Engineering Commons^™

Ground Moving Target Detection For Airborne Radar Using Machine Learning Approaches, Rafi Ahmed

FIU Electronic Theses and Dissertations

Airborne radar faces many challenges to suppress unknown interferences from ground reflections to detect slow-moving targets. In this dissertation work, a feature-based machine learning approach is proposed to effectively classify target and interference such as ground clutter without actually removing them using traditional methods. Multiple features are considered for developing the target/clutter classification algorithms of airborne radars with digital arrays. The features we use for classification include the clutter proximity measures and target geometric feature.

The proximity feature is extracted to distinguish target, and clutter in location in the Doppler-angle domain for airborne radar. The Euclidean distance between a signal …

Volitional Control Of Lower-Limb Prosthesis With Vision-Assisted Environmental Awareness, S M Shafiul Hasan

FIU Electronic Theses and Dissertations

Early and reliable prediction of user’s intention to change locomotion mode or speed is critical for a smooth and natural lower limb prosthesis. Meanwhile, incorporation of explicit environmental feedback can facilitate context aware intelligent prosthesis which allows seamless operation in a variety of gait demands. This dissertation introduces environmental awareness through computer vision and enables early and accurate prediction of intention to start, stop or change speeds while walking. Electromyography (EMG), Electroencephalography (EEG), Inertial Measurement Unit (IMU), and Ground Reaction Force (GRF) sensors were used to predict intention to start, stop or increase walking speed. Furthermore, it was investigated whether …