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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8124 条记录，以下是371-380 订阅

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Burning Gas sensor Module Adapter Development and Test Operation Results processing 5

Burning Gas Sensor Module Adapter Development and Test Opera...

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5th international Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2023

作者： Ivantsov, Andrey A. Mironova, Elizaveta A. Ryakhina, Mariya Yu. Joint Stock Company Scientific-Production Enterprise Delta Moscow Russia Bauman Moscow State Technical University Special Machinery Department Moscow Russia

ISBN: (纸本)9798350399523

this paper considers the possibility of import substitution and technical characteristics improvement of the sensor for the gas analyzer and the creation of a module adapter for the domestic producted gas sensor prototype IoffeGAS42. the conducted research shows good prospects for the use of domestic element base for the design of small-sized gas analyzers. the practically obtained transfer function of the optical gas sensor takes into account the temperature drift. Analysis of the data obtained as a result of the tests makes it possible to estimate the expected accuracy of measurements that can be provided by non-dispersive infrared gas sensors of various configurations when measuring the ambient temperature. Novel sensor temperature calibration method was introduced. the results of the development of the module adapter prototype as a part of the dimensional product prototype and the analysis of the test results are presented. © 2023 IEEE.

关键词： Chemical sensors

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5th IEEE international conference on Advanced Information and Communication Technologies, AICT 2023 - Proceedings

5th IEEE International Conference on Advanced Information an...

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5th IEEE international conference on Advanced Information and Communication Technologies, AICT 2023

ISBN: (纸本)9798350372571

the proceedings contain 49 papers. the topics discussed include: disaster recovery solution for on-premises infrastructure using Proxmox backup server;a method of encoding video information to increase its reliability in an information and telecommunications network;prediction of states of information and communication systems using machine learning;Wi-Fi repeater influence on wireless access;method of selective video segment processing for intelligent video image quality enhancement technologies;investigating the efficiency of tournament selection operator in genetic algorithm for solving TSP;an adaptive spectrum time assignment in OFDM datacenter optical networks;big data analysis for startup of supporting Ukraine internet tourism;and AR intelligent real-time method for cultural heritage object recognition.

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Energy Harvesting Integrated sensor Node Architecture for Sustainable IoT networks 5th

Energy Harvesting Integrated Sensor Node Architecture for S...

引用

5th international conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Gopikrishnan, S. Kokila, M. Rivera, Marco Wheeler, Patrick School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Power Electronics Machines and Control PEMC Research Group University of Nottingham Nottingham United Kingdom Universidad de Talca Curicó Chile

ISBN: (纸本)9789819735228

Internet of things (IoT) is a life changing technology which is build on various devices which are connected to the Internet. Wireless sensor nodes (WSN) is one of such devices which plays a major role in deploying IoT in large scale of real-time even monitoring applications. But the energy limitations of those sensor nodes limits the features of IoT networks. Hence improving the energy limitations in WSN plays a vital role in last decade. this chapter presents the possibilities of enhance the energy limitations of sensor nodes with renewable energy resources. the proposed integrated architecture of this proposed model discusses the implementation of sensor node with flexible solar energy harvester module, multi mode communication capability and multi sensor data module. the special contribution in this chapter includes the hardware specification with budget analysis, sleep-awake strategy to effective use of energy and maximum possible data collection from the environment. the novelty and improvement of the proposed model has been proved with mathematical model as well as the real-time measurements from the prototype. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Solar energy

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A CNN-Based Optical Music Recognition Method 5

A CNN-Based Optical Music Recognition Method

引用

5th international conference on Computers, Information processing and Advanced Education, CIPAE 2024

作者： Chen, Jing Gao, Weixiang Zhang, Kai School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798331527662

Optical Music Recognition (OMR) technology is an important means of music digitalization in the future. Today's society is developing in the direction of digitalization, virtualization, and intelligence, and the traditional music field is also deeply affected by it. In this paper, the OMR system mainly includes four parts: the preprocessing of music score images, the localization and deletion of spectral lines, the extraction and recognition of note motifs, and the reconstruction of music information. Finally, Pyqt5 is used to develop a complete system that is convenient for the input of score images, viewing descore images, text information, and playing audio to realize the conversion of scores. the extraction and recognition of note basis uses the direction gradient histogram and Convolutional Neural networks (CNN) algorithm to train a model with high accuracy. the local connection, weight sharing, and pooling operation of CNN can effectively reduce the complexity of the network, reduce the number of training parameters, make the model have a certain degree of invariability to translation, distortion, and zoom, and have strong robustness and fault tolerance, and also be easy to train and optimize. © 2024 IEEE.

关键词： Convolutional neural networks

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EDiTOK: Energy-Efficient Distributed Transmission Optimization-Based Kalman-Zlib Approaches for Monitoring Smart Farming Irrigation in Internet of things networks 1

引用

5th international conference on Communication and Intelligent Systems (ICCIS)

作者： Hasan, Zahraa Yaseen Idrees, Ali Kadhum Univ Babylon Dept Software Babylon Iraq

ISBN: (数字)9789819720538

ISBN: (纸本)9789819720521;9789819720538

Water scarcity is a major global problem and directly impacts agriculture. Both growth in the population and the need for food exacerbate this issue. therefore, mitigating water scarcity in irrigation systems is one of the most important challenges to be faced in the current era, for smart irrigation systems based on the critical function of Internet of things (IoT) important role in optimizing water use in agriculture and landscaping. this article proposes an energy-efficient distributed transmission optimization for monitoring smart farming irrigation based on the Kalman-Zlib approaches (EDiTOK) in IoT networks. the EDiTOK approach operates at the sensor device level. this approach involves a prediction method to determine whether or not to transmit the most recent set of data readings depending on the similarity threshold value and then implement a lossless compression method to decrease the size of a given data set before transmitting them to the gateway node. Numerous experiments utilize actual sensor data from farming fields. the results demonstrate that the proposed EDiTOK approach achieved satisfactory results compared to other proposed methods regarding energy use, the quantity of sent readings, the proportion of data reduction, and the accuracy of the data.

关键词： IoT Agriculture precision Kalman filter Lossless compression Energy efficiency Data reduction

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Explainable Computer Vision Techniques and Deep Transfer Learning for Tomato Leaf Disease Diagnosis 5

Explainable Computer Vision Techniques and Deep Transfer Lea...

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5th international conference on Image processing and Capsule networks, ICIPCN 2024

作者： Drake, Vvubya Henry, Senyonjo Namuwaya, Winfred Kayongo, Nsereko Lule, Emmanuel Marvin, Ggaliwango Makerere University Department of Computer Science Uganda Makerere University Department of Networks Uganda

ISBN: (纸本)9798350367171

With an increase in demand for tomatoes at both local and export markets, there is growth in commercial tomato growing. Because of this, tomatoes are among widely grown vegetables all over Uganda. But farmers are battling the emergency of tomato leaf diseases like bacterial spot, tomato curl virus and late blight. If these diseases are are not detected early and measures for their prevention taken, they affect both the quantity and quality of the produce. Farmers relied on traditional methods of using their naked eyes or advise from agricultural experts and prominent farmers for detecting tomato leaf diseases. However, the procedure is expensive, time consuming and less accurate, which might be dangerous in most cases. In recent studies, deep learning has greatly proven to have the potential to detect these diseases in their early stages. this enables farmers cure these diseases as soon as possible, reducing on the losses. But due to the multilayer nonlinear structure of deep learning models, they are often criticised as being non-transparent, and their prediction not traceable by humans. In this study, we integrated explainable computer vision techniques and deep transfer learning to bring about explainability and interpretability of deep learning models. We used five pretrained models: Vision Transformers, EfficientNetBO, ResNet50, VGG16 and VGG19, on a plant village dataset to detect nine various types of tomato leaf diseases, achieving accuracies of 98.50%, 97.50%, 90.50%, 86.70%, and 88.20%, respectively. Vision Transformers demonstrating the best overall performance, due to its capability to grasp global information within images, transcending the limitations of local feature extraction of Convolutional Neural *** address the black box limitation of deep learning models, Explainable Artificial Intelligence (XAI) techniques: Layerwise Relevance Backward Propagation (LRP), Grad-CAM and Shaply Additive Explanations (SHAP) were implemented to generated exp

关键词： Fruits

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Design of a high-precision current source system for tunable laser driving 5

Design of a high-precision current source system for tunable...

引用

2023 5th international conference on Energy, Power and Grid, ICEPG 2023

作者： Gui, Peng Zhao, Chuang Zhao, Lei Liu, Wenyang Chen, Zitian Mu, Cheng Zhang, Yuhuan Beijing Spacecraft China Technology Corporation Beijing100094 China Control Engineering Aerospace Science and Beijing Space Power Conversion Research Center Beijing100094 China

Fiber Bragg Grating (FBG), which is an Optical Fiber sensor with high sensitivity, light weight, and strong anti-interference ability, can realize the measurement of physical quantities, such as temperature and strain. the sensor network formed by FBG sensors is through wavelength division multiplexing, which greatly reduced the difficulty of sensor wiring. therefore, FBG sensors are widely used in sensing fields such as aerospace, bridges and tunnels. As a wavelength-modulated Optical Fiber sensor, FBG's sensing accuracy depends not only on the performance of the laser, but also on the stability of the driving current. In this paper, a high-precision current source system for laser S7500 modulation drive is designed, with a resolution of output current is higher than 2uA. and the laser S7500 is continuously linearly scanned at 20pm intervals in the wavelength range of 1527nm to 1567nm. the wavelength accuracy of the output optical signal is better than 2pm. the test results prove that the system has the advantages of high modulation speed, good stability and high reliability, and can be applied to high-precision FBG sensor networks. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： sensor networks

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CSGNet: A network with attention mechanism for automatic modulation recognition 5

CSGNet: A network with attention mechanism for automatic mod...

引用

5th international conference on Signal processing and Computer Science, SPCS 2024

作者： Li, Xianyi School of Control Science and Engineering Shandong University Shandong Jinan250002 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In this paper, a new network called CSGNet is introduced for automatic modulation recognition, designed to improve recognition accuracy in complex electromagnetic environments. this innovative network leverages the strengths of both Convolutional Neural networks (CNN) and Gate Recurrent Units (GRU) while mitigating their inherent weaknesses. Additionally, it incorporates a Self-Attention mechanism to further enhance its perceptual abilities. the primary advantage of this method is its superior recognition accuracy under low SNR conditions compared to traditional methods. the process starts by extracting basic I/Q data from the RadioML 2018.01a dataset as input. Subsequently, the CSGNet model performs an end-to-end modulation recognition task. the results demonstrate that this method excels at multiple SNR levels, highlighting the significant potential of deep learning in solving automatic modulation recognition challenges. this study underscores the effectiveness of integrating CNN, GRU, and Self-Attention mechanisms in enhancing the robustness and accuracy of modulation recognition systems. Furthermore, it demonstrates how deep learning techniques can be effectively applied to complex signal processing tasks. the findings suggest that CSGNet could be a valuable tool for improving communication systems, especially in challenging environments with low SNR. © 2025 SPIE.

关键词： Modulation Signal to noise ratio Feature extraction Deep learning Matrices Data modeling Machine learning Data processing Neural networks Quadrature amplitude modulation

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A Predictive Maintenance Platform for a Conveyor Motor sensor System Using Recurrent Neural networks 5th

A Predictive Maintenance Platform for a Conveyor Motor Senso...

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5th international conference on Neural Computing for Advanced Applications (NCAA)

作者： Kiangala, Kahiomba Sonia Wang, Zenghui Univ South Africa UNISA Dept Elect & Smart Syst Engn ZA-1709 Johannesburg Gauteng South Africa

ISBN: (纸本)9789819770007;9789819770014

Since the effusion of Industry 4.0 (I40) and smart manufacturing, predictive maintenance (PdM) has become critical to prevent severe system breakdowns and costly production downtime in various industries. Several state-of-the-art Artificial Intelligence (AI) approaches, such as machine learning models (ML), empower the PdM design concept to produce more accurate outcomes. In this study, we propose the development of a PdM platform that effectively detects future possible warnings and failures in the vibration speed sensor of a conveyor motor. We build the platform backend using recurrent neural networks (RNN) regression ML models to predict vibration speed values and their trends. We train our RNN models using univariate time-series historical vibration sensor data recorded over the years. the experimental results demonstrate the robustness of our platform built on RNN models compared to other traditional regression machine learning models such as Extreme Gradient Boosting (XGboost). We compare the effectiveness of the PdM platform with two RNN models: a basic RNN model and a long short-term memory (LSTM) model. Unlike other regression PdM systems focusing on predicting devices Remaining Useful Life (RUL), our PdM platform forecasts and generates new vibration sensor data based on the desired future time range. It also pinpoints set warning and failure values and indicates when these faults are more likely to occur. Factories can utilize this feature for analytics purposes.

关键词： Conveyor Motor sensor Predictive Maintenance (PdM) Recurrent Neural networks (RNN) Regression Models time-series data

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Skin Cancer Detection Using A Multi-Scale AI Deep Learning Approach 5

Skin Cancer Detection Using A Multi-Scale AI Deep Learning A...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Appavu, Narenthirakumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350356236

Skin cancer occurrences are increasing, and melanoma is the deadliest type of the disease. Preventing the illness from spreading to other regions of the body requires early identification. In this work, an automated technique for identifying skin cancer without the need for physician assistance is presented. the suggested method for detecting melanoma lesions makes use of transfer learning. To reduce lighting effects and noise, the acquired photos are first pre-processed. By creating more training examples, image augmentation - a crucial deep learning technique - is then used to improve the dataset and raise the training process' efficacy. then, using transfer learning, a Convolutional Neural Network (CNN) is built using pre-learnt weights from the ImageNet dataset. the network is then adjusted to prioritise the detection of melanoma and differentiate it from benign tumours. Each of the three main phases in the method's processing of image data focusses on the central area of the picture where the suspicious lesion is located. the results from these processes are aggregated and used to train a fully connected neural network. the suggested solution outperformed other cutting-edge techniques, as seen by the 90.09% accuracy attained in testing on a collection of photos. through concentrating on the questionable skin region at different phases, the method offers a more precise and trustworthy diagnosis. © 2025 IEEE.

关键词： Convolutional neural networks

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