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Full-Text Articles in 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.
Electromyograph Estimation Of Wheelchair Operators Using Deep Learning, Shimpei Aihara, Ryusei Shibata, Ryosuke Mizukami, Takara Sakai, Akira Shionoya
Electromyograph Estimation Of Wheelchair Operators Using Deep Learning, Shimpei Aihara, Ryusei Shibata, Ryosuke Mizukami, Takara Sakai, Akira Shionoya
International Sports Engineering Association – Engineering of Sport
No abstract provided.
Investigating Dataset Distinctiveness, Andrew Ulmer, Kent W. Gauen, Yung-Hsiang Lu, Zohar R. Kapach, Daniel P. Merrick
Investigating Dataset Distinctiveness, Andrew Ulmer, Kent W. Gauen, Yung-Hsiang Lu, Zohar R. Kapach, Daniel P. Merrick
The Summer Undergraduate Research Fellowship (SURF) Symposium
Just as a human might struggle to interpret another human’s handwriting, a computer vision program might fail when asked to perform one task in two different domains. To be more specific, visualize a self-driving car as a human driver who had only ever driven on clear, sunny days, during daylight hours. This driver – the self-driving car – would inevitably face a significant challenge when asked to drive when it is violently raining or foggy during the night, putting the safety of its passengers in danger. An extensive understanding of the data we use to teach computer vision models – …
Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal
Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal
The Summer Undergraduate Research Fellowship (SURF) Symposium
In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples …