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Waveform and Filter Design for Integrated Sensing and Communication Against Signal-dependent Modulated Jamming

IEEE Transactions on Vehicular technology 2025年

作者： Zhou, Yu Shi, Qiao Zhou, Zhengchun Liu, Zilong Fan, Pingzhi Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China University of Essex School of Computer Science and Electronics Engineering ColchesterCO4 3SQ United Kingdom Southwest Jiaotong University Key Laboratory of Information Coding and Wireless Communications Chengdu611756 China

This paper focuses on an integrated sensing and communication (ISAC) system in the presence of signal-dependent modulated jamming (SDMJ). Our goal is to suppress jamming while carrying out simultaneous communications and sensing. We minimize the integrated sidelobe level (ISL) of the mismatch filter output for the transmitted waveform and the integrated level (IL) of the mismatch filter output for the jamming, under the constraints of the loss in-processing gain (LPG) and the peak-to-average power ratio (PAPR) of the transmitted waveform. Meanwhile, the similarity constraint is introduced for information-bearing transmit waveform. We develop a decoupled majorization minimization (DMM) algorithm to solve the proposed multi-constrained optimization problem. In contrast to the existing approaches, the proposed algorithm transforms the difficult optimization problem involving two variables into two parallel sub-problems with one variable, thus significantly speeding up the convergence rate. Furthermore, fast Fourier transform (FFT) is introduced to compute the closed-form solution of each sub-problem, giving rise to a greatly reduced computation complexity. Simulation results demonstrate the capabilities of the proposed ISAC system which strikes a proper trade-off among sensing and jamming suppression. © 1967-2012 IEEE.

关键词： Constrained optimization

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Big Data Analytics for Healthcare Resource Allocation

Big Data Analytics for Healthcare Resource Allocation

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Brijesh Kumar Sharma Khel Prakash Jayant Purvi Pujari Devendra Vikram Manish Saraswat Kajol Kathuria Department of Computer Applications SRM Institute of Science and Technology Modinagar UP India Computer Science & Engieering Raj Kumar Goel Institute of Technology Ghaziabad UP India Department of Management Vijay Patil School of Management DY Patil University Navi Mumbai India Computer Science & Engieering ABES Institute of Technology Ghaziabad UP India Department of Mechanical Engineering Lloyd Institute of Engineering & Technology Greater Noida UP India GLA University Mathura UP India Dept Computer Science and Information Technology Dronacharya Group of Institutions Greater Noida UP India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Big Data Analytics for Healthcare Resource Allocation is the new mantra as it integrates vast amounts of complementary data to allocate healthcare resources efficiently. There is an increasing demand for healthcare services, and resources are in limited supply, so doing something as simple as allocating resources can ensure that care quality remains high. The process includes gathering and organizing large datasets from several resources, including electronic health records, patient demographics, service utilization and detailed analysis to generate meaningful findings. Utilizing advanced statistical and analytical techniques, by definition, amongst others, Big Data Analytics can help you decipher the data to show you patterns in resource utilization (how resources are being used), areas where inefficiencies occur which have gone unnoticed long enough that cause them become ripe for incremental year over year cost savings targets or staff reactions if not corrected as then gain media attention likely) some even assembled through different techniques to predict future needs based on past use cases. The advantages of this are manifold. It will help identify high-risk patients, enhance clinical decision-making, and facilitate evidence-based resource allocation. It can also help quantify how well-existing resource distribution strategies work and influence resource planning. Big Data Analytics for Healthcare Resource Allocation: Better Outcome, Cutting Cost and Effective Utilization of Resources This have the potential to revolutionize healthcare resource allocation and improve national healthcare systems' efficiency and effectiveness.

关键词： Costs Decision making Medical services Organizations Big Data Strategic planning Predictive models Media Market research Resource management

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Enhanced corrosion resistance of zinc-rich epoxy anti-corrosion coatings using graphene-Fe2O3 and HEDP

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Journal of Industrial and engineering Chemistry 2025年

作者： Ji, Ziyu Shu, Junjie Jing, Haiyan Su, Can Peng, Xiaoxiao Li, Tong Xu, Chunying Xia, Mingzhu Wang, Jie Yang, Junhua Lei, Wu Hao, Qingli School of Chemistry and Chemical Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China Qingdao Institute of Marine Chemical Research Institute Co Ltd Shandong Qingdao266072 China China Electronics Technology Group Corporation 14th Research Institute Jiangsu Nanjing210039 China

The presence of the block of conductive pathways, and the low utilization of zinc powders in coatings are severe challenges for the traditional zinc-rich coatings in practice application. In this paper, we use reduced graphene oxide (rGO), ferric oxide (Fe2O3) and 1-hydroxy ethylidene-1,1-diphosphate (HEDP) to design a ternary nanocomposite for zinc-rich epoxy coatings with long-term anticorrosion performance. The results show that the zinc-rich coatings containing the optimal amount of 1.5 wt% rGO-Fe2O3-HEDP exhibit the most pronounced improvement in corrosion resistance performance due to the combination of three components with synergistic effect. Its cathodic protection is extended to 40 days, and the corrosion current density (Icorr) is 7.8 × 10−9 A cm−2 after 60-day immersion, which decreases by two orders of magnitude compared with that of the pure zinc coating. The rGO-Fe2O3-HEDP plays an important role in inhibition of corrosion-promoting effect of graphene, conducting connective path of zinc particles and substrate, physical shieling, activation of zinc particles, and self-repairing, due to the well-disperse and moderate conductivity of rGO based composite, superior coordination of HEDP with zinc and ferrous ions. The possible anticorrosion mechanism is also proposed. Therefore, this work provides a novel research perspective to solve the traditional heavy-duty zinc-rich anticorrosive coatings in the industry. © 2025 The Korean Society of Industrial and engineering Chemistry

关键词： Zinc coatings

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Merging Clinical Knowledge into Large Language Models for Medical Research and Applications: A Survey

arXiv

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arXiv 2025年

作者： Li, Qiyuan Liu, Haijiang Guo, Caicai Chen, Deyu Wang, Meng Gao, Feng Gu, Jinguang College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China The Key Laboratory of Rich-Media Knowledge Organization Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing100038 China School of Computer Science and Technology Huazhong University of Science and Technology Hubei Wuhan430074 China School of Cyber Science and Engineering Wuhan University Hubei Wuhan430072 China

Clinical knowledge is the collection of information learned from studies on the causes, prognosis, diagnosis, and treatment of diseases. This type of knowledge can improve curing performances, and promote physical health. With the emergence of large language models (LLMs), medical artificial intelligence (medical AI), which aims to apply academic medical AI systems to real-world medical scenarios, has entered a new age of development, resulting in excellent works such as DoctorGPT and Pangu-Drug from academic and industrial researches. However, the field lacks a comprehensive compendium and comparison of building medical AI systems from academia and industry. Therefore, this survey focuses on the building paradigms of medical AI systems including the use of clinical databases, datasets, training pipelines, integrating medical knowledge graphs, system applications, and evaluation systems. We hope that this survey can help relevant practical researchers understand the current performance of academic models in various fields of healthcare, as well as the potential problems and future directions for implementing these scientific achievements. Copyright © 2025, The Authors. All rights reserved.

关键词： Clinical research

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Towards Dermatoscopic Imaging and CNN Based Accurate Skin Cancer Detection

Towards Dermatoscopic Imaging and CNN Based Accurate Skin Ca...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Kaur, Jashanpreet Rani, Shalli Singh, Gurpreet Reddy, R. Archana Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal506371 India

ISBN: (纸本)9798331527495

Skin cancer is the most common and deadliest type of cancer from an incidence viewpoint. Melanomas are lethal skin cell malignancies and among the deadliest forms of cancers globally, however early diagnosis significantly enhances treatment results, thus detection poses an excellent challenge as benign lesions appear to be visually similar to malignant lesions. In order to overcome this problem, we design an automated skin cancer detection system by using CNN. The proposed model is trained and validated using the Skin Cancer MNIST: HAM10000 dataset, including 10,015 DIs, which were acquired from Kaggle for the automated diagnosis and categories of skin cancer in seven different categories of skin lesions, namely AKIEC, BCC, BKL, DF, NV, VASC, and MEL. Generalization was achieved through applied data augmentation methods. Training is done on 80% of the dataset with the validation on the remaining 20%. Our proposed CNN model has achieved the precision accuracy score of 98.88%. Thus, our proposed CNN model is above the SOTA methods, including ELM [2], VGG-16 [4], and many other existing CNN models [5], [6], [8]. Our future plan includes incorporating more data sources, along with using techniques based on TL, to upgrade the robustness of the model and its accuracy for even more effective diagnostics of skin cancer. © 2025 IEEE.

关键词： Cancer cells

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Exploring Kernel Transformations for Implicit Neural Representations

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IEEE Transactions on Multimedia 2025年

作者： Zheng, Sheng Zhang, Chaoning Han, Dongshen Puspitasari, Fachrina Dewi Hao, Xinhong Yang, Yang Shen, Heng Tao University of Electronic Science and Technology of China Center for Future Media School of Computer Science and Engineering Chengdu China Kyung Hee University School of Computing Yongin-si Korea Republic of Beijing Institute of Technology School of Mechatronical Engineering Beijing China University of Electronic Science and Technology of China Institute of Electronic and Information Engineering Guangdong China Peng Cheng Laboratory Shenzhen China

Implicit neural representations (INRs), which leverage neural networks to represent signals by mapping coordinates to their corresponding attributes, have garnered significant attention. They are extensively utilized for image representation, with pixel coordinates as input and pixel values as output. In contrast to prior works focusing on investigating the effect of the model's inside components (activation function, for instance), this work pioneers the exploration of the effect of kernel transformation of input/output while keeping the model itself unchanged. A byproduct of our findings is a simple yet effective method that combines scale and shift to significantly boost INR with negligible computation overhead. Moreover, we present two perspectives, depth and normalization, to interpret the performance benefits caused by scale and shift transformation. Overall, our work provides a new avenue for future works to understand and improve INR through the lens of kernel transformation. © 1999-2012 IEEE.

关键词： Article submission IEEE IEEEtran journal LATEX paper template typesetting

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UAV-Mounted RIS Enabled Maritime Secure Sensing With Joint Beamforming and Trajectory Design

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IEEE Transactions on Vehicular technology 2025年

作者： Hao, Xu Ma, Haoyu Wang, Wei Zeng, Feng Cumanan, Kanapathippillai Bjornson, Emil Nantong University School of Information Science and Technology Nantong226019 China University of York School of Physics Engineering and Technology YorkYO10 5DD United Kingdom KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm16440 Sweden

This paper considers a challenging maritime secure sensing system, in which, a legitimate shipborne radar L needs to detect in real-time the state of a collaborative unmanned aerial vehicle (UAV) in the presence of an illegitimate or unauthorized shipborne radar U. To address this challenge, we propose a novel UAV-mounted reconfigurable intelligent surface (RIS) assisted approach, where the RIS is mounted on the UAV for enhancing/suppressing its reflected signal towards the L/U to facilitate/prevent its detection of the UAV. Furthermore, the UAV target can also adjust its flight route to move closer to/away from the L/U to improve/decrease the sensing performance. Due to the mobility of both the shipborne radars and UAVs, the Doppler shift effects of the RIS-ship channel need to be considered. In such a setup, the maximization of the received power at the L is formulated into an optimization problem while suppressing the received power at the U below a certain threshold by jointly designing the reflecting phase shifts of RIS and the 3D flight trajectory of UAV. The non-convex optimization problem is decomposed into two subproblems and solved via an iterative algorithm. Simulation results are presented to corroborate the effectiveness and tremendous potential of UAV-mounted RISs in the maritime secure sensing. © 1967-2012 IEEE.

关键词： Drones

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Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

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Soft Computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

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Enhanced Anomaly Detection in Embedded Payment Systems using Depthwise Separable CNN with Dandelion Optimizer

Enhanced Anomaly Detection in Embedded Payment Systems using...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Sevinthi Kali Sankar Nagarajan Rabail Adwani Kardeepa Ponnuchamy Harshal Shah Shashikala Gurpur Ramya Maranan Independent Researcher 12460 Lanthimos San Antonio TX USA MS in Data Science Cranston RI USA Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Gujarat Faculty of Law Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Vimannagar Pune Maharashtra Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Due to the prevalence of online payments, it is essential to identify instances of payment fraud. Plans that break rules and regulations within the payment systems not only cost money but also erode customer confidence. Finding these fraudulent transactions can be challenging because of the complexity of fraud and the sheer number of transactions. In this study, a new deep learning technique for identifying online payment fraud is proposed. The classification phase, hybrid feature selection, and sophisticated preprocessing form its foundation. Starting with the IEEE CIS Fraud Dataset and the PaySim Financial Credit Dataset, the suggested approach is implemented. To ensure that all of the data sets have identical scales, a preprocessing step that uses Min-Max scaler normalization is utilized. The optimal qualities are picked using a hybrid approach that combines Gooseneck Barnacle Optimization (GBO) and the Artificial Hummingbird Algorithm (AHA). A depthwise separable convolutional neural network (DS-CNN) is then employed for classification, which facilitates the differentiation between fraudulent and non-fraudulent transactions. Additionally, to increase the detection rate, the Dandelion Optimizer (DO) optimizes the entire system. The aforementioned study indicates that the suggested approach has a high accuracy rate in detecting fraudulent transactions, which means it has almost infinite potential to speed up the entire process. In both datasets, the proposed method achieves an impressive 99% accuracy rate, proving its dependability and efficiency. This exceptional outcome shows how flexible the approach is to complex patterns and data variations, indicating its high level of reliability.

关键词： Accuracy Prevention and mitigation Feature extraction Regulation Fraud Complexity theory Classification algorithms Timing Convolutional neural networks Reliability

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DRL-Driven Localization With UAV in Near-Field Communications

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IEEE Internet of Things Journal 2025年

作者： Khan, Muhammad Fawad Peng, Limei Ho, Pin-Han Chen, Yuguang Dong, FangJie Kyungpook National University School of Computer Science and Engineering Daegu41566 Korea Republic of University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Guangdong China University of Waterloo Department of Electrical and Computer Engineering ON Canada Shenzhen Unicom Innovation Business Capability Center China National Health Commission's Center for Statistics and Information China

In this paper, we propose a Deep Reinforcement Learning (DRL)-based Multi-Point Localization Scheme (MLS) to efficiently localize IoT devices using a single Unmanned Aerial Vehicle (UAV) equipped with a large-scale multi-antenna configuration in near-field communication (NFC). By utilizing the spherical wave-based near-field steering vector, the multi-antenna array on the UAV captures both the Angle of Arrival (AoA) and Received Signal Strength Indicator (RSSI) measurements from IoT devices to estimate their locations relative to the position of the UAV. This approach eliminates the need for multiple hovering points required by a single-antenna UAV (SA-UAV) or the deployment of multiple SA-UAVs. To enhance localization accuracy, key hovering points for the multi-antenna UAV (MA-UAV) are strategically selected, with weights assigned based on signal strength to prioritize stronger and more reliable signals. Furthermore, DRL dynamically adjusts the position of the MA-UAV to optimize the trade-off between localization accuracy and energy consumption. Extensive simulations conducted across rural, urban, and dense urban scenarios demonstrate that the proposed DRL-based MLS significantly improves localization accuracy while reducing the energy consumption of the UAV. © 2025 IEEE.

关键词： Deep reinforcement learning

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