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DopeNet: Range-Doppler Radar-based UAV Detection Using Convolutional Neural Network 14

DopeNet: Range-Doppler Radar-based UAV Detection Using Convo...

引用

14th international conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Aouto, Ali Jun, Taesoo Lee, Jae-Min Kim, Dong-Seong Kumoh National Institute of Technology Networked Systems Lab. Dept. of It Convergence Engineering Gumi Korea Republic of

ISBN: (纸本)9798350335385

The proliferation of commercial unmanned aerial vehicles (UAVs) of various sizes and shapes, equipped with cameras and even signal sabotage devices, has raised concerns regarding privacy and safety. Some websites even offer weapons that can be attached to drones, adding to the security threats. As a result, researchers have been motivated to develop an intelligent system that can be integrated into surveillance systems to classify unauthorized UAVs that are flying in restricted areas. In this paper, we propose a convolutional neural network (CNN) for UAV detection based on Radar image data. The Radar system, called Real Doppler RAD-DAR (Radar with Digital Array Receiver), is a range-doppler system developed by the Microwave and Radar Group. We construct and analyze the CNN by adjusting its hyper-parameters using the RAD-DAR dataset. Our simulation results show that setting the number of filters to 32 results in the best time-wise accuracy. The network achieved an accuracy of 97.63%, which is higher than other benchmark image classifiers. Additionally, we conducted an ablation study to investigate and validate the contribution of each part of the neural network. © 2023 IEEE.

关键词： image classification

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MEVSS: Modulo Encryption based Visual Secret Sharing Scheme for Securing Visual Content 1

引用

4th international conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2022

作者： Saini, Parul Kumar, Krishan Kashid, Shamal Negi, Alok Computer Science and Engineering National Institute of Technology Uttarakhand India

ISBN: (数字)9783031303968

ISBN: (纸本)9783031303951

In recent years, there has been a rapid development in the digital world of image communication over unsecured networks. Many domains, including military, medical, government, and education, require transmitting sensitive data through images. Hence, it raises the primary concern of its secrecy with the advancement of technologies. Traditional methods such as Advanced Encryption Standard (AES) and Data Encryption Standard (DES) have been widely used for image security. However, key management is the main concern in protecting image data. This work uses Visual Secret Sharing Scheme (VSS) or Visual Cryptography (VC) to handle the above mentioned issue. Modulo encryption-based VSS scheme (MEVSS) is proposed to encrypt the secret image shared among n number of users. The secret image is recovered loss-less by XORing n shares followed by modulo decryption. Experimental results show that the proposed technique outperforms the exiting techniques Binary tree-based Secret Sharing Scheme and RSA-based VSS in Unified average changing intensity (UACI) and Number of Pixel Change Rate (NPCR), While, the proposed model outperforms the existing techniques in term of Pixel to signal Noise Ratio (PSNR). Moreover, it is faster than traditional techniques as it works directly on color image without converting it into another form like binary or grayscale. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cryptography

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A Novel Transfer Learning-based Classification Method for Painting Defect Detection of Balance Beam of Port Machinery 5

A Novel Transfer Learning-Based Classification Method for Pa...

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5th international conference on Intelligent Control, Measurement and signal Processing, ICMSP 2023

作者： Kuang, Yahao Wu, Fengqi Liu, Long Logistics Engineering College Shanghai Maritime University Shanghai China Shanghai Institute of Special Equipment Inspection and Technical Research Shanghai China

ISBN: (纸本)9798350336030

A novel transfer learning-based classification method for steel structure painting defects recognition is designed, which is used to solve the problem of identifying painting defects in balance beams of port machinery. This paper proposes an AlexNet model based on transfer learning. Firstly, the weight of the original pre-training model is fine-tuned, and then the last full connection layer (FCL) in the AlexNet model is replaced. Finally, the target image data set is taken as the new input, and the weight of the model is fine-tuned through the back propagation mode, so as to realize the model migration. The experimental results show that the success rate of classification with and without defects can reach more than 93%;the success rate of point, line and surface defect classification reaches more than 88%;the accuracy rate for orange peel test reaches more than 91%. It can meet the requirements of defect detection and classification. © 2023 IEEE.

关键词： Learning systems

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image Filtration using Graph-based Low Pass Filter 1

Image Filtration using Graph-based Low Pass Filter

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1st international conference on Recent Trends in Microelectronics, Automation, Computing and Communications systems, ICMACC 2022

作者： Sonti, Nagaraju Rukmini, M.S.S. Pamulapati, Venkatappa Reddy Vignan's Foundation for Science Technology & Research Department of Ece Andhra Pradesh Vadlamudi India

ISBN: (纸本)9781665496049

This paper is based on filtering various noises from the image data through the graph-based low pass filter. The linear noise (salt & pepper noise) and nonlinear noise (Gaussian noise) are taken for the research purpose, those noises are randomly added to the image data. In this paper, the graph filter is proposed to remove the linear as well as non-linear noise. This graph-based low pass filter also allows the noise from the bulk image dataset. In the graph-based low pass filter, the image dataset is converted into a structure like a graph and then the image is considered as matrix data. The pixels in the data connected with neighboring pixels that demonstrate the direction and position of the pixel with a connection of neighboring pixels results in angle. These two properties made Graph signal Processing more adaptable for image data. In the simulation, the performance of proposed model is compared with the three conventional filters (Volterra filter, median filter, and stack filters). The performance measurements of the models are based on the Peak signal to Noise Ratio, Mean Square Error, Root means square error, and Structural similarity index. © 2022 IEEE.

关键词： Median filters

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

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

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signal Processing, Communications and Engineering Management conference (ISPCEM), international

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

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

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.

关键词： image recognition Accuracy Education Neural networks Optimization methods signal processing Stability analysis internet Convolutional neural networks Intelligent robots

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Evolutionary Discriminative Deep Belief Network based Diabetic Retinopathy Classification 6th

Evolutionary Discriminative Deep Belief Network Based Diabet...

引用

6th international conference on Soft Computing and signal Processing, ICSCSP 2023

作者： Saranya Rubini, S. Sathya, K. Saveeth, R. Prabhavathy, M. PES University Bengaluru India Vellore Institute of Technology Vellore India Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9789819986279

The development of the glucose level in the blood may lead to vision threatening which is commonly known as Diabetic Retinopathy. Microaneurysms and Hemorrhages are the indications that illustrate the progressiveness of the eye disease named as Diabetic Retinopathy (DR). These indications appear as blood clots in the retinal area causing a serious threat to human vision. Digital fundus photographs from fundus cameras have the potential to explore the retinal area in a high resolution that helps to detect DR. Regular monitoring of fundus photographs helps diabetic patients from vision impairment. In this work, an Evolutionary Discriminative Deep Belief Network (DR-ED2BN) has been proposed to detect the Diabetic Retinopathy. Initially, the fundus photographs are preprocessed using Contrast Limited Adaptive Histogram based Equalization (ADHE) which enlightens the contrast and supports the noise removal in the image. The preprocessed image is subject to canny edge detection and Eigen value analysis to form clusters corresponding to the DR symptoms. The resultant image is processed to extract several statistical, shape and textural features that contribute to the feature set. These features further undergo Particle Swarm Optimization (PSO) to select the features predominantly indicating the DR symptoms. The reduced feature set is fed to Discriminative Deep Belief Network (D2BN) which classifies the fundus image as either DR healthy or DR affected. The proposed DR-ED2BN method works on real-world dataset and achieved accuracy, sensitivity and specificity of 89%, 92% and 65% respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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Vehicle Detection using Artificial Intelligence based Algorithm in Intelligent Transportation systems 4

Vehicle Detection using Artificial Intelligence based Algori...

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4th international conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Waqar, Muhammad Ishaq, Muhammad Afzal, Muhammad Hammad Iqbal, Shahzaib Balochistan University of Engineering and Technology Khuzdar Pakistan National University of Computer and Emerging Sciences Islamabad Pakistan Comsats University Islamabad Pakistan

ISBN: (纸本)9798350335316

Intelligent transportation systems (ITS) are the future of inter- and intra-city mobility. ITS has several features, and vehicle detection is a crucial system component. This research paper implements the You Only Look Once (YOLO) algorithm, which is a neural network-based technique for detecting vehicles on roads and streets. The algorithm detects vehicles rapidly and accurately. The YOLO implementation of this study confirms the efficacy of this algorithm, detecting vehicles successfully in various scenarios. Vehicles are detected and displayed separately within boundary boxes. Our implementation of this method is limited to detecting up to five vehicles on a single road or street. The vehicles could be parked or waiting at a traffic signal for it to turn green. Vehicle tracking has increased the effectiveness of the system because it follows the vehicle's path until it disappears from the CCTV image. MATLAB simulations are used to validate the results of this study. © 2023 IEEE.

关键词： MATLAB

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Parameter Identification of Resonant System based on Step Response Method

Parameter Identification of Resonant System Based on Step Re...

引用

2023 IEEE international conference on image Processing and Computer Applications, ICIPCA 2023

作者： Zhang, Yongli Wang, Xiaoli Geng, Lihui Li, Xinqi School of Information Technology Beijing Institute of Technology Guangdong Zhuhai519088 China Tianjin University of Technology and Education Tianjin300222 China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin300222 China

ISBN: (纸本)9798350314670

A lot of 3practical systems, such as mechanical system, vehicle shockproof system, the seismic system of the building etc, can be simplized as a resonance system, which usually involves multiple components. There are still some unknown parameters in the practical application of the resonance system, and there are also some external disturbances. The coordination between each component cannot be completely idealized. Hence, in this paper, an experimental platform of resonant system was designed and bulit, and the system identification method based on the step response method is presented for the system. By analyzing the mechanical mechanism of the resonance system, the mechanism model of the system is built using the Lagrange method. Then the step input signal suitable for the resonance system test is designed, the corresponding experimental data is collected, and the model parameters are identified by the step response nonlinear fitting method. Finally, the step response based on the identified transfer function was compared with the actual experimental data, and the validity of the identification was verified. The results shows that the step response method has a better identification fitting degree than the transfer function method, and the model has a higher identification accuracy. The presented identification method in this paper provides a theoretical basis for improving the performance and design efficiency of the controller. © 2023 IEEE.

关键词： Transfer functions

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A Special-Purpose Video Streaming Codec for internet-based Remote Driving

A Special-Purpose Video Streaming Codec for Internet-based R...

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IEEE/ASME (AIM) international conference on Advanced Intelligent Mechatronics

作者： Neel Adwani Kevin Silvestrini-Cordero Roberto Rojas-Cessa Tao Han Cong Wang Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

Real-time teleoperation of robotic systems over the internet is a desirable technology in many ways. Latency of the video feedback has been hampering its development. This paper takes the application of remote driving to introduce an unconventional codec that provides a very low latency for internet-based video streaming. The proposed method preserves just enough information in the video for essential perception and decision making of a remote driver. Thanks to a unique integration of several image processing and data streaming techniques, the proposed codec can realize a glass-to-glass latency of around 90 ms. A series of tests are conducted over the real consumer internet to analyze the latency and verify the effectiveness of remote driving with the proposed codec.

关键词： Codecs Mechatronics image processing Decision making Streaming media Real-time systems internet

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Wi-Fi Received signal Strength-based Indoor Localization System Using K-Nearest Neighbors fingerprint integrated D*algorithm 24

Wi-Fi Received Signal Strength-based Indoor Localization Sys...

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24th international conference on Advanced Communication technology (ICACT) - Artificial Intelligence Technologies toward Cybersecurity

作者： Jarawant, Tanatthep Kamsingt, Patcharin Torteekat, Peerapong Manuthasnat, Shariff Hematulint, Warunyu Choorakst, Tachodom Phisannupawongt, Thaweerath Sangkarakt, Sitthirak Mungkhudt, Sookaseam Somjitt, Thanaporn King Mongkuts Inst Technol Ladkrabang Int Acad Aviat Ind Dept Aeronaut Engn Air Space Control Optimizat & Management Lab Bangkok 10520 Thailand Natl Astron Res Inst Thailand Chiang Mai 50180 Thailand

ISBN: (纸本)9791188428090

The indoor localization system is essential since the Global Positioning System cannot give an accurate position indoors, especially when several floor plans are considered. Wi-Fi received signal strength becomes an alternative indicator for indoor localization systems. The experiment proposed a localization system created by integrating and working between the K-Nearest Neighbors algorithm and the D*algorithm. The result illustrates the optimal path from the start point to the target point by avoiding the obstacle performing exceptionally well. The K-Nearest Neighbors algorithm provide the result for localization with Root Mean Square Errors of displacement at 1.190 meters, 2.491 meters, and 1.363 meters of X-Axis Y-Axis, respectively. The proposed indoor localization system can have various applications considering different environmental factors in different applications, such as the size of unmanned aerial vehicles when applying indoor unmanned aerial vehicles.

关键词： Indoor localization Wi-Fi Received signal Strength Indicator K-Nearest Neighbor D*Algorithm Indoor positioning

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