LRTCFPan: Low-Rank Tensor Completion Based Framework for Pansharpening.

Pansharpening refers to the fusion of a low spatial-resolution multispectral image with a high spatial-resolution panchromatic image. In this paper, we propose a novel low-rank tensor completion (LRTC)-based framework with some regularizers for multispectral image pansharpening, called LRTCFPan. The tensor completion technique is commonly used for image recovery, but it cannot directly perform the pansharpening or, more generally, the super-resolution problem because of the formulation gap.

Model Lightweighting for Real-time Distraction Detection on Resource-Limited Devices.

Detecting distracted driving accurately and quickly with limited resources is an essential yet underexplored problem. Most of the existing works ignore the resource-limited reality. In this work, we aim to achieve accurate and fast distracted driver detection in the context of embedded devices where only limited memory and computing resources are available.

A long-term survival rat model of spinal cord ischemia injury: Thoracic aortic occlusion combined with aortic bypass circulation.

Objective: This study aims to investigate the methods for rat spinal cord ischemia injury models with a high long-term survival rate.

Methods: The rats were divided into three groups: the treatment group, the control group, and the sham operation group. The treatment group had a blocked thoracic aorta (landing zone 3 by Ishimaru - T11) + aortic bypass circulation for 20 min.

Asymmetrical alterations of grey matter among psychiatric disorders: A systematic analysis by voxel-based activation likelihood estimation.

Schizophrenia (SZ), bipolar disorder (BD) and major depression disorder (MDD) have been regarded as highly diverged independent entities in current psychiatric diagnosis. However, ample new evidence suggests that they may have common biological traits. Neuroimaging studies showed that psychiatric disorders might associated with altered grey matter (GM) asymmetry compared to controls; however, the degree to which SZ, BD and MDD have common and/or distinct asymmetrical alterations in GM is still ambiguous.

A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data.

Human driving behaviors are personalized and unique, and the automobile fingerprint of drivers could be helpful to automatically identify different driving behaviors and further be applied in fields such as auto-theft systems. Current research suggests that in-vehicle Controller Area Network-BUS (CAN-BUS) data can be used as an effective representation of driving behavior for recognizing different drivers. However, it is difficult to capture complex temporal features of driving behaviors in traditional methods.

Characterization of fine motor development: dynamic analysis of children's drawing movements.

In this study, we investigated children's fine motor development by analyzing drawing trajectories, kinematics and kinetics. Straight lines drawing task and circles drawing task were performed by using a force sensitive tablet. Forty right-handed and Chinese mother-tongue students aged 6-12, attending classes from grade 1 to 5, were engaged in the experiment.