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Intelligent Perception Method of Shipborne Radar Jamming Signal Based on YOLOv8

Intelligent Perception Method of Shipborne Radar Jamming Sig...

引用

ieee International conference on Electronic Information and Communication Technology (ICEICT)

作者： Xuan Zhu Hao Wu Fangmin He Zhaopeng Shi Xi Yu Jin Meng School of Electrical Engineering and Automation Wuhan University National Key Laboratory of Electromagnetic Energy Naval University of Engineering Wuhan China Institute of Military Electrical Science and Technology Naval University of Engineering School of Electronic Information and Communications Huazhong University of Science and Technology

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

To improve the jamming signal perception ability and anti-jamming performance of shipborne radar in complex environment, this paper proposes a visual perception method of jamming signal based on YOLOv8n. Firstly, the time-frequency image dataset of jamming signal is constructed, and the noise is suppressed by contrast limited adaptive histogram equalization (CLAHE). Second, the YOLOv8n object detection algorithm is used to realize the recognition and location of jamming signals. Then, combined with the location information of the jamming signal in the time-frequency image, the values of key parameter estimation can be obtained by numerical calculation. Finally, the values of parameter estimation are corrected by the Euclidean distance clustering algorithm. The experimental results demonstrate that the jamming signal category perception precision of the proposed method can reach 98.01%, and the parameter estimation error is 2.36%, which is 7.47% lower than that before correction.

关键词： Time-frequency analysis Parameter estimation Radar measurements Noise Clustering algorithms Radar Euclidean distance

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Autoencoder for image Retrieval System using Deep Learning Technique with Tensorflow and Kears

Autoencoder for Image Retrieval System using Deep Learning T...

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Integrated Circuits and Communication Systems (ICICACS), ieee International conference on

作者： Kanchan Wangi Aziz Makandar Dept. Compter Science Karnataka State Akkamahadevi Women's University Vijayapura Karnataka

In today's era image retrieval is most emerging trends of digital image processing techniques. Traditional methods for retrieval such as Text and Query based image retrieval of image it will take more time for searching and retrieving images from large datasets. So, to reduce the time for searching and results, in this research work we used deep learning neural network technique is called Autoencoder. Autoencoder is neutral networks which reconstruct images. The proposed work, the image are retrieves similar visual content textures, color and shape of image this technique recognized as “Content Based image Retrieval (CBIR)”. Current research work, we used the autoencoder technique to form image into feature encoded vector format which will helpful for searching image from larger dataset. Proposed the encoder architecture to evaluate the input image then applied two training approaches pre-train and post train to generate good quality encoded representation. Three autoencoder are used for this research work Simple autoencoder, Deep autoencoder and Convolutional autoencoder. To reduced search indexing time, for extracting similar images from the dataset we used Approximate Nearest Neighbor (ANN) approach. Finally, developed end to end application system for image retrieval on MNIST and CIFER 10 datasets.

关键词： Deep learning Training visualization Shape image retrieval Neural networks Market research

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Corneal Ulcer Feature Extraction and image Classification using a Deep Convolutional Network and the VGG 16 Model 1

Corneal Ulcer Feature Extraction and Image Classification us...

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1st ieee International conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Janet Grace Susila, S. Kavitha, D. Department of Computer Science and Applications St. Peter's Institute of Higher Education and Research Chennai India

ISBN: (纸本)9781665460842

The most common kind of corneal issue is keratitis, an infection of the cornea commonly referred to as a corneal ulcer. In order to minimize the overall problem of visual heaviness that happens throughout the many phases of ulceration and the consequences, such as permanent vision loss or blindness, a technical solution that permits a distinction to be formed is needed. An image categorization technique using Deep Convolution Neural Networks is described in this article. This technique allows for the diagnosis of a wide variety of corneal ulcers using the data from ulcer images. The suggested technique is very useful and permits the automated extraction of crucial traits, which has significant ramifications in both the theoretical and practical arenas. The VGG16model integrates the two goals, serving as both a method and a high-throughput network for the categorization of a large number of photos for investigating CUs data processing problems. By expediting the reduction of provocative application and induction monitoring of customized medical treatment, we provide a contribution to the area of information technology-based healthcare. Early corneal ulcer identification helps to achieve this. There are two different employment categories to choose from. Data preprocessing is used to extract features and classify data during the initial phase of the process. DCNN is used in the second assignment to extract features from the data. According to the results of experimental research, the classification accuracy obtained using features produced from DCNN is only slightly better than that obtained using features from neural networks. classification on DCNN. © 2022 ieee

关键词： Feature extraction

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DeepDream Algorithm for Data Augmentation in a Neural Network Ensemble Applied to Multiclass image Classification 14th

DeepDream Algorithm for Data Augmentation in a Neural Networ...

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14th Asian conference on Intelligent Information and Database Systems (ACIIDS)

作者： Viaktin, Dmitrii Garcia-Zapirain, Begonya Zorrilla, Amaia Mendez Univ Deusto Unibertsitate Etorb 24 Bilbao 48007 Bizkaia Spain Northern Arctic Fed Univ Severnaya Dvina Embankment 17 Arkhangelsk Arkhangelsk Reg Russia

ISBN: (纸本)9789811982330;9789811982347

This paper presents the application of Deep-Dream algorithm for data augmentation applied to images. This algorithm analyzes the image by a trained neural network and hides main features of the image. Importance and place of the features is estimated based on neural network layers output values. The new neural network trained on the processed data are forced to search and train a new set of features in the data, since the known features have been hidden. Trained on original and processed data neural networks are used in an ensemble. Experiments were conducted with a balanced images dataset with 5000 images in 10 classes. Experiment was conducted with a neural network based on InceptionV3 architecture in two variations: with non pretrained weights and with pretrained weights. The neural network received a 256 x 256 pixel image as input. Training was conducted using categorical cross entropy loss function, accuracy metric, Adam optimizer with a learning rate of 0.0001. The improvements of the algorithm are almost insignificant when classifying an ensemble of neural network models for a small number of classes, but the impact of the algorithm increases as the number of classes increases. For binary classification there may be no improvement in ensemble accuracy, but when the number of classes increases and becomes more than 5, the influence of the algorithm on the accuracy of the final ensemble increases. The improvement in ensemble accuracy can be 0.5-4%, depending on the initial training conditions without the use of other types of data processing and augmentation.

关键词： Neural network Augmentation DeepDream Ensemble Algorithm

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Physical Layer Identity Authentication Technology for Broadband Power Line Communication Equipment

Physical Layer Identity Authentication Technology for Broadb...

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ieee International conference on Signal and image processing (ICSIP)

作者： Pujiang Ren Bohan Zhang Yu Jiang Chengqian Yuan School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China School of Engineering Unversity of Central Lancashire Lancashire England

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

High speed power line carrier communication systems are increasingly exposed to security threats, including device identity forgery, data tampering, and unauthorized intrusion. To address these challenges, this study introduces an identity authentication technology based on physical layer characteristics. This approach leverages the unique hardware attributes of the signal precursor code and the distinct features of the power line channel to construct a singular identity fingerprint for each device. This technique can effectively deter adversaries from counterfeiting the identity of legitimate devices, thereby enhancing the security of power line communication systems. The efficacy of this proposed method has been validated within an experimental high-speed power line carrier communication system environment. Furthermore, the performance of this approach under varying channel conditions has been thoroughly analyzed. These findings offer fresh insights and a novel technical framework for bolstering the physical layer security of power line carrier communication systems.

关键词： Performance evaluation image processing Power line communications Authentication Fingerprint recognition Physical layer security Hardware

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Unraveling Instance Associations: A Closer Look for Audio-visual Segmentation

Unraveling Instance Associations: A Closer Look for Audio-Vi...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuanhong Chen Yuyuan Liu Hu Wang Fengbei Liu Chong Wang Helen Frazer Gustavo Carneiro Australian Institute for Machine Learning University of Adelaide St Vincent's Hospital Melbourne Centre for Vision Speech and Signal Processing University of Surrey

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Audio-visual segmentation (AVS) is a challenging task that involves accurately segmenting sounding objects based on audio-visual cues. The effectiveness of audio-visual learning critically depends on achieving accurate cross-modal alignment between sound and visual objects. Successful audio-visual learning requires two essential components: 1) a challenging dataset with high-quality pixel-level multiclass annotated images associated with audio files, and 2) a model that can establish strong links between audio information and its corresponding visual object. However, these requirements are only partially addressed by current methods, with training sets containing biased audio-visual data, and models that generalise poorly beyond this biased training set. In this work, we propose a new cost-effective strategy to build challenging and relatively unbiased high-quality audio-visual segmentation benchmarks. We also propose a new informative sample mining method for audio-visual supervised contrastive learning to leverage discriminative contrastive samples to enforce cross-modal understanding. We show empirical results that demonstrate the effectiveness of our benchmark. Furthermore, experiments conducted on existing AVS datasets and on our new benchmark show that our method achieves state-of-the-art (SOTA) segmentation accuracy 1 1 This work was supported by Australian Research Council through grant FT190100525.

关键词： Training visualization image segmentation Computer vision Accuracy Contrastive learning Benchmark testing

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Improving CLIP for Biomedical Retrieval: A Multi-Margin Contrastive Loss Approach

Improving CLIP for Biomedical Retrieval: A Multi-Margin Cont...

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ieee International conference on Big Data

作者： Ejan Shakya Haritha Prasad Rayakota Pei-Chi Huang Department of Computer Science University of Nebraska at Omaha Omaha Nebraska USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Biomedical documents contain a wealth of multimodal data, including visual evidence and natural language descriptions. Effectively navigating this vast and expanding data source poses significant challenges, requiring labor-intensive manual curation processes at scale. Multimodal learning methodologies offer a promising approach to mitigate these bottle-necks in domain-specific information retrieval tasks, especially in biomedical sciences. This research investigates the potential of multimodal learning methodologies for various downstream tasks, such as information retrieval and classification based on paired multimodal image-text datasets. The proposed solution employs state-of-the-art transformer-based deep learning techniques and a contrastive learning approach to design a foundational model capable of adapting to biomedical vision-language processing. Through experiments and comparative studies, the effectiveness of our model was validated, demonstrating superior performance compared to existing approaches across diverse standard datasets. This study emphasizes the challenges in domain-specific environments and underscores the critical role of high-quality multimodal data in advancing biomedical AI applications, fostering innovation and progress in healthcare research and practice.

关键词： Adaptation models visualization Accuracy Biological system modeling Lungs Medical services Manuals Transformers Biomedical imaging image classification

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Deep Learning based Determination of Graf Standart Plane on Hip Ultrasound Scans

Deep Learning based Determination of Graf Standart Plane on ...

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ieee Signal processing and communications Applications (SIU)

作者： Baran Pelit Huseyin Abay Burhan Bilal Akkas Aysun Sezer Bilgisayar Mühendisligi Bölümü Biruni Üniversitesi Istanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Graf’s ultrasonography (US) method is one of the most commonly used imaging techniques for developmental dysplasia of the hip (DDH) and is universally accepted for the assessment of neonatal hips [1]. However, the training process is lengthy and requires supervision until the evaluator achieves expertise. Computer-based segmentation and object detection tools may assist less experienced evaluators in identifying anatomical structures and classifying hip US images. This method involves anatomical description as well as measuring bone and soft tissue coverage in coronal two-dimensional (2D) US images of the hip. During scanning with the ultrasound probe, the physician has to decide whether the image is in the standard plane and whether the image is measurable. An image must contain a straight iliac wing, lower limb of the ilium, and the labrum to be classified as measurable [2, 3]. Graf’s method is prone to interpreter variability due to the anatomical complexity of the hip structures, which can lead to misclassification [4]. When anatomical regions are not precisely identified, the selection of points for angle calculations may not be accurately determined, rendering the image unacceptable for measurements. This study comparatively measured success using a different model of the YOLOv8 algorithm to detect the labrum, lower limb of the ilium, and iliac wing regions in 200 measurable hip ultrasonography images obtained in the standard plane. With the YOLOv8x configuration, the labrum, ilium, and acetabulum were detected with success rates of 93.57%, 98.30% and 94.25% respectively, with an intersection over union (IoU) of 0.25. Our findings indicate that the YOLOv8x-based algorithm shows significant promise for the detection of labrum, ilium, and iliac wing regions in the standard plane.

关键词： Training Ultrasonic variables measurement Two-dimensional displays Signal processing algorithms Ultrasonography Signal processing Rendering (computer graphics)

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Vehicle Number Plate Detection and Recognition using YOLO-V3 and OCR Method

Vehicle Number Plate Detection and Recognition using YOLO-V3...

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International conference on Mobile Networks and Wireless communications (ICMNWC)

作者： Shashidhar, R. Manjunath, A. S. Kumar, Santhosh R. Roopa, M. Puneeth, S. B. JSS Sci & Technol Univ Dept Elect & Commun Engn Mysuru India JSS Sci & Technol Univ Dept Comp Applicat Mysuru India Dayananda Sagar Coll Engn Dept Elect & Commun Bengaluru India Presidency Univ Dept Elect & Commun Bengaluru India

ISBN: (纸本)9780738146379

Automatic Number Plate Recognition is one of the many information systems which is used data extraction from given vehicle image and apply the data for further usage in safe, secure and modernistic Transportation System. The Novelty of the project is that even if the image is blurred, our system can deblur the given image and apply it to the Machine Learning models further. In the proposed work, You Only Look Once [YOLO] V3 model for Region of Interest [ROI];Convolution Neural Network [CNN] for optical character recognition was implemented. After the ROI is detected, it will be enhanced with pre-processing steps before it is fed to CNN model. Dataset of different Indian Number Plates'Font was created, consisting of 6439 images of different alpha- numerical characters. Accuracy of 91.5 percent is obtained. The extracted and sorted characters of the number plate is cross- checked with the Indian RTO database and the information regarding which RTO the input vehicle image belongs to, is provided.

关键词： Number plate detection CNN YOLOV3 OCR RTO

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Semi-automatic Analysis of cells in honeybee comb images

Semi-automatic Analysis of cells in honeybee comb images

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Neha Rathore Praveen Kumar Tyagi Dheeraj Agrawal Department of electronics and communication engineering Maulana Azad National Institute of Techonology Bhopal India

Our environment depends on honeybees. In the face of global honey bee decline, colony strength assessment can help apiary management and provide valuable research data. Counting honey, brood, pollen, larvae, and bee cells manually and classifying them based on visual judgement and estimation is time-consuming, error-prone, and requires a qualified inspector. Digital image processing and AI developed automated and semi-automatic solutions to make this arduous job easier. Prior to classification of cells, it is necessary to detect all the cells correctly. The objective of this study is to provide an efficient method to identify all the uncapped cells in honey comb images. The paper is organized with the discussion of problem's background and evaluation first. Further, we give a thorough review of the existing work and explore a possible application. After that, we discussed the methodology adopted in literature. Overall, our investigation is thorough and extensive.

关键词： Computer science visualization image segmentation image edge detection Digital images Estimation Transforms

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