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Artificial Intelligence and Robotics Commons™
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- Deep learning (2)
- Adaptive graph (1)
- Autism Spectrum Disorder Diagnose (1)
- Biometrics (1)
- Electrophysiology (1)
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- Gait Recognition (1)
- Geometric angles (1)
- Graph Neural Network (1)
- Morphology (1)
- Multimodal Data (1)
- Multimodal fusion (1)
- Neuron classification (1)
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- Raw waveform (1)
- Remote Sensing (1)
- Representation learning (1)
- Self supervised (1)
- Skeleton-base action recognition (1)
- Unmanned Aerial Vehicles (1)
- Videos (1)
Articles 1 - 3 of 3
Full-Text Articles in Artificial Intelligence and Robotics
Implementing Unmanned Aerial Vehicles To Collect Human Gait Data At Distance And Altitude For Identification And Re-Identification, Donn E. Bartram
Implementing Unmanned Aerial Vehicles To Collect Human Gait Data At Distance And Altitude For Identification And Re-Identification, Donn E. Bartram
Graduate Theses, Dissertations, and Problem Reports
Gait patterns are a class of biometric information pertaining to the way a person moves and poses. Gait information is unique to each person and can be used to identify and reidentify people. Historically, this task has been achieved through the use of multiple ground-based imaging sensors. However, as Unmanned Aerial Vehicles (UAVs) advance, they present the opportunity to evolve the process of persons identification and re-identification. Collecting human gait data using UAVs at distances ranging from 20m to 500m and altitudes ranging from 0m to 120m is a challenging task. The current biometric data collection methods, primarily designed for …
Multimodal Neuron Classification Based On Morphology And Electrophysiology, Aqib Ahmad
Multimodal Neuron Classification Based On Morphology And Electrophysiology, Aqib Ahmad
Graduate Theses, Dissertations, and Problem Reports
Categorizing neurons into different types to understand neural circuits and ultimately brain function is a major challenge in neuroscience. While electrical properties are critical in defining a neuron, its morphology is equally important. Advancements in single-cell analysis methods have allowed neuroscientists to simultaneously capture multiple data modalities from a neuron. We propose a method to classify neurons using both morphological structure and electrophysiology. Current approaches are based on a limited analysis of morphological features. We propose to use a new graph neural network to learn representations that more comprehensively account for the complexity of the shape of neuronal structures. In …
Image And Video-Based Autism Spectrum Disorder Detection Via Deep Learning, Mindi Ruan
Image And Video-Based Autism Spectrum Disorder Detection Via Deep Learning, Mindi Ruan
Graduate Theses, Dissertations, and Problem Reports
People with Autism Spectrum Disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories but was particularly successful at classifying photos of people with >80% accuracy. Importantly, the visualization of our model revealed critical features that led …