检索结果-内蒙古大学图书馆

IEEE International Conference on Communications (ICC)

作者： Mohamed I. Ibrahem Hussien AbdelRaouf Ahmad Alsharif Mostafa M. Fouda Zubair Md Fadlullah Ahmed Aleroud School of Computer and Cyber Sciences Augusta University Augusta GA USA Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt Department of Computer Science University of Alabama AL USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Science Western University London ON Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Load forecasting (LF) in smart grids is beneficial not only in mitigating equipment failures and power outages but also in facilitating effective power dispatching and infrastructure planning. To predict future loads accurately, the consumers' fine-grained energy consumption readings are fed into machine-learning (ML) models. However, revealing these readings enables adversaries to deduce confidential information about consumers, including details about their lifestyle, and hence their privacy is violated. To address this privacy issue, the existing works only focus on using federated learning (FL)-based approaches to train and obtain an accurate global LF model. Nevertheless, addressing the privacy violation problem during the LF process (in the deployment phase) after obtaining the global model for AMI networks has not been well investigated yet. Therefore, this paper proposes a novel, efficient, and decentralized approach that enhances the precision of LF while safeguarding the privacy of consumers. The proposed scheme incorporates inner product functional encryption (IPFE) to allow smart meters (SMs) to encrypt their readings with no need for a trusted key distribution center (KDC) while allowing LF without divulging or acquiring knowledge of the consumers' readings to protect their privacy. In addition, a hybrid deep learning approach is developed to construct an LF model that can yield precise forecasts. To show the feasibility of the proposed scheme, the performance of our scheme was assessed on a real energy consumption readings dataset, and the results demonstrate proficiency in LF while providing robustness and privacy preservation with reasonable communication efficiency.

关键词： Privacy Energy consumption Accuracy Load forecasting Predictive models Smart meters Robustness

来源：评论

学校读者我要写书评

暂无评论

Fusion of Time and Frequency Domain Attributes for the Automated Recognition of Emotion using EEG Recordings 21

Fusion of Time and Frequency Domain Attributes for the Autom...

引用

21st OITS International Conference on Information Technology, OCIT 2023

作者： Maithri, M. Raghavendra, U. Gudigar, Anjan Sambrama Praharaj, Samir Kumar Manipal Institute of Technology Manipal Academy of Higher Education Department of Mechatronics Manipal576104 India Manipal Institute of Technology Manipal Academy of Higher Education Department of Instrumentation and Control Engineering Manipal576104 India North Carolina State University Department of Electrical and Computer Engineering United States Manipal Academy of Higher Education Kasturba Medical College Manipal Department of Psychiatry Manipal India

ISBN: (纸本)9798350358230

Emotions have a significant impact on a person's behavior. Expression reflects how people perceive events, interactions, and judgment. It is possible to identify emotions and categorize them using several techniques, including electroencephalography (EEG) signals, gestures, facial expressions, speech patterns, etc. However, emotion recognition using physiological signals has gained popularity owing to its authenticity. In this study, 54 attributes were extracted from each EEG channel to gather emotional information. These attributes were then fed to multiple classifiers and the results were compared. The study attained a maximum accuracy of 89.52% for channel T7 - 24 using ensemble bagged tree classifiers. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Deep Neural Network aided Sparse Bayesian Learning for Wireless Access Channel Estimation in mm-Wave Massive MIMO Cloud Radio Access Network Systems 1

Deep Neural Network aided Sparse Bayesian Learning for Wirel...

引用

1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Datta, Jayanta Zabala-Blanco, David Soria, Francisco Ruben Castillo University of Chile Department of Electrical Engineering Santiago Chile Universidad Católica de Maule Department of Computer Science and Industry Talca Chile Autonomous University of San Luis Potosi Telecommmunications Engineering Faculty of Sciences San Luis Potosi Mexico

ISBN: (纸本)9781665450461

Cloud Radio Access Network systems with mmWave Massive MIMO framework can be considered as a potential candidate for next generation wireless communications due to its promise of increased spectral efficiency and distributed signal processing capability. State-of-the-art compressive sensing algorithms like sparse Bayesian learning can exploit the inherent sparsity of the mm-Wave wireless channels to estimate the channel connecting the remote radio head and the user equipment in the wireless access link. The performance of the sparse Bayesian learning based channel estimation can be adversely affected by impairments due to optical fiber based front-haul channel and quantization noise. As a result, it is necessary to compensate for the performance degradation by applying methods which can combat the effects of the front-haul channel. Contemporary research has demonstrated the capability of deep learning algorithms in signal enhancement under low signal-to-noise ratio conditions, such as hybrid beamforming design, channel estimation as well as feedback of channel state in heterogeneous multi-antenna wireless systems. Motivated by their de-noising and signal prediction capabilities, convolutional long short term memory networks are employed in this work to jointly remove the quantization noise and optical fiber impairment due to the front-haul channel, which can improve the performance of sparse Bayesian learning in estimating the wireless access channel at the base-band processing unit. computer simulation results show that the proposed methodology performs well under low signal-to-noise ratio conditions. © 2022 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Classification of traditional ulos of Batak Toba ssing probabilistic neural network 1

Classification of traditional ulos of Batak Toba ssing proba...

引用

1st South East Asia Science, Technology, engineering and Mathematics International Conference, SEA- STEM 2020

作者： Siregar, B. Panggabean, I.P.S. Fahmi Hizriadi, A. Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Sumatera Utara Indonesia

Ulos are commonly used in traditional Batak events. Ulos has many variations of pattern and the pattern almost similar to one another. Ulos also have different functions in its use. So, people are often wrong to call the name of Ulos because the similarity of its pattern. Therefore, the research is doing Ulos classification to increase the introduction of Batak Toba's Ulos. In this research, the method used in classification is Probabilistic Neural Network method. The stages before classification are pre-processing, extraction feature using Gray Level Co-Occurrence Matrix, and then classified using Probabilistic Neural Network. The research uses 650 data to be used as training data and test data. After testing in this research, it was concluded that the Probabilistic Neural Network method had the ability to classify Ulos into five patterns namely Bintang Maratur, Ragi Hidup, Ragi Hotang, Mangiring and Sibolang. The result showed that the accuracy level is 80%. © Published under licence by IOP Publishing Ltd.

关键词： Ability testing

来源：评论

学校读者我要写书评

暂无评论

Development of a person's facial expression recognition system using a convolutional neural network 1

Development of a person's facial expression recognition syst...

引用

1st South East Asia Science, Technology, engineering and Mathematics International Conference, SEA- STEM 2020

作者： Siregar, B. Wirtjes, J.S. Nababan, E.B. Fahmi Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Sumatera Utara Indonesia

Humans naturally and intuitively use important and powerful facial expressions to communicate and show their emotions during social interactions. In that interaction, the effort to interpret someone's emotional condition is important in good communication. A person's emotional condition is reflected in the form of words, gestures, and especially facial expressions. Although humans can recognize expressions well, research on expression recognition carried out by machines is continuously being carried out in order to be able to carry out expression recognition in human and computer interactions. This research conducted facial expression recognition using the Convolutional Neural Network method. To perform facial expression recognition, the Convolutional Neural Network is trained with expression image data. The training process is carried out using different optimizer values, batch size, and epoch to get the best model. To overcome overfitting, data augmentation was carried out on training data and validation data. The experimental results in this research indicate that the system method built is able to recognize a person's facial expressions with an accuracy rate of 80%. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Sum-Rate Maximization for UAV Relay-Aided Fluid Antenna System with NOMA

Sum-Rate Maximization for UAV Relay-Aided Fluid Antenna Syst...

引用

IEEE International Symposium on Telecommunication Technologies (ISTT)

作者： Salah Boubkar Salah Abdou Wee Kiat New Chee Yen Leow SeungHwan Won Kai-Kit Wong Zhiguo Ding Wireless Communication Centre Faculty of Electrical Engineering Universiti Teknologi Malaysia (UTM) Johor Bahru Malaysia Department of Electronic and Electrical Engineering University College London London United Kingdom Connected Intelligence Research Group University of Southampton Malaysia Iskandar Puteri Johor Malaysia School of Electrical and Computer Engineering Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350392074

ISBN: (纸本)9798350392081

Fluid Antenna System (FAS), Non-Orthogonal Multiple Access (NOMA), and Unmanned Aerial Vehicle (UAV) have been explored as promising technologies for sixth-generation (6G) wireless networks. However, a single technology alone will not be sufficient to meet the stringent performance requirements of 6G. Thus, in this paper, we propose a NOMA-based UAV Relay-Aided FAS to address this challenge. Specifically, we jointly optimize the active port, power allocation, and UAV altitude to maximize the achievable sum-rate of Ground Users (GUs) while satisfying minimum rate requirements. To tackle this non-convex optimization problem, we decouple it into two sub-problems: a large-scale fading optimization problem and a small-scale fading optimization problem. We demonstrate that the large-scale fading problem can be solved using a one-dimensional search method, while the small-scale fading problem can be addressed with closed-form expressions. Our simulation results show that FAS consistently outperforms Traditional Antenna System (TAS). Moreover, the combination of FAS and NOMA greatly benefits from the joint optimization of both small-scale and large-scale fadings in UAV relay-aided communications, resulting in substantial performance improvements over the existing TAS and Orthogonal Multiple Access (OMA) combination.

关键词： Fading channels 6G mobile communication NOMA Fluids Closed-form solutions Autonomous aerial vehicles Resource management Relays Optimization Antennas

来源：评论

学校读者我要写书评

暂无评论

Design and Experiment of a Wideband Circularly Polarized Stacked Patch Antenna for Universal UHF RFID Readers

Design and Experiment of a Wideband Circularly Polarized Sta...

引用

2021 Research, Invention, and Innovation Congress: Innovation electricals and Electronics, RI2C 2021

作者： Konjunthes, Sathaporn Chalermwisutkul, Suramate Akkaraekthalin, Prayoot Thaiwirot, Wanwisa King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Bangkok Thailand

ISBN: (纸本)9781665403009

This paper presents a wideband circularly polarized stacked patch antenna for universal UHF RFID readers. The proposed antenna consists of a slanted elliptical microstrip patch layer, corner truncated patch layer, a horizontally meandered strip (HMS) layer, and a corner truncated ground plane. A slated elliptical patch, which is the main radiating patch, is etched on one side of a FR4 substrate in order to achieve circular polarization. The slated elliptical patch is fed through the HMS. This HMS is placed below the slanted elliptical patch to improve the impedance bandwidth. One end of the HMS is connected to the ground plane via SMA connector, while the other end is connected to the main radiating patch by the probe. In order to further enhance the bandwidth, the parasitic truncated patch is placed above the main radiating patch. In addition, the truncation of the ground plane is used to control the AR bandwidth. The proposed antenna has an overall size of 250 x 250 x 54 mm3. The measured results show that the proposed antenna can provide the impedance bandwidth of about 149 MHz (817 - 966 MHz) or 16.7%, a 3-dB axial ratio (AR) bandwidth of about 170 MHz (830 - 1000 MHz) or 18.5%, and maximum gain of about 8.4 dBi. The proposed antenna can operate at the UHF band of 840 - 960 MHz, which covers the bandwidth of universal UHF RFID readers. © 2021 IEEE.

关键词： Bandwidth

来源：评论

学校读者我要写书评

暂无评论

IoT-Based Cultivation of Mitragyna Speciosa

IoT-Based Cultivation of Mitragyna Speciosa

引用

International electrical engineering Congress (iEECON)

作者： S. Meesomklin P. Ruaangchaiwuttikhun J. Duangprateep S. Saleekhao P. Akkaraekthalin Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper proposes a smart farm system, applying to cultivate mitragyna speciosa plants in Thailand. The greenhouse system with several sensors, microcontroller, and cultivation devices has been designed. The temperature, light intensity and moisture in the area of cultivation can be measured and the microcontroller has been used to read all data and then they are shown through the website. The cultivation data have been processed and then used for controlling temperature, light intensity and moisture inside the greenhouse. The proposed system is very effective, that the temperature values, light intensity, and humidity can be monthly, daily and hourly viewed and displayed on the computer and smartphone. When the proposed system has been applied for cultivating mitragyna speciose, the better growth results have been found comparing with the traditional cultivation.

关键词： Temperature measurement Smart agriculture Temperature sensors Temperature distribution Microcontrollers Plants (biology) Moisture Process control Sensor systems Temperature control

来源：评论

学校读者我要写书评

暂无评论

SarcNet: A Novel AI-Based Framework to Automatically Analyze and Score Sarcomere Organizations in Fluorescently Tagged HIPSC-CMS

SarcNet: A Novel AI-Based Framework to Automatically Analyze...

引用

IEEE International Symposium on Biomedical Imaging

作者： Huyen Le Khiet Dang Tien Lai Nhung Nguyen Mai Tran Hieu Pham College of Engineering & Computer Science VinUniversity VinUni-Illinois Smart Health Center VinUniversity Faculty of Sciences and Technologies University of Burgundy Department of Electrical & Computer Engineering University of Illinois Urbana-Champaign (UIUC) College of Health Science VinUniversity

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Quantifying sarcomere structure organization in human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is crucial for understanding cardiac disease pathology, improving drug screening, and advancing regenerative medicine. Traditional methods, such as manual annotation and Fourier transform analysis, are labor-intensive, error-prone, and lack high-throughput capabilities. This paper proposes SarcNet, a novel deep learning-based framework that leverages cell images and integrates cell features to automatically evaluate the sarcomere structure of hiPSC-CMs from the onset of differentiation. The proposed framework is a cell-features concatenated and linear layers-added ResNet-18 module, to output a continuous score ranging from 1 to 5 that captures the level of sarcomere structural organization. SarcNet achieves a Spearman correlation of 0.831 with expert evaluations, demonstrating superior performance and an improvement of 7.5% over the previous approach, which uses linear regression. Our results also show a consistent pattern of increasing organization from day 18 to day 32 of differentiation, aligning with expert evaluations. This study contributes to the development of a new automated tool for quantifying sarcomere structural organization in hiPSC-CMs, advancing cardiac research.

关键词： Drugs Deep learning Pathology Fourier transforms Linear regression Organizations Manuals Fluorescence Distance measurement Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Impact of Finger Type in Contactless Fingerprint Verification

引用

Procedia computer Science 2024年 246卷 3928-3937页

作者： Karama Abdeljabbar Islem Jarraya Tarek M. Hamdani Adel M. Alimi REGIM-Laboratory—REsearch Groups in Intelligent Machines National Engineering School of Sfax (ENIS) Sfax Tunisia Higher Institute of Computer Science Mahdia (ISIMa) University of Monastir Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Contactless fingerprint authentication has gained popularity as a field of research in biometrics in recent years. Unlike traditional fingerprint recognition systems that require direct contact of the person’s finger with the sensor, contactless fingerprint systems offer several advantages, among them ease of capture and cost-effectiveness. Despite the progress made in this field, poor contrast, background noise, and limited image information continue to pose difficulties for fingerprint recognition in contactless environments. Furthermore, the number of images in published fingerprint biometric datasets for each person is restricted, and there is insufficient data for efficient training. Nevertheless, Convolutional Neural Network (CNN) architectures have been widely used, necessitating large databases. To address these issues, this paper introduces a Siamese network designed for the purpose of identity Verification, using the contactless thumb fingerprint modality to enhance recognition results. The Siamese network is able to extract pertinent features from noisy images with low contrast and limited information, even if they have limited information and low contrast. Additionally, this work proposes the use of the contactless thumb fingerprint modality instead of the contactless index fingerprint modality, which is more commonly used in related works. Consequently, the Mobile FingerPrint (MFP) dataset is introduced and constructed for evaluation. Experimental results demonstrate the efficiency of the proposed method, achieving an accuracy of 98.68% for thumb fingerprint Verification.

关键词： Contactless Fingerprint Authentication Thumb Fingerprint Deep Learning Siamese Network Biometric Verification

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：