systems Weizmann Institute of Science Rehovot7610001 Israel

A new incoherent 3D imaging system with a rotating point spread function has been developed. Different computational reconstruction methods such as non-linear reconstruction and the Lucy-Richardson-Rosen algorithm wer... 详细信息

ISBN: (纸本)9781957171128

关键词： Optical transfer function

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