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Sensor signal processing for Defence (SSPD)

作者： Mengwei Sun Richard Hodgskin-Brown Mike E. Davies Ian K. Proudler James R. Hopgood Institute of Digital Communications University of Edinburgh Edinburgh U.K. Department of Electronics & Electrical Engineering Electromagnetic Sensing Group University of Manchester Manchester UK Department of Electronic & Electrical Engineering Centre for Signal & Image Processing (CeSIP) University of Strathclyde Glasgow UK

This paper proposes the use of the adaptive kernel Kalman filter (AKKF) to track metallic targets using magnetic anomaly detection (MAD). The proposed AKKF-based approach enables accurate tracking of moving metallic targets using magnetometer sensors, even in the presence of dynamic and unknown magnetic moments. The experimental results demonstrate that the proposed method exhibits favourable tracking and estimation performance with reduced computational complexity compared with the bootstrap particle filter (PF). For example, in magnetic moment strength estimation, the relative root mean square error (RRMSE) of the proposed algorithm using 50 particles can approach 2.5% with a computation time of 0.18 seconds, whereas the RRMSE of the PF using 2000 particles is 4.5% with a computation time of 1.4 seconds. This study highlights the potential of AKKF in MAD for metallic target tracking using magnetometer sensors.

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Transient Analysis of Space-Time Frequency-Dispersive Metal Slabs

Transient Analysis of Space-Time Frequency-Dispersive Metal ...

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International Congress on Advanced Electromagnetic Materials in Microwaves and Optics (METAMATERIALS)

作者： Pablo H. Zapata-Cano Salvador Moreno-Rodríguez Stamatios Amanatiadis Antonio Alex-Amor Zaharias D. Zaharis Carlos Molero School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Signal Theory Telematics and Communications Research Centre for Information and Communication Technologies (CITIC-UGR) Universidad de Granada Granada Spain Department of Electrical and Systems Engineering Philadelphia University of Pennsylvania Pennsylvania United States

ISBN: (数字)9798350373493

ISBN: (纸本)9798350373509

In this paper, we analyze the scattering of an incident plane wave on a space-time metal slab in the presence of dispersion. Frequency dispersion is taken into account here by means of the Drude model. Analytical results, obtained via first-principle calculations, are in good agreement with in-house finite-difference time-domain (FDTD) computations.

关键词： Transient response Time-frequency analysis Metals Scattering Time-domain analysis Slabs Transient analysis Dispersion Physics Finite difference methods

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Implementation of Adaptive Kernel Kalman Filter in Stone Soup

Implementation of Adaptive Kernel Kalman Filter in Stone Sou...

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Sensor signal processing for Defence (SSPD)

作者： James S. Wright James R. Hopgood Mike E. Davies Ian K. Proudler Mengwei Sun Dstl Dstl Porton Down Salisbury Wiltshire UK Institute of Digital Communications University of Edinburgh Edinburgh UK Department of Electronic & Electrical Engineering Centre for Signal & Image Processing (CeSIP) University of Strathclyde Glasgow UK

The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF has significantly improved performance, reducing computational complexity and avoiding resampling. It has been applied in various tracking scenarios, such as multi-sensor fusion and multi-target tracking. By using existing Stone Soup components, along with newly established kernel-based prediction and update modules, we demonstrate that the AKKF can work in the Stone Soup platform by being applied to a bearing–only tracking (BOT) problem. We hope that the AKKF will enable more applications for tracking and estimation problems, and the development of a whole class of derived algorithms in sensor fusion systems.

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A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas

arXiv

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arXiv 2023年

作者： Fister, Iztok Fister, Iztok Fister, Dušan Podgorelec, Vili Salcedo-Sanz, Sancho Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 Maribor2000 Slovenia Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares Madrid28805 Spain

Association rule mining is intended for searching for the relationships between attributes in transaction databases. The whole process of rule discovery is very complex, and involves pre-processing techniques, a rule mining step, and post-processing, in which visualization is carried out. Visualization of discovered association rules is an essential step within the whole association rule mining pipeline, to enhance the understanding of users on the results of rule mining. Several association rule mining and visualization methods have been developed during the past decades. This review paper aims to create a literature review, identify the main techniques published in peer-reviewed literature, examine each method’s main features, and present the main applications in the field. Defining the future steps of this research area is another goal of this review paper. Copyright © 2023, The Authors. All rights reserved.

关键词： Visualization

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Novel Personalized Multimedia Recommendation Systems Using Tensor Singular-Value-Decomposition

Novel Personalized Multimedia Recommendation Systems Using T...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Wu, Yiyan 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 Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin541004 China Guilin University of Electronic Technology Department of Information and Telecommunication Guilin541004 China Northwestern State University of Louisiana Department of Engineering Technology NatchitochesLA71497 United States Communications Research Centre Canada OttawaONK2H 8S2 Canada

ISBN: (纸本)9798350321524

Nowadays, multimedia data are often produced by various sources, e.g., Internet of Things (IoT), social media, customer databases, etc. As tremendous multimedia data are produced every day, users or E-commerce customers cannot infer information from such data by themselves and therefore recommender systems have been developed to help users to select the products or services which better fit users' preferences or requirements. Nonetheless, there exists few works on the incorporation of side information or multiple attributes about items into the design of a more robust recommender system. In this work, we propose a novel approach based on the third-order tensor singular-value-decomposition (T3-SVD) to design new personalized multimedia recommender systems (PMRSs) for internet users. A PMRS can dynamically adjust its recommendation strategy subject to a particular user's on-line transaction behavior. To evaluate the effectiveness of our proposed PMRS based on T3-SVD, we compare the performances of our proposed new PMRS and two other existing tensor-based recommendation systems over realworld data in terms of normalized root-mean-square error (NRMSE). As a result, our proposed new PMRS greatly outperforms the other two existing systems. © 2023 IEEE.

关键词： Recommender systems

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NOMA Assisted Semi-Grant-Free Transmission for UAV Networks: A Multi-User Scheduling Approach

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IEEE Transactions on communications 2025年

作者： Huang, Junlin Lu, Huabing Tang, Jie Zhao, Nan Shi, Zhaoyuan Wang, Xianbin Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Nanchang University School of Information Engineering Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications Nanchang330031 China South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Anqing Normal University School of Computer and Information Anqing246052 China Western University Department of Electrical and Computer Engineering LondonN6A 5B9 Canada

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission, which enables grant-based users to share their spectrum with grant-free (GF) users, becomes an effective solution to the challenge of massive connections. However, this improvement is limited as most of the existing schemes can only admit one GF user. In this paper, we propose a novel NOMA-assisted SGF transmission scheme to support the access of multiple GF users in unmanned aerial vehicle networks. Moreover, in order to further enhance the advantages of the multi-user SGF scheme, two SGF schemes with power matching are devised to further improve the performance by employing the benefits of distributed contention. For ease of performance evaluation, the theoretical expressions of achievable sum rate and average age of information of the three SGF schemes are derived by applying order statistics. We also investigate the high signal-to- noise approximation expressions of sum rate for these schemes to give more insight. Finally, simulation results are provided to demonstrate the performance improvement of three proposed SGF schemes and validate the correctness of the theoretical analysis expressions. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compression

arXiv

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arXiv 2023年

作者： Burakhan Koyuncu, A. Jia, Panqi Boev, Atanas Alshina, Elena Steinbach, Eckehard The Technical University of Munich School of Computation Information and Technology Department of Computer Engineering Media Technology Munich80333 Germany The Friedrich-Alexander University Department of Electrical-Electronic-Communication Engineering Multimedia Communications and Signal Processing Erlangen91058 Germany The Huawei Munich Research Center Munich80992 Germany

Entropy estimation is essential for the performance of learned image compression. It has been demonstrated that a transformer-based entropy model is of critical importance for achieving a high compression ratio, however, at the expense of a significant computational effort. In this work, we introduce the Efficient Contextformer (eContextformer) - a computationally efficient transformer-based autoregressive context model for learned image compression. The eContextformer efficiently fuses the patch-wise, checkered, and channel-wise grouping techniques for parallel context modeling, and introduces a shifted window spatio-channel attention mechanism. We explore better training strategies and architectural designs and introduce additional complexity optimizations. During decoding, the proposed optimization techniques dynamically scale the attention span and cache the previous attention computations, drastically reducing the model and runtime complexity. Compared to the non-parallel approach, our proposal has ∼145x lower model complexity and ∼210x faster decoding speed, and achieves higher average bit savings on Kodak, CLIC2020, and Tecnick datasets. Additionally, the low complexity of our context model enables online rate-distortion algorithms, which further improve the compression performance. We achieve up to 17% bitrate savings over the intra coding of Versatile Video Coding (VVC) Test Model (VTM) 16.2 and surpass various learning-based compression models. © 2023, CC BY-NC-ND.

关键词： Image compression

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Integrating HL7 FHIR into Clinical Decision Support Systems: A Real-World Application with Pepper Humanoid Robot in Hospital During Doctor Visits

Integrating HL7 FHIR into Clinical Decision Support Systems:...

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International Conference on Mathematics and computers in Sciences and in Industry (MCSI)

作者： Valentino Šafran Samo Horvat Bojan Ilijevec Igor Rober Roj Vojko Flis Izidor Mlakar Laboratory of Digital Signal Processing University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia Department of Thoracic Surgery University Medical Centre Maribor Maribor Slovenia Clinical Department of Abdominal and General Surgery University Medical Centre Maribor Maribor Slovenia Department of Nurses in Clinic for Surgery University Medical Centre Maribor Maribor Slovenia Clinical Department of Cardiac Surgery University Medical Centre Maribor Maribor Slovenia

ISBN: (数字)9798350377224

ISBN: (纸本)9798350377231

This study explores the integration of HL7 Fast Healthcare Interoperability Resources (FHIR) into a Clinical Decision Support System (CDSS) utilizing a Pepper robot to enhance doctor visits in a hospital setting. We evaluate a real-world implementation where the Pepper robot acts as an interface for data collection from patients, clinical monitors, and hospital information systems. The CDSS leverages FHIR to seamlessly integrate this data and present it visually, aiming to improve clinical decision-making processes for doctors. Our research investigates the potential of this FHIR-based CDSS with a robot interface to optimize healthcare delivery and potentially improve patient outcomes.

关键词： Decision support systems Surveys Hospitals Service robots Soft sensors Transforms User experience Usability Monitoring Interoperability

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An Integrated Web Solution for Planetary Data Visualization and Exploration

An Integrated Web Solution for Planetary Data Visualization ...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Ananya Maheshwari Love Lakhwani Pariav Naik Abhishek Kumar Jai G. Singla Amitabh Nitant Dube VijayPal Singh Dhaka School of Computing and Intelligent Systems Manipal University Jaipur Dehmi Kalan Jaipur Rajasthan India Department of Computer Engineering Charotar University of Science and Technology Anand Gujarat India Department of Electrical Engineering Indian Institute of Technology Bhubaneshwar Bhubaneshwar Odisha India Signal and Image Processing Area Space Applications Centre ISRO Ahmedabad Gujarat India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

The exploration and study of planetary bodies have produced vast amounts of geospatial data, creating a need for effective visualization and analysis tools. Many existing solutions are constrained by platform limitations and are resource-intensive, reducing their accessibility for broader use. To overcome these challenges, we developed a lightweight, web-based platform that visualizes interplanetary data and supports multiple geospatial data formats, including shapefiles, KML, and CZML. Our solution includes unique features such as 2D and 2.5D terrain visualization, 3D tiles, and tools for navigating to specific coordinates and measuring distances. These enhancements allow users to explore and interact with geospatial data from various planetary bodies across multiple devices without requiring specialized hardware or software. By modifying the underlying system, we extended its application to planetary data while preserving accessibility and performance across different platforms. This paper discusses the methodology behind these customizations, the challenges we encountered, and the potential applications of our platform in scientific research and education.

关键词： Performance evaluation Three-dimensional displays Coordinate measuring machines Navigation Data visualization Geoscience and remote sensing Extraterrestrial measurements Software Hardware Geospatial analysis

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Cell-Free MIMO Perceptive Mobile Networks: Cloud vs. Edge processing

arXiv

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arXiv 2024年

作者： Jeong, Seongah Kang, Jinkyu Simeone, Osvaldo Shamai, Shlomo The School of Electronics Engineering Kyungpook National University Daegu Korea Republic of The Department of Information and Communication Engineering Myongji University Gyeonggi-do Korea Republic of The King's Communications Learning and Information Processing The Centre for Intelligent Information Processing Systems King's College London LondonWC2B 4BG United Kingdom The Department of Electrical and Computer Engineering Technion Haifa3200003 Israel

Perceptive mobile networks implement sensing and communication by reusing existing cellular infrastructure. Cell-free multiple-input multiple-output, thanks to the cooperation among distributed access points, supports the deployment of multistatic radar sensing, while providing high spectral efficiency for data communication services. To this end, the distributed access points communicate over fronthaul links with a central processing unit acting as a cloud processor. This work explores four different types of PMN uplink solutions based on Cell-free multiple-input multiple-output, in which the sensing and decoding functionalities are carried out at either cloud or edge. Accordingly, we investigate and compare joint cloud-based decoding and sensing (CDCS), hybrid cloud-based decoding and edge-based sensing (CDES), hybrid edge-based decoding and cloud-based sensing (EDCS) and edge-based decoding and sensing (EDES). In all cases, we target a unified design problem formulation whereby the fronthaul quantization of signals received in the training and data phases are jointly designed to maximize the achievable rate under sensing requirements and fronthaul capacity constraints. Via numerical results, the four implementation scenarios are compared as a function of the available fronthaul resources by highlighting the relative merits of edge- and cloud-based sensing and communications. This study provides guidelines on the optimal functional allocation in fronthaul-constrained networks implementing integrated sensing and communications. © 2024, CC BY.

关键词： Wireless networks

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