Tabrizi, M. K., Chi, M., Dey, Bir Bikram, Yuan, K., and Solbach, M. D., Liu, Y., Jenkin, M. and Tsotsos, J. K., DIJIT: A Robotic Head for an Active Observer, IEEE Robotics and Automation Letters, 1-8, 2026.
We present DIJIT, a novel binocular robotic head expressly designed for mobile agents that behave as active observers. DIJIT's unique breadth of functionality enables active vision research and the study of human-like eye and head-neck motions, their interrelationships, and how each contributes to visual ability. DIJIT is also being used to explore the differences between how human vision employs eye/head movements to solve visual tasks and current computer vision methods. DIJIT's design features nine mechanical degrees of freedom, while the cameras and lenses provide an additional four optical degrees of freedom. The ranges and speeds of the mechanical design are comparable to human performance. DIJIT attains 85% of the peak human saccade speed. Our design includes the ranges of motion required for convergent stereo, namely, vergence, version, and cyclotorsion. Here, we present DIJIT and some aspects of its performance. We also present a novel method for saccadic camera movements, using a direct relationship between camera orientation and motor values. The resulting saccadic camera movements are close to human movements in terms of their accuracy, with 1.17 deg and 1.14 deg mean error for the left and right cameras, respectively.
Sun, X., Wu, D., Jenkin, M., Zinflou, A., Wang, B., Boulet, B., Optimizing Electric Vehicle Charging Load Forecasting via Ensemble of Diffusion Models, IEEE Transactions on Intelligent Vehicles, 11: 869-891, 2026.
The global adoption of electric vehicles (EVs) has experienced rapid growth in recent years. This has resulted in higher EV charging demands, placing additional pressure and challenges on existing smart grid infrastructure. Accurate and reliable short-term electric vehicle load predictions are essential to optimize smart grid operations, prevent overload of the grid and transformers, and ensure that electricity distribution aligns with actual demand. Although machine learning methods have found wide use in electric vehicle charging load forecasting, they struggle to capture complex feature patterns and accurately quantify uncertainties. In contrast, generative AI has shown great promise in various real-world applications due to its ability to model intricate data distributions and provide robust probabilistic forecasts.
Sun, C., Michael, B., Chen, F., Jenkin, M. and Wu, D. Deep Learning for Electric Vehicle Charging Load Forecasting: A Survey. EEE Open Journal of Intelligent Transportation Systems, 2026 (to appear).
The widespread adoption of electric vehicles (EVs) is reshaping modern energy systems, necessitating accurate and efficient forecasting of EV charging demand to ensure grid stability and infrastructure optimization. Traditional statistical and machine learning models offer foundational insights but often struggle with capturing the complex temporal, nonlinear, and spatial patterns inherent in EV charging behavior. Deep learning has emerged as a powerful alternative, demonstrating superior capabilities in modeling long-range dependencies and integrating diverse contextual factors. This survey presents a comprehensive review of deep learning-based approaches for EV charging load forecasting, categorizing them into point and probabilistic forecasting paradigms. Point forecasting methods provide precise single-value predictions suitable for real-time applications, while probabilistic forecasting models quantify uncertainty, enabling risk-aware energy management. The paper further differentiates single-architecture models, hybrid architectures, and ensemble learning models within each paradigm, highlighting different architectures that integrate convolutional, recurrent, and attention-based components. This work also reviews relevant datasets and identifies current challenges, research gaps, and future directions, emphasizing the critical role of deep learning in advancing intelligent, sustainable, and resilient EV charging systems.
