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IET conference Proceedings

IET Conference Proceedings

引用

8th international conference on Computing in Engineering and technology, ICCET 2023

ISBN: (纸本)9781839539237

The proceedings contain 85 papers. The topics discussed include: performance analysis of radial system with renewable sources;artificial intelligence framework for rice blast disease detection and classification using recurrent neural networks;road anomaly detection using self-supervised label generator with vertical disparity maps;sEMG signal analysis and classification for precision and pulp to pulp grip recognition;internet of Things and robotics: a review of concept, added value and applications;a deep learning based approach to enhance the Quality of Service (QoS) of next generation wireless systems by large intelligent surfaces;new applications of morphological methodology for the early detection of cancer;IoT based automatic fuel level monitoring system for automobiles;evolutionary technique for high utility sequential pattern mining;high utility sequence mining using cuckoo search algorithm;image restoration and object detection in unfavorable weather conditions for autonomous vehicles using deep learning approaches: a review;and a review of image super-resolution approaches based on deep learning methods.

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A Grouping Strategy-based Progressive Fusion Network for Hyperspectral image Super-Resolution

A Grouping Strategy-Based Progressive Fusion Network for Hyp...

引用

2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Lv, Guohua Zhang, Baodong Gao, Yongbiao Zhao, Guixin Wang, Guotao Wang, Juncan Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Ministry of Education China Beihang University Beijing China

ISBN: (纸本)9798350368741

Hyperspectral super-resolution involves combining low-resolution hyperspectral images with high-resolution multispectral images to produce a high-resolution hyperspectral image. Recently, although many methods for hyperspectral image super-resolution have been proposed, they often fail to fully utilize the high similarity among adjacent bands to enhance fusion performance. Therefore, we propose a grouping strategy-based progressive fusion network (GPFNet) for hyperspectral super-resolution. The core of GPFNet is the grouping strategy fusion block (GPF block), in which grouping-based spatial-spectral information fusion and spatial information refinement are performed. We design the spatial-spectral information fusion module (SSIFM) based on grouped convolutions to capture the feature differences from adjacent bands. To refine spatial details, we develop the spatial information enhancement module (SpaEM), which leverages the hierarchical features extracted by the multi-scale feature extraction module (MIEM). Additionally, a progressive fusion strategy, which involves using multiple upsampled hyperspectral images and downsampled multispectral images, further preserves spectral integrity and spatial details. Extensive experiments show that GPFNet outperforms state-of-the-art methods both qualitatively and quantitatively. © 2025 IEEE.

关键词： group convolution Hyperspectral image image fusion super-resolution

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Machine Learning based Detection System for Identifying Deepfake images and Social Bots 6

Machine Learning Based Detection System for Identifying Deep...

引用

6th international conference on Advancements in Computing, ICAC 2024

作者： Liyanage, Chamath Dematgoda, Hirushini Harindi, Dewmini Perera, Nirmal Fernando, Harinda Rathnayake, Samadhi Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

This paper presents a machine learning-based approach to detect two major forms of misinformation on social media platforms: deepfake images and social bots. For deepfake image detection, we propose a novel hybrid model combining DenseNet121 with custom Convolutional Neural Networks, achieving 97% accuracy on a dataset of 140,000 images. The social bot detection component utilizes a dual-branch architecture, processing tweet content through LSTM networks and user metadata through dense layers, attaining 91 % accuracy on the CIC truth seeker dataset. Both models demonstrate balanced performance across classes, addressing the challenges of evolving misinformation tactics. By integrating these detection systems, we aim to enhance the integrity and reliability of information ecosystems in digital spaces. Our approach contributes to the ongoing efforts to combat the spread of misinformation on social media platforms. © 2024 IEEE.

关键词： Bot (internet)

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Low-light Road image Enhancement based on Optimized MSR 3

Low-light Road Image Enhancement based on Optimized MSR

引用

3rd international conference on Advanced Manufacturing technology and Electronic Information, AMTEI 2023

作者： Lan, Haoqiang Huang, Kaiqi Huang, Zihao School of Electrical Engineering and Automation Jiangxi University of Science and Technology Ganzhou341000 China

ISBN: (纸本)9781510674806

Utilizing machine vision to acquire road environment information is a crucial factor influencing the performance of advanced driver assistance systems, but under low-light conditions, obtaining high-quality road information directly through vision becomes challenging, necessitating image enhancement. Due to uneven illumination in low-light conditions, existing enhancement methods often lead to issues such as glare and blurred details in bright areas. In this paper, we propose an optimized Multi-Scale Retinex (MSR) image enhancement ***, RGB images are converted to the YUV format, and the MSR algorithm is applied to enhance the Y channel. The local background brightness is then incorporated into the Just Noticeable Difference (JND) model to establish a non-linear relationship model between background brightness and adjustment factors. This enables adaptive adjustment of image enhancement intensity. Subsequently, a non-linear bilateral filtering function is applied to smooth the adjusted image, followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance its contrast. Finally, the resulting image is fused with the original image in a 1:1 *** results indicate that, compared to the combination of MSR and histogram equalization, the proposed method achieves a 4.5% increase in standard deviation, a 7% increase in information entropy, and a 25% improvement in peak signal-to-noise ratio. © 2024 SPIE.

关键词： image enhancement

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Explaining Representations in Correlation-based Deep Multiview Representation Learning

Explaining Representations in Correlation-based Deep Multivi...

引用

2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Kuschel, Maurice Alkhatib, Amr Hasija, Tanuj Boström, Henrik Signal and System Theory Group Paderborn University Paderborn Germany Division of Software and Computer Systems KTH Royal Institue of Technology Stockholm Sweden

ISBN: (纸本)9798350368741

Multiview representation learning techniques based on deep correlation maximization have become increasingly popular for learning meaningful and compact representations from multiview data. Even though their performance is state-of-the-art in many interpretability-critical fields, their black-box behavior poses a problem and restricts their usability. To overcome this restriction, we propose XDCCA (eXplanations for Deep Canonical Correlation Analysis), an explanation strategy using characteristic rules in combination with SHAP that exploits the inherent structure of latent spaces created by correlation maximization techniques. We demonstrate how XDCCA allows for interpreting learned representations and their correlation using real medical time series and synthetic image data. © 2025 IEEE.

关键词： CEGA Deep Canonical Correlation Analysis Explainable AI Multiview Representation Learning SHAP

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Enhanced Object Detection by Integrating Camera Parameters into Raw image-based Faster R-CNN 26

Enhanced Object Detection by Integrating Camera Parameters i...

引用

IEEE 26th international conference on Intelligent Transportation systems (ITSC)

作者： Wei, Chuheng Wu, Guoyuan Barth, Matthew Chan, Pak Hung Donzella, Valentina Huggett, Anthony Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92507 USA Univ Warwick WMG Coventry CV4 7AL England ONSEMI Greenwood House Bracknell RG12 2AA England

ISBN: (纸本)9798350399462

The rapid progress in intelligent vehicle technology has led to a significant reliance on computer vision and deep neural networks (DNNs) to improve road safety and driving experience. However, the image signal processing (ISP) steps required for these networks, including demosaicing, color correction, and noise reduction, increase the overall processing time and computational resources. To address this, our paper proposes an improved version of the Faster R-CNN algorithm that integrates camera parameters into raw image input, reducing dependence on complex ISP steps while enhancing object detection accuracy. Specifically, we introduce additional camera parameters, such as ISO speed rating, exposure time, focal length, and F-number, through a custom layer into the neural network. Further, we modify the traditional Faster R-CNN model by adding a new fully connected layer, combining these parameters with the original feature maps from the backbone network. Our proposed new model, which incorporates camera parameters, has a 4.2% improvement in mAP@[0.5,0.95] compared to the traditional Faster RCNN model for object detection tasks on raw image data.

关键词： Raw image object detection faster R-CNN deep neural networks

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Efficient Classification of Bird Species Using Photographic images: A MobileViT based Approach 3

Efficient Classification of Bird Species Using Photographic ...

引用

3rd international conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Polisetty, Venkata Ramana Murthy Chokkalingam, Sp Chennai601103 India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai601103 India

ISBN: (纸本)9798350372120

In the quest to enhance biodiversity conservation and support ecological monitoring, the identification of bird species through automatic recognition systems has emerged as a vital tool. This study introduces a novel approach employing the Mobile Vision Transformer model, capitalizing on its efficiency in handling complex visual data across a comprehensive dataset encompassing 525 bird species. MobileViT integrates the advantages of Convolutional Neural Networks (CNNs) with Transformer technology, surpassing typical deep learning models in terms of feature extraction and contextual comprehension abilities. Incorporating advanced data augmentation methods has bolstered the dataset's variability, ensuring the model's preparedness for a wide spectrum of imaging scenarios. With a direct and impressive outcome, the model achieved a 97.5% accuracy rate in species identification. The adoption of MobileViT promises a scalable and potent solution for bird species recognition within ornithological research, marking a forward leap in employing machine learning for environmental conservation. © 2024 IEEE.

关键词： image classification

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Node Layout Algorithm based Environmental Monitoring System for Industrial IoT 4

Node Layout Algorithm Based Environmental Monitoring System ...

引用

4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Ren, Xinyi Li, Kangle Liu, Yangsen Fei, Junshu He, An Zhuge, Xiying Chengdu University of Technology School of Computer and Network Security Sichuan Chengdu China

ISBN: (纸本)9798331534622

As the internet of Things (IoT) technology continues to revolutionize, modern industrial production systems are undergoing profound changes. Current production methods require the expansion of production space to accommodate more equipment, raw materials and workers. The traditional monitoring systems mostly use a simple sensor monitoring method. This kind of monitoring method has the problems of too much monitoring data which is not easy to manage, and the system can not respond in time after the disaster occurs. based on this situation, this paper proposes a node layout algorithm based industrial IoT environment monitoring system. The system utilizes Zigbee wireless communication technology to construct the network. The master station uses the Loongson 2K1000LA development board as the display and calculation terminal to interact with the cloud platform. The sub-station uses Loongson 1C102 development board as the main control chip to monitor the environmental data. The cloud platform carries out image recognition and sends peripherals control commands. Compared with the traditional monitoring system, this system reduces the peripheral response delay by more than 1.5 seconds, improves the monitoring efficiency by 33%, The peripheral response time difference is reduced by 1.499 seconds, an increase of 61.3%.and facilitates managers to formulate effective security prevention strategies. © 2024 IEEE.

关键词： Zigbee

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Wind Turbine Fault Diagnosis based on Gramian Angle and Federated Learning 4

Wind Turbine Fault Diagnosis Based on Gramian Angle and Fede...

引用

4th international conference on Energy Engineering and Power systems, EEPS 2024

作者： Yuan, Lu Chen, Dawei Shanghai DianJi University Shanghai China

ISBN: (纸本)9798350366914

To improve the efficiency and accuracy of wind turbine fault detection while maintaining data confidentiality, we propose a convolutional neural network model that integrates Gram's Angle Summation Field (GAF) with a federated learning algorithm. Initially, the one-dimensional time series signal is converted into a two-dimensional image using GAF technology, and a Convolutional Neural Networks model is developed for feature extraction. To address the issue of uneven client data distribution and the tendency to get stuck in local optima, local drift variables are introduced to monitor the disparity between the learned local model and the global model, thereby enhancing fault identification accuracy in diverse data sets. Experimental findings demonstrate that the proposed model algorithm outperforms other algorithms significantly, showcasing its superiority and effectiveness. This study serves as a valuable addition to contemporary wind turbine fault diagnosis technology. © 2024 IEEE.

关键词： Federated learning

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Operation state monitoring method of double column center fracture horizontal rotary disconnector based on laser point cloud 3

Operation state monitoring method of double column center fr...

引用

3rd international conference on Energy internet and Power systems

作者： Lin, Hongdong Li, Fupeng Xie, Xingsheng Jiang, Yuhuan Zhongshan Power Supply Bur Guangdong Power Grid C Zhongshan 528400 Guangdong Peoples R China

A technique utilizing laser point cloud data has been developed for the surveillance of the operational status of a double-column center fracture horizontal rotary disconnector switch. The method includes an action monitoring model for isolating switches to identify and segment point cloud data of isolating switch conducting arm. The model consists of a local feature extraction module, a point cloud feature dimension attention module, a classification module, and a segmentation module. The segmentation module realized the point cloud segmentation function by Interpolate and Unit Pointnet network. The classification module implements the point cloud classification function through a point net layer Pointnet and a fully connected layer (FC). Ultimately, the status of the disconnector's operation is determined by analyzing the differences in the point cloud vectors of the conductive arm between successive frames.

关键词： image segmentation

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