检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Isufi, Elvin Leus, Geert Beferull-Lozano, Baltasar Barbarossa, Sergio Lorenzo, Paolo Di Faculty of Electrical Engineering Mathematics and computer Science Delft University of Technology Delft Netherlands SIGIPRO Department Simula Metropolitan Center for Digital Engineering Oslo Norway Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy

Developing methods to process irregularly structured data is crucial in applications like gene-regulatory, brain, power, and socioeconomic networks. Graphs have been the go-to algebraic tool for modeling the structure via nodes and edges capturing their interactions, leading to the establishment of the fields of graph signal processing (GSP) and graph machine learning (GML). Key graph-aware methods include Fourier transform, filtering, sampling, as well as topology identification and spatiotemporal processing. Although versatile, graphs can model only pairwise dependencies in the data. To this end, topological structures such as simplicial and cell complexes have emerged as algebraic representations for more intricate structure modeling in data-driven systems, fueling the rapid development of novel topological-based processing and learning methods. This paper first presents the core principles of topological signal processing through the Hodge theory, a framework instrumental in propelling the field forward thanks to principled connections with GSP-GML. It then outlines advances in topological signal representation, filtering, and sampling, as well as inferring topological structures from data, processing spatiotemporal topological signals, and connections with topological machine learning. The impact of topological signal processing and learning is finally highlighted in applications dealing with flow data over networks, geometric processing, statistical ranking, biology, and semantic communication. © 2024, CC BY.

关键词： Network theory (graphs)

来源：评论

学校读者我要写书评

暂无评论

Mobile Deep Learning: Exploring Deep Neural Network for Predicting Context-Aware Smartphone Usage

引用

SN computer science 2021年第3期2卷 1-12页

作者： Sarker, Iqbal H. Abushark, Yoosef B. Khan, Asif Irshad Alam, Md Mottahir Nowrozy, Raza Swinburne University of Technology Melbourne 3122 VIC Australia Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong 4349 Bangladesh Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia Victoria University Footscray 3011 VIC Australia

In this paper, we mainly formulate the problem of predicting smartphone usage based on contextual information, which involves both the user-centric and device-centric contexts. In the area of mobile analytics, traditional machine learning techniques, such as Decision Trees, Random Forests, Support Vector Machines, etc. are popular for building context-aware prediction models. However, real-life smartphone usage data may contain higher dimensions of contexts and can be huge in size considering the daily behavioral data of the users. Thus, the traditional machine learning models may not be effective to build the context-aware model. In this paper, we explore “Mobile Deep Learning”, an artificial neural network learning-based model considering multiple hidden layers for predicting context-aware smartphone usage. Our model first takes into account context correlation analysis to reduce the neurons as well as to simplify the network model through filtering the irrelevant or less significant contexts, and then build the deep learning model with the selected contexts. The experimental results on smartphone usage datasets show the effectiveness of the model. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Artificial neural network Context-aware computing Deep learning Intelligent applications Mobile data analytics Personalization Predictive analytics User behavior modeling

来源：评论

学校读者我要写书评

暂无评论

Chinese Temple shaped slotted monopole UWB patch antenna for Microwave Imaging Applications

Chinese Temple shaped slotted monopole UWB patch antenna for...

引用

2021 International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2021

作者： Talukder, Md Siam Samsuzzaman, Md Hasan, Md Mahedi Islam, Md Tarikul Hossain, Ismail Singh, Mandeep Singh Jit Patuakhali Science and Technology Faculty of Computer Science and Engineering Patuakhali Bangladesh Universiti Kebangsaan Malaysia Dept of Electrical Electronic and Systems Engineering Selangor Malaysia Universiti Kebangsaan Malaysia Selangor Malaysia

ISBN: (纸本)9781665414609

A new Chinese Temple shaped (CTS) slotted monopole UWB patch antenna is introduced in this research for the purposes of microwave imaging applications such as breast imaging. Geometrically the antenna was being comprised of a slotted pentagonal radiator with a diminished slotted ground-plane carved rectangularly. On the patch and ground plane, numerous types of slots were explored. Slotted pentagonal-shape patch and slotted rectangular ground offer significant Gain over 2.4 to 7.8GHz frequency ranges, wide bandwidth, along with comparatively balanced radiation properties. Whole dimension of the prototype is 40×40×1.6mm3 with 0.320λ×0.320λ×0.0128λ electrical dimension at starting frequency 2.4GHz. The antenna has a fractional bandwidth of 105.88% and an operating bandwidth of 5.4 GHz (2.4-7.8GHz). Several parameters are taken into account and optimized to maximize Gain, and the maximum gain value is 6 dBi. Efficiency reaches a maximum of 96.23% with an average of 94.32% within 2.4-7.8 GHz frequency ranges. Being obtainable and affordable, the prototype mounted on 1.6 mm thick FR-4 substrate with such a 4.6 and 0.002 Permeability and loss tangent respectively. Simulation performed through 3D simulator software CST Microwave Studio 2019 and HFSS. May the antenna really be a respectable contender to use in MI applications for its lightweight structure and high Gain. © 2021 IEEE.

关键词： Microstrip antennas

来源：评论

学校读者我要写书评

暂无评论

Development of a Machine Learning Model For Big Data Analytics

Development of a Machine Learning Model For Big Data Analyti...

引用

science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Ugbedeojo Musa Marion O. Adebiyi Abayomi A. Adebiyi Avodele A. Adebiyi Department of Computer Science Federal Polytechnic Idah Kogi Nigeria Department of Computer Science Landmark University Umu-Aran Kwara Nigeria Department of Electrical Power Engineering Faculty of Engineering and the Built Environment Durban University of Technology Durban South Africa

The aim of the study was to utilize machine learning approaches in the development of a big data analytics model that can effectively discover patterns and aid in making intelligent decisions. The impact of Big Data on scientific discoveries and technology in Big Data analytics cannot be overstated. In this regard, the study focused on the development of a system to control incoming illegal messages from intruders/unknown users, in order to alleviate illicit contextual communication channels. Unstructured data sets were collected from online blogs and labeled to suit the needs of the machine learning algorithm. The data was preprocessed using Weka machine learning libraries and converted into attribute related file form (arff). The resampling technique was used to partition the data into 80% training set and 20% testing set, which was used to develop the big data model and identify illicit message content to prevent people from being victimized. The Na ï ve Bayes machine learning algorithm was utilized to predict the categorized labels as binary values. The results of the study showed that Naïve Bayes had an accuracy of 96.76% for metrics evaluation of true precision model. It is recommended that the application of other machine learning techniques to the classification model would improve its performance.

关键词： Training Analytical models Machine learning algorithms Machine learning Big Data Data models Virtual machining

来源：评论

学校读者我要写书评

暂无评论

An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering

引用

Array 2025年 26卷

作者： Abualigah, Laith Alomari, Saleh Ali Almomani, Mohammad H. Zitar, Raed Abu Migdady, Hazem Saleem, Kashif Smerat, Aseel Snasel, Vaclav Ezugwu, Absalom E. Computer Science Department Al al-Bayt University Mafraq25113 Jordan Faculty of Science and Information Technology Jadara University Irbid21110 Jordan Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates CSMIS Department Oman College of Management and Technology Barka320 Oman Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh11362 Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom2520 South Africa

Data clustering plays a crucial role in various domains, such as image processing, pattern recognition, and data mining. Traditional clustering techniques often suffer from limitations like sensitivity to initialization, poor convergence, and entrapment in local optima. To address these challenges, this paper proposes an Enhanced Walrus Optimizer (IWO) tailored for clustering tasks. The proposed IWO integrates two powerful strategies–Opposition-Based Learning (OBL) and Mutation Search Strategy (MSS)–to improve population diversity and prevent premature convergence, thereby enhancing both exploration and exploitation capabilities. These enhancements enable more accurate and stable identification of cluster centers. The effectiveness of IWO is validated through extensive experiments on multiple benchmark clustering datasets and compared against several state-of-the-art metaheuristic algorithms, including PSO, GWO, AOA, and others. The results demonstrate that IWO achieves better results, indicating improved compactness and separation of clusters. Statistical validation using p-values and ranking scores further confirms the superiority of the proposed method. These findings suggest that IWO offers a robust and flexible framework for solving complex clustering problems. Future work will explore hybrid deep learning-integrated models and parallel implementations to enhance scalability. © 2025

关键词： Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Optimization of Transmit Beamforming Using Channel Covariances for MISO Downlink Assisted by Reconfigurable Intelligent Surfaces

Optimization of Transmit Beamforming Using Channel Covarianc...

引用

International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Khin Thandar Kyaw Wiroonsak Santipach Kritsada Mamat Kamol Kaemarungsi Kazuhiko Fukawa Department of Electrical Engineering Faculty of Engineering Kasetsart University Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Department of Information and Communications Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

We propose an unsupervised beamforming neural network (BNN) to optimize transmit beamforming in downlink multiple input single output (MISO) channels. Reconfigurable intelligent surface (RIS) panels are used to assist user equipment that does not receive sufficiently s trong s ignals f rom t he base station. To avoid frequent beam updates, the proposed BNN is based on slow-changing channel covariances and is different from most other BNNs that utilize instant channel state information. Numerical simulations show that the proposed BNN can achieve much higher sum rates than zeroforcing beamforming does when the system is heavily loaded and can drastically reduce computation time for large-scale communication systems.

关键词： Array signal processing Neural networks Reconfigurable intelligent surfaces MISO communication Downlink Numerical simulation Telecommunications

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study to Evaluate the Performance of Communication Protocols for Process Industry

A Comparative Study to Evaluate the Performance of Communica...

引用

Australasian Telecommunication Networks and Applications Conference, ATNAC

作者： Xinyu Wang İlhan Mutlu Fatima Rani Lucas Drowatzky Leon Urbas Faculty of Electrical and Computer Engineering Technische Universität Dresden Germany Faculty of Mechanical Science and Engineering Technische Universität Dresden Germany

ISBN: (数字)9781665471039

ISBN: (纸本)9781665471046

With the boom of Industry 4.0 for industrial automation, the data exchange between the different industrial devices has tremendously increased through local (edge) and cloud computing. Nowadays, several communication protocols are available to realize data acquisition in the industry. As a result, understanding the practical capabilities of each communication protocol arises as an essential issue in optimizing the data acquisition and storage in industrial plants. This study aims to develop a software-based test environment to evaluate the performance of two widely used communication protocols within the scope of the Industrial Internet of Things (IIoT). These contemplated protocols are Open Platform Communications Unified Architecture (OPC UA) and Message Queuing Telemetry Transport (MQTT). With the help of the developed test environment, several performance metrics, like packet overhead, latency, packet loss ratio, and CPU utilization, are evaluated for different application scenarios where the varying number of clients and subscribers are considered. Derived results indicate the stronger parts of each protocol under varying communication configurations.

关键词： Industries Protocols Data acquisition Packet loss computer architecture Communications technology Industrial plants

来源：评论

学校读者我要写书评

暂无评论

Multiband highly sensitive metamaterial-based THz absorber for edible oil adulteration sensing

引用

Optik 2024年 307卷

作者： Anowarul Haque, S.M. Sohayeb Hossain, Md Alqahtani, Abdulrahman Islam, Mohammad Tariqul Moniruzzaman, Md. Samsuzzaman, Md. Dept. of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Dept. of Electrical and Electronic Engineering Mymensingh Engineering College Bangladesh Department of Biomedical Technology College of Applied Medical Sciences in Al-Kharj Prince Sattam Bin Ab-dulaziz University Al-Kharj11942 Saudi Arabia Department of Medical Equipment Technology College of Applied Medical Science Majmaah University Majmaah City11952 Saudi Arabia Dept. of Electrical and Electronic Engineering College of Engineering and Technology International University of Business Agriculture and Technology Dhaka Uttara1230 Bangladesh Dept. of Computer and Communication Engineering Faculty of Computer Science and Engineering Patuakhali Science and Technology University Bangladesh Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Dhaka Savar1216 Bangladesh

A terahertz (THz) wave technology is very appealing for applications in detection in biomedicine, biological spectroscopy, material characterization, detection, imaging, sensing, and other convenient applications. Metamaterials (MMs) are artificial structures that have structures in a regular periodic arrangement and don't exist in nature. By utilizing a proposed terahertz metamaterial absorber, a broad spectrum of adulterated lubricants can be detected. It is an efficient detection process that enables the development of a THz detection approach by making use of the metamaterial as a sensor. The proposed terahertz absorber is analyzed and optimized using the CST Microwave Studio 2019 software. The article proposed a multiband THz metamaterial absorber with a unit size of 70 µm. According to the demonstrated results, we can achieve five narrow absorption peaks with perfect absorption within the frequency range between (0.2–4.2) Terahertz. Also, the result shows that, the average absorbance of 99.07% at the resonance frequencies of 0.56, 1.42, 2.676, 3.14, and 3.89 THz. The E (electric) and H (magnetic)-field distributions on the resonator at different resonance frequencies were also analyzed as well as. The recommended metamaterial absorber has a good sensing capability at different resonant frequencies, with refractive index sensitivities of 80 GHz/RIU, 240 GHz/RIU, 480 GHz/RIU, 480 GHz/RIU, and 520 GHz/RIU obtained by modifying the refractive index of the sample holder or the analytic layer, respectively. As a result, the proposed detector has a high refractive index sensitivity, high Q-factor, and high FOM, making it suitable for detecting oil adulteration. © 2024 Elsevier GmbH

关键词： Metamaterials

来源：评论

学校读者我要写书评

暂无评论

Enhancing machine learning performance through intelligent data quality assessment: An unsupervised data-centric framework

引用

Heliyon 2025年第4期11卷

作者： Rahal, Manal Ahmed, Bestoun S. Szabados, Gergely Fornstedt, Torgny Samuelsson, Jörgen Department of Mathematics and Computer Science Karlstad University Universitetsgatan 2 Karlstad 65188 Sweden Department of Computer Science Faculty of Electrical Engineering Czech Technical University Prague 16627 Czech Republic Department of Engineering and Chemical Sciences Karlstad Universitetsgatan 2 Karlstad 65188 Sweden

Poor data quality limits the advantageous power of Machine Learning (ML) and weakens high-performing ML software systems. Nowadays, data are more prone to the risk of poor quality due to their increasing volume and complexity. Therefore, tedious and time-consuming work goes into data preparation and improvement before moving further in the ML pipeline. To address this challenge, we propose an intelligent data-centric evaluation framework that can identify high-quality data and improve the performance of an ML system. The proposed framework combines the curation of quality measurements and unsupervised learning to distinguish high- and low-quality data. The framework is designed to integrate flexible and general-purpose methods so that it is deployed in various domains and applications. To validate the outcomes of the designed framework, we implemented it in a real-world use case from the field of analytical chemistry, where it is tested on three datasets of anti-sense oligonucleotides. A domain expert is consulted to identify the relevant quality measurements and evaluate the outcomes of the framework. The results show that the quality-centric data evaluation framework identifies the characteristics of high-quality data that guide the conduct of efficient laboratory experiments and consequently improve the performance of the ML system. © 2025 The Author(s)

关键词： Automated data evaluation Data quality Data-centric clustering Machine learning Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Characterizing Continual Learning Scenarios and Strategies for Audio Analysis

arXiv

引用

arXiv 2024年

作者： Bhatt, Ruchi Kumari, Pratibha Mahapatra, Dwarikanath Saddik, Abdulmotaleb El Saini, Mukesh The Department of Computer Science & Engineering Indian Institute of Technology Punjab Ropar India The Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Inception Institute of AI Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates School of Electrical Engineering and Computer Science University of Ottawa Canada The Department of Computer Science & Engineering Indian Institute of Technology Punjab Ropar India Abu Dhabi United Arab Emirates

Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis approaches assume that the data distribution at training and deployment time will be the same. However, due to various real-life environmental factors, the data may encounter drift in its distribution or can encounter new classes in the late future. Thus, a one-time trained model might not perform adequately. In this paper, we characterize continual learning (CL) approaches in audio analysis. In this paper, we characterize continual learning (CL) approaches, intended to tackle catastrophic forgetting arising due to drifts. As there is no CL dataset for audio analysis, we use DCASE 2020 to 2023 datasets to create various CL scenarios for audio-based monitoring tasks. We have investigated the following CL and non-CL approaches: EWC, LwF, SI, GEM, A-GEM, GDumb, Replay, Naive, cumulative, and joint training. The study is very beneficial for researchers and practitioners working in the area of audio analysis for developing adaptive models. We observed that Replay achieved better results than other methods in the DCASE challenge data. It achieved an accuracy of 70.12% for the domain incremental scenario and an accuracy of 96.98% for the class incremental scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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：