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ELoran signal message prediction algorithm based on Alexnet-ECA

IET RADAR SONAR AND NAVIGATION 2024年第3期18卷 500-513页

作者： Zhang, Kai Yang, Fan Wang, Weidong Ye, Xiaotao Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo Peoples R China

Enhanced Loran (eLoran) is the most important backup system for Global Navigation Satellite System (GNSS). However, most current eLoran demodulation methods are based on digital signal processing, which may produce large errors under the influence of strong interference or noise. The authors propose an eLoran signal message prediction algorithm based on deep learning. Using time-frequency (TF) analysis convert one-dimensional signal into two-dimensional high-resolution image. It improves Alexnet by adjusting the convolutional layer structure, adding an attention module, and reducing the pooling layer window to construct the Alexnet-ECA network. The network is used to identify TF image features and predict signal message. The experimental results show that novel method for eLoran signal message prediction based on Alexnet-ECA can achieve high accuracy and robustness in various SNR scenarios. The accuracy is more than 90% at SNR greater than 10db and not less than 80% even in the complex environments. Compared with Resnet34 and VGG16 networks, Alexnet-ECA demonstrates its efficiency and effectiveness using real measurement data. This method can enhance the performance and reliability of eLoran receivers and applications. A deep learning algorithm for predicting eLoran signal message is proposed. The algorithm utilises time-frequency analysis to convert one-dimensional signals into high-resolution two-dimensional images. To effectively leverage these images and extract their distinctive features, an enhanced variant of Alexnet called Alexnet-ECA is devised. Subsequently, the proposed network utilises these extracted features to predict the corresponding signal ***

关键词： Loran neural networks signal processing time-frequency analysis

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real-time Batch Detection System for Defects in Highly Curved Spiral Cylindrical Coils

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IEEE ACCESS 2024年 12卷 153370-153384页

作者： Zhuo, Qinghang Kong, Linghua Zheng, Jishi Ding, Zhigang Li, Wenguang Yi, Dingrong Lian, Guofu Fujian Univ Technol Sch Mech & Automot Engn Fuzhou 350118 Peoples R China Fujian Univ Technol Digital Fujian Ind Mfg Internet Things Lab Fuzhou 350118 Peoples R China Fujian Univ Technol Sch Transportat Fuzhou 350118 Peoples R China Huaqiao Univ Sch Mech Engn & Automat Xiamen 361021 Peoples R China

Tungsten coil is one of the important components of the magnetron, and its surface quality and geometry directly affect the service life of the magnetron. The focus of this article is to apply machine vision and deep learning algorithms to study a surface defect detection and geometric measurement system for tungsten coils. Firstly, in order to solve the problem of difficulty, high cost, and low efficiency in manual detection caused by small and random surface cracks on tungsten coils, this paper proposes a surface crack detection method based on deep learning technology. By constructing the dataset and training the semantic segmentation model, the surface cracks were effectively detected and identified. Secondly, a sub-pixel edge detection method based on image processing technology and the Canny algorithm is proposed to address the issue of difficulty in measuring the geometric dimensions of tungsten coils due to their high bending spiral structure. The method accurately measures the length, outer diameter, and the maximum error is only 0.023mm. The results of the verification experiment indicate that the developed system can detect an average of 70 tungsten coil samples in one minute, with sensitivity and accuracy of 99.66% and 98.52%, respectively. This system not only has high robustness and efficiency, but also reduces 95.23% of manual workload, meeting the requirements of production lines for surface defect detection and geometric dimension measurement of tungsten coils, and solving the limitations of traditional manual detection.

关键词： Coils Tungsten Surface cracks Accuracy Defect detection Magnetic field measurement Inspection Machine vision image processing Tungsten coil machine vision defect detection dimensional measurement image processing

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High-Speed Motion Target real-time Detection Based on Lightweight deep Feature learning Network

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IEEE SENSORS JOURNAL 2024年第12期24卷 19577-19589页

作者： Bao, Yi Liu, Zelin Feng, Wanzhu Deng, Yong Huang, Yulin Univ Elect Sci & Technol China UESTC Dept Elect Engn Chengdu 611731 Peoples R China Sichuan Sanlian New Mat Co Ltd Chengdu 610041 Peoples R China

real-time detection of high-speed moving targets is a significant and wildly applicable subject in the field of target detection. It is extremely important in both civilian and military fields. However, most of the current mainstream object detection models have difficulties in fulfilling requirements of accuracy and speed simultaneously. In this article, we propose a lightweight deep feature learning network (LDFL-Network), which can accurately complete detection of high-speed objects in real time. The network utilizes Ghost convolution instead of ordinary convolution to extract image features because of its superior performance in developing a lightweight and efficient network architecture. Utilizing the principles of Ghost convolution, we propose the I-GhostConv module, which employs the involution algorithm to enable flexible feature extraction and improves the network's expressive capacity. In addition, we propose a novel feature fusion method: adaptive cross-fusion structure. This structure adaptively allocates weights to feature maps and integrates various feature information crossly and comprehensively. Experiments are conducted on two representative datasets for high-speed motion target detection. The results of experiments demonstrate that the proposed network can accurately detect the targets in high-speed motion and achieve accuracy speed higher than mainstream target detection models.

关键词： Feature extraction feature fusion feature learning network lightweight real-time detection target detection

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A Directional Generation Algorithm for SAR image Based on Azimuth-Guided Statistical Generative Adversarial Network

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IEEE TRANSACTIONS ON SIGNAL processing 2024年 72卷 5406-5421页

作者： Peng, Guobei Liu, Ming Chen, Shichao Tao, Mingliang Li, Yiyang Xing, Mengdao Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China North Automat Control Technol Inst Taiyuan 030000 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

The high cost of acquiring synthetic aperture radar (SAR) images results in the problem of insufficient data, which limits the performance of deep learning-based automatic target recognition (ATR) models. To solve this problem, a directional generation algorithm for SAR image based on azimuth-guided statistical generative adversarial network (AGSGAN) is proposed in this paper. The proposed algorithm can not only generate images with similar statistical characteristics as the real SAR images, but also control the azimuth of the generated images. Considering that the statistical characteristics of SAR images are different at different azimuth, the proposed algorithm partitions the azimuth intervals of SAR image into adaptive azimuth intervals, and the statistical characteristics of images within each adaptive azimuth interval should be similar. Then, the proposed algorithm obtains the statistical characteristics of real images by using the G(0) distribution to fit the statistical distribution of images within each adaptive azimuth interval. Finally, the random noise sampled from the fitted G(0) distribution and the serial number of adaptive azimuth interval are inputted into AGSGAN. The images that are within a specified adaptive azimuth interval and have statistical characteristics similar to the real images are generated by AGSGAN. Experimental results show that the images generated by the proposed algorithm are more realistic in statistical characteristics, and can effectively improve the recognition accuracy of the deep learning-based SAR automatic target recognition model.

关键词： Radar polarimetry Generative adversarial networks Azimuth Signal processing algorithms Synthetic aperture radar Noise Training image recognition Target recognition Solid modeling Synthetic aperture radar (SAR) automatic target recognition (ATR) azimuth-guided statistical generative adversarial network (AGSGAN) adaptive azimuth interval G(0) distribution

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Application of an Effective Hierarchical deep-learning-Based Object Detection Model Integrated with image-processing Techniques for Detecting Speed Limit Signs, Rockfalls, Potholes, and Car Crashes

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Future Internet 2023年第10期15卷 322-322页

作者： Chung, Yao-Liang Department of Communications Navigation and Control Engineering National Taiwan Ocean University Keelung City202301 Taiwan

Against the backdrop of rising road traffic accident rates, measures to prevent road traffic accidents have always been a pressing issue in Taiwan. Road traffic accidents are mostly caused by speeding and roadway obstacles, especially in the form of rockfalls, potholes, and car crashes (involving damaged cars and overturned cars). To address this, it was necessary to design a real-time detection system that could detect speed limit signs, rockfalls, potholes, and car crashes, which would alert drivers to make timely decisions in the event of an emergency, thereby preventing secondary car crashes. This system would also be useful for alerting the relevant authorities, enabling a rapid response to the situation. In this study, a hierarchical deep-learning-based object detection model is proposed based on You Only Look Once v7 (YOLOv7) and mask region-based convolutional neural network (Mask R-CNN) algorithms. In the first level, YOLOv7 identifies speed limit signs and rockfalls, potholes, and car crashes. In the second level, Mask R-CNN subdivides the speed limit signs into nine categories (30, 40, 50, 60, 70, 80, 90, 100, and 110 km/h). The images used in this study consisted of screen captures of dashcam footage as well as images obtained from the Tsinghua-Tencent 100K dataset, Google Street View, and Google images searches. During model training, we employed Gaussian noise and image rotation to simulate poor weather conditions as well as obscured, slanted, or twisted objects. Canny edge detection was used to enhance the contours of the detected objects and accentuate their features. The combined use of these image-processing techniques effectively increased the quantity and variety of images in the training set. During model testing, we evaluated the model’s performance based on its mean average precision (mAP). The experimental results showed that the mAP of our proposed model was 8.6 percentage points higher than that of the YOLOv7 model—a significant improvement

关键词： Object detection

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A Novel deep learning and image processing Based Technique for Lumpy Skin Disease Detection

A Novel Deep Learning and Image Processing Based Technique f...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Jay Prakash Singh Debolina Ghosh Jagannath Singh Department of Comp. Sci. and Engg. Manipal University Jaipur Jaipur India Department of Information Technology Manipal University Jaipur Jaipur India School of Comp.Engg. KIIT Deemed to be University Bhubaneswar India

Lumpy skin disease is a very infectious disease in cattle. Every year many cattle die because of lumpy skin disease. Recently, Indian states especially Rajasthan, Haryana, Punjab have witnessed the death of many cows due to lumpy skin infection. This disease becomes very dangerous and spreads at a high rate if not treated well at the time of infection. A proper technique is required to detect the lumpy skin infection in cattle so that proper diagnosis can be adopted to stop the infection from spreading and to save the life of the infected cattle. With the emergence of machine learning and deep learning approaches it seems that detection can be done at the inception of infection in the cattle. Many researchers have suggested various ways to detect the lumpy skin disease but there is need of an approach which can guarantee the high accuracy, precision, recall and f1 score for the lumpy skin infection. We have used some of the advanced pre-trained deep learning models and also proposed a modified convolution neural network model for lumpy skin infection detection. We collected dataset from the online platform. We collected total 936 cattle images including healthy and lumpy skin infected cattle. Out of 936 cattle images 421 were lumpy affected and rest 515 were images of the healthy cattle. We split the image in training and testing dataset. We used 80 percent of images for training the models and 20 % of images for testing the performance of the models. We trained InceptionV3, VGG16, ResNet50V2 and modified CNN model under similar condition by properly tuning the hyper parameters. We have observed that proposed modified CNN model outperformed all the reset three models. On training data, modified CNN yield an accuracy of 99.01 %, InceptionV3 furnished an accuracy of 98.85 %, Resnet50V2 has an accuracy of 92.09% and VGG16 guaranteed an accuracy of 92%. On testing data, proposed modified CNN, InceptionV3, ResNet50V2 and VGG 16 has accuracy 97.87 %, 97.34%, 93.09% and 93

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A LEARNABLE RADAR IMAGING PARADIGM DRIVEN BY deep GENERATIVE MODEL 31

A LEARNABLE RADAR IMAGING PARADIGM DRIVEN BY DEEP GENERATIVE...

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2024 International Conference on image processing

作者： Li, Shuang Dong, Ganggang Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9798350349405;9798350349399

The improvement of synthetic aperture radar (SAR) image quality is the constant subject of SAR technology. In previous works, certain physical models were built to form SAR images from raw echoes, such as Range-Doppler algorithm (RDA). Although this family of methods is effective in the ideal conditions, the generalization ability is poor under the special scenarios. Moreover, these methods ignore the large amounts of history SAR data. And their processing speed is not enough to support their application in real-time systems unless draconian restrictions are added. To solve these problems, a learnable radar imaging paradigm driven by deep generative model is proposed in this paper. A deep fully convolutional network is first developed to achieve the mapping from the raw echoes to the imaging results. Then a large amount of historical echo data and corresponding SAR images formed by RDA are used to train and evaluate this network. It has been proven that our imaging paradigm can quickly form high-quality imaging results in various scenarios, through simulations and experiments.

关键词： SAR Imaging deep learning deep Fully Convolutional Network Architecture

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real-time MONOCULAR DEPTH ESTIMATION ON EMBEDDED SYSTEMS 31

REAL-TIME MONOCULAR DEPTH ESTIMATION ON EMBEDDED SYSTEMS

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2024 International Conference on image processing

作者： Feng, Cheng Zhang, Congxuan Chen, Zhen Hu, Weiming Ge, Liyue Beihang Univ Sch Instrumentat & Optoelect Engn Beijing Peoples R China Nanchang Hangkong Univ Sch Measuring & Opt Engn Nanchang Jiangxi Peoples R China Chinese Acad Sci NLPR Inst Automat Beijing Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Depth sensing is of paramount importance for unmanned aerial and autonomous vehicles. Nonetheless, contemporary monocular depth estimation methods employing complex deep neural networks within Convolutional Neural Networks are inadequately expedient for real-time inference on embedded platforms. This paper endeavors to surmount this challenge by proposing two efficient and lightweight architectures, RT-MonoDepth and RT-MonoDepth-S, thereby mitigating computational complexity and latency. Our methodologies not only attain accuracy comparable to prior depth estimation methods but also yield faster inference speeds. Specifically, RT-MonoDepth and RT-MonoDepth-S achieve frame rates of 18.4&30.5 FPS on NVIDIA Jetson Nano and 253.0&364.1 FPS on Jetson AGX Orin, utilizing a single RGB image of resolution 640x192. The experimental results underscore the superior accuracy and faster inference speed of our methods in comparison to existing fast monocular depth estimation methodologies on the KITTI dataset.

关键词： deep learning Convolutional Neural Networks Embedded Systems real-time Depth Estimation

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A Review of CNN Applications in Smart Agriculture Using Multimodal Data

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SENSORS 2025年第2期25卷 472-472页

作者： El Sakka, Mohammad Ivanovici, Mihai Chaari, Lotfi Mothe, Josiane CNRS UMR5505 IRIT Inst Rech Informat Toulouse F-31400 Toulouse France Univ Toulouse F-31400 Toulouse France Transilvania Univ Brasov Dept Elect & Comp Brasov 500036 Romania Toulouse INP F-31000 Toulouse France Univ Toulouse INSPE F-31400 Toulouse France

This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency. Key approaches analyzed involve image classification, image segmentation, regression, and object detection methods that use diverse data types ranging from RGB and multispectral images to radar and thermal data. By processing UAV and satellite data with CNNs, real-time and large-scale crop monitoring can be achieved, supporting advanced farm management. A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. Future directions point toward integrating IoT and cloud platforms for real-time data processing and leveraging large language models for regulatory insights. Potential research advancements emphasize improving increased data accessibility and hybrid modeling to meet the agricultural demands of climate variability and food security, positioning CNNs as pivotal tools in sustainable agricultural practices. A related repository that contains the reviewed articles along with their publication links is made available.

关键词： convolutional neural network smart agriculture weed detection crop disease detection crop classification yield prediction water management remote sensing machine learning

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MERRC: A Memristor-Enabled Reconfigurable Low-Power Reservoir Computing Architecture at the Edge

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第1期71卷 174-186页

作者： Nowshin, Fabiha Huang, Yi Sarkar, Md. Rubel Xia, Qiangfei Yi, Yang Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA Univ Massachusetts Amherst Dept Elect & Comp Engn Amherst MA 01003 USA

The massive growth in the Internet of Things (IoT) has led to an increase in demand for devices receiving and transmitting data to and from the cloud during operations. Edge computing has been developed in an attempt to bring computations in proximity to the devices to overcome latency and cost overhead. IoT applications heavily reliant on machine learning (ML) tasks such as image or voice recognition can benefit from edge devices that facilitate real-time operations without costly data transmission back and forth from memory. In this work, we develop MERRC, a memristor-enabled reconfigurable architecture that incorporates processing-in-memory and reservoir computing to carry out ML tasks of image classification at the edge. This design uses a novel masking circuit to allow for image segmentation, which is combined with a delay-based reservoir to form a recurrent neural network. We further implement the final stage of classification using a fabricated memristor crossbar. Our hardware measurement results on the MNIST dataset of the delay-based reservoir with memristor crossbar arrays provide a recognition accuracy of 98%. On a more complex image classification dataset of CIFAR-10, MERRC shows a high accuracy of 88%, further highlighting the edge computing capabilities of the architecture.

关键词： Reservoirs Computer architecture deep learning Task analysis Memristors Training image edge detection Edge device memristor image recognition reservoir computing processing-in-memory analog integrated circuit design

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