检索结果-内蒙古大学图书馆

International Conference on computer Science and engineering (UBMK)

作者： Elov Botir Boltayevich Eşref Adalι Khamroeva Shahlo Mirdjonovna Abdullayeva Oqila Xolmo'Minovna Xusainova Zilola Yuldashevna Xudayberganov Nizomaddin Uktamboy O'g'li Dept. of Computational Linguistics and Digital Technologies Tashkent State University of Uzbek Language and Literature named Alisher Navo'i Tashkent Uzbekistan Computer Engineering and Informatics Faculty Istanbul Technical University Istanbul Türkiye

The number of possible word forms in agglutinative languages is theoretically unlimited. This, in turn, creates the problem of POS tagging (part-of-speech) of out-of-vocabulary (OOV) words in agglutinative languages. In agglutinative languages, words are formed by adding suffixes to the stem. Due to the occurrence of phonetic harmony and disharmony while adding suffixes to the stem, it is necessary to analyze both phonetic and morphological changes. When solving many NLP tasks, it is necessary to reduce word forms to the stem (stemming). Removing all inflectional affixes from a word and lemmatizing the rest of the word is considered one of the important tasks of natural language processing (NLP), and this process is called stemming. The stemming process is important in information retrieval (IR) systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mamba-Based Super-Resolution and Segmentation Network for UAV-Captured Blueberry Farmland Imagery

Mamba-Based Super-Resolution and Segmentation Network for UA...

引用

Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Fan Zhao Jian Song Jiaqi Wang Yijia Chen Dianhan Xi Xinlei Shao Y ongying Liu Bangzhang Ma Yinyin He Guochen Zhang Qingyang Wu Zhengyue Tu Mengya Wu Yulun Chen Jiaolin Guo Chenyu Zhang Graduate School of Frontier Science The University of Tokyo Kashiwa Japan Dept. Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong Dept. of Computer and Information Sciences Hosei University Tokyo Japan Dept. Environmental Health Sciences University of California Los Angeles LA CA US Dept. Mechanical Engineering The University of Tokyo Bunkyo Japan Dept. Environmental Science Southwest Forestry University Kunming China Yunnan Yijiu Agricultural Development Co. Ltd. Yuxi City China Dept. Geographic Information Science Fujian Normal University Fuzhou China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Traditional methods of inspecting blueberry maturity primarily rely on manual observation, which is inefficient and prone to visual errors. The high similarity in shape and color between immature blueberries and blueberry flowers poses significant challenges for accurate visual assessment. This study proposes a novel method for evaluating the overall maturity of a blueberry plantation using super-resolution reconstruction (SRR) and semantic segmentation. We designed and evaluated various SRR models, including SRCNN, EDSR, RCAN, Real-ESRGAN, SwinIR, HAT, and MambaIR, to enhance the detailed representation of blueberries at different maturity stages in low-resolution images. These enhanced images are then processed by the proposed semantic segmentation network to accurately segment blueberry flowers, leaves, and blueberries at different maturity stages. Experimental results demonstrate that MambaIR achieves 82.26% SSIM in the SRR task, while the image segmentation model developed based on the Mamba algorithm attains an average intersection over union (mIoU) of 80.48. In summary, the proposed method shows significant accuracy and efficiency advantages in assessing blueberry maturity.

关键词： Visualization Accuracy Shape Semantic segmentation Superresolution Plantations Manuals Flowering plants Image reconstruction Optimization

来源：评论

学校读者我要写书评

暂无评论

Local Binary Pattern Histogram for Face Recognition in Student Attendance System 3

Local Binary Pattern Histogram for Face Recognition in Stude...

引用

3rd International Conference on computer and informatics engineering, IC2IE 2020

作者： Alexander, Allan D Salkiawati, Ratna Lubis, Hendarman Rahman, Fathur Herlawati, Herlawati Handayanto, Rahmadya Trias Universitas Bhayangkara Jakarta Raya Bekasi Dept. of Informatics Engineering Bekasi Indonesia Universitas Islam 45 Bekasi Dept. of Computer Engineering Bekasi Indonesia

ISBN: (纸本)9781728182476

Student attendance record has an important role in the educational process. Universitas Bhayangkara Jakarta Raya, as a case study, uses attendance record as the factor for final grade calculation. Many attendance recording systems were developed using biometrics, e.g. face recognition, iris recognition, and fingerprint recognition. In this study, face recognition was proposed since the face cannot be duplicated and can eliminate fraud committed by students. In addition, this contactless method could minimize the risk of COVID-19 spread with some additional treatments. The local binary pattern (LBP) was proposed in this study. This method has the ability to describe the texture and shape of an image by dividing the image into small portions of feature extraction. The result showed that the proposed system can identify students with 86% accuracy. © 2020 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

引用

Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Yu Li Rana Das Cuong Duong Teng-Teeh Lim Timothy Haithcoat Ilker Ersoy Chi-Ren Shyu Department of Electrical Engineering and Computer Science University of Missouri Columbia MO US Biological Engineering University of Missouri Columbia MO US Plant Science and Technology Extension University of Missouri Columbia MO US Institute for Data Science and Informatics University of Missouri Columbia US Institute for Data Science and Informatics University of Missouri Columbia MO US Electrical Eng. and Computer Science Dept. Institute for Data Science and Informatics University of Missouri Columbia MO US

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri Department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Energy-Autonomous SWIPT RFID Tag for Communication in the 2.4 GHz ISM Band 3

An Energy-Autonomous SWIPT RFID Tag for Communication in the...

引用

3rd URSI Atlantic and Asia Pacific Radio Science Meeting, AT-AP-RASC 2022

作者： Paolini, Giacomo Quddious, Abdul Chatzichristodoulou, David Masotti, Diego Nikolaou, Symeon Costanzo, Alessandra University of Bologna Dei 'Guglielmo Marconi' Bologna Italy Kios Research and Innovation Center of Excellence Nicosia Cyprus Frederick Research Center Nicosia Cyprus Rf and Microwave Solutions Ltd Cyprus Frederick University Dept. Electrical Computer Engineering and Informatics Nicosia Cyprus University of Bologna Dei 'Guglielmo Marconi' Cesena Italy

ISBN: (纸本)9789463968058

This paper presents the design of an energy autonomous radiofrequency identification (RFID) tag for communication and/or indoor localization. It makes use of the energy harvesting (EH) technique in the 915 MHz ultra-high frequency (UHF) band for the feeding of a system-on-a-chip (SoC), working also as a radio transceiver, in order to communicate data to a reader, or a gateway, at 2.4 GHz. First, the design of a multiple-input multiple-output (MIMO) antenna is described, with the dimensions of the whole tag limited to 47 × 100 mm2. Subsequently, the description and the performance of the UHF rectifier are reported, with its connection to the related power management unit (PMU). The PMU is then connected to a transceiver that can send information about its identification number (ID) and the received signal strength indicator (RSSI) without the need of a battery. © 2022 URSI.

关键词： MIMO systems

来源：评论

学校读者我要写书评

暂无评论

High Speed Photonic Neuromorphic Computing Using Recurrent Optical Spectrum Slicing Neural Networks

arXiv

引用

arXiv 2022年

作者： Sozos, K. Bogris, A. Bienstman, P. Sarantoglou, G. Deligiannidis, S. Mesaritakis, Charis Dept. of Informatics and Computer Engineering University of West Attica Aghiou Spiridonos Egaleo 12243 Greece Dept. of Information Technology Ghent University-Imec Technologiepark Zwijnaarde 126 Gent9052 Belgium Dept. Information and Communication Systems Engineering Engineering School University of the Aegean Palama 2 Samos83200 Greece

Neuromorphic Computing implemented in photonic hardware is one of the most promising routes towards achieving machine learning processing at the picosecond scale, with minimum power consumption. In this work, we present a new concept for realizing photonic recurrent neural networks and reservoir computing architectures with the use of recurrent optical spectrum slicing. This is accomplished through simple optical filters placed in an loop, where each filter processes a specific spectral slice of the incoming optical signal. The synaptic weights in our scheme are equivalent to filters' central frequencies and bandwidths. This new method for implementing recurrent neural processing in the photonic domain, which we call Recurrent Optical Spectrum Slicing Neural Networks (ROSS-NNs), is numerically evaluated on a demanding, industry-relevant task such as high baud rate optical signal equalization (>100 Gbaud), exhibiting ground-breaking performance. The performance enhancement surpasses state-of-the-art digital processing techniques by doubling the reach while minimizing complexity and power consumption by a factor of 10 compared to state-of-the-art solutions. In this respect, ROSS-NNs can pave the way for the implementation of ultra-efficient photonic hardware accelerators tailored for processing high-bandwidth (> 100 GHz) optical signals in optical communication and high-speed imaging applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical communication

来源：评论

学校读者我要写书评

暂无评论

Channel Attention HalfU-Net for Skin Lesion Segmentation

Channel Attention HalfU-Net for Skin Lesion Segmentation

引用

Advanced engineering and Technologies (ICONNIC), International Conference on

作者： Heri Prasetyo Abid Ammar Mahdy Abrar Dwi Fairuz Nadhif Jing-Ming Guo Chih-Hsien Hsia Department of Informatics Universitas Sebelas Maret (UNS) Surakarta Indonesia Department of Electrical Engineering National Taiwan Univ. Sci. Technolgy Taipei Taiwan Dept. Computer Sci. Infor. Engineering National Ilan University Yilan Taiwan

U-shaped architecture have gain popularity in the image segmentation task, especially for biomedical image. Regardless of its good performance, U-shaped architecture generally has huge number of parameters. Considering the resource constraint in actual medical environment, the needs lightweight model is essential. In order to overcome this problem, we propose Channel Attention HalfU-Net. The proposed method implement the existing medical image segmentation model named HalfU-Net. HalfU-Net can reduce the computational cost by simplify the decoder part of the UNet. Channel attention is assigned between ghost model for every stage in encoder part to capture more relevant feature. This leads to performance improvement in skin lesion segmentation problem. Despite that our model achieve state-ofthe-arts performance by surpassing U-Net in aspects of DSC and IoU metrics by 0.86% and 3.48% respectively, the number of parameter of our proposed method is more than 200 times less then U-Net.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Development of Image-Based Emotion Recognition using Convolutional Neural Networks 7

Development of Image-Based Emotion Recognition using Convolu...

引用

7th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2021

作者： Latif, Atiya Gunawan, Teddy Surya Kartiwi, Mira Arifin, Fatchul Mansor, Hasmah International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Universitas Negeri Yogyakarta Electronics and Informatics Eng. Dept. Yogyakarta55281 Indonesia

ISBN: (纸本)9781728175225

In recent years, artificial intelligence has been utilized in many applications. One of the prominent applications is detecting emotion from an image, which can help an intelligent automatic response system respond appropriately based on the user's emotion. This paper presented the development of emotion recognition using Convolutional Neural Networks (CNN) on image input. First, the extended Cohn-Kanade image emotion database was selected with five defined emotions: happy, sad, anger, fear, surprise, and neutral. Second, face detection and facial landmarks extraction was applied to the input image. Then, the AlexNet model is used as the selected deep learning architecture for transfer learning. Results showed that around 98.2% recognition accuracy could be achieved. Furthermore, precision, recall, and F1-score were evaluated, and it showed the effectiveness of our proposed algorithm. © 2021 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

A SHA-3 Co-Processor for IoT Applications

A SHA-3 Co-Processor for IoT Applications

引用

2020 Workshop on Communication Networks and Power Systems, WCNPS 2020

作者： Azevedo, Igor L.R. Nery, Alexandre S. Da Sena, Alexandre Dept. of Electrical Engineering University of Brasília Brazil Dept. of Informatics and Computer Science State University of Rio de Janeiro Brazil

ISBN: (纸本)9781728187914

The Secure Hash Algorithm 3 (SHA-3) is the latest member of the secure hash family of algorithms (SHA) on top of which several technologies are built upon, such as in Blockchain, security applications and protocols, including TLS, SSL, PGP, SSH, IPsec, and S/MIME. Due to tighter processing and power efficiency constraints often present in embedded applications, hardware architectures such as FPGAS (Field-Programmable Gate Array) can be employed to enable the design and implementation of efficient hardware accelerators. Thus, this work implements a SHA-3 Co-Processor in FPGA suitable for IoT applications. Performance, Circuit-area and Energy consumption results show that the Co-Processor is about 65% faster than the ARM Cortex-A9 processor that is also equipped in the FPGA chip, as well as in many IoT embedded systems. © 2020 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Converging for Security: Blockchain, Internet of Things, Artificial Intelligence - Why Not Together?

Converging for Security: Blockchain, Internet of Things, Art...

引用

International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Ahmad Anwar Zainuddin Dini Handayani Isyraq Haziq Mohd Ridza Siti Husna Abdul Rahman Saidatul Izyanie Kamarudin Khairul Zakwan Ahmad Mirza Darwisy Mahazir Muazzam Hazmi Sukhaimi Krishnan Subramaniam Mohamad Irfan Firdaus Basri Nurul Hanis Mohd Dhuzuki Dept. of Computer Science KICT International Islamic University Malaysia Gombak Malaysia Dept. of Computer Science KICT International Islamic University Malaysia Kuala Lumpur Malaysia Faculty of Computing Informatics Multimedia University Cyberjaya Selangor Malaysia Kolej Pengajian Pengkomputeran Informatik dan Matematik (KPPIM) Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Mechanical Engineering Mila University Nilai Negeri Sembilan Malaysia

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

Blockchain, renowned for its decentralized and secure nature, and Artificial Intelligence, the pinnacle of machine intelligence, stand as pillars of innovation. However, despite the recognition of their few successful collaborations, the interrelation among these triumvirates - Internet-of-Things (IoT), Blockchain, and Artificial Intelligence (AI) - still remains relatively unexplored terrain. Issues arise regarding their synergy: the challenges of integration, the potential improvements in existing technologies, and the careful consideration required before their unification. While evidence suggests their individual prowess, understanding their convergence and the implications thereof is crucial for shaping a technologically robust future. This paper delves into the complex interplay between IoT, Blockchain, and AI, navigating their relationship and exploring opportunities for enhancement. In addition, this paper will also look into several challenges and complexities hindering seamless integration.

关键词： Ethics Technological innovation Navigation Collaboration Blockchains Complexity theory Stakeholders

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：