检索结果-内蒙古大学图书馆

Fair and Verifiable Identity-Based Broadcast Proxy Re-Encryption With Designated Sender Feasible for Medical Internet of Things

引用

IEEE Transactions on information Forensics and Security 2025年 20卷 6033-6045页

作者： Liqing Chen Shiyu Chen Hao Zhang Jian Weng Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an Jiangsu China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou Fujian China College of Information Science and Technology Jinan University Guangzhou Guangdong China

With the development of big data, cloud computing is widely used by individual and enterprise users due to its powerful computing and storage capabilities. How to share the encrypted data stored in cloud servers flexibly to a group of users and provide effective access control to the users is a hot topic in current research. For that, this paper proposes fair and verifiable identity-based broadcast proxy re-encryption with designated sender (FV-IBBPRE-DS) for Medical Internet of Things (MIoT), which gives the system model, formal definition, and security model of FV-IBBPRE-DS, and constructs a concrete FV-IBBPRE-DS scheme. The data owner in the FV-IBBPRE-DS scheme is able to designate a sender, which is given the decryption privilege of the encrypted data to a set of receivers. The scheme ensures that the participating entities perform the process of data sharing fairly and are able to verify the consistency of the ciphertexts. The FV-IBBPRE-DS scheme achieves chosen ciphertext attack security. The performance analysis demonstrates that the scheme in this paper has significant advantages in re-encrypted ciphertext generation and decryption phase, and can be applied to resource-constrained MIoT devices.

关键词： Receivers Encryption Hospitals Cryptography Cloud computing Genetics Servers Access control Sun software engineering

来源：评论

学校读者我要写书评

暂无评论

Data-driven Online Decision Support for Hotel Site Selection 9

Data-driven Online Decision Support for Hotel Site Selection

引用

9th Edition of IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2021

作者： Kumarage, Imal Weerakeshara, Nuwanga Chamika, Thanuja Rathnapala, Heshan Nawinna, Dasuni Gamage, Narmada Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665432405

Location is one of the fundamental factors that determine hotel success. The location, once selected, cannot be changed without a significant investment. This research aims to identify the location-specific factors that affect Sri Lankan coastal hotels. The factors that affect the location rating have been assessed under location attraction, accessibility, and popularity. An ensemble learning model has been trained to predict the location score of a hypothetical location, assess the manner accessibility affects hotel performance, and predict location popularity based on the surrounding competition. The results show that this method can assess hotel location and performance, with significant accuracy and the identified location-related factors that contribute to a hotel's success can be used by hoteliers and investors to improve decision making. © 2021 IEEE.

关键词： Location

来源：评论

学校读者我要写书评

暂无评论

Design and implementation of unmanned aerial vehicle localization and light tracking system

引用

The Journal of China Universities of Posts and Telecommunications 2023年第4期30卷 67-74页

作者： Xu Ming Dong Chen Li Hanlu Luo Qiming Wang Lizi Zhang Wanru Liu Huixin School of Digital Media and Design Arts Beijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Unmanned aerial vehicles(UAV)are applied widely and profoundly in various ***,high-precision positioning and tracking in multiple scenarios are the core requirements for UAV *** ensure stable communication of UAVs in denial environments with substantial electromagnetic interference,a systematic solution is proposed based on a deep learning algorithm for target detection and visible light for UAV *** the cost and computational power limitations on the hardware,the you only look once(YOLO)v4-Tiny model is used for static target detection of the UAV *** UAV tracking,and a light tracker that can adjust the angle of emitted light and focus it on the target is used for dynamic tracking ***,achieving the primary conditions of UAV optical communication with good secrecy is also suitable for dynamic *** UAV tracker positions the UAV model by returning the coordinates and calculating the time delay,and then controls the spotlight to target the *** order to facilitate the deployment of deep learning models on hardware devices,the lighter and more efficient model is selected after *** trained model can achieve 99.25%accuracy on the test *** dynamic target detection can reach 20 frames per second(FPS)on a computer with an MX520 graphics processing unit(GPU)and 6 GB of random access memory(RAM).Dynamic target detection on a Jetson Nano can reach 5.4 FPS.

关键词： unmanned aerial vehicles deep learning light tracking target detection unmanned aerial vehicles location unmanned aerial vehicles tracking

来源：评论

学校读者我要写书评

暂无评论

Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

引用

computers, Materials & Continua 2022年第6期71卷 5639-5657页

作者： Faisal Saeed Mohammad Al-Sarem Muhannad Al-Mohaimeed Abdelhamid Emara Wadii Boulila Mohammed Alasli Fahad Ghabban College of Computer Science and Engineering Taibah UniversityMedina41477Saudi Arabia School of Computing and Digital Technology Birmingham City UniversityBirminghamB47XGUnited Kingdom Information System Department Saba’a Region UniversityMareebYemen Computers and Systems Engineering Department Al-Azhar UniversityCairo11884Egypt RIADI Laboratory National School of Computer SciencesUniversity of ManoubaManouba2010Tunisia

Several millions of people suffer from Parkinson’s disease ***’s affects about 1%of people over 60 and its symptoms increase with *** voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech *** the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this *** classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many *** paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and *** dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 *** experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.

关键词： Filter-based feature selection methods machine learning parkinson’s disease wrapper-based feature selection methods

来源：评论

学校读者我要写书评

暂无评论

CardiacMamba: A Multimodal RGB-RF Fusion Framework with State Space Models for Remote Physiological Measurement

arXiv

引用

arXiv 2025年

作者： Wu, Zheng Xie, Yiping Zhao, Bo He, Jiguang Luo, Fei Deng, Ning Yu, Zitong School of Computing and Information Technology Great Bay University Dongguan523000 China Dongguan Key Laboratory for Intelligence and Information Technology Dongguan523000 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen518060 China

Heart rate (HR) estimation via remote photoplethysmography (rPPG) offers a non-invasive solution for health monitoring. However, traditional single-modality approaches (RGB or Radio Frequency (RF)) face challenges in balancing robustness and accuracy due to lighting variations, motion artifacts, and skin tone bias. In this paper, we propose CardiacMamba, a multimodal RGB-RF fusion framework that leverages the complementary strengths of both modalities. It introduces the Temporal Difference Mamba Module (TDMM) to capture dynamic changes in RF signals using timing differences between frames, enhancing the extraction of local and global features. Additionally, CardiacMamba employs a Bidirectional SSM for cross-modal alignment and a Channel-wise Fast Fourier Transform (CFFT) to effectively capture and refine the frequency domain characteristics of RGB and RF signals, ultimately improving heart rate estimation accuracy and periodicity detection. Extensive experiments on the EquiPleth dataset demonstrate state-of-the-art performance, achieving marked improvements in accuracy and robustness. CardiacMamba significantly mitigates skin tone bias, reducing performance disparities across demographic groups, and maintains resilience under missing-modality scenarios. By addressing critical challenges in fairness, adaptability, and precision, the framework advances rPPG technology toward reliable real-world deployment in healthcare. The codes are available at: https://***/WuZheng42/CardiacMamba. Copyright © 2025, The Authors. All rights reserved.

关键词： Fast Fourier transforms

来源：评论

学校读者我要写书评

暂无评论

Distributionally Robust Optimization for Computation Offloading in Aerial Access Networks

Distributionally Robust Optimization for Computation Offload...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Jiang, Guanwang Jia, Ziye He, Lijun Dong, Chao Wu, Qihui Han, Zhu Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Northwestern Polytechnical University School of Software Xi'an710072 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350351255

With the rapid increment of multiple users for data offloading and computation, it is challenging to guarantee the quality of service (QoS) in remote areas. To deal with the challenge, it is promising to combine aerial access networks (AANs) with multi-access edge computing (MEC) equipments to provide computation services with high QoS. However, as for uncertain data sizes of tasks, it is intractable to optimize the offloading decisions and the aerial resources. Hence, in this paper, we consider the AAN to provide MEC services for uncertain tasks. Specifically, we construct the uncertainty sets based on historical data to characterize the possible probability distribution of the uncertain tasks. Then, based on the constructed uncertainty sets, we formulate a distributionally robust optimization problem to minimize the system delay. Next, we relax the problem and reformulate it into a linear programming problem. Accordingly, we design a MEC-based distributionally robust latency optimization algorithm. Finally, simulation results reveal that the proposed algorithm achieves a superior balance between reducing system latency and minimizing energy consumption, as compared to other benchmark mechanisms in the existing literature. © 2024 IEEE.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

Thinking in Granularity: Dynamic Quantization for Image Super-Resolution by Intriguing Multi-Granularity Clues

arXiv

引用

arXiv 2024年

作者： Wang, Mingshen Zhang, Zhao Li, Feng Xu, Ke Miao, Kang Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Artificial Intelligence Anhui University Hefei China

Dynamic quantization has attracted rising attention in image super-resolution (SR) as it expands the potential of heavy SR models onto mobile devices while preserving competitive performance. Existing methods explore layer-to-bit configuration upon varying local regions, adaptively allocating the bit to each layer and patch. Despite the benefits, they still fall short in the trade-off of SR accuracy and quantization efficiency. Apart from this, adapting the quantization level for each layer individually can disturb the original inter-layer relationships, thus diminishing the representation capability of quantized models. In this work, we propose Granular-DQ, which capitalizes on the intrinsic characteristics of images while dispensing with the previous consideration for layer sensitivity in quantization. Granular-DQ conducts a multi-granularity analysis of local patches with further exploration of their information densities, achieving a distinctive patch-wise and layer-invariant dynamic quantization paradigm. Specifically, Granular-DQ initiates by developing a granularity-bit controller (GBC) to apprehend the coarse-to-fine granular representations of different patches, matching their proportional contribution to the entire image to determine the proper bit-width allocation. On this premise, we investigate the relation between bit-width and information density, devising an entropy-to-bit (E2B) mechanism that enables further fine-grained dynamic bit adaption of high-bit patches. Extensive experiments validate the superiority and generalization ability of Granular-DQ over recent state-of-the-art methods on various SR models. Code and supplementary statement can be found at https://***/MmmingS/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Granular computing

来源：评论

学校读者我要写书评

暂无评论

Semantic recommendation through Reinforcement Learning and weighted meta-paths

Semantic recommendation through Reinforcement Learning and w...

引用

2022 International Conference on Electronic information engineering, Big Data, and computer Technology, EIBDCT 2022

作者： Yu, Jing Lang, Yue Li, Xuewen Zhang, Jin Zheng, Shaojie Gong, Jibing School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Key Laboratory for Software Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9781510655133

Explainable recommendation systems, which can produce high-accuracy recommendations and help users make quick decisions, have become a hotspot in research field. Most of existing research algorithms committed to improving the accuracy of recommendation, but ignored the interpretability of recommendation. We propose a semantic recommendation algorithm through Reinforcement Learning and weighted meta-paths which analyzes and selects the meta-path, and uses Reinforcement Learning network to train the weight of meta-path. This method can effectively solve data sparsity, improve the accuracy of recommendation, and increase interpretability. Compared with the baseline method, indicators of the recommended methods for integrating Reinforcement Learning with Heterogeneous information Networks have been greatly improved, as verified and compared on a real MovieLens 1M dataset. Experimental results prove that the algorithm can effectively improve the accuracy and interpretability of the recommendation. © 2022 SPIE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI

Self-supervised Contrastive Learning for 6G UM-MIMO THz Comm...

引用

IEEE International Conference on Communications Workshops, ICC

作者： Rafid Umayer Murshed Md Saheed Ullah Mohammad Saquib Moe Z. Win Department of Electrical and Computer Engineering The University of Texas Dallas Texas USA Department of Electrical and Computer Engineering University of Delaware Delaware USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology Massachusetts USA

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

This paper investigates the potential of contrastive learning in 6G ultra-massive multiple-input multiple-output (UM-MIMO) communication systems, specifically focusing on hybrid beamforming under imperfect channel state information (CSI) conditions at THz. UM-MIMO systems are promising for future 6G wireless communication networks due to their high spectral efficiency and capacity. The accuracy of CSI significantly influences the performance of UM-MIMO systems. However, acquiring perfect CSI is challenging due to various practical constraints such as channel estimation errors, feedback delays, and hardware imperfections. To address this issue, we propose a novel self-supervised contrastive learning-based approach for hybrid beamforming, which is robust against imperfect CSI. We demonstrate the power of contrastive learning to tackle the challenges posed by imperfect CSI and show that our proposed method results in improved system performance in terms of achievable rate compared to traditional methods.

关键词： 6G mobile communication Wireless communication Array signal processing Heuristic algorithms Conferences System performance Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Multi-layered Minutiae Extractor Based on OCT Fingerprints 16th

A Novel Multi-layered Minutiae Extractor Based on OCT Finge...

引用

16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Zeng, Wenfeng Zhang, Wentian Liu, Feng Tan, Xu Li, Qin College of Computer Science and Software Engineering Shenzhen University Shenzhen China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Senzhen China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9783031202322

Fingerprints of Optical Coherence Tomography (OCT) imaging provide 3D volume data which have the nature property of multi-layered tissue structure. This paper, for the first time, attempts to extract minutiae for OCT-based fingerprint by making full use of the merits multi-layered structure of OCT imaging and powerful convolution al neural network (CNN). In particular, a novel multi-layered feature fusion minutiae extraction network is proposed, involving a multi-layered feature extractor to integrate the rich information of multiple subsurface fingerprints, and a two-stage object detection framework using reweighted concatenation feature to detect minutiae points. Compared with the results achieved by typical traditional and learning-based methods, our method outperforms the best one by about 2% of F1-score in minutiae extraction, and achieves the improvement of 0.09% than the best one in matching performance, which further demonstrate the effectiveness and robustness of our proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：