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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Liu, Changxin Tao, Jun Huang, Weimin School of Information Science and Engineering Southeast University Nanjing210096 China Pengcheng Laboratory Shenzhen518000 China Memorial University of Newfoundland Department of Electrical and Computer Engineering St. John'sNLA1B 3X5 Canada

ISBN: (纸本)9798218142186

A direct adaptive equalizer (DAE) coupled with a phase-locked loop (PLL) has been proved as an efficient receiver scheme for single-carrier coherent underwater acoustic (UWA) communications. Ever since its birth, numerous improvements have been proposed for the DAE. In contrast, the PLL has been somehow overlooked. A PLL with predetermined coefficients suffers performance degradation when channel conditions change, then a variable-parameter (VP) PLL designed via gradient descent (GD) method was proposed. The GD-VP-PLL is sensitive to parameter initialization though. In this paper, we propose a new VP-PLL scheme in light of the recursive Gauss-Newton (RGN) method. The resulting RGN-VP-PLL has been tested by at-sea experimental data, and showed better performance than the GD-VP-PLL and a PLL with fixed coefficients. Moreover, it is insensitive to initialization of parameters. © 2023 The Marine Technology Society (MTS).

关键词： Underwater acoustic communication

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Improving Stain Invariance of CNNs for Segmentation by Fusing Channel Attention and Domain-Adversarial Training

arXiv

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arXiv 2023年

作者： Abutalip, Kudaibergen Saeed, Numan Khan, Mustaqeem Saddik, Abdulmotaleb El Computer Vision Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

Variability in staining protocols, such as different slide preparation techniques, chemicals, and scanner configurations, can result in a diverse set of whole slide images (WSIs). This distribution shift can negatively impact the performance of deep learning models on unseen samples, presenting a significant challenge for developing new computational pathology applications. In this study, we propose a method for improving the generalizability of convolutional neural networks (CNNs) to stain changes in a single-source setting for semantic segmentation. Recent studies indicate that style features mainly exist as covariances in earlier network layers. We design a channel attention mechanism based on these findings that detects stain-specific features and modify the previously proposed stain-invariant training scheme. We reweigh the outputs of earlier layers and pass them to the stain-adversarial training branch. We evaluate our method on multi-center, multi-stain datasets and demonstrate its effectiveness through interpretability analysis. Our approach achieves substantial improvements over baselines and competitive performance compared to other methods, as measured by various evaluation metrics. We also show that combining our method with stain augmentation leads to mutually beneficial results and outperforms other techniques. Overall, our study makes significant contributions to the field of computational pathology. © 2023, CC BY.

关键词： Image enhancement

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Electron charge qubit with 0.1 millisecond coherence time

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Nature Physics 2024年第1期20卷 116-122页

作者： Zhou, Xianjing Li, Xinhao Chen, Qianfan Koolstra, Gerwin Yang, Ge Dizdar, Brennan Huang, Yizhong Wang, Christopher S. Han, Xu Zhang, Xufeng Schuster, David I. Jin, Dafei Center for Nanoscale Materials Argonne National Laboratory LemontIL United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CambridgeMA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States James Franck Institute and Department of Physics University of Chicago ChicagoIL United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States Department of Applied Physics Stanford University StanfordCA United States Department of Physics and Astronomy University of Notre Dame Notre DameIN United States

Electron charge qubits are compelling candidates for solid-state quantum computing because of their inherent simplicity in qubit design, fabrication, control and readout. However, electron charge qubits built on conventional semiconductors and superconductors suffer from severe charge noise that limits their coherence time to the order of one microsecond. Here we report electron charge qubits that exceed this limit, based on isolated single electrons trapped on an ultraclean solid neon surface in a vacuum. Quantum information is encoded in the motional states of an electron that is strongly coupled with microwave photons in an on-chip superconducting resonator. The measured relaxation and coherence times are both on the order of 0.1 ms, surpassing all existing charge qubits and rivalling state-of-the-art superconducting transmon qubits. The simultaneous strong coupling of two qubits with a common resonator is also demonstrated, as the first step towards two-qubit entangling gates for universal quantum computing. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Superconducting resonators

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching 39

DAMPER: A Dual-Stage Medical Report Generation Framework wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diagnosis

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Clustering Interval and Triangular Granular Data: Modeling, Execution, and Assessment

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10000-10014页

作者： Yiming Tang Wenbin Wu Witold Pedrycz Jianwei Gao Xianghui Hu Zhaohong Deng Rui Chen Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada School of Computer and Information Hefei University of Technology Hefei China Faculty of Automatic Control Electronics and Computer Science Silesian University of Tecghnology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul TÃrkiye School of Computer Science and Engineering Southeast University Nanjing China School of Artificial Intelligence and Computer Jiangnan University Wuxi Jiangsu China

In current granular clustering algorithms, numeric representatives were selected by users or an ordinary strategy, which seemed simple; meanwhile, weight settings for granular data could not adequately express their structural characteristics. Aiming at these problems, in this study, a new scheme called a granular weighted kernel fuzzy clustering (GWKFC) algorithm is put forward. We propose the representative selection and granularity generation (RSGG) algorithm enlightened by the density peak clustering (DPC) algorithm. We build interval and triangular granular data on the strength of numeric representatives obtained by RSGG under the principle of justifiable granularity (PJG), in which we establish some combinations of functions and boundary constraints and prove their properties. Furthermore, we present a novel distance formula via the kernel function for granular data and design new weights to affect the coverage and specificity of granular data. In addition, based upon these factors, we come up with the GWKFC algorithm of granular clustering, and its performance with different granularity is assessed. To sum up, a macro framework involving granular modeling, granular clustering, and assessment has been set up. Lastly, the GWKFC algorithm and ten other granular clustering algorithms are compared by experiments on some artificial and UCI datasets together with datasets with large data or those of high dimensionality. It is found that the GWKFC algorithm can provide better granular clustering results by contrast with other algorithms. The originality is embodied as follows. First, we improve the previous density radius and present the RSGG algorithm to acquire numeric representatives. Second, we propose a new strategy to determine granular data boundaries and further obtain novel weights enlightened by the idea of volume. Lastly, we employ the kernel function to calculate the distance between granular data, which has a stronger spatial division ability than the pr

关键词： Clustering algorithms Data models Computational modeling Numerical models Kernel Phase change materials Granular computing Uncertainty Fuzzy sets Noise

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Imaging atomic-scale chemistry from fused multi-modal electron microscopy

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npj Computational Materials 2022年第1期8卷 164-171页

作者： Jonathan Schwartz Zichao Wendy Di Yi Jiang Alyssa J.Fielitz Don-Hyung Ha Sanjaya D.Perera Ismail El Baggari Richard D.Robinson Jeffrey A.Fessler Colin Ophus Steve Rozeveld Robert Hovden Department of Materials Science and Engineering University of MichiganAnn ArborMIUSA Mathematics and Computer Science Division Argonne National LaboratoryLemontILUSA Advanced Photon Source Facility Argonne National LaboratoryLemontILUSA Dow Chemical Co. MidlandMIUSA Department of Material Science and Engineering Cornell UniversityIthacaNew YorkUSA School of Integrative Engineering Chung-Ang UniversitySeoulRepublic of Korea Department of Physics Cornell UniversityIthacaNYUSA The Rowland Institute at Harvard CambridgeMAUSA Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMIUSA National Center for Electron Microscopy Lawrence Berkeley National LaboratoryMolecular FoundryBerkeleyCAUSA Applied Physics Program University of MIchiganAnn ArborMIUnited States

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic ***,fused multi-modal electron microscopy offers high signal-to-noise ratio(SNR)recovery of material chemistry at nano-and atomic-resolution by coupling correlated information encoded within both elastic scattering(high-angle annular dark-field(HAADF))and inelastic spectroscopic signals(electron energy loss(EELS)or energy-dispersive x-ray(EDX)).By linking these simultaneously acquired signals,or modalities,the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector *** many cases,the dose requirements can be reduced by over one order of *** high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials.

关键词： materials chemistry fused

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Cloud and Edge based Smart Agriculture: A Real-World Deployment

Cloud and Edge based Smart Agriculture: A Real-World Deploym...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Et-Taibi, Bouali Abid, Mohamed Riduan Boufounas, El-Mahjoub Benhaddou, Driss Moulay Ismail University of Meknès Reipt Laboratory Faculty of Sciences and Technology of Errachidia Morocco Al Akhawayn University in Ifrane School of Science and Engineering Ifrane53000 Morocco Columbus State University Tsys School of Computer Science ColumbusGA31907 United States University of Houston Department of Engineering Technology 4800 Calhoun Rd HoustonTX77004 United States Alfaisal University College of Engineering Electrical Engineering Department Riyadh Saudi Arabia

ISBN: (纸本)9798350365306

Smart Agriculture presents significant opportunities for transforming farming methods, although its widespread adoption remains constrained by cost barriers, especially for small and medium-sized farmers. To bridge this divide, we propose a cost-effective solution tailored specifically for soilless cultivation within drip irrigation systems. Our approach harnesses the power of IoT, cloud computing, and edge computing to bolster connectivity and streamline data management. Central to our solution is a three-tiered open-source software architecture, which encompasses local sensors, edge computing, and cloud computing layers. Locally deployed Wireless Sensor Networks facilitate real-time monitoring and data acquisition, while the edge tier orchestrates irrigation tasks in the field using Fuzzy Logic algorithms. The cloud layer serves as the backbone for data analytics, storage, and visualization, ensuring optimal resource management. Through the utilization of IoT protocols, seamless communication is established between sensors, edge devices, and the cloud, enabling efficient data exchange and analysis. Our tangible prototype validates the effectiveness of this platform in optimizing drip irrigation systems. This offering provides an accessible solution for small and medium-scale farmers, empowering them to enhance farming productivity and sustainability. © 2024 IEEE.

关键词： Smart agriculture

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Edge-aware Feature Aggregation Network for Polyp Segmentation

arXiv

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arXiv 2023年

作者： Zhou, Tao Zhang, Yizhe Chen, Geng Zhou, Yi Wu, Ye Fan, Deng-Ping PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering NPU Xi’an China School of Computer Science and Engineering Southeast University Nanjing China Computer Vision Lab ETH Zürich Zürich Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundaries, it is still a challenging task to achieve satisfactory segmentation performance with different scales and shapes. In this study, we present a novel Edge-aware Feature Aggregation Network (EFA-Net) for polyp segmentation, which can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation. Specifically, we first present an Edge-aware Guidance Module (EGM) to combine the low-level features with the high-level features to learn an edge-enhanced feature, which is incorporated into each decoder unit using a layer-by-layer strategy. Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation. Further, a Cross-level Fusion Module (CFM) is proposed to effectively integrate the cross-level features, which can exploit the local and global contextual information. Finally, the outputs of CFMs are adaptively weighted by using the learned edge-aware feature, which are then used to produce multiple side-out segmentation maps. Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness. Our implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Practical and Ethical Considerations for Generative AI in Medical Imaging 2nd

Practical and Ethical Considerations for Generative AI in...

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2nd International Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the International Conference on Medical Image Computing and computer Assisted Interventions, MICCAI 2024

作者： Jha, Debesh Rauniyar, Ashish Hagos, Desta Haileselassie Sharma, Vanshali Tomar, Nikhil Kumar Zhang, Zheyuan Isler, Ilkin Durak, Gorkem Wallace, Michael Yazici, Cemal Berzin, Tyler Biswas, Koushik Bagci, Ulas Machine and Hybrid Intelligence Laboratory Northwestern University Evanston United States SINTEF Digital Trondheim Norway Department of Electrical Engineering and Computer Science Howard University WashingtonDC United States Department of Computer Science University of Central Florida Orlando United States Mayo Clinic Florida Jacksonville United States Department of Medicine University of Chicago Chicago United States Beth Israel Deaconess Medical Center Harvard Medical School Boston United States

ISBN: (纸本)9783031727863

Generative Artificial Intelligence (AI) has the potential to transform medicine. It is helpful to clinicians and radiologists for diagnosis, screening, treatment planning, interventions, and drug development. It benefits the clinical flow with real-time decision-support systems. While generative AI can potentially improve healthcare, it also introduces new ethical issues that require careful analysis and mitigation strategies. This work emphasizes the ethical aspects of generative AI in medical imaging, aiming to ensure that advancements in this field align with established ethical principles and societal values. We delve into the ethical implications surrounding bias, fairness, patient privacy, consent, transparency, explainability, intellectual property, and data ownership. Furthermore, we discuss regulations governing the use of synthetic medical data. To promote equitable application of these powerful tools, we also propose clear guidelines for promoting fairness, mitigating bias, and ensuring diversity within generative AI models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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Innovative Self-Powered 3D-Printed Triboelectric Nanogenerator for Multimodal Force Sensing

Innovative Self-Powered 3D-Printed Triboelectric Nanogenerat...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Shingirirai Chakoma Jerome Rajendran Xiaochang Pei Anita Ghandehari Jorge Alfonso Tavares Negrete Rahim Esfandyarpour Department of Electrical Engineering and Computer Science Laboratory for Integrated Nano BioElectronics Innovation The Henry Samueli School of Engineering University of California Irvine Irvine CA USA

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

In this study, we present the development and characterization of a self-powered, flexible, and multi-material 3D-printed Triboelectric Nanogenerator-Unit Cell Force (TENG-UCF) sensor designed for multimodal force sensing and energy harvesting. Traditional force sensors, limited by their reliance on single-modal electrical readouts and continuous external power sources, are insufficient for comprehensive sensory applications. Our TENG-UCF sensor overcomes these limitations by leveraging the unique properties of MXene/P ANI composites and SEBS tribo layers, enabling simultaneous force sensing and energy generation. This innovative sensor generates electricity through triboelectric charges produced during contact and separation cycles, with output voltage and capacitance directly correlating with applied force. The TENG-UCF achieves high sensitivity and stability, with a maximum output voltage of 700V and a power density of 816.6 mW/m 2 under optimal conditions. Extensive testing demonstrated the sensor's mechanical stability, and long-term operational viability without significant degradation. Our TENG-UCF sensor represents a significant advancement in sustainable, renewable, and multimodal force sensing technologies, with broad implications for the development of self-powered sensor systems.

关键词： Power system measurements Density measurement Force Voltage Three-dimensional printing Sensor systems Sensors Energy harvesting Triboelectricity Testing

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