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Lightweight Transformer with GRU Integrated Decoder for image Captioning 16

Lightweight Transformer with GRU Integrated Decoder for Imag...

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16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Sharma, Dhruv Dingliwal, Rishabh Dhiman, Chhavi Kumar, Dinesh Delhi Technol Univ Dept ECE Delhi India

ISBN: (纸本)9781665464956

image captioning develops a relationship between visual and text information to generate a sequence of words as captions. Transformers perform machine translation and language comprehension together using encoder and decoder structure. With the aim of building a lightweight and production deployment friendly model, we present the Lightweight Transformer with a GRU integrated decoder for image Captioning. In the presented model, the number of encoders and decoders in standard architecture are reduced to single encoder and GRU integrated decoder. Also, Multi-level rich visual features from incepptionV3 improves single unit encoder encoding performance. To validate the efficiency of the proposed Lightweight Transformer architecture extensive experiments are carried out on MSCOCO image captioning dataset. The model achieves appreciable performance in comparison to other state-of-the-art.

关键词： Transformers image Captioning Gated Recurrent Unit (GRU) MSCOCO

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Design and implementation of a scenario classification system for the internet of things application 3

Design and implementation of a scenario classification syste...

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3rd international conference on Advanced Algorithms and signal image Processing, AASIP 2023

作者： Zheng, Hao Li, Hong Xu, Bo Heilongjiang Provincial Research Institute of Spatial Planning China

ISBN: (纸本)9781510668522

With the rapid development of the internet of Things industry today, it is imperative to classify the internet of Things scenarios to adapt to the needs of the internet of Things in different scenarios. In recent years, Convolutional Neural Network (CNN) has succeeded in scene parsing. The methods based on CNN generally extract the visual features of the input images through the step-by-step abstraction of multiple convolutional layers and then parse the corresponding segmentation results of the output images through the step-by-step restoration of multiple deconvolutions. Screen the scene pictures of the internet of Things from the large data set, use the image recognition algorithm to extract the features of the photographs, and use the extracted features to cluster the image set (for example, it can be divided into a family environment, car environment, etc.), divide the pictures with similar features into the same set, and finally measure the clustering accuracy to realize the internet of Things scene classification. Finally, scene recognition accuracy is about 87%, and the clustering of feature vectors is also satisfactory. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： internet of things

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FPGA Implementation of Various Division Algorithms for image Processing Applications - A Comparative Analysis

FPGA Implementation of Various Division Algorithms for Image...

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2024 international conference on signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Jayanthi, B. Kumar, L.S. Someshwaran, A. Sandya, M. Bharadwaj, Nandhini National Institute of Technology Puducherry Department of ECE Karaikal India

ISBN: (纸本)9798331540685

Division is one of the most commonly sort after algorithm for performing image processing operations such as normalization, filtering, enhancement, deconvolution etc. Hence, the design of efficient division algorithm is highly essential in order to obtain a better image processing algorithm The first and foremost step in the implementation of any algorithm in hardware is to identify the number systems used to represent the inputs and outputs. The inputs, outputs and complexity of the algorithm varies based on the number system used. This paper proposes a recursive subtraction based fixed-point division algorithm. This work also provides a comparative analysis on various aspects such as area, power, delay and throughput, on comparing the proposed recursive subtraction based fixed-point division algorithm with the existing floating-point division, restoring integer division and non-restoring integer division algorithms and operator based fixed-point division algorithm. © 2024 IEEE.

关键词： image enhancement

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Deep Metric Learning for Transparent Classification of Covid-19 X-Ray images 16

Deep Metric Learning for Transparent Classification of Covid...

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16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Calderaro, Salvatore Lo Bosco, Giosue Rizzo, Riccardo Vella, Filippo Univ Palermo DMI Palermo Italy Natl Res Council Italy ICAR Palermo Italy

ISBN: (纸本)9781665464956

This work proposes an interpretable classifier for automatic Covid-19 classification using chest X-ray images. It is based on a deep learning model, in particular, a triplet network, devoted to finding an effective image embedding. Such embedding is a non-linear projection of the images into a space of reduced dimension, where homogeneity and separation of the classes measured by a predefined metric are improved. A KNearest Neighbor classifier is the interpretable model used for the final classification. Results on public datasets show that the proposed methodology can reach comparable results with state of the art in terms of accuracy, with the advantage of providing interpretability to the classification, a characteristic which can be very useful in the medical domain, e.g. in a decision support system.

关键词： image diagnosis Covid-19 Chest-X-ray embeddings

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Application of Smart Terminals in the Power Industrial internet

Application of Smart Terminals in the Power Industrial Inter...

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international Hydropower Development conference, IHDC 2024

作者： Fuqiang, Huang Zhengpu, Chen Jiangtao, Xiao Zhaoan, Jin Jun, Chen Hai, Yang Xiluodu Hydropower Station China Yangtze Power Co. Ltd. Yunnan Province Yongshan County657300 China State Grid International Development Co. Ltd. Hong Kong Shanghai Liqian Power Technology Co. Ltd. Shenzhen China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (纸本)9789819791835

The Power Industrial internet serves as an essential platform for the digital transformation of the electric power industry, during its implementation, the integration of Information technology (IT) and Operational technology (OT) in power production and maintenance has posed significant challenges, necessitating the development of intelligent equipment based on Cyber-Physical systems (CPS) to bridge this gap. Microcomputer platforms are well-established and reliable systems capable of running various complex algorithms. However, traditional microcomputer-based protection devices face limitations such as firmware programming, limited types of signal sampling, and insufficient data transmission capabilities. In the current era of digital transformation, there is an urgent need for intelligent devices and smart terminals that can overcome these shortcomings through cloud-edge collaboration to assist the digital transformation of the power industry, continuously creating effective applications that enhance quality and efficiency, and fostering new productive forces. This article starts from the strategic orientation of industrial development, the problem orientation of digital transformation, and the demand orientation for quality improvement and efficiency enhancement. It focuses on the key issues in the advancement of the industrial internet, proposes solutions based on microcomputer platforms, and explores new directions in the development of intelligent and digital transformation in the field of relay protection specialization. © The Author(s) 2025.

关键词： Cloud-Edge Collaboration Cyber-Physical systems Industrial internet Microcomputer Platform Power Industry Smart Terminal

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An accelerated algorithm for ECG signal denoising 16

An accelerated algorithm for ECG signal denoising

引用

16th international conference on signal-image technology and internet-based systems (SITIS)

作者： De Luca, Pasquale Galletti, Ardelio Marcellino, Livia Parthenope Univ Naples Dept Sci & Technol Naples Italy

ISBN: (纸本)9781665464956

The Electrocardiogram (ECG) signal is an important tool for cardiovascular diseases analysis. However, still today acquisition devices produce noisy signals that degrades the quality of information by corrupting important features. To improve the quality of the acquired data a filtering process is mandatory. Moreover, a real-time filtering of ECGs, in order to obtain a diagnosis as quickly as possible is a very interesting challenge. In this paper, we consider as denoising filter, the Savitzky-Golay method and we propose a parallel algorithm implementing it. The procedure exploits the computational power of Graphics Processing Units (GPUs). Results in terms of performance and quality are provided.

关键词： ECG denoising SG filter parallel algorithms GP-GPU

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Text-to-image Retrieval based on Zero-shot Transfer Learning with CLIP Model and Vector Database 11

Text-to-image Retrieval Based on Zero-shot Transfer Learning...

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11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Xie, Junfeng Chen, Junying School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798331531904

With the continuous increase in the number of internet users, the explosion of multimodal data on the web has led to a growing demand for image retrieval. Current text-to-image retrieval systems often rely on keyword matching, which frequently fails to accurately capture users' retrieval needs. This work proposes a text-to-image retrieval method based on zero-shot transfer learning with the CLIP model and vector database, designed to be applied in text-to-image retrieval systems. By leveraging the semantic alignment capability of the CLIP model for text and images, this study effectively addresses the semantic gap problem in text-to-image retrieval. Additionally, the method utilizes the Milvus database for efficient storage and retrieval of image feature vectors, thereby enhancing system stability. Experimental results on the Flickr30k dataset demonstrate that this method improves the accuracy of text-to-image retrieval, verifying its effectiveness. Furthermore, this work extends the proposed method by incorporating the iFLYTEK Spark Cognitive Model to support image retrieval using Chinese text descriptions, further broadening the system's applicability and reliability. © 2024 IEEE.

关键词： Zero-shot learning

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Equipment Fault image Recognition Method based on Deep Integrated Learning with Hyperparameter Optimization 6

Equipment Fault Image Recognition Method Based on Deep Integ...

引用

6th IEEE international conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Chen, Pintong Guan, Di Chen, Jing Guangdong University of Technology Guangzhou China Liaoning Technical University Huludao China Harbin Institute of Technology Weihai China

ISBN: (纸本)9798350374315

In order to solve the problem of low precision and low stability of deep learning network for industrial equipment fault recognition under strong background noise, a method of equipment fault image recognition based on deep integrated learning with hyperparameter optimization was proposed. Firstly, the time-frequency image data set is constructed by using two-dimensional wavelet transform of the original vibration signal. Secondly, the noise resistance of each CNN network architecture is tested, and the change of network performance is studied by embedding different noise intensities into the data. Then, group optimization algorithm is used to optimize the learning rate and batch sampling parameters of the optimal architecture model. Finally, decision fusion is used to integrate the trained model, so as to improve the stability and image recognition accuracy of the integrated network. Verified by the bearing data of Western Reserve University, the proposed algorithm has good diagnostic accuracy and stability. © 2024 IEEE.

关键词： Wavelet transforms

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Semantic Communication based on Content Style Adaptation for image Transmission 16

Semantic Communication Based on Content Style Adaptation for...

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16th international conference on Wireless Communications and signal Processing, WCSP 2024

作者： Song, Mengshu Ma, Nan Liu, Yiming Shen, Linzi Dong, Chen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Peng Cheng Laboratory Department of Broadband Communication Shenzhen China

ISBN: (纸本)9798350390643

The development of semantic communication has promoted the research on image transmission. Content and style are two crucial characteristics of image information. Existing studies have explored image information transmission and recovery methods in semantic communication systems, but do not consider both image style information and content information simultaneously. To address this issue, based on deep joint source-channel coding (JSCC) structure, a novel system named semantic communication based on content style adaption (CSASC) for image transmission is proposed in this paper. Specifically, a novel semantic information extraction method are designed to extract the semantic content information and semantic style information of images, meanwhile an image content-style adaptive reconstruction method is proposed by fusing semantic content and semantic style features to improve the image reconstruction accuracy. In addition, the impact of the weighting of semantic style and content information on image reconstruction quality is analyzed. Results demonstrate that the proposed method has better recovery results compared with existing image semantic communication methods. © 2024 IEEE.

关键词： content information image reconstruction semantic information style information

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Enhanced Edge Attention-based SRCNN for Retinal image Super-Resolution 1

Enhanced Edge Attention-Based SRCNN for Retinal Image Super-...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Koshika, Manoj Kandukuri, Prabhakar Moppey, Shyam Kumar Chaitanya Bharathi Institute of Technology Department of Artificial Intelligence & Machine Learning Gandipet Hyderabad India

ISBN: (纸本)9798331529246

The early recognition of retinal disorders such as cataracts, glaucoma, and retinal problems is highly dependent on high-resolution retinal imaging, which is a complex issue because of poor image quality and the lack of important features. The proposed Enhanced Edge Attention Super Resolution Convolutional Neural Network (EEASRCNN) is designed to improve the quality of retinal images high precisely. The model preserves important details like blood vessels and the optic disc by using a custom edge loss function, sharpening filters, and combined attention mechanisms. With a high structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) score and an accuracy of 97.16% in preserving structural details, EEASRCNN outperforms traditional super-resolution methods after being trained on a dataset of diverse retinal images. EEASRCNN offers effective image super resolution in real-time medical image diagnosis. © 2025 IEEE.

关键词： Ophthalmology

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