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Unmanned Aerial Vehicle Hyperspectral Remote Sensing image Classification based on Parallel Convolutional Neural Network 11

Unmanned Aerial Vehicle Hyperspectral Remote Sensing Image C...

引用

11th international conference on Information systems and Computing technology, ISCTech 2023

作者： Jiang, Linhuan Zhang, Zhen Hu, Yabin Kunming University of Science and Technology Faculty of Land Resources Engineering Kunming China Frist Institute of Oceanography Ministry of Natural Resources Lab of Marine Physics and Remote Sensing Qingdao China

ISBN: (纸本)9798350342406

Unmanned aerial vehicle (UAV) hyperspectral images (HSI) are widely used in remote sensing fine-grained monitoring and classification owing to the abundant spatial- spectral information. Deep learning (DL) for HSI classification is a popular method because of the strong information extraction ability. The existing DL methods mainly include pixel-level convolutional neural network (CNN) methods and semantic segmentation algorithms. However, the HSI of UAV has rich spatial-spectral information, and it also shows the characteristics of severe shadow, poor signal-to-noise ratio and redundant spectral information. Pixel-level CNN shows a large amount of salt and pepper noise in classification results for these HSI, while the semantic segmentation algorithm shows inaccurate boundaries and small fragments, and requires a large number of training samples. Therefore, to address this problem, we hereby propose a parallel neural network model based on ICA-2D-CNN branch and 3D-CNN branch for UAV HSI classification. Among them, the ICA-2D-CNN branch extracts HSI spatial features using ICA and 2D-CNN, and the 3D-CNN branch is designed to extract spatial and spectral information of HSI to alleviate the classification problem of salt noise and boundary inaccuracy. Taking the 5cm spatial resolution of UAV HSI as an example, the overall accuracy (OA) of the proposed model is 71.87%, the average accuracy reaches 72.9%, and the Kappa coefficient is 0.639. Compared with 1D-CNN, 2D-CNN and 3D-CNN, OA increased by 3.6%, 9.3% and 6.5%, respectively. The parallel neural network proposed by this paper can achieve satisfactory classification results, which showing robust classification performance and significant advantages for UAV HSI classification. © 2023 IEEE.

关键词： Remote sensing

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Cutting-Edge technology: Exploring the Experimental Possibilities of Smart Virtual Wardrobe Design Using internet of Things

Cutting-Edge Technology: Exploring the Experimental Possibil...

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Advances in Computing, Communication and Applied Informatics (ACCAI), international conference on

作者： B. Arunsundar P Anusha K. Mekala Devi M Manimaraboopathy R. Ratheesh Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of ECE R.M.K. Engineering College Kavaraipettai Chennai Department of ECE S.A. Engineering College Chennai Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science Technology Chennai Department of ECE Agni College of Technology Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Modern technological developments have mostly centered on three areas: communication, the workplace, and entertainment. Choosing an outfit is a daily struggle for everyone. The issue isn’t a lack of clothing; rather, it’s the inability to distinguish between those that you use frequently and those that have been in storage for quite some time. In addition to keeping tabs on your garments, a smart wardrobe can also provide outfit recommendations based on your calendar’s events. In order to assist you in deciding which garments to sell or donate, the system creates statistical data based on the frequency of wear. An intelligent wardrobe system for garment management, identification, and recommendation is suggested in this study, along with a virtual try-on system based on image processing. To test how well the suggested system works, it is cross-validated with the traditional model known as Virtual Wardrobe (VW). This system is dubbed Smart IoT Virtual Wardrobe (SIoTVW). In order to recognize and categorize the garments in the closet, the system makes use of machine learning algorithms and neural network based approaches. In order to provide the user with outfit recommendations that are suitable for various events and climates, the smart system also remembers their daily routine and clothing preferences. In addition, the system generates virtual fit-on dress models using image processing and neural network based approaches, allowing users to virtually try on various garments and view how they appear before deciding what to wear. In order to keep the clothes in pristine condition, the suggested system uses internet of Things (IoT) sensors and application programming interfaces to track the wardrobe’s status, which includes illumination, humidity, and temperature. The system’s study reveals that it is capable of accurately detecting garments, offering clothing recommendations to users, and simulating virtual try-on dress models, which makes it an attractive option for c

关键词： Temperature sensors Schedules image processing Clothing Neural networks Sensor systems and applications Real-time systems

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image Compressive Sensing With Adaptive Sampling by Median Filtering

Image Compressive Sensing With Adaptive Sampling by Median F...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Yanfeng Wu Chen Hui Ronghua Liao Shaohui Liu Debin Zhao Department of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep unfolding compressive sensing (CS) has experienced remarkable advancements. However, there still exist two challenges: (1) Many algorithms either use uniform block-based sampling, which ignore the fact that the content of different blocks is different, or allocate the sampling rate referring to complete signal before CS sampling, which is not always feasible in real-world scenarios. (2) Traditional CNN is difficult to capture broader contextual priors during iterative recovery. In this paper, we propose a novel network ASMFNet to solve the above two issues. Specifically, to address the first issue, we introduce a dual-branch network featuring a basic sampling branch to acquire reference image and an adaptive sampling branch by median filtering for allocating remaining sampling rate adaptively. For the second problem, we use Swin Transformer and feature fusion block to increase the feature interactions. Experimental results demonstrate that our proposed method outperforms existing methods.

关键词： Adaptive systems Filtering signal processing algorithms signal processing Transformers Iterative algorithms Sensors Speech processing Compressed sensing image reconstruction

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Lightweight image Quality Prediction Guided by Perceptual Ranking Feedback

Lightweight Image Quality Prediction Guided by Perceptual Ra...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Aymen Sekhri Mohamed-Chaker Larabi Seyed Ali Amirshahi CNRS Université de Poitiers XLIM Poitiers France Norwegian University of Science and Technology Gjøvik Norway

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Automatic image Quality Assessment (IQA) remains a difficult challenge due to the complexity of mimicking the Human Visual System (HVS) and the limitations of traditional objective image Quality Metrics (IQM). Existing learnable methods often involve high computational costs and fail to adequately capture the nuanced perceptual characteristics of the HVS, including the human ability to rank image quality and human sensitivity to differences in areas with high-frequency. In this study, we propose an effective approach that addresses these challenges by incorporating the characteristics of HVS and the perceptual classification into a lightweight IQM framework based on the transformer architecture. This allows our method to capture long-range dependencies effectively. Our approach leverages Objective Error Maps (OEMs) to enhance sensitivity to visual errors and employs a ranking module as an objective function, providing feedback on the perceptual quality at the feature level. Experimental results demonstrate that our approach not only achieves competitive performance compared to state-of-the-art IQMs but also significantly reduces computational complexity.

关键词： image quality Visualization Sensitivity Computer architecture Visual systems signal processing Transformers Linear programming Computational efficiency Speech processing

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Covid-19 Detection using Chest X-Rays with image based Deep Learning 3

Covid-19 Detection using Chest X-Rays with Image based Deep ...

引用

3rd international conference on Electronics and Sustainable Communication systems, ICESC 2022

作者： Bhuvana, S. Anjhanaa, A.A. Bala Keerthana, K.K.B. Sivadharshini, K. Psg Institute of Technology and Applied Research Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

The COVID-19 has become a global pandemic, which has already affected the health care and lifestyle of literally every person on earth. It has affected all over the world. The global healthcare system is being overwhelmed by the exponential increase in COVID-19 patients. One of the critical factors driving the rapid spread of the COVID-19 pandemic is the lengthy clinical testing time. In order speed up the process the identification process can be done using chest X-Ray. In this project, a computer vision-based deep learning model for detecting Covid-19 is proposed. A dataset scraped from the internet was used to train the model. NumPy, OpenCV, Matplotlib, and TensorFlow are among the Python 3 packages that are used to create the computer vision model. This method is created by training and testing the model to identify failures and establish recovery strategies. As a result, early diagnosis of Covid-19 using computer vision-based approaches is employed to lower the mortality rate. The work suggests using Convolutional Neural Networks (CNNs) to detect Covid-19. The model is fed data from chest X-ray images. The neural network is trained using a dataset of classified X-Ray images and classified between normal image and covid-19 image. It approaches the problem as a binary classification problem, that is, either Covid-19 or not. The goal is to achieve high accuracy and detect Covid-19 using the defined training model. © 2022 IEEE.

关键词： COVID-19

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Application of AI Virtual Host in TV Programs in the Information technology Era

Application of AI Virtual Host in TV Programs in the Informa...

引用

Integrated Circuits and Communication systems (ICICACS), IEEE international conference on

作者： Bin Yao College of Journalism and Communication Bohai University Jinzhou Liaoning China

based on artificial intelligence technology, computer simulation is used to generate a virtual world of three-dimensional space. This provides the user's visual, auditory and tactile sensory simulation, allowing the user to observe things in three-dimensional space in a timely and unlimited manner as if they were there. Virtual studio and virtual host are two forms of program expression, which have been used in various TV programs. As a new thing, virtual host is an inevitable trend to expand the scope of application and increase the frequency of use, and has obvious advantages in the era of internet TV and interactive TV. based on artificial intelligence, this paper studies key technologies such as 3D virtual engine architecture, image recognition and motion capture, and promotes the research and application of AI virtual hosts in TV programs in the information technology era.

关键词： Solid modeling Visualization TV Three-dimensional displays Media internet Artificial intelligence

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Improving Small-signal Stability of Grid-Connected Inverters based Phase-Locked Loop Compensation Under Weak Grid

Improving Small-Signal Stability of Grid-Connected Inverters...

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Smart Power & internet Energy systems (SPIES), international conference on

作者： Yukai Huang Shilong Lei Songtao Huang Jinbang Xu Bingying Zheng Zhuang Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Engineering Kings College London London United Kingdom

ISBN: (数字)9798350368864

ISBN: (纸本)9798350368871

In weak grid scenarios, the dynamic behavior of the phase-locked loop (PLL) can adversely affect system stability due to the influence of weak grid impedance. Establishing a small-signal output impedance model of grid-connected inverters is common practice for studying oscillation instability issues between the inverter and the weak grid. This paper proposes a stabilizing control strategy based on symmetric compensation of feed-forward perturbations to address the impact of PLL small perturbation coupling on current loop control. The strategy takes into full consideration the characteristics of small perturbation coupling in the control coordinate system and formulates a system of equations to derive the expression of the small perturbation feedforward compensation controller. By inversely compensating the small perturbation components that lead to current loop destabilization, the stability of the system under weak grid conditions is improved. Simulation results demonstrate that this strategy does not require adjusting controller parameters and directly compensates for disturbance components.

关键词： Couplings Perturbation methods Simulation Power system stability Stability analysis Inverters Impedance Phase locked loops Feedforward systems Oscillators

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AgriSense: Advanced Environmental Monitoring for Crops

AgriSense: Advanced Environmental Monitoring for Crops

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international conference on Communications and signal Processing

作者： S Harish B Harikumaran S Vimala Prakash T. Rajalakshmi S Vasudevan Suryakala Department of Electronics and Communication Engineering SRM Institute of science and Technology Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In addressing the challenges of irrigation in agriculture, the implementation of internet of Things (IoT) technology emerges as a promising solution. The growing need for effective water management has put farmers at a disadvantage in optimizing irrigation systems to suit crop requirements while preserving resources. A smart IoT-based irrigation system that provides an effective solution to overcome these problems. By incorporating various sensors like humidity and moisture sensors with Arduino Mega, the system continuously monitors soil conditions and crop needs in real-time. Data collected from these sensors is transmitted to the cloud via IoT connectivity for analysis and storage. A user-friendly mobile application provides remote access to this data, offering insights into soil moisture levels, environmental factors, and irrigation schedules. Moreover, the app allows manual control of the irrigation system, enabling users to remotely toggle the pump on/off as required. Crucially, the microcontroller powering the system is solar-powered, enhancing sustainability and reducing dependence on external power sources. Combining IoT technology, Arduino Mega microcontrollers, and solar energy, this system represents a scalable and environmentally conscious approach to smart irrigation, addressing the pressing need for sustainable water management in agriculture.

关键词： Irrigation Microcontrollers Crops Solar energy Sensor systems internet of Things Sustainable development

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AGCN: Augmented Graph Convolutional Network for Lifelong Multi-Label image Recognition

AGCN: Augmented Graph Convolutional Network for Lifelong Mul...

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2022 IEEE international conference on Multimedia and Expo, ICME 2022

作者： Du, Kaile Lyu, Fan Hu, Fuyuan Li, Linyan Feng, Wei Xu, Fenglei Fu, Qiming Suzhou University of Science and Technology China Tianjin University China Suzhou Institute of Trade & Commerce China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

The Lifelong Multi-Label (LML) image recognition builds an online class-incremental classifier in a sequential multilabel image recognition data stream. However, training on the data with different Partial Labels may result in more serious Catastrophic Forgetting in old classes. To solve the problem, the study proposes an Augmented Graph Convolutional Network (AGCN)to build an Augmented Correlation Matrix (ACM) across the sequential partial-label tasks and sustain the catastrophic forgetting. First, in ACM, the intra-task relations derive from the hard label statistics, while the inter-task relations further leverage the soft labels from a stored expert network. Then, based on the ACM, AGCN captures label dependencies with dynamic augmented structure and yields effective class representations. Our method is evaluated on two multi-label image benchmarks and the results show that the proposed method is effective for LML image recognition. © 2022 IEEE.

关键词： image recognition

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

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

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Intelligent systems and Computational Networks (ICISCN), international conference on

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

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

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.

关键词： Training image quality Adaptation models Attention mechanisms PSNR image edge detection Computational modeling Superresolution Retina Convolutional neural networks

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