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共 6129 条记录，以下是331-340 订阅

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Online Visual Localization Combining Space and Feature Distance Constraints 3

Online Visual Localization Combining Space and Feature Dista...

引用

3rd international conference on Computer Science, Electronic Information Engineering and Intelligent Control technology, CEI 2023

作者： Chen, Zequan Xu, Dong Zhang, Ming Liu, Yang Guangzhou Urban Planning & Design Survey Research Institute Guangzhou China

ISBN: (纸本)9798350372052

Visual Localization is widely used in autonomous driving, virtual reality, and autonomous robot applications. However, the existing visual localization solutions require a cumbersome sparse scene model and suffer from coarse place recognition, making inefficient and inaccurate localization. To resolve these challenges, we present a visual localization solution adopting an online local reconstruction method and a space and feature distance constraints (SFDC) place recognition strategy. Comprehensive experiments confirm that the SFDC improves place recognition precision by about 50% over the popular visual localization method. Besides, the proposed method can achieve faster localization results by performing local online reconstruction of the retrieved images than traditional methods. © 2023 IEEE.

关键词： robot applications

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Research on the Application of Spatiotemporal Feature Analysis in Radar Target Classification 24

Research on the Application of Spatiotemporal Feature Analys...

引用

3rd international conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Xie, Tingyao Xiao, Jiankang Qi, Yang Feng, Feng Feng, Yangqian Shi, Shuhai Shanghai Spaceflight Electronic and Communication Equipment Research Institute Shanghai China Shanghai Powerwin Scientists Technology Developing Co Ltd. Shanghai China

ISBN: (纸本)9798400710834

This paper explores the application of spatiotemporal feature analysis in radar target classification. Radar technology, as an important remote sensing tool, is widely used in both military and civilian fields. Target classification technology is crucial for enhancing the intelligence and target recognition capabilities of radar systems. This paper employs spatiotemporal feature analysis methods to thoroughly analyze the motion trajectories of targets in radar echoes. Traditional signal processing methods were used to extract detailed temporal features, spatial distribution characteristics, and reflection properties of the targets, including motion speed, acceleration, positional information, radar cross-section (RCS) values, height, and both mean and variance of RCS. Based on these features, an expert system was designed, which can effectively handle target classification tasks under multi-feature conditions, significantly improving classification accuracy and robustness. The results of this study not only theoretically enrich the application of spatiotemporal feature analysis methods in radar target classification but also provide in-depth technical support and guidance for practical applications. © 2024 Copyright held by the owner/author(s).

关键词： Radar cross section

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Comprehensive Review of AI, IoT, and ML in Enhancing Urban Mobility and Reducing Carbon Footprints 3

Comprehensive Review of AI, IoT, and ML in Enhancing Urban M...

引用

3rd international conference on Sustainable Mobility applications, Renewables and technology, SMART 2024

作者： Hossain, Mahmood Khalid, Hamad Rao, Avent Prakasa Lootah, Mohammad Al-Mohammedi, Salah Salim Khalaf Majeed, Salih Rashid School of Engineering Applied Science and Technology Canadian University Dubai City Walk Dubai United Arab Emirates

ISBN: (纸本)9798331532925

The rise in vehicle ownership and urbanization, which exacerbates traffic congestion and carbon emissions, are significant barriers to sustainable urban development. This study investigates how artificial intelligence (AI), machine learning (ML), and the internet of things (IoT) could enhance urban transport systems, reduce traffic, and lower urban regions' carbon footprints. The study examines how urban infrastructures can be made more efficient and flexible through the use of technologies including demand-responsive transport systems, integrated mobility solutions, predictive maintenance, and personalized trip planning. A recent study of educators, students, and other participants at Canadian University Dubai (CUD) found that people are becoming increasingly accepting of AI-driven solutions that reduce fuel consumption and travel time. These technologies have promise even if there are still challenges to be solved, such as the requirement for ongoing validation in dynamic urban contexts and the reliance on historical data. The study concludes with recommendations for integrating AI, IoT, and ML to develop responsive urban mobility solutions and emphasizes the importance of practical implementations to realize these benefits. © 2024 IEEE

关键词： Traffic congestion

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5G Wireless Communication : Major Challenges And Opportunities 3

5G Wireless Communication : Major Challenges And Opportuniti...

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3rd international conference for Innovation in technology, INOCON 2024

作者： Atri, Khushi Chauhan, Rahul Bhatt, Chandradeep Devliyal, Swati Lande, Ayapal Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Computer Science and Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350381931

Numerous nations have either already implemented or will shortly implement fourth-generation wireless communication systems. Nevertheless, despite the proliferation of wireless mobile devices and services, several issues remain unaddressed by 4G technology, including spectrum scarcity and excessive energy usage. Since new wireless applications constantly increase the need for high data rates and mobility, wireless system designers have begun researching fifth generation wireless systems, which are anticipated to be used beyond 2020. The difficulties in deploying 5G networks, such as spectrum distribution, security issues, and infrastructure needs, are methodically examined in this article. It also highlights how 5G technology has the potential to disrupt industries and improve quality of life by examining the revolutionary prospects it presents across a range of sectors, including healthcare, transportation, and smart cities. © 2024 IEEE.

关键词： 5G mobile communication systems

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Smart Surveillance Object Detection System 3

Smart Surveillance Object Detection System

引用

3rd international conference on Artificial intelligence For Internet of Things, AIIoT 2024

作者： Chaudhary, Gaurav Pathak, Shreya Choudhary, Vikas Chauhan, Love Tripathi, Abhishek N School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350372120

This paper presents an integrated Internet of Things based approach to smart surveillance and urban parking management systems, emphasizing robust object detection and precise parking occupancy monitoring. Our solution adopts cost-effective hardware like the ESP32 CAM module, leveraging its built-in camera and powerful processor for real-time image processing. For surveillance applications, we implement a custom object detection model built with OpenCV, a renowned Python library. This model, finely-tuned for efficient person detection, runs directly on the ESP32 CAM, eliminating the need for external processing units. Additionally, the ESP32 CAM's Wi-Fi connectivity enables seamless integration with existing networks, simplifying deployment. This integrated approach, combining the cost-effectiveness of ESP32 CAM modules with the image processing capabilities of OpenCV, offers a comprehensive solution for smart surveillance and urban parking management. By utilizing readily available hardware and open-source software, the system promotes widespread adoption and affordability, making it a valuable tool for creating smarter and more sustainable cities. © 2024 IEEE.

关键词： Computer vision

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MIMO Microstrip Patch Antenna using Coaxial Feed for 5G mmWave 3

MIMO Microstrip Patch Antenna using Coaxial Feed for 5G mmWa...

引用

3rd international conference on Applied Artificial intelligence and Computing, ICAAIC 2024

作者： Josephine, D.Diana Akila, I.S. Pratheek, Paveen Poonia, Aditya Rajadurai, S. Coimbatore Institute of Technology Department of Electronic and Communication Engineering Tamil Nadu Coimbatore641014 India Electronic and Communication Engineering Coimbatore Institute of Technology Tamil Nadu Coimbatore641014 India

ISBN: (纸本)9798350375190

The design and analysis of a Multiple Input Multiple Output (MIMO) microstrip patch antenna operating at 28 GHz, catering to the millimeter-wave (mmWave) spectrum for 5G communication systems. The proposed antenna employs a coaxial feed mechanism for efficient power transfer. The design process involves careful consideration of substrate properties, patch geometry, and coaxial feed positioning to achieve optimal performance. The antenna's characteristics, including impedance matching, radiation pattern, and gain, are systematically investigated through electromagnetic simulations in ADS. The MIMO configuration is tailored to meet the demands of 5G applications, ensuring minimal mutual coupling and desirable correlation between antenna elements. Fabrication details and measurement results validate the simulated performance, highlighting the antenna's suitability for 28 GHz 5G mmWave communication systems. The presented MIMO microstrip patch antenna offers a promising solution for achieving high data rates and reliable communication in next-generation wireless networks. © 2024 IEEE.

关键词： Microstrip antennas

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Development of Garuda 45 An Android Gamification Application for Historical Education 3

Development of Garuda 45 An Android Gamification Application...

引用

3rd international conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Christofher Hermawan, Feri Arizona, Ken Indiana Yanfi Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Binus Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350367492

Gamification has emerged as a powerful strategy for enhancing educational programs by boosting engagement and motivation. This study introduces Garuda 45, an Android-based application designed to teach the history of Indonesian independence through gamified learning. Utilizing the Rapid Application Development (RAD) methodology, the application was developed iteratively with continuous user feedback to ensure it met educational needs effectively. Garuda 45 integrates gamification elements such as levels, medals, challenges, and points to create an engaging and interactive learning environment. Survey results showed a significant increase in users' interest and comprehension of historical content, highlighting gamification's potential to revolutionize traditional history education. This application aims to make history more engaging while enhancing retention and understanding through active user participation, demonstrating the technical and educational advancements achievable through modern software development practices. © 2024 IEEE.

关键词： Mobile applications

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Enhanced Malware Image Classification through Ensemble model 3

Enhanced Malware Image Classification through Ensemble model

引用

3rd international conference on Applied Artificial intelligence and Computing, ICAAIC 2024

作者： Kotha, Eshwar Palivela, Lakshmi Harika Begum, Afruza Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

Classification of malwares and viruses is a very important work in the cyber security field to protect the computers and systems from threats and attacks. In this paper, we proposed a novel approach for the classification of the malwares by combining 2 models EfficientNet B0 and EffiCBNet models. Here we have used Malimg dataset which is available in Kaggle and it contains around 9,339 malware images and these malwares are from 25 different families, for evaluating the models. EffcientNetB0 is a Convolution Neural Network (CNN) model, which showed great and high results on various image classification works. We used transfer learning to fine tune the EfficientNet model;whereas the proposed EffiCBNet model is a deep learning model which contains of convolution layers and batch normalization layers. The results have shown that the proposed model has given the highest accuracy of 99.70 on the dataset we have used, exceeding all other models. The proposed model is the combination of EfficientNet and EffiCBNet, which showed great results for the classification. The proposed model can also be used in real time applications for detecting malware attacks and threats © 2024 IEEE.

关键词： Image classification

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Artificial intelligence-programmable robot based on the Aelos Blockly platform 3

Artificial intelligence-programmable robot based on the Aelo...

引用

3rd international conference on Control and Intelligent robotics, ICCIR 2023

作者： Qiu, Shuxia Wang, Bin Dong, Mengnan Chizhou Vocational and Technical College No. 389 Jianshe West Road Anhui Province Chizhou247000 China

ISBN: (纸本)9781510671867

This paper presents a human intelligent editable program robot based on aelos platform. The robot uses artificial intelligence technology and features editable programs that allow users to program and customize them according to their own needs. Through the analysis of the Aelos Blockly platform, this paper proves that this robot is highly flexible and applicable, and can be applied to many fields, such as home, education, entertainment, etc. This paper also introduces the design principle and technical details of the robot, and gives some application examples to prove its practical effect. This study provides an important theoretical and practical basis for the development of the AI field, and also provides important enlightenment for the in-depth research and application of the AI technology in the future. © 2023 SPIE.

关键词： Intelligent robots

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd international conference for Innovation in technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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