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Mobile Robots (MRs), typically equipped with single-antenna radios, face many challenges in maintaining reliable connectivity established by multiple wireless access points (APs). These challenges include the absence of direct line-of-sight (LoS), ineffective beam searching due to the time-varying channel, and interference constraints. This paper presents REMARKABLE, an online learning based adaptive beam selection strategy for robot connectivity that trains kernelized bandit model directly in real-world settings of a factory floor. REMARKABLE employs reconfigurable intelligent surfaces (RISs) with passive reflective elements to create beamforming toward target robots, eliminating the need for multiple APs. We develop a method to create a beamforming codebook, reducing the search space complexity. We also develop a reconfigurable rotational mechanism to expand RIS coverage by rotating its projection plane. To address non-stationary conditions, we adopt the bandit over bandit idea that employs adaptive restarts, allowing the system to forget outdated observations and safely relearn the optimal interference-constrained beam. We show that our approach achieves a dynamic regret and the violation bound of Õ(T^(3/4)B^(1/4)) where T is the total time, and B is the total variation budget which captures the total changes in the environment without even assuming the knowledge of B. Finally, experimental validation with custom-designed RIS hardware and mobile robots demonstrates 46.8% faster beam selection and 94.2% accuracy, outperforming classical methods across diverse mobility settings. 

We report a two-step etching process involving inductively coupled plasma (ICP) etching followed by wet chemical etching to achieve smooth and vertical sidewalls, being beneficial for AlGaN-based electronic and optoelectronic devices. The influence of ICP power on the roughness of etched sidewalls is investigated. It is observed that ICP etching alone does not produce smooth sidewalls, necessitating subsequent wet chemical etching using tetramethyl ammonium hydroxide (TMAH) to enhance sidewall smoothness and reduce tilt angle. The morphological evolution of the etched sidewalls with wet etch time for the device structures is also thoroughly investigated. Consistent etch results are achieved for Al_xGa_1-xN alloys with Al compositions up to 70%, indicating the effectiveness of our etching process. 

In this paper, we address the challenges of asynchronous gradient descent in distributed learning environments, particularly focusing on addressing the challenges of stale gradients and the need for extensive communication resources. We develop a novel communication efficient framework that incorporates a gradient evaluation algorithm to assess and utilize delayed gradients based on their quality, ensuring efficient and effective model updates while significantly reducing communication overhead. Our proposed algorithm requires agents to only send the norm of the gradients rather than the computed gradient. The server then decides whether to accept the gradient if the ratio between the norm of the gradient and the distance between the global model parameter and the local model parameter exceeds a certain threshold. With the proper choice of the threshold, we show that the convergence rate achieves the same order as the synchronous stochastic gradient without depending on the staleness value unlike most of the existing works. Given the computational complexity of the initial algorithm, we introduce a simplified variant that prioritizes the practical applicability without compromising on the convergence rates. Our simulations demonstrate that our proposed algorithms outperform existing state-of-the-art methods, offering improved convergence rates, stability, accuracy, and resource consumption.