computer vision technology is used for autonomous driving and road traffic safety. Accordingly, studies on deep learning models that detect and analyze objects through images or videos are ongoing. On the other hand, ...
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Gender detection assumes a vital role in different spaces, including security, promotion, medical care, and the human-PC connection. This examination paper presents a strong, constant gender detection framework in lig...
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For computers to understand human activity or behavior in a variety of scenarios, reliable 3D human posture estimation is a prerequisite. Several difficulties have made such work more complex as it is influenced by va...
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The Internet of Things (IoT) has emerged as a transformative technology, connecting a wide array of devices and enabling seamless communication and data exchange. However, the rapid proliferation of IoT devices has br...
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Data rewarding is a novel business model that leads to an economic trend in mobile networks. In this scheme, the advertiser incentivizes mobile users (MUs) to watch advertisement (ads) and, in return, receive a reward...
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This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed *** LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changin...
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This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed *** LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of ***,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO *** comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been *** accuracy and stability of simulated data and real data in the parameter extraction of PV systems are *** to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent *** further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and *** summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.
Currently,e-learning is one of the most prevalent educational methods because of its need in today’s *** classrooms and web-based learning are becoming the new method of teaching *** students experience a lack of acc...
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Currently,e-learning is one of the most prevalent educational methods because of its need in today’s *** classrooms and web-based learning are becoming the new method of teaching *** students experience a lack of access to resources commonly the educational *** remote loca-tions,educational institutions face significant challenges in accessing various web-based materials due to bandwidth and network infrastructure *** objective of this study is to demonstrate an optimization and queueing tech-nique for allocating optimal servers and slots for users to access cloud-based e-learning *** proposed method provides the optimization and queue-ing algorithm for multi-server and multi-city constraints and considers where to locate the best *** optimal server selection,the Rider Optimization Algo-rithm(ROA)is utilized.A performance analysis based on time,memory and delay was carried out for the proposed methodology in comparison with the exist-ing *** proposed Rider Optimization Algorithm is compared to Par-ticle Swarm Optimization(PSO),Genetic Algorithm(GA)and Firefly Algorithm(FFA),the proposed method is more suitable and effective because the other three algorithms drop in local optima and are only suitable for small numbers of user *** the proposed method outweighs the conventional techniques by its enhanced performance over them.
The increasing concern regarding animal attacks within rural communities and among forestry workers highlights the pressing need for effective wild animal tracking systems. Surveillance cameras and drones have become ...
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In contemporary organizational landscapes, the strategic retention of employees stands as a paramount concern for sustained success. To address this challenge, this research endeavors to harness the power of predictiv...
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computer vision methods for depth estimation usually use simple camera models with idealized optics. For modern machine learning approaches, this creates an issue when attempting to train deep networks with simulated ...
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computer vision methods for depth estimation usually use simple camera models with idealized optics. For modern machine learning approaches, this creates an issue when attempting to train deep networks with simulated data, especially for focus-sensitive tasks like Depth-from-Focus. In this work, we investigate the domain gap caused by off-axis aberrations that will affect the decision of the best-focused frame in a focal stack. We then explore bridging this domain gap through aberration-aware training (AAT). Our approach involves a lightweight network that models lens aberrations at different positions and focus distances, which is then integrated into the conventional network training pipeline. We evaluate the generality of network models on both synthetic and real-world data. The experimental results demonstrate that the proposed AAT scheme can improve depth estimation accuracy without fine-tuning the model for different datasets. The code will be available in ***/vccimaging/Aberration-Aware-Depth-from-Focus. Author
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