检索结果-内蒙古大学图书馆

14th International Conference on Frontier Computing, FC 2024

作者： Andriyadi, Anggi Wijaya, Chandra Chen, Shih-Yen Huang, Ding-Hsiang Yang, Chao-Tung Department of Industrial Engineering and Enterprise Information Tunghai University Taichung City Taiwan Department of Computer Science Tunghai University Taichung City Taiwan Department of Information System Institut Informatika dan Bisnis Darmajaya Bandar Lampung Indonesia Department of Informatics Universitas Katolik Parahyangan Bandung Indonesia Research Center for Smart Sustainable Circular Economy Tunghai University Taichung City Taiwan

ISBN: (纸本)9789819623945

Fire incidents pose a significant security threat that requires attention from all stakeholders. For example, Taipei reported 893 fire incidents in 2023 alone. This study aims to enhance security surveillance by promptly detecting fire incidents through the development of a patrol robot. The robot captures sensor signals from the MQ-5 and transmits them via MQTT to the Raspberry Pi control center, delivering real-time reports of smoke and fire to fire officials. Additionally, equipped with a camera, the robot streams footage using RTSP to Jetson for fire and smoke object recognition using the You Look Only Once (YOLO) algorithm. Our robot effectively detected smoke and fire using the MQ5-Sensor and transmitted the signals via MQTT. Furthermore, it analyzed the visual stream to identify fire and smoke patterns with YOLOv8, resulting in a significant increase in accuracy. The evaluation of our model yielded impressive performance metrics, including a precision of 93%, a recall of 97%, a precision-recall of 97.90%, and an F1-Score of 97%. In the future studies we will integrate a mini camera and Large Language Model (LLM) to enable the robot to communicate with humans to warn of early fire incidents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smoke detectors

来源：评论

学校读者我要写书评

暂无评论

Synergy of machine learning and Mie-tronics: tailoring resonant response of meta-atom on demand 28

Synergy of machine learning and Mie-tronics: tailoring reson...

引用

Integrated Optics: Devices, Materials, and Technologies XXVIII 2024

作者： George, Renee C. Sedeh, Hooman Barati Lai, Fangxing Li, Hao Li, Wenhao Gao, Jiannan Sun, Jingbo Xiao, Shumin Kuznetsov, Arseniy Litchinitser, Natalia M. Department of Electrical and Computer Engineering Duke University DurhamNC United States Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen Shenzhen518055 China School of Materials Science and Engineering Tsinghua University Beijing100084 China A*STAR Institute Singapore Singapore

ISBN: (纸本)9781510670389

Optical metasurfaces are engineered 2D electromagnetic structures enabling flat optical elements with properties not readily found in nature. Their unit cells, meta-atoms, usually are represented by a set of electric and magnetic multipoles. All-dielectric-based metasurfaces have recently attracted significant attention owing to their virtually lossless transmission properties at optical frequencies. A majority of reported dielectric metamaterials are composed of relatively simple meta-atoms such as spheres, cubes, and cylinders, whose electromagnetic response is dominated by the electric dipole. However, magnetic dipoles and higher-order multipoles may enable new optical properties and functionalities, including directional scattering, beam steering, and new frequency generation. Despite impressive progress in the field of optical metamaterials and nanofabrication technologies, engineering meta-atoms that support such higher-order resonances is still challenging. Here, we demonstrate that designed titanium dioxide meta-atoms can enable dominant magnetic dipole response. We apply a machine-learning model to predict a meta-atom shape with a strong magnetic dipole resonant mode at the operating wavelength of 750 nm. Using finite-element-based numerical simulations implemented in COMSOL Multiphysics, we found that the optimized meta-atom is robust against experimental variations and conditions such as a non-perfectly collimated incident beam, nanofabrication inaccuracies, and an added substrate. The meta-atoms have been fabricated using two approaches, focused ion beam lithography and an electron beam lithography followed by reactive ion etching, and characterized using white light spectroscopy. To the best of our knowledge, this is the first experimental realization of a machine-learning-based optimization of a magnetic dipole mode at optical frequencies. © 2024 SPIE.

关键词： Atoms

来源：评论

学校读者我要写书评

暂无评论

A Development of 5G Wireless Sensor Network using Man Design 4

A Development of 5G Wireless Sensor Network using Man Design

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Vennila, C. Almusawi, Muntather Kumar, K. Sriram Priya, B. Rawat, Vidhi Amirthayogam, G. Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h TN India The Islamic University of Babylon College of Technical Engineering Department of Computers Techniques Engineering Babylon Iraq Karpagam Institute of Technology Department of Information Technology TN India Nehru Institute of Engineering and Technology Department of Computer Science and Business System Tamil Nadu T.M.Palayam India Ies College of Technology Department of Electrical & Electronics Engg Madhya Pradesh Bhopal India Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

This article investigates the key drivers influencing the design of the forthcoming Metropolitan Area Network (MAN), focusing on the integration of two pivotal elements: Cloud Radio Access Network (C-RAN) for 5G implementation and the deployment of edge computing. As we delve into these aspects, the article systematically uncovers the crucial architectural components essential for effectively addressing the challenges inherent in implementing C-RAN and edge computing. Through this strategic approach, the objective is to ensure a cost-effective solution that aligns with the specific requirements of these applications. The article aims to provide insights into the symbiotic relationship between CRAN and 5G, emphasizing their collaborative role in enhancing network efficiency. Simultaneously, it underscores the significance of edge computing, highlighting its potential to optimize data processing and reduce latency within the evolving landscape of metropolitan networks. This comprehensive exploration contributes to a nuanced understanding of the future MAN design, grounded in innovative solutions and considerations for seamless integration. © 2024 IEEE.

关键词： Radio access networks

来源：评论

学校读者我要写书评

暂无评论

Empowering Human-Machine Communication in Code Generation with NLP

Empowering Human-Machine Communication in Code Generation wi...

引用

Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： Upendra Singh Aswal Pappala Mohan Rao B. R. Supreeth G. Manikandan R. Srinivasan Mohit Tiwari Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Computer Science and System Engineering Andhra University College Of Engineering AU College Of Engineering Visakhapatnam AP Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Ullal Bangalore Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam India Department of Computer Science and Engineering St. Martins Engineering College Secunderabad Telangana Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Paschim Vihar Delhi

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

The symbiotic interaction between people and computers in the realm of software development is experiencing a fundamental transition, spurred by improvements in Natural Language Processing (NLP). This study investigates how natural language processing (NLP) is changing the software development process by facilitating better human-machine collaboration throughout the coding phase. Data collection, NLP model creation, and assessment are all part of the rigorous process used in our work, which yields important results. The possibility for improving code quality and efficiency while also democratizing code production is discussed in the context of a fictional situation. Ethical concerns are also highlighted, along with the need to minimize bias in NLP-driven code production. To the benefit of developers, organizations, and society at large, this study not only adds empirical insights but also imagines a future in which software development is more inclusive, efficient, and morally responsible. From improved natural language processing models to real-time feedback loops and multimodal techniques, the future of this transforming discipline promises to further influence the landscape of software development.

关键词： Symbiosis Codes Human-machine systems Computational modeling Production Organizations Market research

来源：评论

学校读者我要写书评

暂无评论

Number systems for Deep Neural Network Architectures: A Survey

arXiv

引用

arXiv 2023年

作者： Alsuhli, Ghada Sakellariou, Vasileios Saleh, Hani Al-Qutayri, Mahmoud Mohammad, Baker Stouraitis, Thanos Department of Electrical Engineering and Computer Science System on Chip Center Khalifa University United Arab Emirates

Deep neural networks (DNNs) have become an enabling component for a myriad of artificial intelligence applications. DNNs have shown sometimes superior performance, even compared to humans, in cases such as self-driving, health applications, etc. Because of their computational complexity, deploying DNNs in resource-constrained devices still faces many challenges related to computing complexity, energy efficiency, latency, and cost. To this end, several research directions are being pursued by both academia and industry to accelerate and efficiently implement DNNs. One important direction is determining the appropriate data representation for the massive amount of data involved in DNN processing. Using conventional number systems has been found to be sub-optimal for DNNs. Alternatively, a great body of research focuses on exploring suitable number systems. This article aims to provide a comprehensive survey and discussion about alternative number systems for more efficient representations of DNN data. Various number systems (conventional/unconventional) exploited for DNNs are discussed. The impact of these number systems on the performance and hardware design of DNNs is considered. In addition, this paper highlights the challenges associated with each number system and various solutions that are proposed for addressing them. The reader will be able to understand the importance of an efficient number system for DNN, learn about the widely used number systems for DNN, understand the trade-offs between various number systems, and consider various design aspects that affect the impact of number systems on DNN performance. In addition, the recent trends and related research opportunities will be highlighted. © 2023, CC BY.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

引用

computers, Materials & Continua 2022年第9期72卷 4509-4523页

作者： Elias Hossain Md.Shazzad Hossain Md.Selim Hossain Sabila Al Jannat Moontahina Huda Sameer Alsharif Osama S.Faragallah Mahmoud M.A.Eid Ahmed Nabih Zaki Rashed Department of Software Engineering Daffodil International UniversityDhaka 1207Bangladesh Department of Computing and Information System Daffodil International UniversityDhaka 1207Bangladesh Department of Computer Science&Engineering BRAC UniversityDhaka 1212Bangladesh Department of Information and Communication Engineering Bangladesh University of Professionals(BUP)Dhaka 1216Bangladesh Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenouf 32951Egypt

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep *** is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.

关键词： Brain cancer segmentation 3D U-Net ResNet50 Dice measurement ROC-AUC

来源：评论

学校读者我要写书评

暂无评论

Arabic Abstractive Summarization Using the Multilingual T5 Model

Arabic Abstractive Summarization Using the Multilingual T5 M...

引用

Computing and Informatics (ICCI), International Conference on

作者： Abdelrhman A. Karim Mohammed Usama Manar A. Ibrahim Youssef Hatem Waleed Wael Ahmad T. Mazrua Ghada K. El-Monayer Magy Elbanhawy Khaled Foad Ibrahim F. Moawad Artificial Intelligence Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer and System Department Faculty of Engineering Portsaid University Egypt Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables computers to comprehend, generate, and manipulate human language. Natural language processing can interrogate the data with natural language text or voice. Abstractive Text Summarization is based on the Natural Language Processing technique that tries to provide new and more concise textual summaries for huge texts. Artificial intelligence and deep learning techniques are used in abstractive summarization to examine the text's essential information and create a new summary that conveys the content more concisely and accurately. This type of summary differs from normal extractive summarizing in that it can generate new summaries beyond simply extracting key lines from the source text. This study presents an abstractive Arabic summarization based on the Multilingual T5 (MT5) model AASMT5 to address these concerns. This technique is based on deep neural networks and models like transformers, which have changed the ability to summarize text. This research has explored the various types of summarizations and highlighted the significance of two prominent techniques: abstractive and extractive summarization. This research describes a complete process for developing an Arabic abstractive summarization model using the MT5 architecture. Experiments on different datasets show that this model achieves state-of-the-art results across MT5.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Vacancy Modulated Analog Resistive Switching Memory Device Based on Bilayer of Zn@ZnO/ZnO for Neuromorphic Computing

Vacancy Modulated Analog Resistive Switching Memory Device B...

引用

IEEE International Conference on Electronics, Circuits and systems (ICECS)

作者： Muhammad Umair Khan Baker Mohammad Department of Electrical Engineering and Computer Science System on Chip Center Khalifa University Abu Dhabi UAE

This work proposed a vacancy-modulated analog resistive random-access memory (ReRAM) for neuromorphic computing, utilizing a structure composed of Ag/Zn@ZnO/ZnO/FTO. The resistive switching behavior can be modulated by the movement of oxygen vacancies under positive or negative voltages. This characteristic leads to the desired resistive switching behavior for neuromorphic computing. The Ag/Zn@ZnO/ZnO/FTO based analog ReRAM exhibits several desirable features, including a low operation current, forming-free operation, and self-compliance. The conductance change rate of the device can be adjusted efficiently by modifying the amplitudes and pulse intervals of spike pulses. The device can be used for short-time memory (STM) and long-term memory (LTM) memory operations with good retention time. These characteristics align with the requirements for next-generation ReRAM. The introduction of the Ag/Zn@ZnO/ZnO/FTO-based device structure paves the way for the future development of neuromorphic computing memory applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Efficient Hybrid PAPR Reduction for 5G NOMA-FBMC Waveforms

引用

computers, Materials & Continua 2021年第12期69卷 2967-2981页

作者： Arun Kumar Sivabalan Ambigapathy Mehedi Masud Emad Sami Jaha Sumit Chakravarty Kanchan Sengar Department of Electronics and Communication Engineering JECRC UniversityJaipur303905India Center for System Design Chennai Institute of TechnologyIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Computer Engineering Kennesaw State UniversityGAUSA

The article introduces Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC),known as hybrid waveform(NOMAFBMC),as two of the most deserving contenders for fifth-generation(5G)*** spectrum access and clampdown of spectrum outflow are unique characteristics of hybrid *** compare the spectral efficiency of Orthogonal Frequency Division Multiplexing(OFDM),FBMC,NOMA,and *** is seen that the hybrid waveform outperforms the existing *** to Average Power Ratio(PAPR)is regarded as a significant issue in multicarrier *** combination of Selective Mapping-Partial Transmit Sequence(SLM-PTS)is an effective way to minimize large peak power *** SLM,PTS,and SLM-PTS procedures are applied to the NOMA-FBMC *** hybrid structure is applied to the existing ***,the correlated factors like Bit Error Rate(BER)and Computational Overhead(CO)are studied and computed for these *** outcome of the work reveals that the NOMA-FBMC waveform coupled with the SLM-PTS algorithm offers superior performance as compared to the prevailing systems.

关键词： 5G NOMA-FBMC SLM-PTS PAPR BER OFDM

来源：评论

学校读者我要写书评

暂无评论

Cuckoo Optimized Convolution Support Vector Machine for Big Health Data Processing

引用

computers, Materials & Continua 2022年第11期73卷 3039-3055页

作者： Eatedal Alabdulkreem Jaber S.Alzahrani Majdy M.Eltahir Abdullah Mohamed Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha 62529Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many *** cloud-based IoT health providers have been described in the literature ***,there are a number of issues related to time consumed and overall network performance when it comes to big data *** the existing method,less performed optimization algorithms were used for optimizing the *** the proposed method,the Chaotic Cuckoo Optimization algorithm was used for feature selection,and Convolutional Support Vector Machine(CSVM)was *** research presents a method for analyzing healthcare information that uses in future *** major goal is to take a variety of data while improving efficiency and minimizing process *** suggested method employs a hybrid method that is divided into two *** the first stage,it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight,opposition-based learning,and distributor *** the second stage,CSVM is used which combines the benefits of convolutional neural network(CNN)and *** CSVM modifies CNN’s convolution product to learn hidden deep inside data *** improved economic flexibility,greater protection,greater analytics with confidentiality,and lower operating cost,the suggested approach is built on fog *** results of the experiments show that the suggested method can minimize the number of features in the datasets,enhances the accuracy by 82%,and decrease the time of the process.

关键词： Healthcare convolutional support vector machine feature selection chaotic cuckoo optimization accuracy processing time convolutional neural network

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：