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4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021

作者： Zhang, Gang Zhang, Zhuowei Li, Rui Zhu, Lei Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing School of Electrical and Automation Engineering Nanjing Normal University NNU Nanjing China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (纸本)9781733509619

A direct synthesis design technique of multi-band filtering power dividers (FPDs) with high frequency selectivity is proposed, which is promising in both industrial and academic field. After a set of complete closed-form analytical equations are established for direct synthesis design, the proposed method is intensively described to reveal its powerful capacity in design of a multi-band FPD with any pre-specified responses, high multiband isolations and matching. To validate this design concept, a demonstrator triple-band microstrip FPD has been designed, fabricated, measured. What's more, a triple-band three-way FPD with 90° port-to-port phase differences as an extended example of the synthesis design technique has also been implemented, which furtherly shows the flexibility of the proposed method. © 2021 Applied Computational Electromagnetics Society (ACES).

关键词： Design

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Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs

arXiv

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arXiv 2022年

作者： Rivera, Cody Di, Sheng Tian, Jiannan Yu, Xiaodong Tao, Dingwen Cappello, Franck Department of Computer Science University of Alabama TuscaloosaAL United States School of Electrical Engineering and Computer Science Washington State University PullmanWA United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States

More and more HPC applications require fast and effective compression techniques to handle large volumes of data in storage and transmission. Not only do these applications need to compress the data effectively during simulation, but they also need to perform decompression efficiently for post hoc analysis. SZ is an error-bounded lossy compressor for scientific data, and cuSZ is a version of SZ designed to take advantage of the GPU’s power. At present, cuSZ’s compression performance has been optimized significantly while its decompression still suffers considerably lower performance because of its sophisticated lossless compression step—a customized Huffman decoding. In this work, we aim to significantly improve the Huffman decoding performance for cuSZ, thus improving the overall decompression performance in turn. To this end, we first investigate two state-of-the-art GPU Huffman decoders in depth. Then, we propose a deep architectural optimization for both algorithms. Specifically, we take full advantage of CUDA GPU architectures by using shared memory on decoding/writing phases, online tuning the amount of shared memory to use, improving memory access patterns, and reducing warp divergence. Finally, we evaluate our optimized decoders on an Nvidia V100 GPU using eight representative scientific datasets. Our new decoding solution obtains an average speedup of 3.64× over cuSZ’s Huffman decoder and improves its overall decompression performance by 2.43× on average. © 2022, CC BY.

关键词： Decoding

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An Improved Machine Learning Technique with Effective Heart Disease Prediction System

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computers, Materials & Continua 2021年第12期69卷 4169-4181页

作者： Mohammad Tabrez Quasim Saad Alhuwaimel Asadullah Shaikh Yousef Asiri Khairan Rajab Rihem Farkh Khaled Al Jaloud College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology P.O.Box 6086Riyadh11442Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar University1002Tunisia

Heart disease is the leading cause of death *** heart disease is challenging because it requires substantial experience and *** research studies have found that the diagnostic accuracy of heart disease is *** coronary heart disorder determines the state that influences the heart valves,causing heart *** indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep *** work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial *** first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)*** this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was *** CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods *** proposed method offers an illustrative framework that helps predict heart attacks with high *** proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

关键词： Machine learning deep recurrent neural network effective heart disease prediction framework

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A Comprehensive Survey of Spectrum Sharing Schemes from a Standardization and Implementation Perspective

arXiv

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arXiv 2022年

作者： Parvini, Mohammad Zarif, Amir Hossein Nouruzi, Ali Mokari, Nader Javan, Mohammad Reza Abbasi, Bijan Ghasemi, Amir Yanikomeroglu, Halim The Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran The Department of Electrical and Computer Engineering Shahrood University of Technology Iran The Commutations Research Centre Innovation Science and Economic Development Canada OttawaONK2H 8S2 Canada The Department of Systems and Computer Engineering Carleton University Ottawa Canada

As the services and requirements of next-generation wireless networks become increasingly diversified, it is estimated that the current frequency bands of mobile network operators (MNOs) will be unable to cope with the immensity of anticipated demands. Due to spectrum scarcity, there has been a growing trend among stakeholders toward identifying practical solutions to make the most productive use of the exclusively allocated bands on a shared basis through spectrum sharing mechanisms. However, due to the technical complexities of these mechanisms, their design presents challenges, as it requires coordination among multiple entities. To address this challenge, in this paper, we begin with a detailed review of the recent literature on spectrum sharing methods, classifying them on the basis of their operational frequency regime-that is, whether they are implemented to operate in licensed bands (e.g., licensed shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing (DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to narrow the gap between the standardization and vendor-specific implementations, we provide a detailed review of the potential implementation scenarios and necessary amendments to legacy cellular networks from the perspective of telecom vendors and regulatory bodies. Next, we analyze applications of artificial intelligence (AI) and machine learning (ML) techniques for facilitating spectrum sharing mechanisms and leveraging the full potential of autonomous sharing scenarios. Finally, we conclude the paper by presenting open research challenges, which aim to provide insights into prospective research endeavors. © 2022, CC BY.

关键词： Standardization

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Group-equivariant autoencoder for identifying spontaneously broken symmetries

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Physical Review E 2023年第5期107卷 054104-054104页

作者： Devanshu Agrawal Adrian Del Maestro Steven Johnston James Ostrowski Department of Industrial and Systems Engineering University of Tennessee Knoxville Tennessee 37996 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee 37996 USA Institute for Advanced Materials and Manufacturing University of Tennessee Knoxville Tennessee 37996 USA

We introduce the group-equivariant autoencoder (GE autoencoder), a deep neural network (DNN) method that locates phase boundaries by determining which symmetries of the Hamiltonian have spontaneously broken at each temperature. We use group theory to deduce which symmetries of the system remain intact in all phases, and then use this information to constrain the parameters of the GE autoencoder such that the encoder learns an order parameter invariant to these “never-broken” symmetries. This procedure produces a dramatic reduction in the number of free parameters such that the GE-autoencoder size is independent of the system size. We include symmetry regularization terms in the loss function of the GE autoencoder so that the learned order parameter is also equivariant to the remaining symmetries of the system. By examining the group representation by which the learned order parameter transforms, we are then able to extract information about the associated spontaneous symmetry breaking. We test the GE autoencoder on the 2D classical ferromagnetic and antiferromagnetic Ising models, finding that the GE autoencoder (1) accurately determines which symmetries have spontaneously broken at each temperature; (2) estimates the critical temperature in the thermodynamic limit with greater accuracy, robustness, and time efficiency than a symmetry-agnostic baseline autoencoder; and (3) detects the presence of an external symmetry-breaking magnetic field with greater sensitivity than the baseline method. Finally, we describe various key implementation details, including a quadratic-programming-based method for extracting the critical temperature estimate from trained autoencoders and calculations of the DNN initialization and learning rate settings required for fair model comparisons.

关键词： Phase transitions Structural order parameter Data analysis

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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IEEE Conference on Wireless Communications and Networking

作者： Haoxuan Liu Nan Qi Xiaojie Li Alexandros A. A. Boulogeorgos Theodoros A. Tsiftsis Ming Xiao Juha Röning Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden Biomimetics and Intelligent Systems Group University of Oulu Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

关键词： Training Data privacy Accuracy Federated learning Transfer learning Channel estimation Deep reinforcement learning Data models Surface treatment Convergence

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Unveiling the impact of drug on single cell metabolism in human gut microbiome by an SRS-FISH platform

Unveiling the impact of drug on single cell metabolism in hu...

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Frontiers in Optics + Laser science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser science 2023

作者： Ge, Xiaowei Pereira, Fátima C. Zhu, Yifan Wagner, Michael Cheng, Ji-Xin Department of Electrical and Computer Engineering Boston University BostonMA02215 United States School of Biological Sciences University of Southampton SouthamptonSO17 1BJ United Kingdom Centre for Microbiology and Environmental Systems Science Department of Microbiology and Ecosystem Science University of Vienna Vienna1030 Austria Department of Chemistry Boston University BostonMA02215 United States Department of Chemistry and Bioscience Aalborg University Aalborg Denmark Department of Biomedical Engineering Boston University BostonMA02215 United States Photonics Center Boston University BostonMA02215 United States

This study presents a multi-modal chemical imaging technique that delineates single-bacteria metabolism activeness and drug accumulation with identities, enabling the study of the impact of two host-targeted drugs on ... 详细信息

ISBN: (纸本)9781957171296

关键词： Beam splitters Imaging techniques Phase modulation Phase shift Raman scattering Stimulated Raman scattering

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Adaptive Distributed Learning with Byzantine Robustness: A Gradient-Projection-Based Method

Adaptive Distributed Learning with Byzantine Robustness: A G...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xinghan Wang Cheng Huang Jiahong Ning Tingting Yang Xuemin Shen School of Cyber Science and Engineering Southeast University Nanjing China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada School of Dalian Maritime University Dalian China Peng Cheng Laboratory Shenzhen China

In this paper, we propose an adaptive distributed learning algorithm that not only resists three types of Byzantine attacks (i.e., gradient negative direction attacks, gradient partial dimension zeroing attacks, gradient scaling attacks) but also ensures high model accuracy. The proposed algorithm is built on a fully distributed model: clients share their local model updates with a group of dynamic committee clients, who cooperatively and iteratively train a global model. Specifically, to counter gradient negative direction attacks, we design a method based on gradient projection that maps clients' local gradients into small subspaces. The design allows committee clients to efficiently and precisely filter out adversarial clients by comparing angles between these subspaces. Moreover, considering that data heterogeneity among clients may cause misdetections of gradient partial dimension zeroing and scaling attacks, thereby reducing model accuracy, we introduce an adaptive multi-dimensional scoring method, which is applied after the gradient-projection-based filtering. The method assists committee clients in scoring and selecting most suitable clients for model aggregation using three hyperparameters, and thus achieves a balance between model accuracy and security. Finally, we conduct extensive experiments on real-world datasets to show the proposed algorithm's effectiveness: it can achieve Byzantine robustness and simultaneously maintain high model accuracy.

关键词：

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Criteria-based Unsupervised Feature Selection Techniques: A Comparative Study Over Chest CT Scan

Criteria-based Unsupervised Feature Selection Techniques: A ...

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Information Technology (ACIT)

作者： Shrouq Al-Daja Ala’ Alyabrodi Abdallah Moubayed MohammadNoor Injadat Ali Elrashidi Data Science & AI Department Zarqa University Zarqa Jordan Cyber Security Department Zarqa University Zarqa Jordan Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Research on classifying chest CT scans as normal or abnormal using machine learning and deep learning has garnered significant attention. To address this, various feature selection (FS) methods are employed to reduce dimensionality and identify key features. This paper evaluates criteria tailored to unsupervised binary datasets, such as feature subsets, redundancy, and diversity, by comparing Mutual Information-Based Methods (MI) with Clustering-Based Feature Selection (Agglomerative Clustering) ones. Additionally, it examines Graph Structure, Spectral Properties, and Quality criteria by comparing Graph-Based Methods with Spectral Feature Selection-be (SPEC). For feature reduction, Agglomerative Clustering may offer a slight advantage due to better handling of diversity and redundancy, though the difference is minimal. If these aspects are crucial, Agglomerative Clustering might be preferred. Conversely, for Graph-Based vs. SPEC, SPEC Method is more suitable for feature reduction due to its coherent feature selection and focus on capturing significant patterns related to medical interventions.

关键词： Deep learning Computed tomography Termination of employment Feature extraction Information technology

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Evaluation of Chest CT-Scan Anomaly Detection Models Using Principal Component Analysis

Evaluation of Chest CT-Scan Anomaly Detection Models Using P...

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Information Technology (ACIT)

作者： Ala’ Alyabrodi Shrouq Al-Daja MohammadNoor Injadat Abdallah Moubayed Ali Elrashidi Cyber Security Department Zarqa University Zarqa Jordan Data Science & AI Department Zarqa University Zarqa Jordan Computer Engineering Department and Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Machine learning-based improvements in anomaly detection, visualization, and segmentation are made possible by the growing digitization of medical imaging, which reduces the workload for medical specialists. Nevertheless, trustworthy labeled data is necessary for supervised machine learning and is sometimes expensive, time-consuming, or impossible to get. Consequently, techniques that need either little labeling (unsupervised methods) or partial labeling (semi-supervised methods) have been used increasingly been used. One approach that can be used to tackle medical imaging tasks like segmentation and classification is anomaly detection, which makes use of both unsupervised and semi-supervised methods. This study investigates the anomaly detection capabilities of deep learning and machine learning models trained on chest CT scans. To adopt this approach, firstly, by evaluating several feature extraction techniques (Principal Component Analysis (PCA), Autoencoder, and variance threshold), PCA achieves the best results on the selected dataset. After that, different machine learning and deep learning models (Isolation Forest, K-means, feed-forward neural networks, and convolutional neural networks) are assessed to detect anomalies using PCA for dimensionality reduction. The experiments show that the NN-Feedforward model with PCA outperformed the other models, also attains remarkably low false alarm rate, demonstrating its effectiveness in reducing false positives and precisely detecting anomalies.

关键词： Deep learning Image segmentation Forestry Feature extraction Feedforward neural networks Labeling Information technology Anomaly detection Principal component analysis Biomedical imaging

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