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1. Epitaxial mid-IR nanophotonic optoelectronics

   https://doi.org/10.1063/5.0086774  

   Nordin, L.; Wasserman, D. (May 2022, Applied Physics Letters)

   There are a range of fundamental challenges associated with scaling optoelectronic devices down to the nano-scale, and the past decades have seen significant research dedicated to the development of sub-diffraction-limit optical devices, often relying on the plasmonic response of metal structures. At the longer wavelengths associated with the mid-infrared, dramatic changes in the optical response of traditional nanophotonic materials, reduced efficiency optoelectronic active regions, and a host of deleterious and/or parasitic effects makes nano-scale optoelectronics at micro-scale wavelengths particularly challenging. In this Perspective, we describe recent work leveraging a class of infrared plasmonic materials, highly doped semiconductors, which not only support sub-diffraction-limit plasmonic modes at long wavelengths, but which can also be integrated into a range of optoelectronic device architectures. We discuss how the wavelength-dependent optical response of these materials can serve a number of different photonic device designs, including dielectric waveguides, epsilon-near-zero dynamic optical devices, cavity-based optoelectronics, and plasmonic device architectures. We present recent results demonstrating that the highly doped semiconductor class of materials offers the opportunity for monolithic, all-epitaxial, device architectures out-performing current state of the art commercial devices, and discuss the perspectives and promise of these materials for infrared nanophotonic optoelectronics. 

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2. Optoelectronic synapse using monolayer MoS2 field effect transistors

   https://doi.org/10.1038/s41598-020-78767-4  

   Islam, Molla Manjurul; Dev, Durjoy; Krishnaprasad, Adithi; Tetard, Laurene; Roy, Tania (December 2020, Scientific Reports)

   null (Ed.)

   Abstract Optical data sensing, processing and visual memory are fundamental requirements for artificial intelligence and robotics with autonomous navigation. Traditionally, imaging has been kept separate from the pattern recognition circuitry. Optoelectronic synapses hold the special potential of integrating these two fields into a single layer, where a single device can record optical data, convert it into a conductance state and store it for learning and pattern recognition, similar to the optic nerve in human eye. In this work, the trapping and de-trapping of photogenerated carriers in the MoS 2 /SiO 2 interface of a n-channel MoS 2 transistor was employed to emulate the optoelectronic synapse characteristics. The monolayer MoS 2 field effect transistor (FET) exhibits photo-induced short-term and long-term potentiation, electrically driven long-term depression, paired pulse facilitation (PPF), spike time dependent plasticity, which are necessary synaptic characteristics. Moreover, the device’s ability to retain its conductance state can be modulated by the gate voltage, making the device behave as a photodetector for positive gate voltages and an optoelectronic synapse at negative gate voltages. 

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3. Real‐time driving style classification based on short‐term observations

   https://doi.org/10.1049/cmu2.12405  

   Zheng, Xinhu; Yang, Pengtao; Duan, Dongliang; Cheng, Xiang; Yang, Liuqing (May 2022, IET Communications)

   Abstract Vehicle behaviour prediction provides important information for decision‐making in modern intelligent transportation systems. People with different driving styles have considerably different driving behaviours and hence exhibit different behaviour tendency. However, most existing prediction methods do not consider the different tendencies in driving styles and apply the same model to all vehicles. Furthermore, most of the existing driver classification methods rely on offline learning that requires a long observation of driving history and hence are not suitable for real‐time driving behaviour analysis. To facilitate personalised models that can potentially improve vehicle behaviour prediction, the authors propose an algorithm that classifies drivers into different driving styles. The algorithm only requires data from a short observation window and it is more applicable for real‐time online applications compared with existing methods that require a long term observation. Experiment results demonstrate that the proposed algorithm can achieve consistent classification results and provide intuitive interpretation and statistical characteristics of different driving styles, which can be further used for vehicle behaviour prediction. 

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4. Lifelong Personalization via Gaussian Process Modeling for Long-Term HRI

   https://doi.org/10.3389/frobt.2021.683066  

   Spaulding, Samuel; Shen, Jocelyn; Park, Hae Won; Breazeal, Cynthia (June 2021, Frontiers in Robotics and AI)

   null (Ed.)

   Across a wide variety of domains, artificial agents that can adapt and personalize to users have potential to improve and transform how social services are provided. Because of the need for personalized interaction data to drive this process, long-term (or longitudinal) interactions between users and agents, which unfold over a series of distinct interaction sessions, have attracted substantial research interest. In recognition of the expanded scope and structure of a long-term interaction, researchers are also adjusting the personalization models and algorithms used, orienting toward “continual learning” methods, which do not assume a stationary modeling target and explicitly account for the temporal context of training data. In parallel, researchers have also studied the effect of “multitask personalization,” an approach in which an agent interacts with users over multiple different tasks contexts throughout the course of a long-term interaction and learns personalized models of a user that are transferrable across these tasks. In this paper, we unite these two paradigms under the framework of “Lifelong Personalization,” analyzing the effect of multitask personalization applied to dynamic, non-stationary targets. We extend the multi-task personalization approach to the more complex and realistic scenario of modeling dynamic learners over time, focusing in particular on interactive scenarios in which the modeling agent plays an active role in teaching the student whose knowledge the agent is simultaneously attempting to model. Inspired by the way in which agents use active learning to select new training data based on domain context, we augment a Gaussian Process-based multitask personalization model with a mechanism to actively and continually manage its own training data, allowing a modeling agent to remove or reduce the weight of observed data from its training set, based on interactive context cues. We evaluate this method in a series of simulation experiments comparing different approaches to continual and multitask learning on simulated student data. We expect this method to substantially improve learning in Gaussian Process models in dynamic domains, establishing Gaussian Processes as another flexible modeling tool for Long-term Human-Robot Interaction (HRI) Studies. 

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5. On the Long-Term Performance of Solitary Wave-Based Transducers for Nondestructive Evaluation Applications

   https://doi.org/10.1115/1.4054391  

   Jalali, Hoda; Rizzo, Piervincenzo (November 2022, Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems)

   Abstract A nondestructive evaluation (NDE) technique based on highly nonlinear solitary waves (HNSWs) has been developed recently by a few groups worldwide. The technique is based on the propagation and detection of these waves along a one-dimensional monoperiodic array of spherical particles in which one end of the array is in contact with the material/structure to be inspected, and the particle at the opposite end induces the waves by means of a mechanical impact. Several studies have demonstrated that the dynamic interaction between the waves and the element to be evaluated is dependent on the geometric and mechanical properties of the structure, and such dependency can be monitored by sensing the waves reflected at the interface between the array and the structure. This NDE technique is typically performed by using the so-called HNSW transducer. The term transducer indicates a portable device that consists of a monoperiodic array of particles, a device to trigger the waves, and a sensing element to detect the waves. In the study presented in this article, the long-term performance of three transducers was investigated by placing them above a test object whose mechanical and geometric properties were left constant for a week while the transducers triggered and detected thousands of waves. Any variability of the waves was quantified by extracting simple features such as amplitude, time of flight, and cross-correlation. To investigate the cause of variabilities, 16 measurements were captured with short videos at ∼1000 fps. The results of the study demonstrate that the traveling time of the solitary waves is the most reliable parameter for long-term monitoring with the lowest variability and the least susceptibility to physical changes within the array. In addition, the findings of this study allow the framing of a valid strategy to improve the design of the transducers in order to make the HNSW-based technique suitable for long-term monitoring. 

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