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Defects and dopant atoms in solid state materials are a promising platform for realizing single photon sources and quantum memories, which are the basic building blocks of quantum repeaters needed for long distance quantum networks. In particular, trivalent erbium (Er$^{3+}$) is of interest because it couples C-band telecom optical transitions with a spin-based memory platform. In order to produce quantum repeaters at the scale required for a quantum internet, it is imperative to integrate these necessary building blocks with mature and scalable semiconductor processes. In this work, we demonstrate the optical isolation of single Er$^{3+}$ ions in CMOS-compatible titanium dioxide (TiO$_2$) thin films monolithically integrated on a silicon-on-insulator (SOI) photonics platform. Our results demonstrate a first step toward the realization of a monolithically integrated and scalable quantum photonics package based on Er$^{3+}$ doped thin films.

Connected and autonomous vehicles have the potential to minimize energy consumption by optimizing the vehicle velocity and powertrain dynamics with Vehicle-to-Everything info en route. Existing deterministic and stochastic methods created to solve the eco-driving problem generally suffer from high computational and memory requirements, which makes online implementation challenging. This work proposes a hierarchical multi-horizon optimization framework implemented via a neural network. The neural network learns a full-route value function to account for the variability in route information and is then used to approximate the terminal cost in a receding horizon optimization. Simulations over real-world routes demonstrate that the proposed approach achieves comparable performance to a stochastic optimization solution obtained via reinforcement learning, while requiring no sophisticated training paradigm and negligible on-board memory.

Optically active solid-state spin qubits thrive as an appealing technology for quantum interconnect and quantum networking, owing to their atomic size, scalable creation, long-lived coherence, and ability to coherently interface with flying qubits. Trivalent erbium dopants in particular emerge as a compelling candidate with their telecom C band emission and shielded 4f intra-shell spin-optical transitions. However, prevailing top-down architecture for rare-earth qubits and devices has not allowed simultaneous long optical and spin coherence necessary for long-distance quantum networks. Here we demonstrate dual erbium telecom spin-photon interfaces in an epitaxial thin-film platform via wafer-scale bottom-up synthesis. Harnessing precise controls over the matrix purity, dopant placement, and symmetry unique to this platform, we simultaneously achieve millisecond erbium spin coherence time and $<$3 kilohertz optical dephasing rate in an inversion-symmetry protected site and realize both optical and microwave control in a fiber-integrated package for rapid scaling up. These results demonstrate a significant prospect for high-quality rare-earth qubits and quantum memories assembled using a bottom-up method and pave the way for the large-scale development of quantum light-matter interfaces for telecommunication quantum networks.

Erbium-doped solids are prime candidates for optical quantum communication networks due to erbium's telecom C-band emission. A long-lived electron spin of erbium with millisecond coherence time is highly desirable for establishing entanglement between adjacent quantum repeater nodes while long-term storage of the entanglement could rely on transferring to erbium's second-long coherence nuclear spins. Here we report GHz-range electron spin transitions of $^{167}\mathrm{Er}^{3+}$ in yttrium oxide ($\mathrm{Y_2O_3}$) matrix with coherence times that are consistently longer than a millisecond. Instead of addressing field-specific Zero First-Order Zeeman transitions, we probe weakly mixed electron spins with the field orientation along the lower g-factors. Using pulsed electron spin resonance spectroscopy, we find paramagnetic impurities are the dominant source of decoherence, and by polarizing them we achieve a Hahn echo spin $\mathrm{T_2}$ up to 1.46 ms, and a coherence time up to 7.1 ms after dynamical decoupling. These coherence lifetimes are among the longest found in crystalline hosts especially those with nuclear spins. We further enhance the coherence time beyond conventional dynamical decoupling, using customized sequences to simultaneously mitigate spectral diffusion and Er-Er dipolar interactions. Despite nuclear and impurity spins in the host, this work shows that long-lived erbium spins comparable to non-nuclear spin hosts can be achieved. Our study not only establishes $^{167}\mathrm{Er}^{3+}$: $\mathrm{Y_2 O_3}$ as a significantly promising quantum memory platform but also provides a general guideline for engineering long-lived erbium spins in a variety of host materials for quantum technologies., Comment: 10 pages, 4 figures

Vehicle control algorithms exploiting connectivity and automation, such as Connected and Automated Vehicles (CAVs) or Advanced Driver Assistance Systems (ADAS), have the opportunity to improve energy savings. However, lower levels of automation involve a human-machine interaction stage, where the presence of a human driver affects the performance of the control algorithm in closed loop. This occurs for instance in the case of Eco-Driving control algorithms implemented as a velocity advisory system, where the driver is displayed an optimal speed trajectory to follow to reduce energy consumption. Achieving the control objectives relies on the human driver perfectly following the recommended speed. If the driver is unable to follow the recommended speed, a decline in energy savings and poor vehicle performance may occur. This warrants the creation of methods to model and forecast the response of a human driver when operating in the loop with a speed advisory system. This work focuses on developing a sequence to sequence long-short term memory (LSTM)-based driver behavior model that models the interaction of the human driver to a suggested desired vehicle speed trajectory in real-world conditions. A driving simulator is used for data collection and training the driver model, which is then compared against the driving data and a deterministic model. Results show close proximity of the LSTM-based model with the driving data, demonstrating that the model can be adopted as a tool to design human-centered speed advisory systems., Comment: 6 pages, 9 figures

Purpose: The objective of this investigation is to provide a comprehensive analysis of state-of-the-art methods for video-based assessment of surgical skill in the operating room. Methods: Using a data set of 99 videos of capsulorhexis, a critical step in cataract surgery, we evaluate feature based methods previously developed for surgical skill assessment mostly under benchtop settings. In addition, we present and validate two deep learning methods that directly assess skill using RGB videos. In the first method, we predict instrument tips as keypoints, and learn surgical skill using temporal convolutional neural networks. In the second method, we propose a novel architecture for surgical skill assessment that includes a frame-wise encoder (2D convolutional neural network) followed by a temporal model (recurrent neural network), both of which are augmented by visual attention mechanisms. We report the area under the receiver operating characteristic curve, sensitivity, specificity, and predictive values with each method through 5-fold cross-validation. Results: For the task of binary skill classification (expert vs. novice), deep neural network based methods exhibit higher AUC than the classical spatiotemporal interest point based methods. The neural network approach using attention mechanisms also showed high sensitivity and specificity. Conclusion: Deep learning methods are necessary for video-based assessment of surgical skill in the operating room. Our findings of internal validity of a network using attention mechanisms to assess skill directly using RGB videos should be evaluated for external validity in other data sets.

Connected and Automated Vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the Eco-Driving problem seeks to design optimal speed and power usage profiles based on available information from connectivity and advanced mapping features to minimize the fuel consumption over an itinerary. This paper presents a hierarchical multi-layer Model Predictive Control (MPC) approach for improving the fuel economy of a 48V mild-hybrid powertrain in a connected vehicle environment. Approximate Dynamic Programming (ADP) is used to solve the Receding Horizon Optimal Control Problem (RHOCP), where the terminal cost for the RHOCP is approximated as the base-policy obtained from the long-term optimization. The controller was extensively tested virtually (using both deterministic and Monte Carlo simulations) across multiple real-world routes where energy savings of more than 20\% have been demonstrated. Further, the developed controller was deployed and tested at a proving ground in real-time on a test vehicle equipped with a rapid prototyping embedded controller. Real-time in-vehicle testing confirmed the energy savings observed in simulation and demonstrated the ability of the developed controller to be effective in real-time applications., Comment: This work has been submitted to ASME (journal) for possible publication and is under review. Paper summary: 17 pages, 15 figures

The Eco-Driving control problem seeks to perform fuel efficient speed planning for a Connected and Autonomous Vehicle (CAV) that can exploit information available from advanced mapping, and from Vehicle-to-Everything (V2X) communication. The ability of an Eco-Driving strategy to adapt in real time to variable traffic scenarios where surrounding vehicles can be either connected or unconnected is critical for further development and deployment of this technology in the transportation sector. In this work, the Eco-Driving strategy, formulated as a receding-horizon optimal control problem, is integrated with a target vehicle speed prediction model and solved via Dynamic Programming (DP) to determine the optimal speed trajectory in the presence of a human-driven target vehicle. An encoder-decoder architecture analyzes the patterns in the target vehicle velocity recorded over a historic window using a Gated-Recurrent-Unit (GRU) based encoder and generates an estimate of the future velocity trajectory using the GRU based decoder. A sensitivity study is done to analyze the effect of the historical and prediction windows on the accuracy of the velocity predictor. The proposed Eco-Driving controller is evaluated through microscopic simulations using a traffic simulator., Comment: 8 pages, 11 figures, conference paper

Connected and Automated Hybrid Electric Vehicles have the potential to reduce fuel consumption and travel time in real-world driving conditions. The eco-driving problem seeks to design optimal speed and power usage profiles based upon look-ahead information from connectivity and advanced mapping features. Recently, Deep Reinforcement Learning (DRL) has been applied to the eco-driving problem. While the previous studies synthesize simulators and model-free DRL to reduce online computation, this work proposes a Safe Off-policy Model-Based Reinforcement Learning algorithm for the eco-driving problem. The advantages over the existing literature are three-fold. First, the combination of off-policy learning and the use of a physics-based model improves the sample efficiency. Second, the training does not require any extrinsic rewarding mechanism for constraint satisfaction. Third, the feasibility of trajectory is guaranteed by using a safe set approximated by deep generative models. The performance of the proposed method is benchmarked against a baseline controller representing human drivers, a previously designed model-free DRL strategy, and the wait-and-see optimal solution. In simulation, the proposed algorithm leads to a policy with a higher average speed and a better fuel economy compared to the model-free agent. Compared to the baseline controller, the learned strategy reduces the fuel consumption by more than 21\% while keeping the average speed comparable., Comment: This work has been submitted to the IEEE for possible publication and is under review. Paper summary: 13 pages, 11 figures

Predictive energy management of Connected and Automated Vehicles (CAVs), in particular those with multiple power sources, has the potential to significantly improve energy savings in real-world driving conditions. In particular, the eco-driving problem seeks to design optimal speed and power usage profiles based upon available information from connectivity and advanced mapping features to minimize the fuel consumption between two designated locations. In this work, the eco-driving problem is formulated as a three-state receding horizon optimal control problem and solved via Dynamic Programming (DP). The optimal solution, in terms of vehicle speed and battery State of Charge (SoC) trajectories, allows a connected and automated hybrid electric vehicle to intelligently pass the signalized intersections and minimize fuel consumption over a prescribed route. To enable real-time implementation, a parallel architecture of DP is proposed for an NVIDIA GPU with CUDA programming. Simulation results indicate that the proposed optimal controller delivers more than 15% fuel economy benefits compared to a baseline control strategy and that the solver time can be reduced by more than 90% by the parallel implementation when compared to a serial implementation., Comment: This work has been accepted by the 2021 European Control Conference. Paper summary: 6 pages, 9 figures

Connected and Automated Vehicles (CAVs), in particular those with multiple power sources, have the potential to significantly reduce fuel consumption and travel time in real-world driving conditions. In particular, the Eco-driving problem seeks to design optimal speed and power usage profiles based upon look-ahead information from connectivity and advanced mapping features, to minimize the fuel consumption over a given itinerary. In this work, the Eco-driving problem is formulated as a Partially Observable Markov Decision Process (POMDP), which is then solved with a state-of-art Deep Reinforcement Learning (DRL) Actor Critic algorithm, Proximal Policy Optimization. An Eco-driving simulation environment is developed for training and evaluation purposes. To benchmark the performance of the DRL controller, a baseline controller representing the human driver, a trajectory optimization algorithm and the wait-and-see deterministic optimal solution are presented. With a minimal onboard computational requirement and a comparable travel time, the DRL controller reduces the fuel consumption by more than 17% compared against the baseline controller by modulating the vehicle velocity over the route and performing energy-efficient approach and departure at signalized intersections, over-performing the more computationally demanding trajectory optimization method, Comment: This work has been accepted by IEEE Transactions on Vehicular Technology. Paper summary: 13 pages, 11 figures, 4 tables