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Smart Irrigation Management System 16

Smart Irrigation Management System

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

作者： Sellami, Bassem El Fazziki, Abdelaziz Bennani, Mohamed Taha Ben Yahia, Sadok Univ Tunis El Manar Dept Comp Sci Fac Sci Tunis Tunisia Cadi Ayyad Univ Dept Comp Sci Comp Syst Engn Lab Marrakech Morocco Tallinn Univ Technol Dept Software Sci Tallinn Estonia

ISBN: (纸本)9781665464956

The significant problem in the area of sustainable development is the effective management of water resources. There are a number of approaches for resolving this issue in the literature, particularly in the agricultural sector, which serves as the primary consumer through irrigation. As a result, irrigation management is a crucial and cutting-edge discipline that has been the focus of several research and study projects to address the various activities, behaviors, and conflicts between the various users. This paper introduces an intelligent irrigation system based on smart sensors that can be used sparingly and affordably to monitor farms. By incorporating some connected electronic devices and other useful tools frequently used in the field of IoT, it calculates the water requirement of each farm based on the water loss caused by the process of evapotranspiration. Data gathered from a number of sensors placed across the plantation farm are used to determine the amount of water needed. It may be utilized by farmer organizations whose endowments and irrigation planning are determined according to the requirement and quantity of water available in the rural municipality. This project focuses on smart irrigation based on IoT. Each node in the system functions as an IoT device and incorporates a microprocessor with the integration of sensors, actuators, and valve modules. An irrigation management system simplifies the operation of each node and configuration settings for irrigation allow for the direct monitoring of environmental factors. The Penman model is used to get the daily evapotranspiration baseline and is used to calculate the quantity of water needed for irrigation. The suggested irrigation approach is anticipated to help reduce water usage and distribute it fairly without affecting crop productivity compared to the traditional irrigation system.

关键词： Smart Irrigation internet of Things Planning and scheduling Evapotranspiration Planification

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Indigenous IoT and Software Framework for Implementing Digital Twin Over Indian Region Towards Sustainable Development 1

Indigenous IoT and Software Framework for Implementing Digit...

引用

1st international conference on Computing, Sciences and Communications, ICCSC 2024

作者： Agarwal, Nikunj Singla, JaiG. Dhaka, Vijaypal Singh School of Computing and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur India Space Applications Centre ISRO Signal and Image Processing Area Gujarat Ahmedabad India

ISBN: (纸本)9798350353648

Digital Twin is a digital replication of physical things. This technology has been in works for over a decade, and has been implemented in various regions. This study shows the integration of internet of Things(IoT) with the Digital Twin technology seamlessly within the Indian ecosystem to lead towards sustainable development. The Digital Twin model utilizes the data collected through an indigenous IoT solution equipped with LoRa technology and Node MCU. However, with the diverse geographic properties of the Indian subcontinent, it poses a significant challenge to collect comprehensive data of the entirety of the region. This paper presents an approach to develop an indigenous and integrated solution to simplify the complexities of integrating data from the sensors spread across the region into one simple framework. This paper further present show this Digital Twin model can play a huge role in showcasing data received from indigenous IoT devices to nroduce more accurate and actionable insishts. © 2024 IEEE.

关键词： Sustainable city

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Energy Harvesting-enabled Cognitive Radio-internet of Things Using Machine Learning

Energy Harvesting-enabled Cognitive Radio-Internet of Things...

引用

IEEE international conference on Consumer Electronics (ICCE)

作者： Sipon Miah, Md Garcia Armada, Ana UC3M Dept Signal Theory & Commun Madrid 28911 Spain

ISBN: (纸本)9781665491303

The cognitive radio (CR) is a modern technology in cognitive radio-internet of things (CR-IoT) networks. In contrast, each CR-IoT user is unable to achieve both a better sensing gain, and an enhanced system sum rate in conventional energy detection technique based CR-IoT networks with the present energy harvesting (EH) and security threats due to under-utilized the reporting framework. For this reason, we proposed EH-enabled CR-IoT networks using machine learning (ML) algorithms in which each normal CR-IoT user is assisted by finite capacity battery systems and energy harvested. In this paper, Firstly, the proposed hybrid detection technique based on EH-enabled CRIoT networks using ML algorithms is separating the trusted (normal) and untrusted (malicious users) CR-IoT users where all untrusted CR-IoT users are not participating in spectrum sensing due to they degraded the performance like sensing gain and system sum rate;Secondly, the proposed scheme is utilized the reporting framework where only trusted CR-IoT users are obtained longer sensing time slot which enhanced the sensing gain, the EH, and the sum rate;and Finally, the simulation results show that the proposed hybrid scheme outperformed the conventional schemes in terms of security, sensing gain, EH, and system sum rate.

关键词： Energy detection p-norm detection spectrum sensing internet of things energy harvesting system sum rate machine learning

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A High Quality Secure Medical image Steganography Method 3

A High Quality Secure Medical Image Steganography Method

引用

3rd international conference on Computing and Information technology, ICCIT 2023

作者： Hameed, Mohamed Abdel Hassaballah, M. Bekhet, Saddam Narmatha, C. Kenk, Mourad A. Luxor University Faculty of Computers and Information Department of Computer Science Luxor Egypt Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Department of Computer Science AlKharj Saudi Arabia South Valley University Faculty of Commerce Qena Egypt University of Tabuk Faculty of Computers and Information Technology Tabuk City Saudi Arabia South Valley University Faculty of Computers and Artificial Intelligence Department of Computer Science Hurghada Egypt

ISBN: (纸本)9798350321487

The exchange of medical images over the internet has prompted substantial claims on the last years due to the presentation of cloud-based medical data systems according to various web services in the area of telemedicine applications. The preservation of confidential information has ever been a key indicator of the effectiveness of these systems. In this paper, an efficient data hiding technique is proposed for Digital Imaging and Communications in Medicine (DICOM) images hence security can be specified for the highly invaluable and private sensitive data of the patient. The proposed method is developed for DICOM images to embed Patient Record Information (PRI) for maintaining the fidelity of both the critical components of the image and private patient data. The proposed method is very effective in telemedicine applications as it can increase the security of the PRI during its transmission over the internet from different remote places. © 2023 IEEE.

关键词： Steganography

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Cross-modal image text retrieval based on Cross-optimization and Joint strategy

Cross-modal image text retrieval based on Cross-optimization...

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2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： Zheng, Wei Han, Na Hu, Yan Kang, Peipei Zhang, Yue Yang, Aqing Zhang, Lei School of Computer Science Guangdong Polytechnic Normal University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering East University of Heilongjiang China

ISBN: (纸本)9781510680425

In the fields of computer vision and natural language processing, cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been made by globally aligning images and sentences or locally aligning regions and words. However, most of these methods rely on attention mechanisms, which may be affected by noise and cannot balance the differences between modalities, resulting in suboptimal image-text alignment. In addition, training with attention mechanisms consumes a lot of computational resources, and the retrieval process is time-consuming. To address these challenges, we develop a novel multigranularity image-text alignment model, which we call DFVLM. First, we independently train the intra-modal encoder and cross-modal encoder to reduce interference between different modal encoders and better learn intra-modal and inter-modal information. Then, we propose a joint strategy to combine the intra-modal and inter-modal information to better capture the information between image and text. In addition, we introduce a hard negative pair-based method to train the performance of the intra-modal multi-granularity encoder without using attention mechanisms. Unlike existing methods, We treat image and text retrieval as a two-way process and delve into the intrinsic connections between the two matrices. We propose a cross-validation method to optimize the retrieval result based on common image-to-text alignment scenarios in daily life, i.e., during the image-to-text retrieval process, a text-to-image reverse verification is performed on the retrieved corresponding text. Through extensive qualitative experiments and analysis, our approach performs well on both Flickr30K and MSCOCO datasets and also significantly reduces the time required for testing. © 2024 SPIE.

关键词： Natural language processing systems

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MobileNet-V3-based sea-land segmentation for maritime search radars 15

MobileNet-V3-based sea-land segmentation for maritime search...

引用

15th international conference on signal Processing systems, ICSPS 2023

作者： Xue, Jian Ma, Manshan School of Communications and Information Engineering Xi’an University of Posts and Telecommunications China

ISBN: (纸本)9781510675056

The effective perception of marine detection scenarios is essential for maritime search pulse radar to detect and track maritime targets. However, the dynamically changing and complex marine environment makes it challenging for maritime search pulse radar to accurately perceive the sea and land regions. To improve the accuracy of sea-land region perception, and reduce the computation and complexity of algorithm of the network, this paper proposes a MobileNetV3-based maritime search pulse radar sea-land segmentation method. Firstly, a radar sea-land segmentation dataset based on PPI (Plan Position Indicator) is constructed using real measured data from different scenarios and sea conditions. Subsequently, the MobileNetV3 network is trained on this dataset to achieve sea-land segmentation for maritime search pulse radar. Experimental results demonstrate that the segmentation accuracy of the MobileNetV3-Lbased radar sea-land segmentation method surpasses its competitors. © 2024 SPIE.

关键词： image segmentation

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Accurate Shadow height measurement technology of the SAR image 2023

Accurate Shadow height measurement technology of the SAR ima...

引用

5th international Symposium on signal Processing systems, SSPS 2023

作者： Li, Lei Hong, Huiyu Nanjing Institute of Electronic Technology Nanjing210013 China

ISBN: (纸本)9798400700040

Aiming at the problem of three-dimensional target positioning in a single two-dimensional synthetic aperture radar image, a high-precision height measurement technology based on target shadow is proposed based on the results of conventional two-dimensional SAR imaging. Firstly, the height measurement error model is derived, and the influence of various parameters on the accuracy of target height estimation is quantitatively analyzed. On the basis of the precise measurement model of the grazing angle, a high-precision shadow height measurement method of SAR image is proposed, which effectively improves the target 3D positioning accuracy of SAR image and provides an effective way for the 3D precise attack of air to ground targets. Finally, the effectiveness of the method is verified by simulation and measured data. © 2023 ACM.

关键词： Synthetic aperture radar

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A New Deception Jamming signal Detection Technique based on YOLOv7 23

A New Deception Jamming Signal Detection Technique Based on ...

引用

23rd IEEE international conference on Communication technology, ICCT 2023

作者： Zhu, Xuan Wu, Hao He, Fangmin Meng, Jin School of Electrical Engineering and Automation Wuhan University Wuhan China Institute of Military Electrical Science and Technology Naval University of Engineering Wuhan China

ISBN: (纸本)9798350325959

In order to improve the jamming recognition and anti-jamming capability of cognitive intelligent radar in modern warfare, this paper proposes a radar deception jamming signal detection method based on YOLOv7. Firstly, four typical jamming signals including distance deception jamming (DDJ), dense false target jamming (DFTJ), interrupted sampling and repeating jamming (ISRJ), smart noise jamming (SNJ) and linear frequency modulation (LFM) signal commonly used in radar are obtained by digital signal processing simulation. Then, the LFM and four jamming signals are transformed into time-frequency spectrum by short-time Fourier transform (STFT) to construct the radar jamming signal image dataset. Finally, the YOLOv7 object detection algorithm is used to detect the jamming signals. The experimental results show that the detection accuracy (mAP) of the proposed method is 99.63%, which can accurately detect the radar deception jamming signal. © 2023 IEEE.

关键词： Cognitive intelligent radar jamming signal detection short-time Fourier transform (STFT) YOLOv7

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GAN-based Two Stage Network for De-occlusion Face image

GAN-Based Two Stage Network for De-occlusion Face Image

引用

2024 IEEE international conference on Consumer Electronics, ICCE 2024

作者： Lee, Dong Gyu Han, Dong Seog Kyungpook National Univ Electronic and Electrical Engineering Daegu Korea Republic of

ISBN: (纸本)9798350324136

In deep learning applications, facial recognition systems such as facial recognition, facial emotion recognition, facial-based criminal investigation, and facial-based driver monitoring have been researched. These applications show very high performance in recognition and analysis, but there is a problem that the performance is degraded when the face is covered. The purpose of this paper is to restore facial occlusion covered by obstruction. An image generation network is used to restore the face from object such as glasses, makeup, masks, and intentional obstruction. The image generation network recognizes facial occlusion in the input image and fills the area with as natural a look as possible. The proposed model is a three-stage model, and in the first step, the occluded part is recognized and then the occluded object is separated. In the second step, two generators are connected to generate an image from which the occluded object is removed. We used two discriminators to determine the overall appearance and the hidden part, and the resulting results affect the generator's learning compared to the correct answer image. The model's learning uses the original image and the occulded image dataset using CelebA and FFHQ. For the performance analysis of the model, we compared the performance of the proposed model with the comparative models using Fréchet inception distance (FID), SSIM and Peak signal to Noise ratio (PSNR) and the proposed model show 0.8 lower at FID and 0.012 higher at SSIM compared to other models. © 2024 IEEE.

关键词： Deep-learning Face occlusion GAN

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image Denoising based on Hybrid L0 and L1-norm Regularization 6

Image Denoising Based on Hybrid L0 and L1-norm Regularizatio...

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6th conference on Frontiers in Optical Imaging and technology: Novel Imaging systems

作者： Han, Xiaoyu Yang, Yannan Dong, Wende College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Tianjin100048 China Key Laboratory of Space Photoelectric Detection and Perception Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Nanjing211106 China

ISBN: (数字)9781510679719

ISBN: (纸本)9781510679702

In cases of insufficient lighting conditions, the obtained images of optical systems usually suffer from heavy noise, which subsequently has a negative impact on tasks like image segmentation, target detection, and edge extraction. image denoising requires preserving the integrity of original information while eliminating irrelevant data from the signal. Regularization is an effective way to improve the performance of denoising algorithms, it achieves this by introducing additional constraints to ensure stable solutions. In this paper, we propose a hybrid regularization method which is based on the weighted combination of the L0-norm and L1-norm of image gradients. In order to obtain reliable denoising results, we have also developed a highly efficient alternately minimization algorithm to solve the resulting complex optimization problem. The algorithm utilizes variable splitting and Lagrange multipliers to determine the optimal solution, effectively transforming the initial problem into a simple convex optimization problem and a quadratic optimization problem, which can be rapidly solved in frequency domain. In the end, we conducted experiments to prove the efficiency of the proposed method. The results show that it is stable, efficient and the quality of the denoised images is comparable to some state-of-the-art methods. © 2024 SPIE.

关键词： image denoising

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