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Proceedings - 2022 international conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2022

Proceedings - 2022 International Conference on Artificial In...

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2022 international conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2022

ISBN: (纸本)9781665462877

The proceedings contain 96 papers. The topics discussed include: beamforming of frustum of a cone conformal array based on convex optimization theory;research on signal integrity technology of missile borne integrated control unit;an intelligent network intrusion detector using deep learning model;an improved simulation model of duffing oscillator with Lyapunov characteristic exponents;a novel end-to-end object detection model based on multi-scale deformable attention module;intrusion detection method based on improved conditional generative adversarial network;a residual combined attention mechanism based on U-Net network for kidney tumor image segmentation;research and development of intelligent protection capabilities against internet routing hijacking and leakage;and automatic control system of wind power generation in mountain area based on internet of things technology.

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2nd international conference on Science, Engineering Management and Information technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd international conference on Science, Engineering Management and Information technology, SEMIT 2023

ISBN: (纸本)9783031722868

The proceedings contain 46 papers. The special focus in this conference is on Science, Engineering Management and Information technology. The topics include: Verification and Validation of Knowledge Engineering systems: A Life Cycle Framework;The Critical Factors of Success of Gamification in Digital Banking Services: Using Analytic Hierarchy Process (AHP) Approach;e-Shopping Sites Preference Analysis with Multi-criteria Decision-Making Methods;advancing Anemia Diagnosis: Harnessing Machine Learning Methods for Accurate Detection;monkeypox Detection with K-mer Using Machine Learning Algorithms;cloud Computing Model for Handling Medical Big Data: A Mobile Hospital Pervasive Healthcare Application;Harnessing Advanced AI Techniques: An In-Depth Analysis of Machine Learning Models for Improved Diabetes Prediction;image Processing in Toxicology: A Systematic Review;augmented Reality Immersive World with Hologram Special Effect in Early Childhood Education;improving the Visual Ergonomics of Computerised Workplaces Through the Use of Specialised Eye-Rest Software;a Diagnosis Model based on Federated Learning for Lung Cancer Classification;Arrhythmia Detection from ECG Traces images Using Transfer Learning Approach;enhancing Traffic Flow Prediction in Urban Areas Through Deep Learning and Probe Information: A Comparative Study;an Approach to Multi-agent Deep Q-Network Optimization of signal Control in Multi-intersection Road Environments to Enhance Urban Traffic Flow;blockchain-Driven Smart Contracts: An Overview of Application Areas and Gap Identification in Construction Management Literature;stochastic Optimization Methodology for Production Planning with Uncertain Demand and Lead Time based on the Digital Twin;role of Top Management Commitment and Information technology Investment in Digital Transformation;antecedents of Mobile Banking Apps Adoption Among Consumers in Ghana.

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Oct image Blind Despeckling based on Gradient Guided Filter with Speckle Statistical Prior 48

Oct Image Blind Despeckling Based on Gradient Guided Filter ...

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48th IEEE international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Li, Sanqian Xiong, Muxing Yang, Bing Zhang, Xiaoqing Higashita, Risa Liu, Jiang Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Shenzhen China Tomey Corporation Japan

ISBN: (纸本)9781728163277

Optical coherence tomography (OCT) imaging technique has been widely used for ocular disease diagnosis. However, speckles occur in OCT images due to the property of coherent imaging, inevitably affecting the visual quality and clinical analysis. To alleviate this problem, we propose a novel gradient-guided speckle image filtering method (GGSF) with structure enhancement for directly removing speckles in OCT images. Specifically, the multiplicative characteristic of speckle noise is incorporated into the guided filtering processing for modeling raw OCT images. To avoid getting trapped in image distortions, we further employ gradient regularization to integrate the structure prior information into the guided speckle image filtering procedure. Additionally, we introduce the statistical property of speckle noise obeying a gamma distribution into the least square method solver for the resulting non-convex GGSF model. Experimental results on the AS-OCT dataset demonstrate the effectiveness of GGSF for OCT image despeckling compared with competitive methods. Furthermore, we validate the benefits of GGSF for subsequent clinical analysis with the CM-OCT dataset. © 2023 IEEE.

关键词： Optical tomography

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Smoke Segmentation Improvement based on Fast Segment Anything Model with YOLOv11 for a Wildfire Monitoring System 10

Smoke Segmentation Improvement Based on Fast Segment Anythin...

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10th international conference on internet of Things, Big Data and Security, IoTBDS 2025

作者： Bunpleng, Puchit Thunyatada, Puthtipong Aksornsuwan, Bhutharit Tungpimolrut, Kanokvate Murata, Ken T. Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand NECTEC National Science and Technology Development Agency Pathum Thani Thailand National Institute of Information and Communications Technology Tokyo Japan

ISBN: (纸本)9789897587504

Forests and wildlife are crucial parts of our ecosystem. Wildfires occurring in dry and hot regions represent a significant threat to these areas, particularly in ASEAN countries during the dry season. While human observers are often employed to detect wildfires, their scarcity and limited availability highlight the need for automated solutions. This study explores the use of machine learning, specifically computer vision, to enhance wildfire detection by segmenting smoke, an approach which potentially gives information regarding the size and the direction of the spread of the smoke, aiding mitigation efforts. We extend prior work by proposing a model to predict the errors and performance of segmentation masks without access to the ground truth, with the aim of facilitating iterative self-improvement of segmentation models. The FireSpot dataset is used to fine-tune a YOLOv11 model to predict bounding boxes of smoke successfully;subsequently, the outputs of this model are used as a prompt to refine a FastSAM model designed to segment the image into a proposed mask containing the smoke. The proposed mask and the corresponding original image are then used to train a machine learning model where the targets are metrics regarding the error rates of the masks. The results show that a gradient boosting model achieves good prediction performance in predicting some error metrics like the IoU (denoted TPP in this paper) between the proposed and actual segmentation masks with an MSE of 0.03 and R2 of 0.46, as well as the proportion of false positives over the union of the proposed and actual masks (denoted FPP in our paper) with an MSE of 0.0002 and R2 of 0.95, while a pre-trained deep learning model fails to learn the distribution, achieving considerably lower performance for IoU with an MSE of 0.05 and R2 of 0.06 and FPP with an MSE of 0.0002 and R2 of -1.15. These findings open the way to future work where the results of the error prediction model can be used as feedback to

关键词： Deep learning

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Communication Interference signal Recognition based on Complex Number Convolutional Neural Network 6

Communication Interference Signal Recognition Based on Compl...

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2022 6th international conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2022

作者： Chen, Yazhong Shan, Jingmiao Zhang, Chuanhao Department of Electrical Information Shandong University of Science and Technology Shandong Jinan250031 China

Modern wireless communication systems are facing an increasingly complex electromagnetic environment, which is affected by a variety of noise and interference signals. In the communication process, if the communication party can effectively identify the type of interference signal, it can take corresponding anti-interference measures to avoid or suppress interference to the maximum extent and reduce the damage of interference to communication quality. In recent years, with the continuous development of deep learning and its outstanding performance in image and speech processing, it has been verified that deep learning has extremely strong nonlinear mapping and data expression capabilities, inspiring researchers to apply deep learning to the field of communication anti-jamming, and improving the antijamming capability of the system. In this paper, an algorithm of interference signal recognition based on a complex convolution neural network is proposed. It also introduces the network architecture. Next, six typical interference signals at each level are identified, and the results are analyzed. The last chapter of the paper summarizes the full text. The experimental results show that the recognition ability of the interference signal recognition algorithm proposed in this paper is better than that of the traditional convolutional neural network. © Published under licence by IOP Publishing Ltd.

关键词： signal interference

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An investigation on digital signal processing in pulse amplitude modulation transmission system 3

An investigation on digital signal processing in pulse ampli...

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3rd international conference on Computer Communication and Network Security, CCNS 2022

作者： Liu, Yexiang School of Information and Electronics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781510660113

With the development of the global internet industry, the demand for the transmission capacity of communication system is increasing rapidly. Optical fiber has become the main transmission media in communication systems because of its unique advantages, such as large bandwidth and low loss. It is necessary to construct large capacity optical communication systems to satisfy the capacity requirements. In this paper, the overall architecture for optical short-reach pulse amplitude modulation (PAM) transmission system is analysed. Then the signal damage factors and corresponding compensation schemes based on digital signal processing (DSP) are studied. The different implementation schemes for clock recovery and channel equalization are described and compared in detail. Finally, the challenges and prospects of DSP technology in short-reach PAM transmission system are discussed. © 2022 SPIE.

关键词： Pulse amplitude modulation

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Mangrove Species Classification in Qi’ao Island based on Gaofen-2 image and UAV LiDAR 1

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international conference on Communications, signal Processing, and systems, CSPS 2023

作者： Sun, Yuchao Wei, Zheng Gao, Yang Ren, Hongkai Chen, Qidong Dong, Di Hu, Ping South China Sea Institute of Planning and Environmental Research State Oceanic Administration Guangzhou510300 China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou510300 China Zhuhai519082 China

ISBN: (数字)9789819975051

ISBN: (纸本)9789819975396

Mangrove species classification is of great significance to the study of mangrove community structure and biodiversity. Most researches use foreign high-resolution remote sensing images or UAV images for mangrove species classification. In order to improve classification accuracy, LiDAR and hyper-spectral data are often used to assist classification. In this paper, based on the Gaofen-2 image and the CHM data obtained by the UAV Lidar, the mangroves in Qi'ao Island, Zhuhai are classified among species by using the random forest classification method. The classified species include 5 types of true mangroves, 3 types of semi mangroves, Phragmites australis and non-vegetation. The results show that the use of Gaofen-2 image can only effectively distinguish the Sonneratia apetala, Acrostichum aureum and non-vegetation, the accuracy of distinguishing other mangrove species is not ideal;After Gaofen-2 image fusion of CHM data, the classification accuracy of each mangrove species has been significantly improved, with the overall classification accuracy reaching 91.44%, which verifies the effectiveness of Gaofen-2 image fusion of external data in mangrove species classification research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image fusion

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Multi-step stereo matching algorithm based on image fragment and confidence propagation 7

Multi-step stereo matching algorithm based on image fragment...

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7th international conference on Communication, image and signal Processing, CCISP 2022

作者： Li, Yingjiang Deng, Yaping Zhong, Yuzhong Chongqing University of Technology Department of Internet of Things Engineering Chongqing China Computer College Sichuan University Chengdu China

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

This paper presents a multi-step stereo matching algorithm that can be applied to multiple scenes. To adapt to different application scenarios, the algorithm divides the stereo matching process into three steps: point, fragment, and plane. First, the texture points of an image are extracted and the stereo matching (point disparity) of these points is performed using the improved self-aware matching measure (SAMM) algorithm. Then, according to the edge information of the image, a smooth region is divided into different fragments in the horizontal direction. The disparity estimation of the smooth region (segment disparity) is obtained through the confidence propagation of disparity values of texture points in the fragments. Finally, based on the similarity of plane disparity, a disparity map is generated using the disparity refining algorithm (plane disparity), and a final high-precision disparity value is obtained. The experimental results show that the proposed algorithm has high operational efficiency and accurate disparity estimation. Moreover, the algorithm may be adapted for more application scenarios. © 2022 IEEE.

关键词： image enhancement

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Enhanced Real-Time Object Detection on Customized Dataset with YOLOv9 Architecture

Enhanced Real-Time Object Detection on Customized Dataset wi...

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2024 IEEE international conference on Intelligent signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Adithiyan, P.V. Kumar, M Ranjith Rahul, K. Nikhil, V. Annamalai, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engg. Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350379525

YOLO has developed into a primary real-time object identification platform for applications such as video surveillance systems, autonomous vehicles, and robots. This research proposes an improved real-time object recognition approach based on the YOLOv9 architecture, trained on a football-specific custom dataset. The 255 annotated images in the dataset are separated into training, validation, and test sets with an 80-15-5 split ratio;there are 204 images in the training set, 38 in the validation set, and 13 in the test set. Our gelan-c model achieves remarkable detection performance with 467 layers, 25,414,044 parameters, and 102.5 GFLOPs. Upon validation, Gelan-c shows an aggregate mAP50 of 75.9%, with mAPs ranging from 32.2% (ball) to 93.7% (goalkeeper) depending on the specified class. The impressive aspect of the Gelan-C is its 31.6 ms picture processing rate, which assures real-time performance. The methodology covers environmental setup, preparation of data, YOLOv9 architecture introduction, and experimental evaluation to demonstrate the model's football-related recognition capabilities. The suggested approach has potential applications in sports event monitoring and evaluation due to its reliability and efficacy in detecting football players, referees, and the ball. © 2024 IEEE.

关键词： image annotation

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Research on Vehicle Parking Aid System based on Parking image Enhancement 10th

Research on Vehicle Parking Aid System Based on Parking Imag...

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10th international conference on Communications, signal Processing, and systems

作者： Ye, Danxian Yin, Xin Dong, Menghan Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin 300387 Peoples R China

ISBN: (纸本)9789811903908;9789811903892

At present, the visual parking assistance system in intelligent driving generally has the problems of unclear parking image quality and high hardware cost. In order to reduce the difficulty of parking and improve the ability to adapt to the environment, this paper proposes a vehicle assistance system based on parking image enhancement. Firstly, Retinex algorithm is used to balance the image illumination information and enhance the color saturation, so that it can adapt to more complex environmental conditions;secondly, Ackerman steering theorem is used to draw the dynamic parking aid line, and the coordinate transformation technology is used to output it to the vehicle screen. The adaptability and effectiveness of the developed system are verified by the relevant experimental research.

关键词： image enhancement Retinex algorithm Parking system Intelligent driving

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