III-nitride nanowires have emerged as an important semiconductor device technology development platform, leveraging the unique physical properties of III-nitride semiconductors such as widely tunable bandgap energies,...
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Automated detection of plant diseases is crucial as it simplifies the task of monitoring large farms and identifies diseases at their early stages to mitigate further plant degradation. Besides the decline in plant he...
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This study examines a novel design for hollow core metal fibers HCMF by systematically altering geometric factors. Various materials, such as gold, silver, aluminum, graphene, and silicon nitride Si3N4, were examined ...
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The work presented in this paper has great significance in improving electromagnetic models based on the strong coupling between the magnetic and electric fields transient equations while considering a realistic rando...
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This paper investigates uncrewed aerial vehicle (UAV)-assisted wireless powered sensor networks (WPSNs). In this system, sensors harvest energy radiated from a UAV and use this energy to transmit collected data back t...
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Oil spills represent significant environmental hazards in ocean ecosystems, requiring rapid and accurate detection and response mechanisms. Due to its efficacy, synthetic aperture radar (SAR) is an important tool for ...
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Question classification (QC) is a process that involves classifying questions based on their type to enable systems to provide accurate responses by matching the question type with relevant information. To understand ...
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Point cloud completion aims to infer complete point clouds based on partial 3D point cloud *** previous methods apply coarseto-fine strategy networks for generating complete point ***,such methods are not only relativ...
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Point cloud completion aims to infer complete point clouds based on partial 3D point cloud *** previous methods apply coarseto-fine strategy networks for generating complete point ***,such methods are not only relatively time-consuming but also cannot provide representative complete shape features based on partial *** this paper,a novel feature alignment fast point cloud completion network(FACNet)is proposed to directly and efficiently generate the detailed shapes of *** aligns high-dimensional feature distributions of both partial and complete point clouds to maintain global information about the complete *** its decoding process,the local features from the partial point cloud are incorporated along with the maintained global information to ensure complete and time-saving generation of the complete point *** results show that FACNet outperforms the state-of-theart on PCN,Completion3D,and MVP datasets,and achieves competitive performance on ShapeNet-55 and KITTI ***,FACNet and a simplified version,FACNet-slight,achieve a significant speedup of 3–10 times over other state-of-the-art methods.
Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of r...
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Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of rice and have a substantial impact on the yield and quality of the crop. In recent times, deep learning methods have gained prominence in predicting rice leaf diseases. Despite the increasing use of these methods, there are notable limitations in existing approaches. These include a scarcity of extensive and diverse collections of leaf disease images, lower accuracy rates, higher time complexity, and challenges in real-time leaf disease detection. To address the limitations, we explicitly investigate various data augmentation approaches using different generative adversarial networks (GANs) for rice leaf disease detection. Along with the GAN model, advanced CNN-based classifiers have been applied to classify the images with improving data augmentation. Our approach involves employing various GANs to generate high-quality synthetic images. This strategy aims to tackle the challenges posed by limited and imbalanced datasets in the identification of leaf diseases. The key benefit of incorporating GANs in leaf disease detection lies in their ability to create synthetic images, effectively augmenting the dataset’s size, enhancing diversity, and reducing the risk of overfitting. For dataset augmentation, we used three distinct GAN architectures—namely simple GAN, CycleGAN, and DCGAN. Our experiments demonstrated that models utilizing the GAN-augmented dataset generally outperformed those relying on the non-augmented dataset. Notably, the CycleGAN architecture exhibited the most favorable outcomes, with the MobileNet model achieving an accuracy of 98.54%. These findings underscore the significant potential of GAN models in improving the performance of detection models for rice leaf diseases, suggesting their promising role in the future research within this doma
In this work, a student project report retrieval system is designed that identifies the relevance of the student queries with appropriate project domains and retrieve the most relevant reports quickly. To design this ...
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