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FEDKA: FEDERATED KNOWLEDGE AUGMENTATION FOR MULTI-CENTER MEDICAL image SEGMENTATION ON NON-IID DATA 49

FEDKA: FEDERATED KNOWLEDGE AUGMENTATION FOR MULTI-CENTER MED...

引用

49th ieee international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Zhang, Yuhao Duan, Shaoming Zha, Xinyu Su, Jinhang Han, Peiyi Liu, Chuanyi Harbin Inst Technol Shenzhen Sch Comp Sci Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Guangdong Prov Key Lab Novel Secur Intelligence T Shenzhen Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Federated learning (FL) allows decentralized medical institutions to collaboratively learn a shared global model without breaching data privacy. However, in the context of medical image segmentation, data distributions across centers may vary a lot due to the diverse imaging protocols, vendors and partial annotation, which usually hampers the optimization convergence and the performance of FL. In this paper, we propose a novel approach called federated knowledge augmentation (FedKA) to address the non-IID (non-independent and identically distributed) problem in medical image segmentation within FL. FedKA first designs a pixel-wise knowledge augmentation method to preserve the knowledge of globally labeled regions for the local model during training, and augments each local feature statistical knowledge based on a mixture of Gaussian distribution. Our experiments on public datasets show the superiority of FedKA over the state-of-the-art methods in test performance.

关键词： Federated learning Knowledge augmentation non-IID data

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Grant-Free Random Access for Private 5G Mobile Networks based internet of Energy 8

Grant-Free Random Access for Private 5G Mobile Networks Base...

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8th ieee international conference on Energy internet, ICEI 2024

作者： Shi, Min-Da Shao, Wei-Ping Zhu, Liang Jiang, Wei Ling, Zhi State Grid Zhejiang Electric Power Co. Ltd. Power Wireless Communication Technology Laboratory Jiaxing Power Supply Company Jiaxing314000 China State Grid Zhejiang Electric Power Co. Ltd Hangzhou310007 China China Electric Power Research Institute Co. Ltd Nanjing210003 China State Grid Zhejiang Electric Power Co. Ltd. Power Wireless Communication Technology Laboratory Hangzhou310007 China

ISBN: (纸本)9798331523558

The internet of Energy (IoE) leveraging private 5G mobile networks represents a novel approach to boosting the overall efficiency of energy infrastructure while minimizing energy waste. As the number of IoE devices grows, grant-free random access (GFRA) has garnered significant attention due to its potential to slash signaling overhead and transmission latency. In our study, we examine GFRA in cell-free massive multiple-input-multiple-output (MIMO) systems within private 5G mobile networks that accommodate mixed-type IoE devices, while also considering the implications of access delay. We introduce a method for identifying colliding devices based on signal-to-noise ratio and calculate the access success probability for each device. Furthermore, we incorporate a backoff mechanism and derive an analytical approximation for the system's average spectral efficiency. By utilizing the gradient descent optimization algorithm, we determine the optimal backoff parameters for each device type. Simulation results indicate a strong correlation among the optimal backoff parameters of diverse device types. Notably, these parameters are significantly influenced by delay constraints, yet their impact diminishes as the devices' delay constraints approach the minimal loose boundary value. © 2024 ieee.

关键词： signal to noise ratio

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Research on SAR image and Optical image Fusion Classification based on Deep Learning 2

Research on SAR Image and Optical Image Fusion Classificatio...

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2nd ieee international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Wu, H. Li, C.H. Tong, G.L. Mnr School of Aerospace Engineering Land Satellite Remote Sensing Application Center Beijing China Mnr Land Satellite Remote Sensing Application Center Satellite Planning Department Beijing China Mnr The Fourth Topographic Surveying Team Technical Quality Management Department Harbin China

ISBN: (纸本)9798331515669

In order to better integrate and classify multi-source image features, we constructed a multi-source remote sensing data feature extraction and fusion classification framework U-DSDNet based on a combination of dual channel convolutional neural networks and self attention. Build two independent channel networks to extract feature information. Introducing self attention mechanism to fuse multi-source image feature information, improving classification accuracy and stability. Meanwhile, we use Sentinel series satellite data to demonstrate the classification accuracy of U-DSDNet. Through comparative analysis with UNet, DeeplabV3+, and PSPNet, the experimental results show that in the SEN-OPT-SAR dataset, the overall accuracy of the U-DSDNet model is 3.8%, 0.2%, and 1.6% higher than the three models, respectively, verifying the superiority and effectiveness of the U-DSDNet model in land classification tasks. © 2024 ieee.

关键词： Semantic Segmentation

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Attending to Transforms: A Survey on Transformer-based image Captioning 2

Attending to Transforms: A Survey on Transformer-based Image...

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2nd international conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and signal Processing, PCEMS 2023

作者： Ambilduke, Kshitij Jayakumar, Thanmay Farooqui, Luqman Padole, Himanshu Singh, Anamika Visvesvaraya National Institute of Technology Nagpur India Indian Institute of Technology Bhubaneswar India

ISBN: (纸本)9798350310719

image captioning is a challenging task that lies at the intersection of Computer Vision and Natural Language Processing. There exists a legion of works that generate meaningful and realistic descriptions of images. Recently, with the advent of attention mechanisms and transformers, there has been a drastic shift in modelling both language and vision tasks. However, there are very few extensive studies that review these approaches based on their progression, advantages and disadvantages. This paper presents a detailed summary of transformer-based models employed for tackling image captioning. In addition to this, we provide an overview of various pre-training tasks, datasets and metrics used for image captioning. Finally, the performance of all the reviewed approaches are compared on the COCO Captions dataset. © 2023 ieee.

关键词： Computer vision

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SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing image Dehazing Using Satellite-to-Ground NDVI Knowledge

SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remot...

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2023 ieee international conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Liu, Zepeng Yang, Zhicheng Zhu, Mingye Wong, Andy Wei, Yibing Han, Mei Yu, Jun Lai, Jui-Hsin University of Science and Technology of China China Ping An Technology China PAII Inc. United States University of Wisconsin - Madison United States

ISBN: (纸本)9798350313154

image dehazing is a meaningful low-level computer vision task and can be applied to a variety of contexts. In our industrial deployment scenario based on remote sensing (RS) images, the quality of image dehazing directly affects the grade of our crop identification and growth monitoring products. However, the widely used peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) provide ambiguous visual interpretation. In this paper, we design a new objective metric for RS image dehazing evaluation. Our proposed metric leverages a ground-based phenology observation resource to calculate the vegetation index error between RS and ground images at a hazy date. Extensive experiments validate that our metric appropriately evaluates different dehazing models and is in line with human visual perception. © 2023 ieee.

关键词： Remote sensing

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LLCNN-based image Enhancement with Squeeze and Excitation Network for Improved Visual Quality 16

LLCNN-based Image Enhancement with Squeeze and Excitation Ne...

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16th ieee international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Subburaj, Maheswari Shanmugam, Thirumurugan Sivaraman, Arun Kumar Nithiyanantham, Janakiraman Ravindran, Priya Sivam, Babu School of Cse Vellore Institute of Technology Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman Digital Engineering Solution Centre Photon Interactive Inc. Ontario Canada Department of Ece K L N College of Engineering Tamilnadu India

ISBN: (纸本)9798331505264

image enhancement, particularly in low-light conditions, has long been a research focus to improve visual quality. Low-light settings often cause visibility issues and detail loss, which recent deep-learning techniques have effectively addressed. This study explores the combination of a low-light convolutional neural network (LLCNN), a convolutional neural network designed explicitly for low-light image enhancement, with a Squeeze and Excitation Network (SEN), which recalibrate features to emphasise critical details and suppress noise. Integrating SEN into LLCNN enhances image details while reducing noise and artefacts typically present in low-light conditions. Experimental results show that this combined approach outperforms existing methods in objective metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), as well as visual quality. © 2024 ieee.

关键词： Convolutional neural networks

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ResNet50 for Accurate Plant Leaf Disease Detection and Classification 2

ResNet50 for Accurate Plant Leaf Disease Detection and Class...

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2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Rani, Ritu Gupta, Sheifali Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India

ISBN: (纸本)9798331540661;9798331540678

ResNet50 deep learning network in this research report obviously finds plant diseases. Divided into "Healthy," "Powdery," and "Rust," the data compiles 1530 images overall and generates testing, training, and sets. Every image is modified and scaled using techniques of validation data augmentation aimed to increase variance. ResNet50 models gain from pre-trained weights to be multi-class classification focused. Changes in learning rate and early training phase stopping help to prevent overfits. To ensure good performance, the model is assessed with accuracy, precision, recall, F1-score, and confusion matrix. Plotting of training and validation accuracy and loss curves helps one to track learning development. After that, the model is ready for use with an easy interface;field testing is carried out to confirm its performance on fresh data. Consistent updates and active learning strategies help to guarantee ongoing development. This approach seeks to generate an accurate, scalable, efficient system for plant disease classification thereby improving agricultural output and sustainability.

关键词： Plant Disease Classification ResNet50 Deep Learning image Recognition Agricultural technology Disease Detection Convolutional Neural Networks (CNN) ect

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A Deep Learning-based Pre-Trained VGG19 Model for Optical Character Recognition 2

A Deep Learning-based Pre-Trained VGG19 Model for Optical Ch...

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2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Singh, Gurpreet Guleria, Kalpna Sharma, Shagun Chitkara Univ Inst Engn & Technol Rajpura Punjab India

ISBN: (纸本)9798331540661;9798331540678

Optical Character Recognition (OCR) stands as a pivotal technology in the digitization and processing of textual information from images. In this study, we propose a novel approach to OCR leveraging the VGG19 convolutional neural network (CNN) architecture. VGG19, renowned for its depth and performance in image classification tasks, is repurposed here to tackle the intricate challenges of character recognition. Through extensive experimentation and evaluation, this study demonstrate the efficacy of the proposed approach in achieving state-of-the-art accuracy and robustness in extracting textual information from images. This study uses diverse datasets comprising printed and handwritten text samples, augmenting them using various techniques to enhance model generalization. The VGG19 model is trained end-to-end, with its convolutional layers serving as feature extractors for character recognition. This paper presents a novel approach to Optical Character Recognition (OCR) using the VGG19 convolutional neural network (CNN). OCR is a fundamental technology that converts printed or handwritten text into digital format, facilitating document digitization and information retrieval. The proposed method leverages the hierarchical features learned by VGG19 to accurately extract textual information from images. This study has conducted experiments using publicly available datasets, achieving significant improvements in both training and test accuracy across epochs. Specifically, the proposed model has achieved a training accuracy of 94.34% and a test accuracy of 94.96% after ten epochs of training. Furthermore, we observed a consistent decrease in both training and test loss throughout the training process, indicating effective convergence and refinement of the model parameters. These results demonstrate the efficacy of the VGG19-based OCR model in accurately recognizing characters from diverse input images, highlighting its potential for various real-world applications such a

关键词： Machine learning Deep learning Handwriting VGG19 Model image recognition CNN Layers

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An In-Depth Analysis of Application Protocols Performances in Various IoT Network Environments 8

An In-Depth Analysis of Application Protocols Performances i...

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8th ieee international conference on image and signal Processing and their Applications, ISPA 2024

作者： Belkheir, Mohammed Rouissat, Mehdi Boukhobza, Mohamed Achraf Mokaddem, Allel Belkhira, Hichame Sid Ahmed Lorenz, Pascal Bouziani, Merahi Beneddine, Messaouda Reguieg, Aya Nour Bachir University Center LIMA Laboratory El-Bayadh Algeria Nour Bachir University Center El-Bayadh Algeria Haute Alsace University Mulhouse France Djilali Liabes University Sidi Bel Abbes Algeria

ISBN: (纸本)9798350309249

The internet of Things (IoT) has emerged as a transformative technology, revolutionizing critical domains such as healthcare, transportation, and energy management by enabling seamless connectivity, real-time data insights, and enhanced automation. Therefore, considerations for both intrinsic and extrinsic constraints are essential in the design of application protocols for IoT networks. IoT nodes often have limited processing power, memory capacity, and battery life. Furthermore, these nodes are interconnected wirelessly within a topology that undergoes dynamic and random changes. This in-depth study aims to analyze the performance of MQTT-SN (Message Queuing Telemetry Protocol for Sensor Networks) and CoAP (Constrained Application Protocol), which are widely regarded as prominent options for the IoT application layer. The analysis focuses on evaluating the control overhead and total energy consumption associated with these protocols under various scenarios of topologies and densities of IoT *** simulation results reveal a significant difference in the generation of control packets at the network level between CoAP and MQTT-SN. Specifically, CoAP exhibits a higher control overhead, which varies based on the network density, while maintaining equivalent energy consumption for both protocols. Moreover, CoAP demonstrates superior efficiency in transmitting application data messages compared to MQTT- SN under similar network constraints. © 2024 ieee.

关键词： internet of things

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A Hybrid Joint Source-Channel Coding Scheme for Mobile Multi-hop Networks 59

A Hybrid Joint Source-Channel Coding Scheme for Mobile Multi...

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59th Annual ieee international conference on Communications (ieee ICC)

作者： Bian, Chenghong Shao, Yulin Gunduz, Deniz Imperial Coll London Dept Elect & Elect Engn London England Univ Macau State Key Lab Internet Things Smart City Taipa Macau Peoples R China Univ Macau Dept Elect & Comp Engn Taipa Macau Peoples R China

ISBN: (纸本)9781728190549

We propose a novel hybrid joint source-channel coding (JSCC) scheme for robust image transmission over multi-hop networks. In the considered scenario, a mobile user wants to deliver an image to its destination over a mobile cellular network. We assume a practical setting, where the links between the nodes belonging to the mobile core network are stable and of high quality, while the link between the mobile user and the first node (e.g., the access point) is potentially time-varying with poorer quality. In recent years, neural network based JSCC schemes (called DeepJSCC) have emerged as promising solutions to overcome the limitations of separation-based fully digital schemes. However, relying on analog transmission, DeepJSCC suffers from noise accumulation over multi-hop networks. Moreover, most of the hops within the mobile core network may be high-capacity wireless connections, calling for digital approaches. To this end, we propose a hybrid solution, where DeepJSCC is adopted for the first hop, while the received signal at the first relay is digitally compressed and forwarded through the mobile core network. We show through numerical simulations that the proposed scheme is able to outperform both the fully analog and fully digital schemes. Thanks to DeepJSCC it can avoid the cliff effect over the first hop, while also avoiding noise forwarding over the mobile core network thank to digital transmission. We believe this work paves the way for the practical deployment of DeepJSCC solutions in 6G and future wireless networks.

关键词： Semantic communications multi-hop channel Neural compression

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