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Real-Time signal Processing in IoT-based Embedded systems Using Hybrid AI-Enhanced Edge Computing

Real-Time Signal Processing in IoT-Based Embedded Systems Us...

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2024 international conference on Artificial Intelligence and Quantum Computation-based Sensor Applications, ICAIQSA 2024

作者： Rajan, Chozha P. Ahmed, A. Sarfaraz Divya, Vyshnava Nagendar, Y. Mohan, Ashwala Atheeswaran, Athiraja Bannari Amman Institute of Technology Department of AIDS Tamilnadu Erode India Sri Eshwar College of Engineering Department of CSE Tamil Nadu Coimbatore India Malla Reddy College of Engineering Department of CSE Hyderabad India SR University Department of School of Computer Science and Artificial Intelligence Telangana Warangal India Guru Nanak Institutions Technical Campus Department of ECE Telangana Ranga Reddy India Hyderabad India

ISBN: (纸本)9798331517953

The rapid expansion of the internet of Things (IoT) has necessitated real-time signal processing capabilities in embedded systems, but traditional cloud-based models often fall short in meeting the low-latency demands of these applications. To address this, we propose a hybrid AI-enhanced edge computing framework that integrates machine learning with conventional signal processing techniques, optimizing real-time processing at the edge. Our framework was tested in a smart factory environment, where edge devices equipped with lightweight AI models were employed for signal classification and processing tasks. The results demonstrate a 40% reduction in latency, with processing times as low as 50 milliseconds per signal, and an accuracy improvement of 15% over traditional methods, achieving a 92% accuracy rate in signal classification. These findings underscore the potential of hybrid AI at the edge to significantly enhance both the speed and accuracy of real-time signal processing in IoT-based embedded systems. This approach not only meets the critical latency requirements of modern IoT applications but also improves overall system reliability, making it a promising solution for applications in smart manufacturing, healthcare, and autonomous systems. Future work should explore further optimization of AI models for edge deployment and scalability across broader IoT ecosystems. © 2024 IEEE.

关键词： Smart manufacturing

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Ontology for Contextual Fake News Assessment based on Text and images 32

Ontology for Contextual Fake News Assessment based on Text a...

引用

32nd Euromicro international conference on Parallel, Distributed and Network-based Processing (PDP)

作者： Chandrasekaran, K. Kandasamy, A. Venkatesan, M. Prabhavathi, P. Gokuldhev, M. Aishwarya, C. NITK Surathkal CSE Dept Surathkal India NIT Puducherry CSE Dept Pondicherry India VIT Vellore CSE Dept Vellore India VelTech Univ CSE Dept Chennai India

ISBN: (纸本)9798350363074;9798350363081

The spread of false news on social networks is a major challenge in the digital age across various sectors, encompassing technology, politics, public health, and finance. This paper introduces an ontology-based method that combines text and image analysis to evaluate the accuracy of news stories in the context of social media. We investigate the role of social engineering tactics in crafting and dispersing fake news and advocate for a comprehensive multi-contextual perspective that covers content, source, social media, psychological, and impact aspects. Using OWL (Web Ontology Language), we present an ontology framework for assessing fake news, providing a structured approach to analyze text, visuals, audio, audience behavior, source credibility, and news propagation patterns. This framework serves as a foundation for advanced detection systems, contributing to the fight against digital misinformation.

关键词： Fake news Contextual Social Engineering OWL Ontology Data Analysis Audience Behavior Source Credibility Quantum Machine Learning Quantum Deep Learning

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A User-friendly AIoT-based Crop Recommendation system (UACR): concept and architecture

A User-friendly AIoT-Based Crop Recommendation system (UACR)...

引用

international IEEE conference on signal-image Technologies and internet-based System

作者： Dalhatu Muhammed Ehsan Ahvar Shohreh Ahvar Maria Trocan Institut Supérieur d’Electronique de Paris (ISEP) Paris France ESIEA Graduate Engineering School LDR Research Lab Paris France

Smart Agriculture plays an important role in providing foods and manufacturing products. It covers various food-related applications, from weed and pest identification to crop recommendation. Selecting an appropriate crop to grow for a farm directly affects the quality and quantity of products. This paper proposes a user-friendly crop recommendation system called UACR. The users only need to insert their farm location and the system can automatically obtain other necessary information (e.g., rain level, soil type) based on the requested location and recommends the best crop(s) for growing in the location. To support and implement the proposed system, an architecture based on Artificial Intelligence of Things (AIoT) technology, as one of the main enablers for smart agriculture, is also proposed.

关键词： Smart agriculture Rain Crops Focusing Soil Manufacturing internet of Things

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Passive Location Model of UAV based on Geometric Derivation and Genetic Algorithm 24

Passive Location Model of UAV Based on Geometric Derivation ...

引用

2024 2nd international conference on internet of Things and Cloud Computing technology, IoTCCT 2024

作者： Liao, Shihao Zhou, Pinlin Marine Engineering College Dalian Maritime University Dalian China School of Maritime Economics and Management Dalian Maritime University Dalian China

ISBN: (纸本)9798400710148

In this paper, a location method of bearing-only UAV based on passive location principle is proposed. First of all, on the basis of establishing the relative plane Cartesian coordinate system, a threepoint positioning model is established by geometric derivation, so that the UAV can locate passively when only the angle information is received. The reliability of the model is verified by Monte Carlo simulation. Secondly, by analyzing the relationship between the polar diameter variation range of the signal receiver and the circumferential angle, the minimum number of UAV is determined. In addition, the genetic algorithm with uniform mutation factor is used to optimize the solution, and the root mean square error between the actual position and the standard position of the UAV is minimized as the fitness function. Finally, through the iterative optimization method, the specific position adjustment scheme of UAV is obtained, and the effective positioning of UAV is realized. This study provides a theoretical reference for the actual UAV formation. © 2024 Copyright held by the owner/author(s).

关键词： Intelligent systems

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Content based image Retrieval System Using CNN based Deep Learning Models 2

Content Based Image Retrieval System Using CNN based Deep Le...

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2nd international conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Gautam, Giriraj Khanna, Anita Dept. of Electronics and Communication Engineering Guru Ghasidas Vishwavidyalaya Koni Bilaspur495009 India

Digital information search archives on the internet and other platforms have been an essential part of our daily lives. With the use of huge amounts of digital images, it is paramount to develop efficient solutions to retrieve images seamlessly. Due to inadequate textual description, the task of automated image retrieval is quite complicated. Therefore, retrieval of images by analysing their visual content is an exciting task and research-worthy. Text based image Search (TIBR) is time consuming as initially, every image has to be linked with a keyword or text to be extracted on query search. In the proposed system, an efficient algorithm for Content based image Retrieval (CBIR) using pre-trained CNN-based Deep Learning models to extract deep features of an image has been developed, which significantly increases the performance of the image retrieval process. Convolutional Neural Networks (CNN) has evolved as an efficient deep learning solution for the CBIR systems that work in image recognition applications. The results of retrieval are evaluated in terms of precision performance parameters. Emphasis has been given to the comparison of the efficiency of the system using two different pre-trained CNN-based Deep Learning models based on VGG16 and ResNet-50 architecture. Transfer learning enables us to develop robust and efficient working systems using pre-trained models. In the proposed system, pre-trained models have been used to derive features using a deep learning CNN network trained for a large image classification dataset imageNet. This approach outperforms many contemporary CBIR systems. As most CBIR systems depend on query images and as most users of such systems are non-professional, we have developed a user interface for the built prototype systems. The efficiency of the CBIR systems with pre-trained models VGG16, and ResNet-50 are compared for better image retrieval and reliability. The proposed system has addressed a few existing problems in CBIR system de

关键词： Convolutional neural networks

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Design of rhubarb processing control system based on internet of Things 4

Design of rhubarb processing control system based on Interne...

引用

4th international conference on Telecommunications, Optics, and Computer Science, TOCS 2023

作者： Sun, Lijuan Wei, Jiansheng Li, Xiangwei Zhao, Xiaoqiang Tang, Jiawei School of Computer Science and Artificial Intelligence Lanzhou Institute of Technology Lanzhou730000 China Li County Rhubarb technology Co. LTD Gansu province Li County Industrial Park Longnan Longnan730000 China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

In order to improve the processing quality of traditional Chinese medicine rhubarb and control the processing process of rhubarb in real time, a programmable logic control system with Ethernet and internet of things remote communication function was designed, and the corresponding programming software and configuration software were developed, which had important engineering application value for the transformation and upgrading of traditional processing equipment of rhubarb. In this paper, based on 5G Narrowband internet of Things (NBiot) technology and industrial Ethernet technology, a programmable logic control system with remote wireless communication function is designed, and the supporting programming software platform is developed to connect to the cloud wireless network. The research results were tested in Li County Rhuai technology Co., LTD. The rhuai production yield was increased by 30%, the processing quality was effectively improved, and the effectiveness of the rhuai processing process control was guaranteed. In addition, the results can provide reference for the research and development of intelligent control systems in traditional Chinese medicine preparation industry, and realize the large-scale and industrialized development of traditional Chinese medicine processing equipment. © 2024 SPIE.

关键词： internet of things

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Integration of UWB and BLE for Indoor Positioning based on Network Adjustment 25

Integration of UWB and BLE for Indoor Positioning Based on N...

引用

25th IEEE international conference on Mobile Data Management (IEEE MDM)

作者： Chen, Yu-Jung Lu, Eric Hsueh-Chan Natl Cheng Kung Univ Dept Geomat Tainan Taiwan

ISBN: (纸本)9798350374551;9798350374568

The development of Indoor Positioning systems (IPS) is being catalyzed by the growing influence of the internet of Things (IoT). In the field of indoor positioning, balancing low cost and high precision has always been a challenging task. Ultra-wideband (UWB) technology stands out for high precision but comes with higher costs, while Bluetooth Low Energy (BLE), on the other hand, is costeffective but faces challenges in signal stability. Therefore, this study explores the integration of BLE and UWB, presenting an innovative approach aimed at enhancing the accuracy of indoor positioning while maintaining cost-effectiveness. The proposed approach leverages BLE trilateration for initial solutions and optimizes coordinates through network adjustment using UWB distance observations between neighboring users, achieving more stable and accurate indoor positioning. The experiments conducted with varying user counts demonstrate that UWB adjustments outperform Bluetooth adjustments and trilateration, significantly improving positioning accuracy by up to 65% of trilateration. The results highlight the potential of this integrated approach to overcome challenges in indoor positioning systems, offering a more robust and precise solution.

关键词： Wireless Localization Indoor Positioning Network Adjustment UWB BLE

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A Differentiable Framework for image-based SAR Target Reconstruction with 3D Gaussian Splatting 2

A Differentiable Framework for Image-Based SAR Target Recons...

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2nd IEEE international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Liu, Zinan Niu, Shengren Qiu, Xiaolan Peng, Lingxiao Zhong, Lihua Ding, Chibiao Chinese Academy of Sciences The National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Beijing China Chinese Academy of Sciences Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Aerospace Information Research Institute Suzhou China Chinese Academy of Sciences Key Laboratory of Technology in Geospatial Information Processing and Application Systems Aerospace Information Research Institute Beijing China

ISBN: (纸本)9798331515669

image-based synthetic aperture radar (SAR) target three-dimensional (3D) reconstruction is an important application for extracting target information from high-resolution two-dimensional (2D) SAR images. However, due to the complex mechanism of SAR imaging, developing a data-driven, fully automated 3D reconstruction framework with explicit physical constraints is very difficult. Thus, this paper proposes a differentiable framework for image-based SAR target reconstruction with 3D Gaussian Splatting, which can automatically realize the 3D target reconstruction from multi-view SAR images. Specifically, the proposed differentiable framework accumulates and generates simulated SAR images according to the SAR imaging geometry and projection during the forward simulation stage. By calculating the loss between the simulated and ground truth SAR images and backpropagating, the spatial positions of the 3D Gaussians are continuously adjusted until convergence, generating the 3D point cloud model of the target. The experiment is conducted on the constructed T72 SAR image dataset and the reconstruction results demonstrate the effectiveness of the proposed framework. © 2024 IEEE.

关键词： 3D reconstruction

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Automatic Modulation Classification based on wavelet image and Convolutional Neural Network 8

Automatic Modulation Classification Based on wavelet Image a...

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8th international conference on Computer and Communication systems, ICCCS 2023

作者： Xiong, Bo Zhong, Yi Pu, Wei Zhang, Feng Southwest China Research Institute of Electronic Equipment Chengdu China

ISBN: (纸本)9781665456128

Automatic modulation recognition is an important fundamental technology in the field of wireless communication, and effective modulation identification of target signals is a necessary prerequisite for applications such as software radio and spectrum management. The complexity of modulation patterns and the increasing congestion of spectrum occupation bring great challenges to traditional classification methods. The authors propose an end-to-end signal modulation classification method based on wavelet image and convolutional neural network, which characterizes the intra-pulse signal based on continuous wavelet transform(CWT), and design a convolutional neural network to extract and classify the characteristics of signal wavelet time-frequency images. At the same time, a small sample dataset of 8 signal modulation types is generated, and the effectiveness of the proposed method is verified by combining data enhancement in 10 signal-to-noise ratio environments, and the classification accuracy is about 87% when the signal-to-noise ratio is 8dB. © 2023 IEEE.

关键词： Wavelet transforms

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Adaptive Dynamic Programming-based Feature Tracking Control for image Disturbances Visual Servoing Manipulators 9

Adaptive Dynamic Programming-Based Feature Tracking Control ...

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9th international conference on Control Science and systems Engineering, ICCSSE 2023

作者： Ren, Xiaolin Yan, Haoyu Ma, Bing Changchun University of Technology Department of Control Science and Engineering Changchun China

ISBN: (纸本)9798350339055

Aiming at the problem of feature point interference and noise in visual servoing (VS) control, an image feature tracking control method based on adaptive dynamic programming (ADP) is proposed. The output signal of the image sensor is used as the feedback signal of the VS manipulators system for closed-loop control. While the adaptive state observer is used to estimate the image disturbances in the VS model of the manipulator, and the cost function is improved according to the observed disturbance values. On the basis of ADP technology, a critic neural network (NN) was constructed to derive the Hamilton-Jacobi-Bellman (HJB) equation. According to Lyapunov theory, it is proved that the visual servoing manipulators system is asymptotically stable. Finally, numerical simulations are given to verify the effectiveness of the algorithm. © 2023 IEEE.

关键词： Manipulators

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