检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Lebedev, Alexey Lebedev, Vladimir Vorobyev, Ilya Deppe, Christian Kharkevich Institute for Information Transmission Problems Russian Academy of Sciences Moscow Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia Institute for Communications Engineering Department of Electrical and Computer Engineering Technical University of Munich Munich Germany

In this paper, we developed new coding strategies for the Z-channel. In particular, we look at the case with two-stage encoding. In this case, the encoder uses noiseless feedback once and adjusts the further encoding strategy based on the previous partial output of the channel. Nevertheless, the developed codes improve the known results with full feedback for small length and 1 error. A tool for the two-stage strategy is the development of a new optimality condition for non-adaptive codes. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

The Adaptive Fault-tolerant Routing Based on an Improved Local Security Information Model of the Exchanged Hypercube

The Adaptive Fault-tolerant Routing Based on an Improved Loc...

引用

IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Yazhi Zhang Chuanwen Luo Guijuan Wang Li Zhang Mengjie Lv Jiguo Yu School of Computer Science and Technology Qilu University of Technology Jinan China School of Information Science and Technology Beijing Forestry University Beijing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Computer Nanjing University of Posts and Telecommunications Nanjing China Big Data Research Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China

With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring Patent Transformation Event: Forecasting Patent Transfer Time

Exploring Patent Transformation Event: Forecasting Patent Tr...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Jinchen Huo Weidong Liu Yang Li Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on patent recommendation method based on graph neural network

Research on patent recommendation method based on graph neur...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Enchao Zhang Weidong Liu Shi Liu Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Federated Edge Learning via Integrated Sensing, Computation, and Communication

Federated Edge Learning via Integrated Sensing, Computation,...

引用

IEEE International Conference on Communications (ICC)

作者： Peixi Liu Guangxu Zhu Shuai Wang Miaowen Wen Wu Luo H. Vincent Poor Shuguang Cui State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Shenzhen Research Institute of Big Data Chinese Academy of Sciences Shenzhen Institute of Advanced Technology School of Electronic and Information Engineering South China University of Technology Department of Electrical and Computer Engineering Princeton University the Guangdong Provincial Key Laboratory of Future Networks of Intelligence School of Science and Engineering (SSE) Future Network of Intelligence Institute (FNii) the Chinese University of Hong Kong Shenzhen

Sensing, computation, and communication (SC 2 ) are highly coupled processes in federated edge learning (FEEL) and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC 2 resources jointly affect the FEEL performance. In this paper, we address the problem of joint SC 2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Specifically, the joint SC 2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time and energy supply of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determining a joint sensing and communication resource allocation that maximizes the total number of samples sensed during the entire training process; the second one concerns the partition of the total number of sensed samples over communication rounds to determine the batch size at each round for convergence speed maximization. Finally, extensive simulation results are provided to validate the superiority of the proposed scheme over several baseline schemes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Effective Hybrid Precoder Logic with mm Wave Massive MIMO using Novel Deep Learning Scheme

Effective Hybrid Precoder Logic with mm Wave Massive MIMO us...

引用

International Conference on science Technology Engineering and Management (ICONSTEM)

作者： Ranjith S P Jesu Jayarin A. Chandra Sekar Department of Electronics and Communication Engineering Jeppiaar Engineering College Chennai Tamilnadu India Department of Data Analytics Institute of IT Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences(SIMATS) Chennai India Department of Computer Science Engineering St. Joseph’s College of Engineering Chennai Tamilnadu India

For establishing a high efficient signal communications, a Millimeter-Wave-Massive-MIMO (mmWave-MMIMO) logics are helpful and more speculated. Although current hybrid precoding schemes are computationally complex, however, they are not using all of the spatial details. The combination of analog and digital precoding logics are considered to be a significant methodology to deal with complexities over hardware area and the higher consumption of energy in association with combination of signal elements. But the basic degradation of the traditional hybrid-precoding methodologies has some issues such as processing complexities and has a lacking to explore the spatial data regarding the channels. To address these limitations, this paper suggests a Novel Deep Learning Scheme (NDLS), in which it is enabled by mmWave-MMIMO system for successful Hybrid Precoder Logic (HDL). As well as each Precoder selection is used to obtain the The optimised-decoder is perceived in the Novel Deep Learning Scheme as a mapping relation. To be more precise, a Hybrid Precoder Logic is extracted via Novel Deep Learning Scheme training for optimizing the Millimeter- Wave-Massive-MIMO precoding method. Additionally, a detailed simulation results are presented to demonstrate the proposed scheme's superior efficiency and the resulting section demonstrate that the Novel Deep Learning Scheme based methodology is competent of lowering the Bit-Error- Rate (BER) and increasing the effectiveness of the frequency of Millimeter-Wave Massive-MIMO (mmWave- MMIMO), gaining a competitive advantage in faster convergence as compared to traditional schemes while significantly reducing the necessary computational difficulty.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Smartphone-based Strep Throat Detection using MTCNN: A Deep Learning Approach for Non-Invasive Diagnosis

Smartphone-based Strep Throat Detection using MTCNN: A Deep ...

引用

International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： G. M. Sathyaseelan L. Sankara Maheswari S. Sophiya M. Malini S. P. Santhoshkumar D. Vikram Department of Information Technology JKK Natraja College of Engineering and Technology Kumarapalayam India Department of Information Technology Sri G.V.G Visalakshi College for Women Udumalpet India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This research proposes a novel approach for strep throat detection utilizing the Multi-Task Cascaded Convolutional Neural Network (MTCNN) algorithm and a smartphone. By incorporating advanced facial detection techniques, MTCNN enhances the accuracy of throat image analysis. The proposed method involves capturing throat images using a smartphone camera with an added device to reduce light reflection. MTCNN is then applied to extract relevant information from the images, enabling a classification model to distinguish between healthy and strep-infected throats. Experimental results demonstrate the effectiveness of this approach, providing a non-invasive and efficient method for strep throat diagnosis. The integration of MTCNN holds significant potential for smartphone-based healthcare applications due to its high accuracy and enhanced feature extraction capabilities.

关键词： Deep learning Accuracy Sensitivity Image analysis Medical services Multitasking Feature extraction Reflection Face detection Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Finger Knuckle Print classification: Leveraging vision Mamba for low complexity and high accuracy

Franklin Open

引用

Franklin Open 2025年 11卷

作者： Bang, Chiron Altaher, Ali Salem Altaher, Ahmed Moamin, Hawraa Alshammari, Thamer Alkanjr, Basmh Altaher, Hasan Al-Jassani, Mohammed G. Zhuang, Hanqi Department of Electrical Engineering and Computer Science Florida Atlantic University 777 Glades Road Boca Raton 33431 FL United States College of Medicine Ibn Sina University of Medical and Pharmaceutical Science Baghdad Iraq Electronic Computer Center Al-Nahrain University Jadriya Baghdad Iraq Ministry of Education Baghdad Iraq Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Al-Jawf Saudi Arabia Department of Computer Science College of Computer and Information Sciences Jouf University Al-Jawf Saudi Arabia Information and Communication Technology Department Baghdad Institute of Technology Middle Technical University Baghdad Iraq

Although FKP has been recognized as a viable alternative to other biometric modalities, its adoption remains in the early stages due to its accuracy being lower than that of more competitive options. This research aims to bridge this performance gap by presenting an advancement in the classification of Finger Knuckle Prints (FKPs) using the Vision Mamba (ViM) model. The experimental study, conducted on a dataset from Hong Kong Polytechnic University containing 7,920 images from 165 individuals, evaluated the ViM model's performance against several pretrained classification models. ViM achieved an impressive accuracy of 99.1%, outperforming other models such as AlexNet (96.2%), SCNN (98.3%), and EfficientNet (98.0%), highlighting its superior capability in FKP classification. With around 7 million parameters, ViM balances complexity and performance, engineered specifically to capture fine-grained FKP features, such as texture and line patterns. Its use of weight decay mitigates overfitting, and it demonstrates resilience in occlusion scenarios by maintaining performance despite missing FKP components. Spatial attention mechanisms further enhance classification accuracy by prioritizing the most informative regions. Seventeen pretrained deep neural networks were evaluated for their effectiveness in classifying FKPs, with experimental results consistently demonstrating the superior performance of the ViM model. ViM exemplifies an advanced deep learning approach for biometric applications, combining high precision with efficient resource usage. Its versatile design – incorporating bidirectional SSMs for global context modeling and position embeddings for spatial awareness – extends its applicability to visual tasks beyond FKP identification. However, users should take its complexity and resource requirements into account for practical implementation. © 2025 The Authors

关键词： Deep learning Finger Knuckle Print Transfer learning Vision Mamba

来源：评论

学校读者我要写书评

暂无评论

Identifying Sources of Soil Pollution in Industrial Areas Using IoT data and Neural networks

Identifying Sources of Soil Pollution in Industrial Areas Us...

引用

Intelligent Technologies (ACOIT), Asian Conference on

作者： Rajesh Kambattan Kovarasan Dr N V Shibu A. Sairam K. K. Manivannan M.V Ishwarya T. R. GaneshBabu Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering KCG College of Technology Chennai Tamil Nadu India Artificial Intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Quick and precise source identification is crucial for developing successful remediation solutions to address soil contamination in industrial locations, which presents serious dangers to both the environment and human health. To locate the origins of soil contamination, this research presents a new method that combines neural network models with data collected from the Internet of Things (IOT). To monitor soil type, pollution levels, and weather conditions in real-time, set up IoT devices. After that, advanced neural network algorithms are used to process and analyze the data to find connections and patterns that might indicate the origins of the pollution. Our findings show that this method is useful for identifying where soil pollution inside industrial zones is coming from. Achieve high accuracy in source identification by integrating IoT technology and machine learning approaches. This allows us to address environmental harm and protect public health via focused remediation activities. Policymakers, environmental scientists, and stakeholders engaged in sustainable development and pollution control may benefit greatly which highlight the potential of IoT data and neural networks in challenges difficult environmental concerns.

关键词： Technological innovation Pollution Environmentally friendly manufacturing techniques Neural networks Pollution control Soil Internet of Things Stakeholders Soil pollution Contamination

来源：评论

学校读者我要写书评

暂无评论

Resource Allocation in UAV-NOMA Communication Systems Based on Proportional Fairness

引用

Journal of Communications and Information networks 2020年第2期5卷 111-120页

作者： Hui Zhang Baoji Wang Chen Chen Xiang Cheng Hang Li State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Shanghai Institute of Microsystem and Information Tech-nology Chinese Academy of SciencesShanghai 200050China Data-Driven Information System Laboratory Shenzhen Re-search Institute of Big DataShenzhen 518172China

This paper investigates the power allocation problem in non-orthogonal multiple access(NOMA)integrated unmanned aerial vehicle(UAV)communication *** particular,we propose a novel resource allocation scheme to increase the transmission rate of the users that have relatively worse channel state information,while reducing the sum rate *** solve this problem efficiently,we decouple the optimization problem into three ***,we solve the problem of user pairing and subchannel ***,the optimum power proportional factor is derived to allocate transmit power among different users on the same *** last,different subchannels are allocated with appropriate power to improve the performance of the *** results show that the proposed scheme can enjoy a better performance than the benchmark methods since it can achieve a proper trade-off between the system sum rate and the proportional fairness.

关键词： UAV communications NOMA power allo-cation proportional fairness

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：