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$\text{MR}^{2}$-Net: Retinal OCTA Image Stitching via Multi-Scale Representation Learning and Dynamic Location Guidance

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IEEE Journal of Biomedical and Health Informatics 2024年第1期29卷 482-494页

作者： Haiting Mao Yuhui Ma Dan Zhang Yanda Meng Shaodong Ma Yuchuan Qiao Huazhu Fu Caifeng Shan Da Chen Yitian Zhao Jiong Zhang Laboratory of Advanced Theranostic Materials and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China School of Cyber Science and Engineering Ningbo University of Technology Ningbo China Department of Computer Science University of Exeter Exeter U.K. Institution of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Institute of High Performance Computing (IHPC) A*STAR Singapore School of Intelligence Science and Technology Nanjing University Nanjing China Qilu University of Technology Jinan China

Optical coherence tomography angiography (OCTA) plays a crucial role in quantifying and analyzing retinal vascular diseases. However, the limited field of view (FOV) inherent in most commercial OCTA imaging systems poses a significant challenge for clinicians, restricting the possibility to analyze larger retinal regions of high resolution. Automatic stitching of OCTA scans in adjacent regions may provide a promising solution to extend the region of interest. However, commonly-used stitching algorithms face difficulties in achieving effective alignment due to noise, artifacts and dense vasculature present in OCTA images. To address these challenges, we propose a novel retinal OCTA image stitching network, named $\text{MR}^{2}$ -Net, which integrates multi-scale representation learning and dynamic location guidance. In the first stage, an image registration network with a progressive multi-resolution feature fusion is proposed to derive deep semantic information effectively. Additionally, we introduce a dynamic guidance strategy to locate the foveal avascular zone (FAZ) and constrain registration errors in overlapping vascular regions. In the second stage, an image fusion network based on multiple mask constraints and adjacent image aggregation (AIA) strategies is developed to further eliminate the artifacts in the overlapping areas of stitched images, thereby achieving precise vessel alignment. To validate the effectiveness of our method, we conduct a series of experiments on two delicately constructed datasets, i.e., OPTOVUE-OCTA and SVision-OCTA. Experimental results demonstrate that our method outperforms other image stitching methods and effectively generates high-quality wide-field OCTA images, achieving a structural similarity index (SSIM) score of 0.8264 and 0.8014 on the two datasets, respectively.

关键词： Feature extraction Image stitching Retina Image registration Convolutional codes Bioinformatics Representation learning

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Mary Kenneth Keller: First US PhD in computer science

arXiv

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arXiv 2022年

作者： Head, Jennifer O'Leary, Dianne P. Archivist Sisters of Charity of the Blessed Virgin Mary DubuqueIA United States Computer Science Department Institute for Advanced Computer Studies University of Maryland United States

In June 1965, Sister Mary Kenneth Keller, BVM, received the first US PhD in computer science, and this paper outlines her life and accomplishments. As a scholar, she has the distinction of being an early advocate of learning-by-example in artificial intelligence. Her main scholarly contribution was in shaping computer science education in high schools and small colleges. She was an evangelist for viewing the computer as a symbol manipulator, for providing computer literacy to everyone, and for the use of computers in service to humanity. She was far ahead of her time in working to ensure a place for women in technology and in eliminating barriers preventing their participation, such as poor access to education and daycare. She was a strong and spirited woman, a visionary in seeing how computers would revolutionize our lives. A condensation of this paper appeared as, "The Legacy of Mary Kenneth Keller, First U.S. Ph.D. in computer science," Jennifer Head and Dianne P. O'Leary, IEEE Annals of the History of computing 45(1):55-63, January-March *** Codes 68.03 © 2022, CC BY-NC-ND.

关键词： Education computing

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Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels

arXiv

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arXiv 2024年

作者： Wang, Zhe Zhang, Jiayi Björnson, Emil Niyato, Dusit Ai, Bo State Key Laboratory of Advanced Rail Autonomous Operation China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Department of Computer Science KTH Royal Institute of Technology Stockholm114 28 Sweden College of Computing & Data Science Nanyang Technological University 639798 Singapore

In this paper, we explore the low-complexity optimal bilinear equalizer (OBE) combining scheme design for cell-free massive multiple-input multiple-output networks with spatially correlated Rician fading channels. We provide a spectral efficiency (SE) performance analysis framework for both the centralized and distributed processing schemes with bilinear equalizer (BE)-structure combining schemes applied. The BE-structured combining is a set of schemes that are constructed by the multiplications of channel statistics-based BE matrices and instantaneous channel estimates. Notably, we derive closed-form achievable SE expressions for centralized and distributed BE-structured combining schemes. We propose one centralized and two distributed OBE schemes: Centralized OBE (C-OBE), Distributed OBE based on Global channel statistics (DG-OBE), and Distributed OBE based on Local channel statistics (DL-OBE), which maximize their respective SE expressions. OBE matrices in these schemes are tailored based on varying levels of channel statistics. Notably, we obtain new and insightful closed-form results for the C-OBE, DG-OBE, and DL-OBE combining schemes. Numerical results demonstrate that the proposed OBE schemes can achieve excellent SE, even in scenarios with severe pilot contamination. Copyright © 2024, The Authors. All rights reserved.

关键词： Fading channels

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Real-time Stream Processing in IoT Environments

Real-time Stream Processing in IoT Environments

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： P. Rajasekar Rupali Shekar Bhosale Indhumathi C Sandeep K V Prasanthi Kumari N Rajendiran M Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electronics and Telecommunication Engineering Bharati Vidyapeeth Institute of Technology Navi Mumbai Maharashtra India Department of Computer Science and Business Systems RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Electronics & Telecommunication Engineering Dayananda Sagar College of Engineering Bengaluru Karnataka India School of Computer Sciences University of Petroleum and Energy Studies Dehradun Uttarakhand 248007 India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT). This article analyzes present methods and technologies. The inquiry begins with large-scale, real-time IoT data analysis. Real-time analytics and bulk processing limits are important for IoT decision-making. We then investigate IoT stream processing frameworks, methodologies, and systems' complexity. We explore Internet of Things technology selection and tradeoffs between scalability, accuracy, latency, and real-time processing system scalability. It covers real-time stream processing system design and deployment and risk mitigation. Case studies and use cases highlight real-time IoT stream processing. Healthcare, smart city, and industrial IoT operational efficiency and proactive decision-making improve with real-time data. Finally, the report suggests the Internet of Things real-time stream processing research. New IoT data analytics protocols, methods, and technologies are highlighted. We teach real-time stream processing to IoT academics, practitioners, and decision-makers. They could then develop more efficient and scalable IoT data stream optimization solutions.

关键词： Privacy Data analysis Protocols Smart cities Scalability Decision making Real-time systems

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A graph-based framework for structured prediction tasks in sanskrit

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Computational Linguistics 2020年第4期46卷 785-845页

作者： Krishna, Amrith Santra, Bishal Gupta, Ashim Satuluri, Pavankumar Goyal, Pawan Department of Computer Science and Technology University of Cambridge United Kingdom Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India School of Computing University of Utah United States School of Linguistics & Literary Studies Chinmaya Vishwavidyapeeth India

We propose a framework using energy-based models for multiple structured prediction tasks in Sanskrit. Ours is an arc-factored model, similar to the graph-based parsing approaches, and we consider the tasks of word segmentation, morphological parsing, dependency parsing, syntactic linearization, and prosodification, a "prosody-level" task we introduce in this work. Ours is a search-based structured prediction framework, which expects a graph as input, where relevant linguistic information is encoded in the nodes, and the edges are then used to indicate the association between these nodes. Typically, the state-of-the-art models for morphosyntactic tasks in morphologically rich languages still rely on hand-crafted features for their performance. But here, we automate the learning of the feature function. The feature function so learned, along with the search space we construct, encode relevant linguistic information for the tasks we consider. This enables us to substantially reduce the training data requirements to as low as 10%, as compared to the data requirements for the neural state-of-the-art models. Our experiments in Czech and Sanskrit show the language-agnostic nature of the framework, where we train highly competitive models for both the languages. Moreover, our framework enables us to incorporate language-specific constraints to prune the search space and to filter the candidates during inference. We obtain significant improvements in morphosyntactic tasks for Sanskrit by incorporating language-specific constraints into the model. In all the tasks we discuss for Sanskrit, we either achieve state-of-the-art results or ours is the only data-driven solution for those tasks. © 2020 Association for Computational Linguistics

关键词： Syntactics

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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A Comprehensive Analysis in Investigating the Impact of Big Data Analytics in Wireless Networks in Failure Prediction, Prevention and Recovery 2

A Comprehensive Analysis in Investigating the Impact of Big ...

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2nd International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Surya, S. Rajasekaran, P. Yuriy, Shvets Johar, Siti Sarawati Tripathi, Amrendra Magno Ventayen, Randy Joy Computer Science and Engineering School of Computing Kalasalingam University India Information Technology Dr. N.G.P Institute of Technology India Financial University under the Goverment of Russian Federation Institute of Control of Science Ras Moscow Russia Centre for General Studies and Co-curricular Universiti Tun Hussein Onn Malaysia Department of Social Science Parit Raja Johor Darul Takzim Batu Pahat86400 Malaysia School of Computer Science Upes Dehradun India Pangasinan State University Lingayen Campus Philippines

ISBN: (纸本)9781665437899

The current economic and competitive environment has enabled many businesses and industries to implement digital for enhancing the business operations and support in addressing the overall objectives. Dur to this there is a mounting avalanche information being shared between the individuals, companies and governments, the increase in the usage of mobile devices, internet, social media and other ecommerce services has supported in increasing the volume of data. This trend is expected to grow exponentially and hence there is a requirement to provide wireless network in an effective manner, the increased usage of data and information has resulted in facing various failures, like call drops for mobile users, low bandwidth for internet users etc. Hence, it is highly necessary to implement tools to understand the failure prediction, prevention and recovery on the network. Hence, this study is focused in investigating the impact of big data analysis in protecting the wireless networks from such failures and support in processing large volume of information in an effective manner, the big data analytics can also extract useful data will enable in addressing the concerned parties so as to make critical decisions. In this study, the researcher intends to conduct the survey using closed ended questionnaire from the individuals so as to measure the effectiveness of applying big data for failure prevention, protection and recovery in the wireless network. © 2022 IEEE.

关键词： Wireless networks

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Variational quantum simulation of the critical Ising model with symmetry averaging

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Physical Review A 2023年第4期107卷 042620-042620页

作者： Troy J. Sewell Ning Bao Stephen P. Jordan Joint Center for Quantum Information and Computer Science College Park Maryland 20742 USA Department of Physics University of Maryland College Park Maryland 20742 USA University of Maryland Institute for Advanced Computer Studies College Park Maryland 20742 USA Computational Science Initiative Brookhaven National Laboratory Upton New York 11973 USA Microsoft Redmond Washington 98052 USA

Here we investigate the use of deep multiscale entanglement renormalization ansatz (DMERA) circuits as a variational ansatz. We use the exactly solvable one-dimensional critical transverse-field Ising model as a test bed. Numerically exact simulation of the quantum circuit ansatz can in this case be carried out to hundreds of qubits by exploiting efficient classical algorithms for simulating matchgate circuits. We find that, for this system, the DMERA strongly outperforms a standard quantum approximate optimization algorithm (QAOA)–style ansatz, and that a major source of systematic error in correlation functions approximated using the DMERA is the breaking of the translational and Kramers-Wannier symmetries of the transverse-field Ising model. We are able to reduce this error by up to four orders of magnitude by symmetry averaging, without incurring additional cost in qubits or circuit depth. We propose that this technique for mitigating systematic error could be applied to noisy intermediate-scale quantum (NISQ) simulations of physical systems with other symmetries.

关键词： Quantum algorithms Quantum simulation 1-dimensional spin chains Quantum many-body systems Approximation methods for many-body systems Ising model Tensor network methods Variational approach

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Parameter Estimation of Multiple Poles by Subspace-Based Method

Parameter Estimation of Multiple Poles by Subspace-Based Met...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Prabhu Vijayan Philippe Dreesen Ivan Markovsky Mariya Ishteva Department of Computer Science ADVISE-NUMA KU Leuven Geel Belgium Department ELEC Vrije Universiteit Brussel Brussel Belgium Department of Advanced Computing Sciences Maastricht University Maastricht The Netherlands Gran Capità International Centre for Numerical Methods in Engineering (CIMNE) Barcelona Spain Catalan Institution for Research and Advanced Studies (ICREA) Barcelona Spain

This work deals with the problem of estimating the parameters (i.e., pole multiplicity structure, pole locations, and complex amplitudes) of exponential polynomials for a given model order. We start from an exponential polynomial signal model and algebraically demonstrate the reasoning behind and the manner in which this model generalizes to the broader class of signals containing multiple poles. The poles and complex amplitudes are estimated by a subspace-based method. With the given model order as prior, a heuristic algorithm is developed to estimate the multiplicity structure of poles. Numerical simulations are conducted to study the performance of the subspace-based method and the sensitivity of poles and multiplicity estimation. Our simulations show that our pole estimation method is accurate for signals with poles of multiplicity greater than one, particularly for signals with lower orders. This allows for successful signal regeneration, producing estimates that closely match the original data. The estimation is sensitive, particularly in the presence of noise, and when the poles are complex and have a higher order of multiplicity.

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments 6

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

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6th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Li, Keqin Chen, Jiajing Yu, Dezhi Dajun, Tao Qiu, Xinyu Lian, Jieting Ji, Ryan Zhang, Shengyuan Wan, Zhenyu Sun, Baiwei Hong, Bo Ni, Fanghao Han, Jiaqi Ama University Department of Computer Science Quezon Philippines Courant Institute New York University 251 Mercer St New YorkNY10012 United States University of California Berkeley University Avenue and Oxford St School of Information BerkeleyCA94720 United States Carnegie Mellon University Master of Science in Mechanical Engineering School of Engineering 5000 Forbes Avenue PittsburghPA United States Northeastern University College of Engineering Seattle United States School of Professional Studies New York University Master of Science in Database Technology 50 West 4th Street New YorkNY United States Cornell University College of Engineering 616 Thurston Ave IthacaNY14853 United States Cornell University Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science 616 Thurston Ave IthacaNY United States Independent Researcher Beijing China University of California Irvine Master of Computer Science Donald Bren School of Information and Computer Sciences 510 Aldrich Hall IrvineCA United States Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350389418

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot's obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments. © 2024 IEEE.

关键词： Deep reinforcement learning

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