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Retinal image segmentation algorithm based on hybrid pooling and multi-dimensional attention

Retinal image segmentation algorithm based on hybrid pooling...

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2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： Zhao, Dequn Li, Chunsheng Ma, Hongwei Zhang, Suming Faculty of Information Technology Beijing University of Technology Beijing China Beijing Institute of Aerospace Systems Engineering Beijing China

ISBN: (纸本)9781510680425

This paper propose a retinal image segmentation model according to hybrid pooling and multi-scale attention mechanism. Due to the crucial importance of retinal vessels for the segmentation of the retina and their intricate structure, which serves as vital diagnostic information for diseases, we focus on retinal images in medical imaging. We introduce a novel medical image segmentation model, MDA-Unet, built upon the Unet architecture. Extensive experiments are conducted on the DRIVE dataset. Addressing challenges such as limited data, varying image quality, and the presence of small structures in segmented target areas causing reduced segmentation accuracy, the proposed enhanced model MDA-Unet aims to overcome these issues. We investigate the impact of incorporating modules such as Exponential Moving Average (EMA), Multi-Path Module (MPM), and serpentine convolutional kernels on the model. The research results indicate that the addition of these modules enhances the ability of the model to be segmented. This improved model is tested with the DRIVE dataset, yielding segmentation results that surpass those of previously proposed segmentation models. Compared with the previous model segmentation results, the method proposed in this paper has achieved good results in fundus vascular segmentation. © 2024 SPIE.

关键词： image segmentation

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The concept of the internet of Things in the development of information and analytical systems based on the method of constructing a scalar assessment of the results of research activities of scientists 14

The concept of the Internet of Things in the development of ...

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14th international conference on Emerging Ubiquitous systems and Pervasive Networks / 13th international conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2023

作者： Biloshchytskyi, Andrii Omirbayev, Serik Mukhatayev, Aidos Biloshchytska, Svitlana Toxanov, Sapar Faizullin, Adil Astana IT University Astana010000 Kazakhstan Information Technologies Kyiv National University of Construction and Architecture Kiev01001 Ukraine

The article describes the creation of tools for building intelligent systems in the field of scientometrics as the concept of the internet of Things. The concept is presented as an information system where scientometric databases such as Scopus, WoS, as well as sites with databases of scientific projects and other databases act as sensors that generate data. systems of data accumulation and processing are systems created using microservices technology. At the output, we get a control signal in the form of a proposal to correct the trajectory of the development of the scientific potential as an organization or a scientist. The system will also suggest how to strengthen the research team by selecting scientific partners from the global scientific community. The article describes the structural model of the concept of the internet of Things in the form of a multi-level structure using microservice technologies. Also, the article suggests methods of constructing a scalar evaluation of the results of scientific research activities of scientists based on the analysis of citations of their publications. © 2024 Elsevier B.V.. All rights reserved.

关键词： internet of things

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An IoT based Electromyography signal Transmission from sEMG Electrodes to Client's Server with ICT Infrastructure 10

An IoT Based Electromyography Signal Transmission from sEMG ...

引用

10th international conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Pavithra, P.M. Balakrishnan, M. Anandaraj, A. Ramya, M. Sudharsan, R. Raja Sri Shanmugha College of Engineering and Technology Department of Artificial intelligence and Data Science Salem India Dr. Mahalingam College of Engineering and Technology Department of Artificial intelligence and Data Science Pollachi India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Sri Shanmugha College of Engineering and Technology Department of Information Technology Salem India Sri Shanmugha College of Engineering and Technology Department of Biomedical Engineering Salem India

ISBN: (纸本)9798350384369

Bio-electric signal like Electromyography (EMG) can measure signal data from nerves and muscles separately in the particular region of affected nerve or muscles from a Human Being. For incorporating these measurements, can use a non-invasive technique. The Surface electrode can be used for measuring these problems in non-invasive fashion. Here, the idea of internet of Things (IoT) can bring included advantage to applications with bio-electric signals to communicate to the doctors and patients such as human-PC cooperation by incorporating different advances for example, sensors, remote correspondence and information science. In this research, we worked on a remote bio-electric signals to another remote observing handling framework. A model with the contextual investigation of nerve and muscle signal acknowledgment utilizing 4-channel sEMG signals is executed. A Gaussian filter is prepared from one individual's sEMG signals with 4-nerve regions of human body such as ulnar, radial, median and radial. The introduced IoT application framework is actualized based on an 11-terminal bio-electric signal measuring device, with Arduino based Controller, Gateway, ESP8266 (Wifi) module, Server being the major mediums of remote transmission of signal data. The signal transmits at 35KB/s. In the framework, the ongoing sEMG signal from surface electrode is filtered from noise and other distortions using is given to the controller for performing the operation. © 2024 IEEE.

关键词： Gateways (computer networks)

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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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Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image Retrieval 29th

Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Te...

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29th international conference on Database systems for Advanced Applications (DASFAA)

作者： Liu, Haoyu Song, Yaoxian Wang, Xuwu Zhu, Xiangru Li, Zhixu Song, Wei Lie, Tiefeng Zhejiang Lab Res Ctr Intelligent Robot Hangzhou Peoples R China Zhejiang Univ Ctr X Mech Dept Engn Mech Hangzhou Peoples R China Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai Peoples R China

ISBN: (纸本)9789819755547;9789819755554

With the explosive growth of multi-modal information on the internet, unimodal search cannot satisfy the requirement of internet applications. Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities. Existing text-image retrieval research is mostly based on general vision-language datasets (e.g. MS-COCO, Flickr30K), in which the query utterance is rigid and unnatural (i.e. verbosity and formality). To overcome the shortcoming, we construct a new Compact and Fragmented Query challenge dataset (named Flickr30K-CFQ) to model text-image retrieval task considering multiple query content and style, including compact and fine-grained entity-relation corpus. We propose a novel LLM-based Query-enhanced method using prompt engineering based on LLM. Experiments show that our proposed Flickr30-CFQ reveals the insufficiency of existing vision-language datasets in realistic text-image tasks. Our LLM-based Query-enhanced method applied on different existing text-image retrieval models improves query understanding performance both on public dataset and our challenge set Flickr30-CFQ with over 0.9% and 2.4% respectively. Our project can be available anonymously in https://***/view/Flickr30K-cfq.

关键词： Text-image Retrieval Natural Query Compact and Fragmented Challenge Set Prompt-enhanced Method

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Automated Waste Classification using Convolutional Neural Network 14

Automated Waste Classification using Convolutional Neural Ne...

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14th international conference on Software technology and Engineering, ICSTE 2024

作者： Thinakaran, Rajermani Somasekar, J. Neerugatti, Vikram Guru Saran, P. INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Department of Computer Science and Engineering Karnataka Banglore India

ISBN: (纸本)9798350378955

Efficient waste management relies on precise waste classification, which facilitates recycling, composting, and appropriate disposal. Conventional waste classification methods often involve manual sorting or simple rule-based systems, which are labor intensive and prone to errors. This study investigates the application of Convolutional Neural Networks (CNNs) for automated waste classification using image data. CNNs have shown significant success in various image classification tasks due to their ability to learn hierarchical features directly from pixel values. The methodology outlined covers data collection, preprocessing, model architecture design, and training procedures for the CNN model. Experimental results demonstrate the effectiveness of the CNN approach in accurately categorizing various waste types. Additionally, we discuss the practical implications of CNN-based waste classification systems for real-world waste management applications. By leveraging CNNs, waste management processes can be optimized, leading to better resource utilization and environmental sustainability. The authors also highlight potential avenues for future research, including exploring advanced CNN architectures and integrating emerging technologies like internet of Things (IoT) sensors for improved waste management solutions. Overall, this study emphasizes the potential of CNNs to transform waste classification practices and enhance the efficiency of waste management systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on high accuracy classification of temperature signals based on OFDR technology and EfficientNet 4

Research on high accuracy classification of temperature sign...

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4th international conference on Advanced Manufacturing technology and Electronic Information, AMTEI 2024

作者： Shang, Chuanyin Li, Peng Tong, Zhengrong Wang, Hao Li, Sibo Tianjin Key Laboratory of Thin Film Electronics and Communication Devices Engineering Research Center for Optoelectronic Devices and Communication Technology the Ministry of Education School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

The development of machine learning is driving revolutionary changes and unprecedented opportunities across various industries. This paper introduces a new temperature monitoring method that combines the efficient deep learning model EfficientNet with Optical Frequency Domain Reflectometry (OFDR). We utilize distributed fiber-optic sensing technology based on OFDR to collect one-dimensional temperature signals under different temperature conditions, which we then transform into a two-dimensional image format suitable for machine learning processing. The dataset is subsequently trained using the EfficientNet deep learning model. The results show that the trained model achieves a high accuracy rate of 97.5% on the test dataset. This research not only demonstrates the feasibility of efficiently and accurately monitoring temperature changes but also provides new solutions and technological pathways for areas such as environmental monitoring, industrial automation, and safety systems. © 2025 SPIE.

关键词： Education and training Data modeling Performance modeling Fiber optics sensors Machine learning Matrices Fiber optics image processing Reliability signal processing

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Advanced Open-Set Fault Diagnosis in Power Grids via Text-image Matching 3

Advanced Open-Set Fault Diagnosis in Power Grids via Text-Im...

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3rd international conference on Artificial Intelligence, internet of Things and Cloud Computing technology, AIoTC 2024

作者： Jiang, Yang Deng, Xing Shi, Hang Miao, Yanwei Chen, Jinan Zhang, Zexi Co. Ltd. Guangdong Shenzhen China

ISBN: (纸本)9798331530280

The reliable operation of power grids is increasingly dependent on advanced fault diagnosis systems capable of identifying both known and emerging fault types. Traditional and many contemporary methods often struggle in open-set environments where new fault types may arise, posing a significant challenge to maintaining grid stability. In this study, we propose an advanced open-set fault diagnosis approach for power grids, utilizing a novel text-image matching technique. Our method integrates a fine-tuned BERT model for extracting domain-specific textual features with a Swin Transformer-based model for capturing multi-scale visual features. These features are fused using a contrastive learning framework, enabling the system to accurately diagnose faults based on textual descriptions, even when those faults have not been previously encountered in the visual domain. This approach not only improves the detection of known faults but also provides robust diagnostic capabilities for new and unforeseen fault types, offering a powerful tool for enhancing the resilience and reliability of modern power grids. © 2024 IEEE.

关键词： Power distribution faults

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Low-cost Prototype for IoT-based Smart Monitoring through Telegram 5

Low-cost Prototype for IoT-based Smart Monitoring through Te...

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5th international conference on Smart systems and Inventive technology, ICSSIT 2023

作者： Samuda, Prathima Sivachandar, K. Praveena, N.G. Nithiya, C. Kamalesh, D. Lokesh, C. Anna University Electronics and Communication Engineering R.M.K College of Engineering and Technology Chennai India

ISBN: (纸本)9781665474672

Surveillance systems have been becoming an essential part of restricted areas, industries, organizations, and homes for automation as well as security purposes. Nowadays, a variety of surveillance systems are available in the market with motion detection (video) or human detection (image) facilities. But, the installation of such surveillance systems will cost huge amounts, and traditional CCTV cameras can only record live video. In this work, a prototype of the hardware and software implementation of the cam motion alert has been developed using Espressif's latest microcontroller ESP32. internet of Things (IoT) is an ongoing development of the internet by which everyday 'things' objects have communication capabilities that allow them to send and receive data. This article presents a lowcost prototype of the smart monitoring system developed by using a motion-triggered image-capturing technology and a Passive Infrared (PIR) sensor. The motion trigger and alert camera consist of a motion sensor along with a vibration sensor that captures images using an ESP32 cam module. The PIR sensor detects a slight movement, immediately it wakes up the ESP32 cam, which will capture the human image and send it to an authentic telegram account anywhere in the world using a continuous Wi-Fi connection. © 2023 IEEE.

关键词： internet of things

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Recurrent Neural Network with Backstepping controller based Fractional-Order Chaotic System based Solution for Secure Communication and image Encryption 5

Recurrent Neural Network with Backstepping controller based ...

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5th international conference on IoT based Control Networks and Intelligent systems, ICICNIS 2024

作者： Narmada, A. Jain, Anuj Shukla, Manoj Kumar Lovely Professional University Electronics and Communication Engineering Punjab Phagwara India Lovely Professional University School of Electronics and Electrical Engineering Punjab Phagwara India University Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798331518097

Synchronisation of Fractional-order Non-linear systems can be used by incorporating additional security attributes. In this work, a Recurrent Neural network with Backstepping controller is designed used for synchronizing two identical non-linear fractional-order chaotic systems in master-slave configuration. The controller is split into two parts. One is designed at the sender's end and the other at receiver section to obtain synchronisation between Master and Slave systems. Also, two signals are transmitted in which one has message signal embedded in it and the other is a complicated combination of master states which is used for synchronisation purpose at the receiver end so that information signal can be recovered. A complex key set is designed with system parameters, fractional orders, initial conditions, so that no intruder can decode the message signal and receiver system should also know the keyset. The efficacy of the proposed scheme is demonstrated with the help of results presented in the end. © 2024 IEEE.

关键词： Recurrent neural networks

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