Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex *** methods,while effective in controlled environments,often fail in scenar...
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Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex *** methods,while effective in controlled environments,often fail in scenarios involving long-range targets,high noise levels,or intricate backgrounds,highlighting the need for more robust *** address these challenges,we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target *** framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty *** utilizing uncer-tainty predictions,our method refines segmentation outcomes,achieving superior detection ***,this marks the first application of uncertainty modeling within the context of infrared UAV target *** evaluations on three publicly available infrared UAV datasets demonstrate the effectiveness of the proposed *** results reveal significant improvements in both detection precision and robustness when compared to state-of-the-art deep learning *** approach also extends the capabilities of encoder-decoder convolutional neural networks by introducing uncertainty modeling,enabling the network to better handle the challenges posed by small targets and complex environmental *** bridging the gap between theoretical uncertainty modeling and practical detection tasks,our work offers a new perspective on enhancing model interpretability and *** codes of this work are available openly at https://***/general-learner/UQ_Anti_UAV(acceessed on 11 November 2024).
In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Pent...
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The proposed system for recognizing Myanmar sign language between individuals who are deaf. The aim of this study is to develop deep learning models for the purpose of accurately identifying dynamic hand gesture image...
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Device connectivity has been redefined by the rapid development of the Internet of Things (IoT) technology, enabling diverse applications in areas such as smart cities, smart homes, and healthcare. These applications ...
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Presently,photocatalytic degradation has emerged as an attractive and efficient technology for wastewater *** order to avoid hurdles,such as difficulty in the suspended photocatalyst segregation from the aqueous syste...
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Presently,photocatalytic degradation has emerged as an attractive and efficient technology for wastewater *** order to avoid hurdles,such as difficulty in the suspended photocatalyst segregation from the aqueous system and low reutilization rate,the strategy of immobilizing photocatalysts with electro-spun fibers has been widely ***,those methods usually require multi-step preparation and complex *** this,a novel metallic Bi-decorated flexible multiphase Bi_(x)Ti_(y)O_(z)/TiO_(2) electrospun carbon nanofibers(Bi/Bi_(x)Ti_(y)O_(z)-TiO_(2)/CNFs)with high photocatalytic efficiency,good mechanical property,good stability,easy separation,and recovery were synthesized via a one-step approach of pre-oxidation and carbonization *** as-prepared Bi/Bi_(x)Ti_(y)O_(z)-TiO_(2)/CNFs with multiphase Bi_(x)Ti_(y)O_(z),anatase TiO_(2),and metallic Bi particles embedded not only enhance the harvest of light but also pro-vide more separation paths for photogenerated carriers,which improve photocatalytic efficiency *** Bi/Bi_(x)Ti_(y)O_(z)-TiO_(2)/CNFs(S3)exhibited excellent photocatalytic performance and the degradation rate of 10 mg L^(-1) Rhodamine B(RhB)was up to 97%in only 30 min under simulated sunlight ***,S3 exhibited stable activity during 5 cycles of experiments since the degradation rates remained at about 97%in 30 *** mechanism of degradation of RhB by Bi/Bi_(x)Ti_(y)O_(z)-TiO_(2)/CNFs in the photocat-alytic process was also proposed based on active species trapping *** work in this paper shows that Bi/Bi_(x)Ti_(y)O_(z)-TiO_(2)/CNFs are easy to prepare and have high photocatalytic ability and stability,thereby offering a new strategy in catalyst immobilization and wastewater treatment.
Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy ...
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Federated Learning (FL) has emerged as a promising approach for privacy-preserving model training across decentralized devices. However, it faces challenges such as statistical heterogeneity and susceptibility to adve...
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Analyzing incomplete data is one of the prime concerns in data analysis. Discarding the missing records or values might result in inaccurate analysis outcomes or loss of helpful information, especially when the size o...
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Analyzing incomplete data is one of the prime concerns in data analysis. Discarding the missing records or values might result in inaccurate analysis outcomes or loss of helpful information, especially when the size of the data is small. A preferable alternative is to substitute the missing values using imputation such that the substituted values are very close to the actual missing values and this is a challenging task. In spite of the existence of many imputation algorithms, there is no universal imputation algorithm that can yield the best values for imputing all types of datasets. This is mainly because of the dependence of the imputation algorithm on the inherent properties of the data. These properties include type of data distribution, data size, dimensionality, presence of outliers, data dependency among the attributes, and so on. In the literature, there exists no straightforward method for determining a suitable imputation algorithm based on the data characteristics. The existing practice is to conduct exhaustive experimentation using the available imputation techniques with every dataset and this requires a lot of time and effort. Moreover, the current approaches for checking the suitability of imputations cannot be done when the ground truth data is not available. In this paper, we propose a new method for the systematic selection of a suitable imputation algorithm based on the inherent properties of the dataset which eliminates the need for exhaustive experimentation. Our method determines the imputation technique which consistently gives lower errors while imputing datasets with specific properties. Also, our method is particularly useful when the real-world data do not have the ground truth for missing data to check the imputation performance and suitability. Once the suitability of a DI technique is established based on the data properties, this selection will remain valid for another dataset with similar properties. Thus, our method can save time an
We propose a frequency stabilization system based on thermal locking, utilizing the thermo-optic effect in a whispering gallery mode (WGM) resonator. By coupling the WGM resonator with an ultra-stable laser, low-frequ...
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With the growing technological advancements in the Internet and advanced functionalities in vehicular networks, it becomes crucial to execute tasks quickly and efficiently. However, the limited onboard computational c...
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