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Intelligent user interfaces for Industry 4.0: Improving workplace security by systematically applying machine learning and image processing to identify face mask usage

Intelligent user interfaces for Industry 4.0: Improving work...

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computer, Electronics & Electrical Engineering & their applications (IC2E3), international conference on

作者： Sandip Mukherjee Sayanti Samanta Bhaswati Roy Niloy Kumar Bhattacherjee Aparna Bhaduri Subir Gupta Dept. of FMS Dr. B. C. Roy Engineering College Durgapur WB India BCREC APC Dr. B. C. Roy Engineering College Durgapur WB India Dept. of CSE Swami Vivekananda University Kolkata West Bengal India

The word "expression" suggests intent. The term "express" can be connoted as implied or imputed within. An emotion might be just a glance at someone's face which denotes a reaction. Reaction br...

The word "expression" suggests intent. The term "express" can be connoted as implied or imputed within. An emotion might be just a glance at someone's face which denotes a reaction. Reaction brings expression. In other words, expressiveness by someone refers to the way of communicating one's feelings. According to research and surveys, humans use 55–57 percent of body languages, 38 percent of tones or tonal variations, and the rest as words to convey meanings in messages for communication. It is also a proven fact that a significant part of the expression is in the form of nonverbal communication. There is sufficient evidence in psychology, pharmacology, and other fields to justify the same. Humans generally communicate through verbal expressions in response to inputs received, which is essential for our daily lives. However, word meanings can sometimes be challenging to detect the purpose accurately thereby necessitating good face recognition. Therefore it would be prudent to have face recognition made automatically and also effectively. In the present study, a facial emotion detection system seems to work correctly. The paper combines A.I. with image processing for such detection. Here Face-recognition software may be able to show the emotions that were captured by a camera. This article offers features related to this detection that has a over 90% success rate. The COVID-19 pandemic redefined the safety protocols of industries necessitating strict adherence to ‘wearing face masks while at work’. The method shown in the paper can thus be used to ensure strict adherence by all employees in the new normal scenario evading any verbal instruction for such compliance.

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Refined Modeling and Simulation Method of Raw Signal Returns for Space-based Radar

Refined Modeling and Simulation Method of Raw Signal Returns...

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Power Electronics, computer applications (ICPECA), ieee international conference on

作者： Zhikang Wang Xingjuan Xi Guisheng Liao Lan Lan Hui Chen National Laboratory of Radar Signal Processing Xidian University Xi’an China Jiangsu Cyber Space Science and Technology Co. Ltd Nanjing China School of Information Science and Engineering Southeast University Nanjing China

Radar echo refinement simulation is an important means to provide data for space-based radar design demonstration. Most of the current simulation methods ignore the real surface topography and complex geomorphic distribution, and the target characteristics are not sufficiently described, resulting in a shortage of refined radar echo data, which is difficult to meet the demonstration requirements of space-based radar early warning detection system. This paper proposes a more refined simulation method for space-based radar echo, mainly from the background clutter and target echo two aspects of fine modeling: Firstly, based on the real ground battlefield environment fluctuation model and complex geomorphic distribution attributes constructed by GIS (Geographic Information System) data, a refined space-time two-dimensional clutter simulation model of space-based radar is constructed which simultaneously includes the characteristics of terrain fluctuation scattering distribution, terrain occlusion and diffraction, earth-sea clutter distribution and earth rotation. Secondly, based on the efficient modeling technology of ray-tracing high-frequency electromagnetic scattering, the target characteristics in the detection scene are finely modeled, which can fully consider the changes of target characteristics under different conditions. Finally, the rationality of space-time two-dimensional echo signal is verified and analyzed by simulation cases. The echo generated by this method is closer to the real situation, and provide support for the research of space-based radar system design and clutter suppression algorithm.

关键词： Fluctuations Diffraction Spaceborne radar Radar clutter Radar detection Radar Radar scattering

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Optimizing Remote Sensing image Classification with Spectral Indices and Convolutional Neural Networks

Optimizing Remote Sensing Image Classification with Spectral...

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ieee conference on Energy Internet and Energy System Integration (EI2)

作者： Wendong Yao Shane Donohue Soumyabrata Dev The ADAPT SFI Research Centre School of Computer Science University College Dublin Dublin Ireland School of Civil Engineering University College Dublin Dublin Ireland The ADAPT SFI Research Centre School of Computer Science University College Dublin Beijing-Dublin International College Beijing China

This paper analyzes the EuroSAT dataset, utilizing spectral channels of images from the dataset to generate spectral indices. We save these indices as new spectral channels of the images to create new datasets. The created datasets are evaluated in conjunction with ResNet-based deep learning models (ResNet-50 and ResNet-152) to assess their impact on the classification accuracy of 27,000 images across ten distinct classes. Our findings demonstrate that spectral indices can significantly enhance object classification accuracy in remote sensing imagery, particularly when used with less complex convolutional neural network models. This research offers valuable insights, highlighting the potential of spectral indices for improved image analysis in earth observation. The study encourages future investigations into additional spectral indices and hybrid approaches, combining spectral indices with visible spectrum (Red Green Blue) or multi-spectral datasets, to advance remote sensing image analysis and broaden its practical applications.

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Split Learning for image Classification in Internet of Drones Networks

Split Learning for Image Classification in Internet of Drone...

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ieee Workshop on High Performance Switching and Routing

作者： Jingjing Yao Department of Computer Science Texas Tech University Lubbock TX USA

Internet of drones (IoD), where drones act as the Internet of things (IoT) devices, has attracted much attention for its application in traffic surveillance, object tracking, and disaster rescue. These applications rely heavily on machine learning (ML) techniques for image classification. The traditional method of training ML models in IoD networks involves sending all data to a centralized ground base station (BS), which can result in privacy and security concerns. Split learning, an approach that separates the training model into a client model and a server model, can mitigate these concerns by allowing the client model to reside in the drones while the server model is in the BS, preserving data privacy without sharing raw data. In this study, we explore the application of split learning in IoD networks and conduct simulations to evaluate our designed algorithm. Simulation results demonstrate that different separations do not significantly impact split learning accuracy, increasing the number of layers on clients can lead to longer training times, communication overhead is a significant bottleneck in split learning for IoD networks, client numbers do not significantly affect accuracy, and training time slightly increases with an increasing number of clients.

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Deep Learning: The Future of Medical image processing

Deep Learning: The Future of Medical Image Processing

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Computational Intelligence and Sustainable Engineering Solutions (CISES), international conference on

作者： Kamred Udham Singh Saroj Kumar Pandey Dhirendra Prasad Yadav Teekam Singh Gaurav Kumar Ankit Kumar School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University Mathura UP India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun

Health care is a vital service that is constantly in high demand since everyone needs it. Individuals have higher hopes for advancement in this profession than for receiving the status quo since they would rather be treated better. Sometimes, or maybe more accurately, in many situations, the findings are not clear and the sickness cannot be understood at the first stage from a manual reading of the report. When it comes to viewing medical images, the fact that they must be interpreted by hand inevitably leads to delays and errors. Many deep learning and machine learning approaches can be used to address this issue. Therefore, although machine learning may be thought of as a subset of deep learning, the reverse is not true. Let's talk about how deep-learning models will be used to process medical images. Medical image processing is one area where deep learning models are having a profound effect. Convolutional neural networks (CNNs) and other deep learning techniques have made it feasible to automate the processing of medical pictures and improve diagnostic and treatment accuracy. It is widely used in radiology to examine medical pictures such as X-rays, CT scans, and MRIs. In order to aid radiologists in their diagnostic work, deep learning models may be taught to identify patterns and characteristics in medical pictures that are diagnostic of certain diseases or ailments.

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Error-Driven Triplet-Based Online Fine-Tuning for Cross-Background image Classification

Error-Driven Triplet-Based Online Fine-Tuning for Cross-Back...

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computer Communication and Artificial Intelligence (CCAI), international conference on

作者： Sheng-Luen Chung Wei-Ting Guo Electrical Engineering Department National Taiwan University of Science and Technology Taipei Taiwan

image classification is a fundamental task in computer vision with numerous real-world applications. However, classification models trained on one set of images may not perform well when tested on another set, especially when the two sets differ in terms of the background environment. To address this challenge, we propose an error-driven triplet-based online fine-tuning approach that leverages misclassified samples to refine the classification model at intervals when enough misclassified samples are collected. Our approach builds on the triplet network architecture, which learns to represent images in a low-dimensional space where images with the same label are clustered together. We use a pre-trained classification model that was trained on a collection of 180 types of images from one background scene. However, when we apply the model to a new background scene with additional types of images, its performance is compromised due to the domain shift. Our proposed approach leverages the misclassified samples by contrast them with positive and negative samples as triplet data to fine-tune the model in new background scene. We use a loss function that combines the triplet loss and the classification loss to update the model weights. We evaluate our approach on two challenging image classification datasets with different background environments. The experimental results demonstrate that our approach achieves significant improvements in accuracy compared to the baseline classification model without fine-tuning. Overall, our error-driven triplet-based online fine-tuning approach shows promising results for adapting classification models to changing background with additionally new classification types, where the performance of pre-trained models is limited.

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FloodSFCP: Quality and Latency Balanced Service Function Chain Placement for Remote Sensing in LEO Satellite Network

FloodSFCP: Quality and Latency Balanced Service Function Cha...

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ieee Communications Society conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Ruoyi Zhang Chao Zhu Xiao Chen Qingyuan Gong Xinlei Xie Xiangyuan Bu School of Cyberspace Science and Technology Beijing Institute of Technology China School of Computer Science and Shanghai Key Lab of Intelligent Information Processing Fudan University China School of Information and Electronics Beijing Institute of Technology China

Prompted by the significant advancements in image processing technologies and their diverse range of applications, remote sensing satellites are poised for rapid expansion. Nonetheless, offloading the vast amount of remote sensing satellite images to the ground gateway station is inefficient due to the exorbitant costs induced by satellite links, while the limited resources of individual satellites hinder local task processing. With the advancement of the network function virtualization (NFV) technology, a new paradigm for service function chain (SFC) has emerged, which can significantly improve the flexibility and resource utilization of network services and alleviate resource conflicts by dividing large services into smaller ones organized in the form of SFCs. As mega-constellations (e.g., Starlink) developed, the number of low earth orbit (LEO) satellites is increasing. By dividing services into small sub-services and organizing them into SFCs throughout the LEO network, services that cannot be completed by a single satellite can be accomplished through multi-satellite cooperation. However, the quality of the remote sensing service is positively correlated with its latency, and the rapidly changing topology of LEO networks also adds complexity to the SFC placement. Hence, how to select appropriate satellites to place the SFC and modulate service levels, in order to obtain better remote sensing results within an acceptable latency, remains a question. To address these issues, this paper proposes the FloodSFCP, an SFC placement method that aims to increase service quality and decrease latency through offline training and online optimization via deep reinforcement learning, taking into account the variation in LEO network topology. By introducing NoisyNet, Dueling, and N-step learning, we improve the model’s generalization ability and reduce the state space, thus enhancing convergence speed while reducing decision and training time. Experimental results demonstrate

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3D image Generation from X-Ray Projections Using Generative Adversarial Networks

3D Image Generation from X-Ray Projections Using Generative ...

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ieee Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Mohammad AlShurbaji Salam Dhou Biomedical Engineering Graduate Program American University of Sharjah Sharjah UAE Department of Computer Science and Engineering American University of Sharjah Sharjah UAE

The human brain can effortlessly imagine a 3D image from only 2D images with a little expertise and imagination, but for machines, this is not a trivial task. Because of this, reconstructing 3D images from 2D ones is a hot topic and has many applications. In this paper, we propose a Generative Adversarial Network (GAN)-based approach that generates CT-like images using pairs of orthogonal X-ray projections taken from different angles. In this work, a variety of orthogonal pairs from different angles, ranging from 0°&90° to 60°&150°, were considered as input to the 3D image generation model. The effectiveness of the proposed method was assessed by measuring the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), which resulted in values of 0.641 and 29.21, respectively. Furthermore, the model's ability to capture the respiratory motion in the input projections and reflect it in the generated images was also assessed. This work demonstrated the feasibility of generating CT-like images from X-ray projections captured from different orthogonal angles taking into consideration the respiratory motion exhibited in these projections.

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Indoor 3D Visible Light Positioning Analysis with Channel Estimation Errors

Indoor 3D Visible Light Positioning Analysis with Channel Es...

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international conference on Systems, Signals and image processing, IWSSIP

作者： Dimitrios Bozanis Nikos G. Evgenidis Vasilis K. Papanikolaou Pavlos S. Bouzinis Sotiris A. Tegos Alexis A. Dowhuszko Panagiotis D. Diamantoulakis George K. Karagiannidis Department of Electrical and Computer Engineering Wireless Communications and Information Processing Group (WCIP) Aristotle University of Thessaloniki Thessaloniki Greece Institute for Digital Communications (IDC) Friedrich-Alexander Universitat Erlangen-Nurnberg (FAU) Germany Department of Applied Informatics University of Macedonia Thessaloniki Greece Department of Communications and Networking Aalto University Espoo Finland Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon

Since radio-frequency signals are affected signifi-cantly by multipath fading, the localization accuracy they offer cannot meet the sixth generations (6G) demanding specifications. On the other hand, visible light positioning (VLP) has emerged as a low cost alternative that takes into account the highly deterministic channel characteristics of visible light communications to provide cm-level accuracy provided that the investigated spaces are adequately covered by LEDs. However, imperfect channel state information and modeling can lead to positioning errors. In this paper, localization accuracy is quantified in terms of channel estimation errors via low complexity lateration algorithm. The channel estimation errors can be caused by a variety of issues and the provided analysis is general so that it can be easily applied to a plethora of applications. Finally, Monte Carlo simulations validate the proposed method and offer valuable insights for the design and performance bounds of VLP systems.

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Learning Path Recommendation Based on Knowledge Tracing and Reinforcement Learning

Learning Path Recommendation Based on Knowledge Tracing and ...

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international conference on Advanced Learning Technologies (ICALT)

作者： Han Wan Baoliang Che Hongzhen Luo Xiaoyan Luo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Image Processing Center School of Astronautics Beihang University Beijing China

Adaptive and intelligent web-based educational systems are made to automate the adaptation of the system to the learners' behaviors and needs. Personalized e-learning platforms should make adaptive adjustments according to the individual students' interactions and their knowledge states (KS). This study proposes a more effective personalized learning path recommendation algorithm to promote the individualized development of students. First, the Dynamic Key-Value Memory Network (DKVMN) is enhanced by integrating a learning behavior module, which is used to trace student knowledge states. Then, the proposed knowledge tracing model is used to simulate virtual students and train recommendation policy based on reinforcement learning (RL). The experimental results show that our personalized learning path recommendation algorithm increases the average knowledge state of students by 12.11% and 5.38% on two different data sets, respectively.

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