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- Affective computing (2)
- 3D face analysis (1)
- Artificial intelligence (1)
- Autism (1)
- Bathymetry estimation (1)
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- Cognitive radio networks (1)
- Computer vision (1)
- Deep capsule network (1)
- Deep convolutional neural network (1)
- Domain adaptation (1)
- Emotion detection (1)
- Human computer interaction (1)
- Machine learning (1)
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- Psychophysical response (1)
- Seagrass detection (1)
- Signal processing (1)
- Software defined radio (1)
- Spectrum sharing (1)
- Transfer learning (1)
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray
Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray
Electrical & Computer Engineering Theses & Dissertations
Affective computing is an exciting and transformative field that is gaining in popularity among psychologists, statisticians, and computer scientists. The ability of a machine to infer human emotion and mood, i.e. affective states, has the potential to greatly improve human-machine interaction in our increasingly digital world. In this work, an ensemble model methodology for detecting human emotions across multiple subjects is outlined. The Continuously Annotated Signals of Emotion (CASE) dataset, which is a dataset of physiological signals labeled with discrete emotions from video stimuli as well as subject-reported continuous emotions, arousal and valence, from the circumplex model, is used for …
Deep Learning Approaches For Seagrass Detection In Multispectral Imagery, Kazi Aminul Islam
Deep Learning Approaches For Seagrass Detection In Multispectral Imagery, Kazi Aminul Islam
Electrical & Computer Engineering Theses & Dissertations
Seagrass forms the basis for critically important marine ecosystems. Seagrass is an important factor to balance marine ecological systems, and it is of great interest to monitor its distribution in different parts of the world. Remote sensing imagery is considered as an effective data modality based on which seagrass monitoring and quantification can be performed remotely. Traditionally, researchers utilized multispectral satellite images to map seagrass manually. Automatic machine learning techniques, especially deep learning algorithms, recently achieved state-of-the-art performances in many computer vision applications. This dissertation presents a set of deep learning models for seagrass detection in multispectral satellite images. It …
Computational Modeling Of Facial Response For Detecting Differential Traits In Autism Spectrum Disorders, Manar D. Samad
Computational Modeling Of Facial Response For Detecting Differential Traits In Autism Spectrum Disorders, Manar D. Samad
Electrical & Computer Engineering Theses & Dissertations
This dissertation proposes novel computational modeling and computer vision methods for the analysis and discovery of differential traits in subjects with Autism Spectrum Disorders (ASD) using video and three-dimensional (3D) images of face and facial expressions. ASD is a neurodevelopmental disorder that impairs an individual’s nonverbal communication skills. This work studies ASD from the pathophysiology of facial expressions which may manifest atypical responses in the face. State-of-the-art psychophysical studies mostly employ na¨ıve human raters to visually score atypical facial responses of individuals with ASD, which may be subjective, tedious, and error prone. A few quantitative studies use intrusive sensors on …
Transparent Spectrum Co-Access In Cognitive Radio Networks, Jonathan Daniel Backens
Transparent Spectrum Co-Access In Cognitive Radio Networks, Jonathan Daniel Backens
Electrical & Computer Engineering Theses & Dissertations
The licensed wireless spectrum is currently under-utilized by as much as 85%. Cognitive radio networks have been proposed to employ dynamic spectrum access to share this under-utilized spectrum between licensed primary user transmissions and unlicensed secondary user transmissions. Current secondary user opportunistic spectrum access methods, however, remain limited in their ability to provide enough incentive to convince primary users to share the licensed spectrum, and they rely on primary user absence to guarantee secondary user performance. These challenges are addressed by developing a Dynamic Spectrum Co-Access Architecture (DSCA) that allows secondary user transmissions to co-access transparently and concurrently with primary …