Semantic segmentation is a computer vision function that incorporates classifying each pixel in an image into a precise category or class. Unlike classification functions where the goal is to assign an exclusive label...
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(纸本)9798331540685
Semantic segmentation is a computer vision function that incorporates classifying each pixel in an image into a precise category or class. Unlike classification functions where the goal is to assign an exclusive label to the whole image, semantic segmentation furnishes a more detailed concern of the image by segmenting it into regions corresponding to distinct objects or structures. Similarly, this paperrepresents the model which scheme for utilizing the datasets of spatial resolutions also site for teaching a completely convolutional neural specification named the U-Net to finish the segmentation of aerial images. The dataset comprises 72 images captured by MBrSC (Mohammed Bin rashid Space Centre) satellites, organized into six larger tiles. Each image is remarked with pixel-sharp semantic segmentation across six distinct classes: Vegetation, Water, Land (unpaved area), road, Building and Unlabeled. Given the variability in image sizes, a crucial preprocessing step involves resizing the images to dimensions divisible by 256 and then extracting manageable patches. This ensures uniformity, enabling seamless processing and analysis. The accompanying mask images, initially provided in rGB format with HEX color codes, undergo conversion to standardrGB values. Subsequently, these rGB values are translated into integer labels, which are further transformed into a one-hot encoded format. This transformation is essential for facilitating the learning procedure of the deep learning model. The core of the project employs a U-Net architecture, a type of convolutional neural network (CNN) prominent for its effectiveness in image segmentation tasks. U-Net's structure, characterized by its symmetric contracting and expansive paths coupled with skip connections, allows for precise localization and classification of each pixel in the image. The network processes the preprocessed patches, generating segmented outputs that categorize each pixel according to the predefined classes.
A flexible radio waveform, generalised frequency division multiplexing (GFdM) allows for significant degrees of freedom in adjusting the number of time slots, subcarriers, and pulse shaping filters. The GFdM is one of...
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A wideband metamaterial absorber (MA) using anti-symmetric geometries is presented in this paper. The absorber unit cell consists of two pairs of circularring slots of different sizes on the top surface of 0.8 m...
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The trend of upgrading data centers (dC) with integration of renewable energy sources (rES), smart micro grids, high level of security management tools like data center infrastructure management (dCIM) contributing to...
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Induction motors are extensively used in artificial and marketable operations due to theirrobustness and effectiveness. To maximize performance and achieve long life from them, speed control and protection systems ar...
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We approach learning characteristic on a neutrosophic graph such as r-edge regular neutrosophic graph, strongly edge regular neutrosophic graph and absolute degree of vertex since a neutrosophic set (Formula Presented...
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Image classification is one of the main parts of computer vision, which is important in applications like self-driving automotives/vehicle systems. While working with image/video data it needs huge amount of resources...
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The Internet of Things is an evolving technology that connects and exchanges data with othertechnology. The communications between the devices are resource constrained in the field of smart environments. The low-powe...
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In the advent of increased transportation systems, the importance and growth of heavy vehicle movements and their applications play a significant role. The transportation sector involving heavy vehicles needs appropri...
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The development of pyrene derivatives possessing the AIE feature have emerged as an intriguing area of research with potential applications in several domains. In the present study, we designed anddeveloped an AIE ac...
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The development of pyrene derivatives possessing the AIE feature have emerged as an intriguing area of research with potential applications in several domains. In the present study, we designed anddeveloped an AIE active pyrene derivative for picric acid sensing in an aqueous medium. The design based on a single step condensation reaction and the product has been meticulously characterized by using NMr and Mass spectrometric techniques. In comparison to pristine pyrene, the synthesized pyrene derivative has a lower bandgap energy as a result of the intramolecular charge transfer between pyrene and 4-(methylthio)-2-oxo-2H-pyran-3-carbonitrile. The synthesized probe demonstrates a moderate level of fluorescence when dissolved in a solution, and it has exhibited a remarkable emission caused by aggregation at higher water percentages. The aggregates were meticulously characterized by dLS, SEM and TEM measurements. The spherically shaped aggregates have shown enhanced fluorescence quantum yield and lifetime, rendering them a more suitable option for sensor applications. The aggregates were effectively employed as a fluorescent probe for the detection of picric acid in an aqueous medium. The probe exhibited a notable level of selectivity and sensitivity towards picric acid, as evidenced by its low detection limit of 4.47 nM. Moreover, the quenching mechanism was ascribed to the inner filter effect, providing supporting evidence from both experimental and mathematical calculations. The findings indicate that the probe has potential as a feasible choice for a picric acid sensor.
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