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

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IoT-based Agriculture Farm Monitoring System and Development of Plant Leaf Disease Detection Using Deep Neural Network

IoT-Based Agriculture Farm Monitoring System and Development...

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signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), international conference on

作者： Tanvir Islam Mim Ivtiaz Ahkam Fahim Nymul Islam Mizanur Rahman Yasin Rahman Nirjhor Rouf Tanbir Ibne Anowar Shuvra Mondal Dept. of Electrical and Electronic Engineering American International University-Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

Throughout history, agriculture or farming has played a vital role in human civilization by providing people's fundamental necessities and significant contributions to a country's economy. Although small-scale smart farming has started in a few nations, it has yet to be adopted scientifically globally. The rapid growth of the world population along with urbanization has rendered modern agriculture increasingly essential day by day. Towards multi-domain modernization, the internet of Things (IoT) technology is a developing paradigm that aims to link many smart physical parameters in smart agriculture. In this paper, an IoT-based smart agricultural farm monitoring system prototype has been proposed which has been designed considering different environmental parameters. Additionally, image processing-based plant disease detection model has been developed in two different methods. The system incorporates various sensors, actuators, and a microcontroller to monitor and control temperature, humidity, soil moisture, and rainfall. It also includes a water pump motor for automated irrigation where a Wi-Fi module allows remote monitoring and controlled by a developed smartphone app. To validate, the proposed hardware model was simulated using Electronic Design Automation (EDA) tools. There have been notable outcomes observed on temperature, humidity, soil moisture, and other monitoring parameters data reading from LCD. Also, deep learning-based image processing algorithm has been proposed using Feature extraction and Convolutional Neural Network (CNN) methods and achieved a high accuracy of about 96%-98% in plant disease detection. The overall proposed system offers improved resource utilization and smart agricultural practices for expanded functionalities and long-term sustainability to ensure food security for the growing population.

关键词： Temperature sensors Temperature measurement Plant diseases Accuracy Soil moisture Sociology Humidity

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Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning

Rogue Emitter Detection Using Hybrid Network of Denoising Au...

引用

ieee international conference on Communications (ICC)

作者： Zeyang Yang Xue Fu Guan Gui Yun Lin Haris Gacanin Hikmet Sari Fumiyuki Adachi College of Telecommunications and Information Engineering NJUPT Nanjing China College of Information and Communication Engineering Harbin Engineering University Harbin China Institute for Communication Technologies and Embedded Systems RWTH Aachen University Aachen Germany International Research Institute of Disaster Science (IRIDeS Tohoku University Sendai Japan

Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications. Existing deep learning-based RED methods have been proposed under friendly environments. However, these methods perform unstably under low signal-to-noise ratio (SNR) scenarios. To address this problem, we propose a robust RED method, which is a hybrid network of denoising autoencoder and deep metric learning (DML). Specifically, denoising autoencoder is adopted to mitigate noise interference and then improve its robustness under low SNR while DML plays an important role to improve the feature discrimination. Several typical experiments are conducted to evaluate the proposed RED method on an automatic dependent surveillance-Broadcast dataset and an ieee 802.11 dataset and also to compare it with existing RED methods. Simulation results show that the proposed method achieves better RED performance and higher noise robustness with more discriminative semantic vectors than existing methods.

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An Improved YOLOv8n Algorithm for Small Object Detection in Aerial images

An Improved YOLOv8n Algorithm for Small Object Detection in ...

引用

ieee international conference on signal and image Processing (ICSIP)

作者： Qinming Wu Xuemei Li Changhan Xu Jingming Zhu School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

The UAV aerial images often contain densely distributed and diverse small target objects, posing challenges for small object detection. The difficulty lies in the interference from background environments, low pixel occupancy, low resolution of the target objects, and susceptibility to occlusion. To address these issues, this paper proposes a small object detection algorithm based on improved YOLOv8n. Firstly, to enhance the focus on small objects, a detection layer targeting large-scale objects was removed from the original YOLOv8n model, and another detection layer for smaller-scale objects was added. Secondly, introducing the recursive gated convolutions to enhance the network's capability to extract feature information. Finally, multiple improved CBAM modules are inserted into the backbone network to reduce attention on non-target features, thereby strengthening the network's focus on small objects in aerial images. In the experimental results on the VisDrone2019 dataset, our model outperformed the baseline YOLOv8n model by achieving a 3.4% increase in mAP@0.50, while also achieving a significant 36.8% reduction in parameters. In summary, the improved YOLOv8n algorithm shows notable enhancement in small object detection in aerial images.

关键词： image resolution Convolution Semantics Object detection Interference Logic gates Feature extraction

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PolSAR image Classification based on Resblock Combined with Attention Model 6

PolSAR Image Classification Based on Resblock Combined with ...

引用

6th international conference on signal and image Processing, ICSIP 2021

作者： Yang, Zhongao Zhang, Qingyuan Chen, Weidong Chen, Chang University of Science and Technology of China School of Microelectronics Anhui Hefei China University of Science and Technology of China Department of EEIS Anhui Hefei China

ISBN: (纸本)9780738133737

Deep learning technology can effectively extract the rich polarimetric information contained in polarimetric Synthetic aperture radar (PolSAR) data. However, the lack of labeled samples limits its performance. The majority of methods use iterative algorithms or need preprocessing on dataset to expand the labeled sample are time consuming. In this paper, an end-to-end method which can better utilize mid-level features and the input polarimetric features of CNN network is proposed. First, high-dimensional polarimetric features are extracted from raw data and sent into the CNN network to produce sufficient polarimetric information, then the Convolutional Block Attention Module (CBAM) is not only inserted into the network layer but also after the input data to achieve better classification performance and accelerate network convergence under tiny amounts of labeled samples. Meanwhile, a feature selection method is proposed based on the channel attention module to reduce the memory consumption and computational complexity while maintaining performance. The experiments tested on Flevoland dataset prove the effectiveness of proposed method compared with traditional method. © 2021 ieee.

关键词： image classification

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Fan-Net: Fourier-based Adaptive Normalization for Cross-Domain Stroke Lesion Segmentation

Fan-Net: Fourier-Based Adaptive Normalization for Cross-Doma...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Weiyi Yu Yiming Lei Hongming Shan Institute of Science and Technology for Brain-Inspired Intelligence Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

Since stroke is the main cause of various cerebrovascular diseases, deep learning-based stroke lesion segmentation on magnetic resonance (MR) images has attracted considerable attention. However, the existing methods often neglect the domain shift among MR images collected from different sites, which has limited performance improvement. To address this problem, we intend to change style information without affecting high-level semantics via adaptively changing the low-frequency amplitude components of the Fourier transform so as to enhance model robustness to varying domains. Thus, we propose a novel FAN-Net, a U-Net–based segmentation network incorporated with a Fourier-based adaptive normalization (FAN) and a domain classifier with a gradient reversal layer. The FAN module is tailored for learning adaptive affine parameters for the amplitude components of different domains, which can dynamically normalize the style information of source images. Then, the domain classifier provides domain-agnostic knowledge to endow FAN with strong domain generalizability. The experimental results on the ATLAS dataset, which consists of MR images from 9 sites, show the superior performance of the proposed FAN-Net compared with baseline methods.

关键词： image segmentation Fans Semantics Neural networks Magnetic resonance Stroke (medical condition) signal processing

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Application of Computer 3D image Vision Algorithm in Intelligent image Recognition System

Application of Computer 3D Image Vision Algorithm in Intelli...

引用

ieee international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Yuan Li Xin Yu Modern Finance Industry School Shandong Institute of Commerce and Technology Jinan Shandong China

In this paper, the 3D space imaging model of machine vision is constructed. Starting from the traditional machine vision image processing algorithm flow, the image denoising process and target tracking process are optimized. The method uses the camera to collect the image and video information of the measured object, and transmits it to the controller. The controller corrects the signal obtained by the wireless sensor in the database to reproduce the position of the measured object and the 3D image. A real-time tracking method of motion trajectory based on computer vision is presented. The object autonomous capture, 3D position and motion trajectory tracking. Simulation experiments show that this method is quite different from conventional image processing methods. This method has the advantages of small computation, fast running speed and good real-time performance. It meets the needs of embedded image processing.

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Detection technology for Dim and Small Targets based on Forward-Looking Sonar images

Detection Technology for Dim and Small Targets Based on Forw...

引用

ieee international conference on Information Communication and signal Processing (ICICSP)

作者： Jingwen Ma Tian Zhou Quan Li Xiaoyang Yu College of Underwater Acoustic Engineering Harbin Engineering University Harbin China National Key Laboratory of Underwater Acoustic Technology Key Laboratory of Marine Information Acquisition and Security College of Underwater Acoustic Engineering Harbin Engineering University Harbin China Nanhai Institute of Harbin Engineering University Sanya China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Due to the complex and changeable characteristics of the underwater environment, the low contrast between the weak target and the background in the forward-looking sonar (FLS) image makes conventional target detection algorithms ineffective at detecting weak targets. This paper proposes a high-precision target detection method that effectively combines the k-means clustering algorithm with the optimized Average Absolute Gray difference (AAGD) algorithm to detect weak targets in a high-intensity background noise environment. The proposed method introduces a multidirectional gradient composite window and adaptive threshold to the AAGD algorithm, overcoming the performance attenuation associated with high-intensity sharp edges. By processing the acquisition FLS of the water tank and the lake experiments, the results show that compared with a number of conventional detection algorithms, the algorithm in this paper is less affected by high background noise and can effectively capture more weak targets, confirming the significant performance improvement of the algorithm in weak target detection.

关键词： Water Storage management signal processing algorithms Clustering algorithms Sonar Object detection signal processing Lakes Background noise Sonar detection

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Traffic Adaptive Transmission Schemes for the internet of Things 4

Traffic Adaptive Transmission Schemes for the Internet of Th...

引用

4th international conference on Communications, signal Processing, and their Applications, ICCSPA 2020

作者： Ferdowsy, Chowdhury Saleha Bouida, Zied Ibnkahla, Mohamed Systems and Computer Engineering Department Carleton University Ottawa Canada

ISBN: (纸本)9781728165356

The growing popularity of the internet of Things’ (IoT) applications comes with new challenges for wireless communications. Indeed, wireless transmission systems should more efficiently support heterogeneous traffic from diverse types of information sources. In this paper, we propose a set of traffic-oriented transmission schemes for a massive MIMO system that adapts the number of antennas based on three types of IoT traffic (i) energy sensitive, (ii) throughput sensitive, and (iii) highly reliable traffic. We jointly consider the uplink and downlink transmission for every IoT traffic and our energy efficient model reveals the optimum number of antennas that ensure each traffic’s Quality of Service (QoS) when communicating with a certain number of IoT nodes. Numerical results are shown in terms of average transmit power, spectral efficiency, average area throughput and energy efficiency. The results demonstrate that the performance is improved with the number of nodes which ensures the scalability of the IoT network. © 2020 ieee

关键词： Quality of service

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Implementation of a Deep Learning Technique for Detection of Objects on Roads for Autonomous Vehicles

Implementation of a Deep Learning Technique for Detection of...

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international conference on Applied Electromagnetics, signal Processing and Communication (AESPC)

作者： Kakarla. Deepti S. Aruna Deepti Ch. Neetu K.R. Deepthi Dept of ECE Vasavi college of Engineering Hyderabad India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

Autonomous driving and vehicle safety things profoundly depend on object detection to guarantee precise an robust right-time performance under different environmental conditions. The effectiveness of this systems depends on precise and efficient processing algorithms. The manuscript aim is to contrast the correctness of three notable image processing algorithms in the context of auto driving: Convolutional Neural Networks (CNN), Retina Net, and You Only Look Once v8 (YOLOv8). The assessment is performed using the Roboflow and kaggle dataset. The first algorithm under assessment is the Convolutional Neural Networks(CNN), Retina Net, and You Only Look Once v8 (YOLOv8). Each algorithm's unique approach to object detection is reviewed, observing their strengths and weaknesses in accurately identifying things in complex road situations. A comparative analysis is performed using the Robo flow and Kaggle dataset, covering various real-world contexts. The algorithms are evaluated based on correctness, considering occurrences with and without occlusion and truncation. Computation time,a vital aspect for real-time applications, is also considered. The precision-recall curve is applied as a metric to determine the algorithms' overall performance. Retina Net, with its focal loss mechanism, is reviewed for its capability to manage occluded and truncated things and its influence on overall correctness. YOLOv8 emerges as a standout performer, displaying superior correctness in discovering challenging road target things under complex situations and adverse weather conditions. Its right-time deployment capabilities, combined with high correctness, position it as a compelling choice for autonomous driving systems. In conclusion, this comparative analysis shines a light on the strengths and deficiencies of notable image processing algorithms for object discovery in autonomous driving systems. YOLOv8 stands outas the most accurate and efficient algorithm, excelling in challenging real-

关键词： YOLO Accuracy Roads image processing Simulation Vehicle safety Retina Real-time systems Convolutional neural networks Autonomous vehicles

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Advanced Phishing Website Detection Techniques in internet of Things Using Machine Learning

Advanced Phishing Website Detection Techniques in Internet o...

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Mobile Networks and Wireless Communications (ICMNWC), ieee international conference on

作者： Gaddam Lakshmi Perumalla Swetha Department of CSE Trinity College of Engineering and Technology Peddapally India Department of CSE Jawaharlal Nehru Technological University Hyderabad India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

There is a serious risk to information privacy and security in the internet of Things (IoT) ecosystem from phishing attacks. Phishing assaults are futile attempts to fool people into disclosing their login credentials or infecting the beacon's computer with malware. This research focuses on identifying phishing websites by using feature extraction, which modifies raw data, image files as a general usage context, and numerical features that operate with machine learning techniques. Phishing attacks are increasingly being conducted mostly through emails. The most important thing in this situation is to recognize phishing websites. Using social media, text messaging, or spam, the attacker(s) creates a website by mimicking reputable websites, and then distributes the URL(s) to their intended audience. An attempt has been made to broaden the present scope of phishing website detection through a number of review studies. The reader gains a better understanding of the many strategies, tactics, advantages, and disadvantages of phishing website detection techniques from this review, along with the relative efficacy of the used algorithms. Subsequent investigations must to take into account the extra information suggested by this study, including typical paperwork along with performance reporting. Researchers attempting to confirm previous findings and evaluate the relative merits of recently presented technologies vs. existing systems may find this survey to be helpful.

关键词： Surveys Wireless communication Uniform resource locators Privacy Reviews Social networking (online) Phishing Machine learning Feature extraction internet of Things

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