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检索条件"任意字段=International Conference on Signal Image Technology and Internet Based Systems"

共 7564 条记录，以下是61-70 订阅

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PLC signal Level based Traffic Light System Using RPi: An Experimental Setup 3

PLC Signal Level Based Traffic Light System Using RPi: An Ex...

引用

3rd international conference for Advancement in technology, ICONAT 2024

作者： Kumawat, Ashok Kumar Manipal University Jaipur Dept. of Mechatronics Engineering Jaipur India

ISBN: (纸本)9798350354171

It is necessary to regularly enhance the traffic light system's control using the newest technology. The PLC-based systems that are now in use make it difficult to adopt modern technologies like artificial intelligence, machine learning, and the internet of things etc. This work aims to convert the logic level signal based on PLC to a logic level signal based on Raspberry Pi. A raspberry pi is used as the control unit in the development of an experimental four direction-based traffic light prototype. Two different types of logic level converters are used in this system to create signals that are compatible with both 24 V traffic light system and Raspberry Pi. Python programming on the Raspberry Pi is used to control the entire system. © 2024 IEEE.

关键词： Traffic signals

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The Analysis of Network Course in Intelligent image Acquisition Education System based on Deep Learning 4

The Analysis of Network Course in Intelligent Image Acquisit...

引用

4th international signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Ge, Qianqian Wei, Cuncun Wu, Dongyan Zhejiang Business Technology Institute Zhejiang Ningbo315012 China

ISBN: (纸本)9798331528676

The internet, big data, and intelligent robots have transformed online education. This study introduces an Enhanced Convolutional Neural Network (CNN+) model optimized with refined weight initialization and strategic activation functions for image recognition. The CNN+ model achieves 99.5% accuracy on MNIST and 75.2% on CIFAR-10, with the Sigmoid-ReLU hybrid boosting performance to 99.2% and 72.3% respectively. These optimizations underscore the importance of strategic CNN architecture choices in enhancing performance, supporting advancements in online education platforms. The CNN+ model represents a significant breakthrough in image recognition for online education. ©2024 IEEE.

关键词： Adversarial machine learning

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Smart Agriculture: CNN-based systems for Early Detection and Diagnosis of Plant Diseases and Pests 2

Smart Agriculture: CNN-based Systems for Early Detection and...

引用

2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Nishitha, Panchareddy Sarika, Tuniki Navya, Soleti Peneti, Subhashini Rajeshwari, Dablikar Vijayakumari, E. N. MLR Inst Technol Hyderabad Dept Comp Sci & Informat Technol Hyderabad India CMR Engn Coll Dept Informat Technol Hyderabad India MLR Inst Technol Dept Comp Sci & Engn Hyderabad India

ISBN: (纸本)9798331540661;9798331540678

Pests impact agricultural production, prompting the need for efficient disease detection in plants. Traditional methods, reliant on manual observation, are time- consuming and imprecise. It uses the Convolutional Neural Network (CNN) for prediction of diseases. The CNNs enhance precision, offering farmers a more effective and streamlined method for disease detection, potentially transforming agricultural practicesUsing transfer learning and pre-trained models, the system successfully extracts information from photographs submitted via a user-friendly website. The CNN based algorithm analyzes these photos using powerful image pre-processing techniques, giving farmers and individuals precise insights into recognized diseases or pest infestations. Plant photos obtained with cellphones are processed using CNN, a specialized image recognition system. A CNN is an effective method in the AI, which scans the image and produces the maximum correct output. It is primarily used in image recognition tasks, where it processes input in the form of pixels. The website design not only allows for easy image submission but also provides results and probable therapy recommendations. This comprehensive solution, which combines cutting-edge CNN technology, transfer learning, and an easy-to-use web interface, enables users to proactively protect their crops, resulting in a more resilient and sustainable agricultural ecosystem.

关键词： Convolutional Neural Network (CNN) Disease Detection Transfer Learning Agricultural Production image Pre-Processing

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Research on the Application of image Preprocessing technology based on Computer Deep Learning in Wireless Communication systems 4

Research on the Application of Image Preprocessing Technolog...

引用

4th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Xu, Xiaoqing School of Information Engineering Eurasia University Shaanxi Xi'an China

ISBN: (纸本)9798350359589

This paper proposes a radio frequency signal identification method based on deep neural network. First, this article abstracts the radio frequency signal into a plane diagram and converts the radio frequency signal identification problem into object detection in image recognition. Then, it is combined with the latest international artificial intelligence technology to further enhance the intelligence of radio frequency signals. At the same time, the ability to identify complex electromagnetic interference is enhanced. Then this paper proposes a method to identify interference signals through radio spectrum images based on deep learning technology, and designs experiments to verify the identification accuracy of this method. The effectiveness of this method was verified through field sampling of a total of 5,432 samples of 15 categories. The proposed method can correctly identify the wave pattern and frequency characteristics of radio frequency signals, with an accuracy rate of 87.14%. The simulation results show that this method is universal and has good identification accuracy for interference at other frequency points. © 2024 IEEE.

关键词： convolutional neural network deep network image object detection radio frequency learning Radio wave recognition

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Deep Learning Enhanced Channel Estimation for the internet of Vehicles OFDM systems

Deep Learning Enhanced Channel Estimation for the Internet o...

引用

2024 IEEE international conference on Computational Electromagnetics, ICCEM 2024

作者： Liu, Zheng Pan, Xiaomin School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798350383317

The deep learning enhanced channel estimation is presented for the OFDM communication in the internet of Vehicles (10V). Using the image enhancement and denoising, the channel responses at unknown positions are predicted under a specified signal-to-Noise Ratio (SNR). Simulation experiments were conducted in predefined urban road *** results show that this method shows significant improvement compared to traditional methods in the minimum mean square error (MMSE) of channel estimation, with an average decrease of 0.03 in a series of experiments with different SNRs. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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An AI based System for Smart Detection of Leaf Diseases using Machine Learning Techniques 10

An AI based System for Smart Detection of Leaf Diseases usin...

引用

10th international conference on Communication and signal Processing, ICCSP 2024

作者： Sharmila, S. Jeyalakshmi, V. Anna University College of Engineering Chennai600025 India Anna University College of Engineering Department of Electronics and Communications Engineering Chennai600025 India

ISBN: (纸本)9798350353068

An internet of things is a system of connecting various smart devices which are able to gather and transfer data over the network. The expansion of internet of Things can be used in smart agriculture to boost the quality and quantity of the crop. Leaf disease prediction is an essential step used in agriculture for screening early diagnosis of disease and provide pesticide at early stage, it is an application used for indoor plantation or terrace gardening users. By using AI and IOT technology, we can able to create a digital system for agriculture which can help the users to take right decision on correct situation in advance. Here, we proposed a system which helps to recognize the leaf disease. In this work, the Mobile camera interface is used to cover the effective leaf image. Quality of the image is improved by Digital image processing techniques using MATLAB. The temperature and soil moisture sensor are placed at various location, which helps to identify the dryness of leaf and for monitoring the soil quality respectively. ARDUINO is a controller device which helps to control those sensors. This system can robotically sense leaf disease at early stage as well as it provides required pesticide to the user through Mobile App. This project is to engage AI and IOT technology in agriculture for more accuracy in detection of leaf disease at early stage. © 2024 IEEE.

关键词： internet of things

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Per-Channel image Super Resolution 16

Per-Channel Image Super Resolution

引用

16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Kamal, George Ghantous, Milad German Univ Cairo Dept Media Engn & Technol Fac Engn Cairo Egypt

ISBN: (纸本)9781665464956

Single image Super Resolution (SISR) is one of the essential tasks in the image processing field, which aims to enhance the resolution of images. With the aid of Deep Learning (DL), SISR recently has made significant improvements and achieved promising results. This paper introduces a novel training technique, called Per-Channel training. This novel training technique proved to obtain the best results in terms of the common Super Resolution (SR) metrics across all testing datasets used, reaching a maximum average Peak signal-to-Noise Ratio (PSNR) of 39.34 dB and Structural Similarity Index Measure (SSIM) of 0.9714 in one of the test datasets.

关键词： image processing super resolution image super resolution per channel deep learning

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Research on transmission delay calibration of new navigation signal 4

Research on transmission delay calibration of new navigation...

引用

4th international conference on signal image Processing and Communication, ICSIPC 2024

作者： Li, Yisong Zhang, Yu Kang, Dingwen Xi’an Institute of Space Radio Technology Xi’an710100 China

ISBN: (纸本)9781510682467

New navigation signal broadcasts from low-orbit communications satellites. In comparison with the GNSS signal, these new navigation signals are designed based on Doppler measurements and share the transmission channel with the communication signal. The Doppler frequency shift simulation analysis of Iridium signal received by the ground is analyzed. The Iridium STL signal structure is analyzed. Delay calibration of a new navigation signal transmission is an important task for satellite design. This paper proposed a delay calibration method of the new navigation signal. And the delay calibration error is analyzed. © 2024 SPIE.

关键词： Satellite communication systems

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Electromagnetic signal Augmentation of Pantograph Arcs Using an Improved DCGAN 12

Electromagnetic Signal Augmentation of Pantograph Arcs Using...

引用

12th international conference on Information systems and Computing technology

作者： Zhang, Chunhui Liang, Yanyue Gao, Zhiwei Zhao, Li Shijiazhuang Inst Technol Sch Artificial Intelligence Shijiazhuang Hebei Peoples R China Shijiazhuang Inst Technol Sch Internet Applicat Technol Shijiazhuang Hebei Peoples R China Shijiazhuang Tiedao Univ Hebei Prov Key Lab Electromagnet Environm Effecs Shijiazhuang Hebei Peoples R China Shijiazhuang Inst Technol Shijiazhuang Hebei Peoples R China Shijiazhuang Tiedao Univ Sch Traff & Transportat Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9798350379860;9798350379877

In response to the insufficient sample issue in the electromagnetic signal data of high-speed railway pantograph arcs, this study proposes an improved Deep Convolutional Generative Adversarial Network (DCGAN) to augment image datasets. By optimizing the network structure, the proposed method significantly improves the quality of generated images and introduces a lightweight design that reduces the computational demands of the model, making it more suitable for resource-constrained environments. After 5,000 iterations, the proposed method achieves a Frechet Inception Distance (FID) score of 36.33, compared to 45.54 for the original DCGAN and 50.21 for the WGAN. Moreover, the Inception Score (IS) of the proposed method is 1.91 +/- 0.11, outperforming the original DCGAN (1.81 +/- 0.23) and WGAN (1.77 +/- 0.04), further demonstrating its effectiveness. This method provides a new technical reference for research on electromagnetic signals in high-speed railways.

关键词： Electromagnetic signal DCGAN image augmentation lightweight network

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Acoustic User Authentication with Smartphones

Acoustic User Authentication with Smartphones

引用

IEEE international conference on Microwaves, Communications, Antennas, Biomedical Engineering and Electronic systems (COMCAS)

作者： Gevirtzer, Neta Ben Yosef, Ofir Eilam, Alon Technion Israel Inst Technol Andrew & Erna Viterbi Fac Elect & Comp Engn Signal & Image Proc Lab SIPL Haifa Israel

ISBN: (纸本)9798350348194;9798350348187

This paper describes a work on an acoustic user authentication system using smartphones. The system implements two-factor authentication for Windows workstations, where the authentication procedure, including locking and unlocking the workstation is transparent to the user. Since workstations and smartphones have built-in microphones and speakers, the system does not require additional hardware. The uniqueness of the solution is being based on acoustic signals. These signals are transmitted by the user's smartphone and received by the workstation microphone. The system is "pure play acoustic" since no wiring or radio transmission is used. The system configuration supports multiple users in the same area. Eavesdropping prevention is provided by sequentially generated random one-time keys. Acoustic communication can be applied either in the audible range or beyond the human hearing range depending on the sampling rate of the smartphone and the workstation.

关键词： Acoustic Android signal Processing Smartphone Two Factor Authentication Windows

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