Art is a powerful medium of emotional expression, using elements like color, sound, and form. It reflects the artist's personality and the era they lived in, telling captivating stories. AI has advanced in generat...
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Tuberculosis (TB) is a serious health issue that kills a lot of people these days. TB is completely curable if it is discovered early enough. One way of diagnosing tuberculosis (TB) early on is to undergo a chest X-ra...
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Using the YOLO v8 model and deep learning approaches, this study explores the field of e-waste management and provides effective item detection. Our research attempts to increase the accuracy and scalability of e-wast...
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This letter proposes a delay-sensitive network design scheme, DSND, for multi-service slice networks. DSND contributes to a virtual processing system where users can share the same application space regardless of dist...
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The Cloud Computing (CC) is a model which treats the resources as an integrated entity on the internet, cloud. Cloud computing is an unique environment or network in which process, access and maintenance are done by a...
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computer vision tasks often involve multiclass image classification, where a picture is labelled by a specified class. Sometimes an image has several objects or qualities, requiring a more detailed method. This study ...
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The emergence of the Internet of Things (IoT) has enabled the proliferation of interconnected devices and sensors, generating vast amounts of often complex and unstructured data. Deep learning (DL), a subfield of mach...
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Scientific text classification (STC) in NLP automatically categorizes texts into predefined scien-tific domains. Although there is enormous progress in STC regarding high-resource languages, it must be explored in low...
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Chest x-ray studies can be automatically detected and their locations located using artificial intelligence (AI) in healthcare. To detect the location of findings, additional annotation in the form of bounding boxes i...
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Interference source localization with high accuracy and time efficiency is of crucial importance for protecting spectrum resources. Due to the flexibility of unmanned aerial vehicles(UAVs), exploiting UAVs to locate t...
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Interference source localization with high accuracy and time efficiency is of crucial importance for protecting spectrum resources. Due to the flexibility of unmanned aerial vehicles(UAVs), exploiting UAVs to locate the interference source has attracted intensive research interests. The off-the-shelf UAV-based interference source localization schemes locate the interference sources by employing the UAV to keep searching until it arrives at the target. This obviously degrades time efficiency of localization. To balance the accuracy and the efficiency of searching and localization, this paper proposes a multi-UAV-based cooperative framework alone with its detailed scheme, where search and remote localization are iteratively performed with a swarm of UAVs. For searching, a low-complexity Q-learning algorithm is proposed to decide the direction of flight in every time interval for each UAV. In the following remote localization phase, a fast Fourier transformation based location prediction algorithm is proposed to estimate the location of the interference source by fusing the searching result of different UAVs in different time intervals. Numerical results reveal that in the proposed scheme outperforms the stateof-the-art schemes, in terms of the accuracy, the robustness and time efficiency of localization.
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