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Image resolution and field of view in far-field optical microscopy are often inversely proportional to one another due to digital sampling limitations imposed by the magnification of the system and the pixel size of the sensor. We present a method including a spatial shifting mechanism and a reconstruction algorithm that bypasses this trade-off by shifting the sample to be imaged by subpixel increments, before registering the images via phase correlation and combining the resulting registered images using the shift-and-add approach. Importantly, this method requires no specific optical components that are uncommon to commercially available or custom-built microscope systems. The findings of the presented study demonstrate an improvement to spatial resolution of ∼42% while maintaining the system’s field of view (FOV), leading to a more than twofold improvement to the system’s space–bandwidth product (SBP). 

Cycling, as a green transportation mode, provides an environmentally friendly transportation choice for short-distance traveling. However, cyclists are also getting involved in fatal accidents more frequently in recent years. Thus, understanding and modeling their road behaviors is crucial in helping improving road safety laws and infrastructures. Traditionally, people understand road user behavior using either purely spatial trajectory data, or videos from fixed surveillance camera through tracking or predicting their paths. However, these data only cover limited areas and do not provide information from the cyclist's field of view. In this paper, we take advantage of geo-referenced egocentric video data collected from the handlebar cameras of cyclists to learn how to predict their behaviors. This approach is technically more challenging, because both the observer and objects in the scene might be moving, and there are strong temporal dependencies in both the behaviors of cyclists and the video scenes. We propose Cycling-Net, a novel deep learning model that tracks different types of objects in consecutive scenes and learns the relationship between the movement of these objects and the behavior of the cyclist. Experiment results on a naturalistic trip dataset show the Cycling-Net is effective in behavior prediction and outperforms a baseline model. 

Urban public transit planning is crucial in reducing traffic congestion and enabling green transportation. However, there is no systematic way to integrate passengers' personal preferences in planning public transit routes and schedules so as to achieve high occupancy rates and efficiency gain of ride-sharing. In this paper, we take the first step tp exact passengers' preferences in planning from history public transit data. We propose a data-driven method to construct a Markov decision process model that characterizes the process of passengers making sequential public transit choices, in bus routes, subway lines, and transfer stops/stations. Using the model, we integrate softmax policy iteration into maximum entropy inverse reinforcement learning to infer the passenger's reward function from observed trajectory data. The inferred reward function will enable an urban planner to predict passengers' route planning decisions given some proposed transit plans, for example, opening a new bus route or subway line. Finally, we demonstrate the correctness and accuracy of our modeling and inference methods in a large-scale (three months) passenger-level public transit trajectory data from Shenzhen, China. Our method contributes to smart transportation design and human-centric urban planning.