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Smart and Safe Conveyor Belt System for Bottling Plants Using IoT 3

Smart and Safe Conveyor Belt System for Bottling Plants Usin...

引用

International conference on Advances in Computing, communication and Applied Informatics (ACCAI)

作者： Parimala, R. Srinivasan, K. Kumaran, Sreejith K. P. Sri Ramakrishna Engn Coll Dept Elect & Instrumentat Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

In modern industrial environments, automation is critical in enhancing efficiency and safety. This paper proposes an innovative approach to controlling industrial conveyors by integrating a Raspberry Pi with a digital image processing system capable of real-time human detection. Consequences aside, the principal objective is to cease conveyor operation immediately upon detecting an approaching individual to prioritize individuals' safety and prevent catastrophes. In 2021, a thorough examination of conveyor accidents across seven discrete heavy industries reaffirmed the imperative nature of implementing such precautions. The system employs the OpenCV module, which comprises object detection algorithms and potentially deep learning models such as YOLO or Faster R-CNN, to discern individuals via digital image processing methods and the Raspberry Pi's computational capabilities. Audible and visual feedback devices provide information on the conveyor's status, and failsafes are incorporated to halt its motion promptly in the event of a malfunction. This endeavor aims to enhance industrial safety by integrating software and physical components. The importance of automation in fostering safer work environments and mitigating potential hazards is underscored.

关键词： Conveyor Safety Raspberry Pi Automation Person Detection Industrial Accident Prevention Digital Image processing

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Optimal Design of communication Digital Twin System Architecture Based on Neural Network Model

Optimal Design of Communication Digital Twin System Architec...

引用

2023 International conference on Computer Simulation and Modeling, information Security, CSMIS 2023

作者： Li, Lei Wang, Yixi Wang, Shengwei Ye, Yuxuan Zhang, Yujie Information & Communication Branch of State Grid Hubei Electric Power Company Hubei Wuhan430077 China

ISBN: (纸本)9798350343724

With the deepening of communication engineering research, many advanced technologies are gradually produced in the signal processing of digital communication, among which the automatic modulation and identification of digital communication signals effectively improve the processing level of communication signals. In order to realize the intelligentization of all-optical network, this article puts forward the architecture optimization strategy of communication digital twin system based on artificial neural network (ANN) model. The average detection accuracy of the proposed method for communication digital twin system is 18.6% higher than that of the traditional method. It is said that the optimization method of communication digital twin system based on ANN shows remarkable effectiveness in the experiment. Through high-precision data acquisition, effective ANN model learning and deduction, and life-cycle prediction ability, this method provides strong support for the optimization of communication system. © 2023 IEEE.

关键词： Signal processing

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Investigating the Effects of Image Restoration Algorithms on Image Quality in Digital Image processing 3

Investigating the Effects of Image Restoration Algorithms on...

引用

3rd International conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Dhabliya, Dharmesh Saini, Rajat Yadav, Dhyan Geetha, G. Singh, Baldev Sripriya, T. Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

This study examines the effect of various image recovery algorithms on photo great in digital photo processing. Especially, the consequences on the visual quality of the photographs are studied through diverse metrics along with contrast, brightness, sharpness, and color saturation. Additionally, the algorithms are compared to every other as a way to decide which algorithm produces the quality effects. The studies will use images from public picture databases and effects can be gathered the use of photo first-rate evaluation software. Moreover, diverse noise and distortion distortions are applied to the photos to be able to investigate the impact of the distinctive algorithms on them. By means of determining variations in photograph first-rate, beneficial insights at the performance of numerous image recuperation algorithms can be obtained. In conclusion, this studies will offer a complete evaluation of the performance of diverse picture recovery algorithms utilized in digital photograph processing, losing light on which algorithms satisfactory enhance the visual nice of snap shots. © 2023 IEEE.

关键词： Complete Losing processing Satisfactory visual

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Enhancing Image Captioning through the Integration of Image processing and NLP 8

Enhancing Image Captioning through the Integration of Image ...

引用

8th IEEE International conference on Computing, communication, Control and Automation, ICCUBEA 2024

作者： Ugale, Sakshi Jain, Druwil Chikmurge, Diptee Barve, Sunita S. Mit Academy of Engineering School of Computer Engineering Alandi Dist. Pune India

ISBN: (纸本)9798350391770

The study offers a novel method for captioning images by combining Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). A feature extractor called VGG19 can be used to take complex photos and extract high-level visual representations from them. The aforementioned visual inputs are subsequently fed into a language model that employs a GRU and Bi-directional LSTM, two widely recognised models for handling sequential dependencies, to produce captions that make sense and are contextually relevant. Selective information update is made possible by the GRU's gating mechanism, making it simpler to capture long-range dependencies in the output captions. The combination of VGG19, GRU, and Bi-directional LSTM provides a comprehensive and hierarchical method for photo captioning that extracts rich visual information required for comprehending complex picture content. GRU's gating technique makes it easier to analyse specific information, which improves the model's comprehension of complex relationships found in output captions. In order to provide more accurate image descriptions, the suggested methodology seeks to improve the synergy between visual and verbal knowledge. Comparing experimental assessments against modern technique approaches on benchmark datasets with metrics like BiLingual Evaluation Understudy (BLEU), METEOR, and CIDEr shows encouraging results. In addition to its potential uses in computer vision(CV), accessibility, and human-computer interaction, this work advances the field of image caption development. © 2024 IEEE.

关键词： Convolutional neural networks

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MD-EPN: An Efficient Multimodal Emotion Recognition Method Based on Multi-Dimensional Feature Fusion 4

MD-EPN: An Efficient Multimodal Emotion Recognition Method B...

引用

4th International conference on Neural Networks, information and communication Engineering, NNICE 2024

作者： Xie, Kaizang Xie, Ziyang Lanzhou University School of Information Science & Engineering Lanzhou China

ISBN: (纸本)9798350394375

Multimodal emotion recognition is extensively applied in diverse areas including driving monitoring, online education, telemedicine, and customer service, marking it as a significant technology in contemporary research. Traditional emotion recognition methods predominantly concentrate on unimodal data processing, which can lead to low accuracy and substantial information loss. In response to these issues, this paper introduces an innovative Multi-Dimensional Emotion Perception Network (MD-EPN) that leverages advanced technologies to integrate textual, auditory, and visual data, thereby aiming to enhance the overall comprehensiveness and precision of emotion recognition. By focusing optimizations on crucial dimensions, the proposed architecture not only bolsters the model's performance but also strikes an effective balance between computational efficiency and accuracy retention, exhibiting exemplary emotion classification accuracy on the CMU-MOSI dataset. © 2024 IEEE.

关键词： Emotion Recognition

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Lightening the Load: Lightweighting Multimodal Understanding for visual Grounding Tasks 4

Lightening the Load: Lightweighting Multimodal Understanding...

引用

4th IEEE International conference on Data Engineering and communication Systems, ICDECS 2024

作者： Pragati Singh, Navjot Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India

ISBN: (纸本)9798350393354

In this paper, we propose a refined approach for visual grounding to find the most relevant object according to a natural language query. Typically, the machine must comprehend the question, recognise the main ideas in the picture, and then locate the target object by providing its bounding box. Our model adopts an end-to-end modulated detector, utilising a transformer-based architecture for the early fusion of text and image modalities, thus significantly improving task efficiency. Large numbers of trainable parameters, long training times, and high computational complexity are common challenges state-of-the-art models for vision language problems are facing nowadays. Our work tackles these problems by minimising the number of parameters and computational complexity while also reducing training time. Using our techniques, we can reduce training time by about 12% without sacrificing accuracy much on datasets flickr30k and MSCOCO. This accomplishment shows how to balance accuracy and processing efficiency in vision-language tasks effectively. Our approach can be easily extended to other problems, such as visual question answering and image caption generation. © 2024 IEEE.

关键词： Computational complexity

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Sign Language Recognition Using Convolutional Neural Network 4th

Sign Language Recognition Using Convolutional Neural Network

引用

4th International conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Potluri, Tejaswi Vallurupalli Nageswara Rao Vignana Jyothi Inst En Vignana Jyothi NagarPragathi Nagar Hyderabad 500090 Telangana India

ISBN: (纸本)9783031837920;9783031837937

Sign language detection is essential for improving accessibility, communication, and inclusion for individuals who are deaf or hard of hearing. This technology enables smooth communication across various environments, including schools, workplaces, healthcare settings, and everyday interactions. By accurately interpreting sign language gestures, it bridges communication barriers, providing equal access to information and services. In education, sign language detection supports language learning and academic success for deaf students, while in healthcare, it ensures that medical information is conveyed accurately, enhancing care quality. In the workplace, it fosters inclusivity by enabling communication between deaf employees and their colleagues, promoting equal opportunities and professional growth. Additionally, during emergencies, it can save lives by enabling swift communication with emergency responders. This study presents a robust sign language detection system based on convolutional neural networks (CNNs), trained on a dataset of 2,515 images representing 36 distinct ASL gestures. The system employs a comprehensive preprocessing pipeline, with images standardized, resized, and split for training and validation, resulting in an accuracy rate of 85.87% across ASL letters and numerals. Real-time testing, facilitated by OpenCV's video capture, demonstrates the model's effectiveness in live gesture recognition with minimal delay. Despite minor errors in misclassification and occlusion, the system shows strong potential to support inclusive communication across settings and to further advance sign language recognition technology.

关键词： Convolutional neural networks Image processing Feature extraction

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Research on adolescent emotion recognition based on visual information

Research on adolescent emotion recognition based on visual i...

引用

2024 International conference on Signal processing and communication Security, ICSPCS 2024

作者： Ji, Mingming Han, Lifeng Yan, Yuchao Zhang, Xihui Yang, Chen Han, Zhenwei Dang, Chenxin School of Information Engineering Xi'an Mingde Institute of Technology Xi'an710124 China

ISBN: (纸本)9781510681699

Convolutional Neural Networks (CNN) are widely utilized in the field of computer vision, garnering particular attention in facial emotion recognition. Literature analysis indicates that attention mechanisms significantly enhance model precision and accuracy. However, existing facial emotion recognition models have not fully considered the interplay between convolutional neural networks and machine vision, nor the correlation between facial emotion recognition and the target user groups. This paper proposes a novel model, MD-CNN, which integrates convolutional neural networks with an attention mechanism and selects appropriate datasets based on the characteristics of the target groups. The model design includes an attention layer and experiments are conducted using the Affect Net dataset. Comparisons with other CNN emotion recognition models demonstrate that the proposed model significantly improves accuracy. © 2024 SPIE.

关键词： Emotion Recognition

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A Chinese Ink-Wash Style Transfer Method for 3D Scenes 9

A Chinese Ink-Wash Style Transfer Method for 3D Scenes

引用

9th International conference on communication, Image and Signal processing, CCISP 2024

作者： Yu, Pengchen Zhu, Qing Zhu, Wenzhe Qi, Na Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9798350356656

Rendering 3D scenes in traditional Chinese ink-wash style has been a topic worth researching. While current 3D ink-wash style rendering methods can produce satisfying static images that reproduce the artistic style of ink-wash paintings, they often lack versatility and fail to deliver dynamic visual effects in certain circumstances. We propose an ink-wash style transfer method for 3D scenes that leverages neural style transfer techniques primarily targeted for images. To begin, we use a landscape ink-wash painting as the style reference to train a style transfer model and transfer ink-wash style presented by the style reference onto given input image, while retaining its original content. Subsequently, we apply this model into a real-time environment and stylize 3D scene objects in the trained ink-wash style. During the training of our model, Sliced Wasserstein loss is leveraged for enhanced style representation and depth loss is introduced to further emphasize depth information of objects. Qualitative and quantitative experiments showed that our approach achieved more appealing visual effects and better performance compared to state-of-the-art methods. © 2024 IEEE.

关键词： 3D modeling

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Fully Optical Integrated Mixture-of-Experts System 24

Fully Optical Integrated Mixture-of-Experts System

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4th International conference on Signal processing and communication Technology, SPCT 2024

作者： Li, Chen Shandong Massive Information Technology Research Institute Shandong Jinan China

ISBN: (纸本)9798400710636

A fully optically integrated Mixture-of-Experts (MoE) system is introduced to address the explosive growth in computational power demands in the development of artificial intelligence technologies. Here, we highlights the potential of optical computing in solving communication challenges for MoE models and proposes a fully optically integrated MoE system. This system integrates on-chip subwavelength diffraction units (SWU) as expert models and uses Mach-Zehnder interferometers (MZI) as gating and tuning modules to achieve system integration. The system not only solves the technical issue of on-chip training difficulty in existing optical diffraction computing models but also overcomes the performance limitations of optical interference computing models. By combining the advantages of both, it enhances the performance of optical computing models and the scalability of optical systems. Finally, the article validates the feasibility of the system in visual tasks through simulation experiments and demonstrates the simulation results of signal input and output using MZI, proving the feasibility of the scheme. © 2024 Copyright held by the owner/author(s).

关键词： Mach-Zehnder interferometers

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