We propose two types of intelligent reflecting systems based on programmable metasurfaces and mirrors to focus the incident optical power towards a visible light communication receiver. We derive the required phase gr...
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Android apps can hold secret strings of themselves such as cloud service credentials or encryption keys. Leakage of such secret strings can induce unprecedented consequences like monetary losses or leakage of user pri...
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Cold chain logistics play an important aspect in storing, preserving and transporting of cargo which is highly sensitive to environmental parameters surrounding it. Not han-dling these medicinal products in the recomm...
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In the coming years, the demand for IoT devices connecting our homes, cities, and industries will grow significantly. We now inhabit a world where interconnected smart devices can be controlled through single commands...
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Existing studies on constrained reinforcement learning (RL) may obtain a well-performing policy in the training environment. However, when deployed in a real environment, it may easily violate constraints that were or...
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Existing studies on constrained reinforcement learning (RL) may obtain a well-performing policy in the training environment. However, when deployed in a real environment, it may easily violate constraints that were originally satisfied during training because there might be model mismatch between the training and real environments. To address this challenge, we formulate the problem as constrained RL under model uncertainty, where the goal is to learn a policy that optimizes the reward and at the same time satisfies the constraint under model mismatch. We develop a Robust Constrained Policy Optimization (RCPO) algorithm, which is the first algorithm that applies to large/continuous state space and has theoretical guarantees on worst-case reward improvement and constraint violation at each iteration during the training. We show the effectiveness of our algorithm on a set of RL tasks with constraints. Copyright 2024 by the author(s)
Hamiltonian Monte Carlo (HMC) has emerged as a promising method for predicting actual-time community visitors. HMC is a specialized form of Markov Chain Monte Carlo (MCMC) sampling and may reduce the computational com...
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The dual-axis tilting quadcopter holds great promise for applications that demand both high performance and full actuation in a 6DOF Cartesian task space;however, control system design is complicated by its complex dy...
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The Internet of Things (IoT) has enabled the adoption of automated aeroponics equipment, which has transformed our understanding of horticulture. Aeroponics is a method of vegetative production that does not use soil....
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This paper presents the design and development of a low-cost and user-friendly Bengali braille embosser for the visually impaired population in Bangladesh. To produce braille text, the embosser employs a shifting mech...
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Numerical methods that can accurately reconstruct rough surface profiles are used in various fields of engineering such as remote sensing, microwave imaging, optics, nondestructive testing, etc. These methods express ...
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ISBN:
(数字)9781733509671
ISBN:
(纸本)9798350362978
Numerical methods that can accurately reconstruct rough surface profiles are used in various fields of engineering such as remote sensing, microwave imaging, optics, nondestructive testing, etc. These methods express the electromagnetic scattered fields measured away from the surface itself as an integral function of the surface profile. This mapping is highly nonlinear and ill-posed (D. Colton and R. Kress, 1998, SpringerVerlag, Berlin), and therefore its inversion for reconstruction of the surface profile from measured scattered fields is challenging. This inversion can done using semi-analytic asymptotic approaches such as the small perturbation and the Rytov approximation methods (A.G. Voronovich, 2013, Springer-Verlag, Berlin), however the range of applicability of these approaches is rather limited. Fully numerical methods that rely on Newton-type iterative linearization techniques and regularization schemes such as those in (S. Arhab, et al., PIERS, pp. 3495–3500, 2017) and (A. Sefer, A. Yapar, IEEE Trans. Geosci Remote Sens., vol. 59, pp. 1041–1051, 2021) have a wider range of applicability but they suffer from convergence and accuracy issues.
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