High-speed three-dimensional (3D) intravital imaging in animals is useful for studying transient subcellular interactions and functions in health and disease. Light-field microscopy (LFM) provides a computational solution for snapshot 3D imaging with low phototoxicity but is restricted by low resolution and reconstruction artifacts induced by optical aberrations, motion and noise. Here, we propose virtual-scanning LFM (VsLFM), a physics-based deep learning framework to increase the resolution of LFM up to the diffraction limit within a snapshot. By constructing a 40 GB high-resolution scanning LFM dataset across different species, we exploit physical priors between phase-correlated angular views to address the frequency aliasing problem. This enables us to bypass hardware scanning and associated motion artifacts. Here, we show that VsLFM achieves ultrafast 3D imaging of diverse processes such as the beating heart in embryonic zebrafish, voltage activity in Drosophila brains and neutrophil migration in the mouse liver at up to 500 volumes per second.
Long-term and frequent blood glucose detection by requiring finger-pick blood become unrealistic. An appropriate non-invasive detection system is thus highly desirable to deal better with it. In this paper, a non-invasive and intelligent dual-sensing system is reported. The feasibility of proposed system has been verified by glucose solution, animal serum and human trials. In the in-vivo experiments, detection signal has a high correlation (r = 0.96) with blood glucose level. An improved convolution neural network (cascade CNN) is purposed to be employed for estimation of blood glucose level (BGL). For the estimation results of BGL, the root mean squared error (RMSE) of 7.3217 mg/dL and mean absolute relative difference (MARD) of 4.7209% were achieved. The estimated results were also 100% fallen into the clinically acceptable zones of the Clarke error grid analysis, which indicated that proposed system could be potentially used for clinical measuring.
Purpose
This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.
Design/methodology/approach
The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.
Findings
The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.
Originality/value
This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.