检索结果-内蒙古大学图书馆

2022 IEEE International Conference and Expo on Real Time Communications at IIT, RTC 2022

作者： Mohamed, Ali Gaber Gamal, Mahmoud Alexandria University Department of Electrical Engineering Communications and Electronics Section Faculty of Engineering Alexandria Egypt Damanhour University Faculty of Engineering Department of Electrical Engineering Damanhour Egypt Higher Institute of Engineering and Technology Department of Computer Engineering El-Behira Egypt

ISBN: (数字)9781665475037

ISBN: (纸本)9781665475037

Cognitive radio can be considered as a viable frequency access framework that overcomes the disadvantages of the licensed-based transmission procedure by allowing the secondary users to access the spectrum to transmit their data. However, due to the evolution of latency-sensitive real-time communication applications such as gaming and extended reality, it becomes more vital that cognitive radio networks should be studied with latency requirements on the data transmission. Recently, Age of Information has introduced itself as a important metric for evaluating the freshness of the transmitted data. In this paper, we investigate the latency and stability analysis of a two-user cognitive radio network that consists of one primary user, one secondary user and their destinations. The latency requirements of the transmitted data packets are taken into consideration by imposing Age of Information constraints on the data transmission of the users. We present two optimization problems, in the first problem, the secondary user stable throughput is maximized under an Age of Information constraint imposed on the data transmission of the secondary user. While, in the second problem, we maximize the stable throughput of the secondary user with respect to Age of Information constraints set on the data transmission of both the primary and secondary users. The resultant problems are found to be non-linear programming optimization problems. An appropriate algorithm is used to solve the problems and provide the numerical solutions. Our results characterize the impact of setting Age of Information constraints on the stability region of the network;we demonstrate that the stability region, in certain cases, is reduced by only 11% with strict Age of Information restrictions if compared to the scenario where no latency requirements is considered. Our results also show the potential accuracy of the algorithm adopted in this paper to solve the formulated optimization problems. © 2022 IEEE.

关键词： Cognitive radio

来源：评论

学校读者我要写书评

暂无评论

Enhanced Circular Parasitic Element at Directional Wideband Wearable Antenna for Microwave Breast Imaging Applications 10

Enhanced Circular Parasitic Element at Directional Wideband ...

引用

10th IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024

作者： Koma'rudin, Nur Atika Binti Salim, Norhuda Binti Zakaria, Zahriladha Hassan, Nornikman Soh, Ping Jack Department of Human Resource Bandar Vendor Taboh Naning Alor Gajah Melaka78000 Malaysia Faculty of Electronics and Computer Technology and Engineering Hang Tuah Jaya Durian Tunggal76100 Malaysia Oulu90014 Finland

ISBN: (纸本)9798350364163

A 3.06 GHz and 7.0 GHz wideband wearable antenna for microwave imaging (MWI) in breast cancer applications has been developed, involving design, simulation, fabrication, and measurement. Simulations for Antenna A, Antenna B, and Antenna C were conducted using CST Microwave Studio. The wearable antenna is intended for MWI breast cancer diagnosis. A hemisphere breast phantom with three fat layers, a round tumor, and skin (thicknesses of 68 mm, 5 mm, and 2 mm) was created and measured. Data from the breast phantom was analyzed using a modified delay and sum (mDAS) imaging technique in MATLAB to identify the tumor's location. For simulation of Antenna C, it shows the resonances at 3.07 GHz and 5.15 GHz with - 37.15 dB and - 38.51 dB, respectively. For measurement, it stated that a 3.29 GHz, 5.51 GHz, and 6.67 GHz, with return losses of - 24.67 dB, - 17.65 dB, and - 19.62 dB, respectively. © 2024 IEEE.

关键词： Phantoms

来源：评论

学校读者我要写书评

暂无评论

Smart Energy Management System Using IoT for Real-Time Monitoring and Theft Detection

Smart Energy Management System Using IoT for Real-Time Monit...

引用

Information technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Rajermani Thinakaran Somasekar Jalari Vikram Neerugatti Ravindra Raman Cholla K Nagendra Rao Faculty of Data Science and Information Technology INTI International University Malaysia Department of Computer Science and Engineering Faculty of Engineering and technology Jain (Deemed-to-be University) Bangalore India Department of Computer Science and Engineering S V College of Engineering Tirupati Andhra Pradesh India

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

The internet of things technology has developed almost all the sectors including energy management. In traditional energy management system meters are used to recording the number of units and electricity used but they are unable to provide the real time information or updates or any theft of electricity. By focusing this problem, we have developed a system which is able to overcome all the problems in traditional electric management system. It monitors the energy usage and real time theft detection of electricity as it cost lot of money. In our system we have used a current sensor which continuously checks the electricity flow. The microcontroller measures the actual energy usage as well as expected values. It compares both the values. If a big difference found, then it could mean the theft of electricity. So, the GSM module is used to send the alert to the user hence they can take required action. The Wi-Fi module sends the data over the cloud for data analytics. Also, user can see the real time data from the cloud platform. Hence user can track the energy usage and get timely alerts so that user can be aware of energy consumption or energy theft. The system is developed by setting up hardware parts as well as integration with software. The system can be operated or monitored from remote place, as Wi-Fi module connects the system with the internet. System can detect the unusual activities like theft of electricity by analyzing the data. Using the IoT technology for energy management, it can become cost effective as well as smart solution of energy management to avoid any theft. Hence it can overcome the loss.

关键词： Meters GSM Energy consumption Renewable energy sources Costs Electricity Real-time systems Recording Monitoring Wireless fidelity

来源：评论

学校读者我要写书评

暂无评论

Enhancing Federated Learning by One-Shot Transferring of Intermediate Features from Clients 10

Enhancing Federated Learning by One-Shot Transferring of Int...

引用

10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023

作者： Deng, Youxingzhu Zhou, Yipeng Liu, Gang Wang, Jessie Hui Shui, Yu Shenzhen University College of Computer Science and Software Engineering Shenzhen China Macquarie University School of Computing Faculty of Science and Engineering Sydney Australia Institute for Network Sciences and Cyberspace Tsinghua University Peking China University of Technology Sydney School of Computer Science Sydney Australia

ISBN: (纸本)9798350345032

Federated learning (FL) is an emerging paradigm using a parameter server (PS) to coordinate multiple decentralized clients for training a common model without exposing their raw data. Despite its amazing capability in preserving data privacy, FL confronts two significant challenges that have not been sufficiently addressed by existing works, which are: 1) heterogeneous data distributed on clients given that the PS cannot alter data locations. 2) limited computation resources of FL clients who may conduct model training with mobile devices. To tackle these challenges, we propose a novel federated one-shot transferring of intermediate features (FedOTF) algorithm. More specifically, FedOTF consists of two stages: feature extraction and model reconstruction. To overcome challenge 1, clients in stage 1 only collaboratively train a small model in a federated manner, with the objective to extract features. In stage 2, intermediate features (generated by the small model trained by stage 1) are transferred from clients to the PS so that a large model can be trained to overcome challenge 2. In addition, the design of FedOTF is robust, which can flexibly diminish the number of communication rounds of stage 1 when network capacity is limited, and reduce the amount of exposed features when privacy is concerned. To verify the superiority of FedOTF, we conduct comprehensive experiments with real datasets. The experiment results demonstrate that FedOTF can significantly improve the model utility of FL because a better model can be finally obtained by the PS without incurring heavy computational load on clients. Besides, we conduct robustness evaluation of FedOTF which can achieve stable performance when varying network capacity and privacy requirement. © 2023 IEEE.

关键词： Privacy-preserving techniques

来源：评论

学校读者我要写书评

暂无评论

RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

IEEE Transactions on AgriFood Electronics

引用

IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

Real-Time Scheduling for Energy Optimization: Smart Grid Integration with Renewable Energy 7

Real-Time Scheduling for Energy Optimization: Smart Grid Int...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gopalakrushna, A. Raj, Vijilius Helena Vani, V. Divya Singh, Navdeep Jethabhai, Patel Yogeshkumar Chakravarthi, M. Kalyan Vignana Bharathi Institute of technology Telangana Hyderabad India New Horizon College of Engineering Department of Applied Sciences Karnataka Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Sankalchand Patel University Faculty of Computer Science Shri C.J. Patel College of Computer Studies Gujarat Visnagar India University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman

ISBN: (纸本)9798350350067

Reliable power system operations, cost reduction, demand and supply balance maintenance, and efficient load scheduling all depend on real-time energy optimisation. Nevertheless, the unpredictability of electric loads and renewable energy sources (RES) makes real-time energy optimization difficult. As a result, the Energy Management Controller (EMC) was developed, which can automatically react to DR signals and resolve energy-related issues. In light of this, EDGE, a hybrid algorithm combining elements of genetic algorithm (GA) and enhanced differential evolution (EDE), is devised in this work. The EDGE algorithm is used to train the EMC to automatically react to DR signals and schedule the 3 classes of household loads - hybrid, non-interruptible, and interruptible - to handle energy management issues. The results show that in terms of power cost reduction, minimizing carbon release, PAR relief, and client discomfort maximization, the suggested EDGE algorithm performs better than benchmark methods. © 2024 IEEE.

关键词： energy optimization renewable energy Smart grid

来源：评论

学校读者我要写书评

暂无评论

Research on the 'Electric Vehicles and Automotive Electronics' Curriculum Aimed at Modernization within the DIGITRANS Project 65

Research on the 'Electric Vehicles and Automotive Electronic...

引用

65th IEEE Annual International Scientific Conference on Power and Electrical engineering of Riga Technical University, RTUCON 2024

作者： Arhun, Shchasiana Hnatov, Andrii Kunicina, Nadezhda Kharkiv National Automobile and Highway University Automobile Faculty Vehicle Electronics Department Kharkiv Ukraine Riga Technical University Riga Latvia Institute of Industrial Electronics Electrical Engineering and Energy Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia

ISBN: (纸本)9798350365771

The rapid evolution of the automotive industry, driven by technological innovations and the shift towards sustainability, necessitates a corresponding transformation in educational curricula. This paper presents a detailed examination of the 'Electric Vehicles and Automotive Electronics' curriculum at Kharkiv National Automobile and Highway University, which has been targeted for modernization under the DIGITRANS project. The project's primary objective is to align the curriculum with the latest industry demands and technological advances, particularly focusing on electric vehicles (EVs) and automotive electronics. This research utilizes a mixed-methods approach, combining quantitative data from industry surveys with qualitative feedback from academic experts and industry stakeholders. The findings highlight significant gaps in the current educational offerings, particularly in areas such as advanced diagnostics, battery management systems, and integration of smart automotive technologies. The paper discusses the development and integration of new course materials that include hands-on laboratory exercises, industry placements, and enhanced e-learning modules. The modernized curriculum aims to equip students with the skills necessary to excel in the fast-evolving automotive sector, emphasizing critical thinking, technical expertise, and practical experience. The outcomes of this research underscore the importance of continuous industry-academia collaboration to keep educational programs relevant and forward-thinking. This initiative not only enhances the employability of graduates but also contributes to the broader goal of advancing electric vehicle technology and sustainability in automotive engineering. © 2024 IEEE.

关键词： Automotive industry

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Convolutional Neural Networks Performance in Remote Sensing for Solar Panel Detection

Comparative Analysis of Convolutional Neural Networks Perfor...

引用

International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Seyedeh Fatemeh Khakzad Tahere Taghizade Firozjaee Reza Mortazavi Erfan Khoshnevisan Sina Khoshnevisan Faculty of Civil Engineering Shahrood University of Technology Shahrood Iran School of Engineering Damghan University Damghan Iran Faculty of Computer Science Shahrood University of Technology Shahrood Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

As the importance of solar energy continues to grow as a pivotal renewable resource, the accurate identification of solar panels within satellite imagery becomes indispensable for both resource assessment and strategic planning. The increasing utilization of solar power signifies a shift towards cleaner and renewable energy sources and serves as a critical measure in mitigating the adverse impacts of climate change, which is predominantly fueled by the widespread use of fossil fuels. This study employs a high-quality, manually labeled dataset comprising 250 satellite images of solar panels in California. Four convolutional neural network (CNN) architectures—DeepLabV3+ VGG16, ResU-Net, UNet3+, and SegNet—are evaluated for their efficacy in detecting solar panels. The models were trained for 50 epochs using a learning rate of 0.0003, the Binary Cross-Entropy loss function, and the Adam optimizer. Performance was assessed using the Dice Coefficient, Recall, and Precision metrics, with DeepLabV3+VGG16 achieving the highest performance with a Dice Coefficient of 94.62% and Recall of 94.23%. These findings provide valuable insights for researchers in selecting appropriate network architectures for accurate semantic segmentation tasks and effective solar panel detection and estimation.

关键词： Training Climate change Accuracy Semantic segmentation Solar energy Satellite images Solar panels Convolutional neural networks Reliability Testing Climate change Solar panels

来源：评论

学校读者我要写书评

暂无评论

Enhancing Investment Forecasting through the Second-Level Thinking Concept

Enhancing Investment Forecasting through the Second-Level Th...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Dan Zhao Chao Wang Zhenyi Shen The College of Computer Science and Technology Zhejiang University China The Faculty of Science Engineering and Technology Swinburne University of Technology Victoria Australia

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Forecasting models that rely on machine learning often lack a transparent trading logic, which can erode trust when they are applied to real-world trading scenarios. To tackle this problem, a model grounded in an investment concept known as ‘second-level thinking’ is proposed. This approach enhances both the interpretability of the model and the prediction accuracy. The model's first-level thinking process makes a broad prediction based on features related to the macroeconomic environment. These features are used to group samples into clusters, each representing a different state of the macroeconomic environment. For each cluster, an associative memory is constructed to improve interpretability and simplify the prediction process, mirroring the function of human memory. A theorem is developed to explore the relationship between the capacity of the associative memory and the threshold used in the clustering step. This helps establish an appropriate threshold for reliably memorizing patterns within each cluster. The second-level thinking process then refines these predictions by fitting the residuals from the first-level process using technical features, further enhancing prediction performance. Experiments conducted on two datasets of Chinese government bonds demonstrate that our proposed model outperforms others in terms of prediction accuracy and trading performance.

关键词： Associative memory Accuracy Philosophical considerations Government Predictive models Reliability theory Macroeconomics Logic Forecasting Investment

来源：评论

学校读者我要写书评

暂无评论

Towards improved human arm movement analysis: Advanced feature engineering and model optimization

引用

engineering Applications of Artificial Intelligence 2025年 156卷

作者： Benachour, Yassine Rehman, Moez Flitti, Farid Faculty of Engineering Higher Colleges of Technology Dubai United Arab Emirates Faculty of Computer Information System Higher Colleges of Technology Dubai United Arab Emirates

Human arm movement (HAM) analysis plays a crucial role in rehabilitation, sports science, and human-computer interaction (HCI). This has led to extensive research, introducing various systems and algorithms to capture and analyze human movement data. This study presents a novel integrated pipeline for analyzing human arm movement time series data collected from an Inertial Measurement Unit (IMU) embedded in an Apple Watch. The pipeline combines time and frequency domain feature extraction, dimensionality reduction using Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE), and embedded feature selection methods including Least Absolute Shrinkage and Selection Operator (Lasso), Random Forest, and Elastic Net, to optimize classification accuracy while reducing computational complexity. Targeting key movements such as elbow extension/flexion, shoulder extension/flexion, and wrist protonation/supination, data were captured using accelerometers and gyroscopes. The initial dataset, consisting of 720 features from both time-domain and frequency-domain analyses, was efficiently reduced to 46 features, mitigating the curse of dimensionality while preserving essential information. As a result, the proposed method achieved a classification accuracy of over 96 %, with precision, recall, and F1-score of 0.95. © 2025

关键词： Feature Selection

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：