Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Publication
- Publication Type
Articles 1 - 6 of 6
Full-Text Articles in Computer Engineering
Robot Learning To Pour Solid Objects Accurately, Juan Wilches, Yu Sun
Robot Learning To Pour Solid Objects Accurately, Juan Wilches, Yu Sun
36th Florida Conference on Recent Advances in Robotics
Pouring is an efficient way to transfer objects from
one container to another. This abstract summarizes a method
to accurately pour solid objects, such as ice cubes. It leverages
visual and proprioceptive feedback together with contextual
information to control the forward and backward rotation of the
pouring container. These feedback signals are fed to a recurrent
neural network that produces the control signal. The proposed
approach can achieve a human-like pouring accuracy in both a
simulation and a real setup.
Generative Spatio-Temporal And Multimodal Analysis Of Neonatal Pain, Md Sirajus Salekin
Generative Spatio-Temporal And Multimodal Analysis Of Neonatal Pain, Md Sirajus Salekin
USF Tampa Graduate Theses and Dissertations
Neonates can not express their pain like an adult person. Due to the lacking of proper muscle growth and inability to express non-verbally, it is difficult to understand their emotional status. In addition, if the neonates are under any treatment or left monitored after any major surgeries (post-operative), it is more difficult to understand their pain due to the side effect of medications and the caring system (i.e. intubated, masked face, covered body with blanket, etc.). In a clinical environment, usually, bedside nurses routinely observe the neonate and measure the pain status following any standard clinical pain scale. But current …
Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy, Troi André Williams
Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy, Troi André Williams
USF Tampa Graduate Theses and Dissertations
This dissertation proposes a novel method called state-dependent sensor measurement models (SDSMMs). Such models dynamically predict the state-dependent bias and uncertainty of sensor measurements, ultimately improving fundamental robot tasks such as localization. In our first investigation, we introduced the state-dependent sensor measurement model framework, described their properties, stated the input and output of these models, and described how to train them. We also explained how to integrate such models with an Extended Kalman Filter and a Particle Filter, two popular robot state estimation algorithms. We validated the proposed framework through a series of localization tasks. The results showed that our …
Adaptive Mobile Eeg Noise Cancellation Using 2d Convolutional Autoencoders For Bci Authentication, Tyree Lewis
Adaptive Mobile Eeg Noise Cancellation Using 2d Convolutional Autoencoders For Bci Authentication, Tyree Lewis
USF Tampa Graduate Theses and Dissertations
Electroencephalography (EEG) signals can be used for many purposes and has the potential to be adapted to various systems. When EEG is recorded from users, these studies are performed primarily in an indoor environment, while the user is stationary. This is due to the levels of noise that are experienced when recording EEG data, to minimize errors in the data. This thesis aims to adapt tasks that are performed indoors to an external environment by removing both noise and artefacts in EEG, using a 2D Convolutional Autoencoder (CAE). The data is recorded from subjects is passed into the 2D CAE …
Strategies In Botnet Detection And Privacy Preserving Machine Learning, Di Zhuang
Strategies In Botnet Detection And Privacy Preserving Machine Learning, Di Zhuang
USF Tampa Graduate Theses and Dissertations
Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the infrastructure that responsible for various of cyber-crimes. Though a few existing work claimed to detect traditional botnets effectively, the problem of detecting P2P botnets involves more challenges. In this dissertation, we present two P2P botnet detection systems, PeerHunter and Enhanced PeerHunter. PeerHunter starts from a P2P hosts detection component. Then, it uses mutual contacts as the main feature to cluster bots into communities. Finally, it uses community behavior analysis to detect potential botnet communities and further identify bot candidates. Enhanced PeerHunter is an …
Learning To Predict Clinical Outcomes From Soft Tissue Sarcoma Mri, Hamidreza Farhidzadeh
Learning To Predict Clinical Outcomes From Soft Tissue Sarcoma Mri, Hamidreza Farhidzadeh
USF Tampa Graduate Theses and Dissertations
Soft Tissue Sarcomas (STS) are among the most dangerous diseases, with a 50% mortality rate in the USA in 2016. Heterogeneous responses to the treatments of the same sub-type of STS as well as intra-tumor heterogeneity make the study of biopsies imprecise. Radiologists make efforts to find non-invasive approaches to gather useful and important information regarding characteristics and behaviors of STS tumors, such as aggressiveness and recurrence. Quantitative image analysis is an approach to integrate information extracted using data science, such as data mining and machine learning with biological an clinical data to assist radiologists in making the best recommendation …