赵一帆

An AI-powered navigation framework to achieve an automated acquisition of cardiac ultrasound images

Echocardiography is an effective tool for diagnosing cardiovascular disease. However, numerous challenges affect its accessibility, including skill requirements, workforce shortage, and sonographer strain. We introduce a navigation framework for the automated acquisition of echocardiography images, consisting of 3 modules: perception, intelligence, and control. The perception module contains an ultrasound probe, a probe actuator, and a locator camera. Information from this module is sent to the intelligence module, which grades the quality of an ultrasound image for different echocardiography views. The window search algorithm in the control module governs the decision-making process in probe movement, finding the best location based on known probe traversal positions and image quality. We conducted a series of simulations using the HeartWorks simulator to assess the proposed framework. This study achieved an accuracy of 99

A lightweight temporal attention-based convolution neural network for driver's activity recognition in edge

Low inference latency and accurate response to environment changes play a crucial role in the automated driving system, especially in the current Level 3 automated driving. Achieving the rapid and reliable recognition of driver’s non-driving related activities (NDRAs) is important for designing an intelligent takeover strategy that ensures a safe and quick control transition. This paper proposes a novel lightweight temporal attention-based convolutional neural network (LTA-CNN) module dedicated to edge computing platforms, specifically for NDRAs recognition. This module effectively learns spatial and temporal representations at a relatively low computational cost. Its superiority has been demonstrated in an NDRA recognition dataset, achieving 81.01

Toward Human-Centered Automated Driving: A Novel Spatiotemporal Vision Transformer-Enabled Head Tracker

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Automatic reconstruction of irregular shape defects in pulsed thermography using deep learning neural network

<scp>Spatial–temporal</scp> graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram

Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review

Multi-Scale Remaining Useful Life Prediction Using Long Short-Term Memory

Pattern Recognition of Barely Visible Impact Damage in Carbon Composites Using Pulsed Thermography

EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning