This paper presents a hybrid rectifier mode control for broad-range output power regulation in wireless power transfer (WPT) systems. The proposed control method employs a secondary-side active rectifier for output tu...
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Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing *** goal of this work is to inves-tigate ...
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Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing *** goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity *** effectiveness of the algorithm will be studied and evaluated in this *** this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of *** algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational *** FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight *** derivation of the algorithm is provided and supported by mathematical convergence *** is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various *** results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter *** outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications.
With the rapid advancement of electronic textiles, the design and integration of connectors have emerged as pivotal challenges. This study emphasizes the development of a connection solution that amalgamates miniaturi...
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In classical Finite Rate of Innovation (FRI) theory, signal reconstruction is initially achieved with precision using the zeroing filter algorithm on noiseless pulse signals. However, with the introduction of noise, t...
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Previous articles on unsupervised skeleton-based action recognition primarily focused on strategies for utilizing features to drive model optimization through methods like contrastive learning and reconstruction. Howe...
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Inverse Reinforcement Learning (IRL) and Reinforcement Learning from Human Feedback (RLHF) are pivotal methodologies in reward learning, which involve inferring and shaping the underlying reward function of sequential...
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Inverse Reinforcement Learning (IRL) and Reinforcement Learning from Human Feedback (RLHF) are pivotal methodologies in reward learning, which involve inferring and shaping the underlying reward function of sequential decision-making problems based on observed human demonstrations and feedback. Most prior work in reward learning has relied on prior knowledge or assumptions about decision or preference models, potentially leading to robustness issues. In response, this paper introduces a novel linear programming (LP) framework tailored for offline reward learning. Utilizing pre-collected trajectories without online exploration, this framework estimates a feasible reward set from the primal-dual optimality conditions of a suitably designed LP, and offers an optimality guarantee with provable sample efficiency. Our LP framework also enables aligning the reward functions with human feedback, such as pairwise trajectory comparison data, while maintaining computational tractability and sample efficiency. We demonstrate that our framework potentially achieves better performance compared to the conventional maximum likelihood estimation (MLE) approach through analytical examples and numerical experiments. Copyright 2024 by the author(s)
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study prop...
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Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
This paper presents a method to control the out-ofband performance of absorptive filters in both narrowband and wideband cases. To verify the method, a narrowband absorptive filter is designed with wideband matching, ...
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This paper explores the development and evaluation of a multimodal system for survival prediction in clinical settings, leveraging both structured electronic health records and unstructured clinical notes. The core ob...
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Inter-satellite optical wireless communication (IsOWC) links are expected to be commonly used for future generation networks for transferring data through low earth orbit (LEO) satellite and geostationary earth orbit ...
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