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Full-Text Articles in Computer Engineering
Tinyml For Gait Stride Classification, Priyanka Rajendra
Tinyml For Gait Stride Classification, Priyanka Rajendra
UNLV Theses, Dissertations, Professional Papers, and Capstones
Human gait classification and analysis become very important when a person has been diagnosed with a neurological disorder or has suffered an injury which has affected their ability to walk correctly. Gait strides are an important parameter to be studied as it helps the doctor to diagnose any underlying gait condition and evaluate what type of treatment suits the best for the patient’s recovery. Studying gait strides also helps athletes to improve their performance.In today’s world, machine learning has emerged as one of the most widely used technology for classification and analysis of gait characteristics. TinyML is a field of …
Analysis Of Microscopic Objects Using Computer Vision Methods, Yuan Dao
Analysis Of Microscopic Objects Using Computer Vision Methods, Yuan Dao
UNLV Theses, Dissertations, Professional Papers, and Capstones
As an essential and powerful tool to observe living organisms, three-dimensional fluorescence microscopy is widely used in biological research and diagnosis. The 4D fluorescence microscopy data can be obtained using time-lapsed videos of 3D images. To analyze and extract useful information from the increasingly large and complex biological image dataset, efficient and effective computational tools are in need but still lagging behind. In analyzing biological data, two major challenges are faced. First, time-lapsed fluorescence microscopic images typically have a low SNR. Second, biological objects often change their morphology and internal structure frequently. As such, conventional image processing methods may not …
Forecasting Pedestrian Trajectory Using Deep Learning, Arsal Syed
Forecasting Pedestrian Trajectory Using Deep Learning, Arsal Syed
UNLV Theses, Dissertations, Professional Papers, and Capstones
In this dissertation we develop different methods for forecasting pedestrian trajectories. Complete understanding of pedestrian motion is essential for autonomous agents and social robots to make realistic and safe decisions. Current trajectory prediction methods rely on incorporating historic motion, scene features and social interaction to model pedestrian behaviors. Our focus is to accurately understand scene semantics to better forecast trajectories. In order to do so, we leverage semantic segmentation to encode static scene features such as walkable paths, entry/exits, static obstacles etc. We further evaluate the effectiveness of using semantic maps on different datasets and compare its performance with already …