In recent years, convolutional neural networks (CNNs) have achieved remarkable advancement in the field of remote sensing image super-resolution due to the complexity and variability of textures and structures in remo...
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ISBN:
(纸本)9798350381566;9798350381559
In recent years, convolutional neural networks (CNNs) have achieved remarkable advancement in the field of remote sensing image super-resolution due to the complexity and variability of textures and structures in remote sensing images (RSIs), which often repeat in the same images but differ across others. Current deep learning-based super-resolution models focus less on high-frequency features, which leads to suboptimal performance in capturing contours, textures, and spatial information. state-of-the-art CNN-based methods now focus on the feature extraction of RSIs using attention mechanisms. However, these methods are still incapable of effectively identifying and utilizing key content attention signals in RSIs. To solve this problem, we proposed an advanced feature extraction module called Channel and Spatial Attention Feature Extraction (CSA-FE) for effectively extracting the features by using the channel and spatial attention incorporated with the standard vision transformer (ViT). The proposed method trained over the UCMerced dataset on scales 2, 3, and 4. The experimental results show that our proposed method helps the model focus on the specific channels and spatial locations containing high-frequency information so that the model can focus on relevant features and suppress irrelevant ones, which enhances the quality of super-resolved images. Our model achieved superior performance compared to various existing models.
India relies heavily on rainfall for agriculture, water management, and climate planning. Satellite missions like the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) provide globa...
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Water leakage poses a critical threat to water distribution systems. This research addresses leak detection by investigating the correlation between acoustic signatures and leak severity. A resource-constrained system...
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ISBN:
(纸本)9798350381771;9798350381764
Water leakage poses a critical threat to water distribution systems. This research addresses leak detection by investigating the correlation between acoustic signatures and leak severity. A resource-constrained system (utilizing LoRa with SF12 and coding rate 4/5) is developed for large-scale deployment, emphasizing affordability and low maintenance. Convolutional Neural Networks (CNNs) are utilized, and experiments with Spectrogram, Mel Spectrogram, and MFCCs features provide crucial insights. While Spectrogram and Mel Spectrogram enable highly accurate classification, MFCCs offer a compact representation (up to 90% reduction) and significantly faster transmission times (over 10 times faster). This size reduction comes at a cost in accuracy, but in the 2-class model, MFCCs remain within a 10% accuracy difference from Mel Spectrograms, making them well-suited for initial leak screening. This system has the potential to optimize water management by proactively identifying leaks and enabling targeted maintenance for a more sustainable network.
This research explores the application of the artificial bee colony (ABC) algorithm in image edge detection to enhance the process of unshelled banana prawn weight prediction. Image edge detection plays a crucial role...
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ISBN:
(纸本)9798350381771;9798350381764
This research explores the application of the artificial bee colony (ABC) algorithm in image edge detection to enhance the process of unshelled banana prawn weight prediction. Image edge detection plays a crucial role in extracting meaningful features from images, which are then utilized for predictive modeling. By integrating the ABC algorithm into the image edge detection stage, the efficiency and effectiveness of feature extraction are enhanced, leading to improved capability in identifying intricate edge patterns and efficiently generating continuous and extracted edges. The experimental findings underscore the effectiveness of our proposed ABC method, showcasing its superiority over existing techniques like Particle Swarm Optimization (PSO) and Bat Algorithm (BA). Therefore, our proposed method is considered suitable for application in the task of edge detection in unshelled banana prawn images. Moreover, the proposed approach also offers a methodology for addressing the challenges associated with weight prediction of unshelled banana prawns, contributing to advancements in aquaculture research and automation technologies.
Emotion identification can be very useful in diverse applications including medical diagnosis, social interaction, marketing, etc. Nevertheless, emotions are complex and still pose numerous challenges. In this work, a...
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As civilization as a whole, the manufacturing sector, and energy consumption keeps expanding, it is vital to keep developing advanced, automated monitoring applications and instruments that will enhance the accuracy o...
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Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for m...
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With the advent of the "Programmable Web"paradigm, the World Wide Web is transitioning into a "Web of Services,"where data and services may be successfully reused across applications. Service disco...
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Process discovery algorithms incorporating domain knowledge can have varying levels of user involvement. It ranges from fully automated algorithms to interactive approaches where the user makes critical decisions abou...
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The escalating prevalence of renewable energy, particularly solar energy, is on a rapid incline, not solely within developed nations, but also evident in oil-producing countries. Various challenges confront photovolta...
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