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Articles 1 - 6 of 6
Full-Text Articles in Signal Processing
Remote Sensing Using I-Band And S-Band Signals Of Opportunity, Kadir Efecik, Benjamin R. Nold, James L. Garrison
Remote Sensing Using I-Band And S-Band Signals Of Opportunity, Kadir Efecik, Benjamin R. Nold, James L. Garrison
The Summer Undergraduate Research Fellowship (SURF) Symposium
Measurement of soil moisture, especially the root zone soil moisture, is important in agriculture, meteorology, and hydrology. Root zone soil moisture is concerned with the first meter down the soil. Active and passive remote sensing methods used today utilizing L-band(1-2GHz) are physically limited to a sensing depth of about 5 cm or less. To remotely sense the soil moisture in the deeper parts of the soil, the frequency should be lowered. Lower frequencies cannot be used in active spaceborne instruments because of their need for larger antennas, radio frequency interference (RFI), and frequency spectrum allocations. Ground-based passive remote sensing using …
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 …
Gui For Mri-Compatible Neural Stimulator And Recorder, Soo Han Soon, Nishant Babaria, Ranajay Mandal, Zhongming Liu
Gui For Mri-Compatible Neural Stimulator And Recorder, Soo Han Soon, Nishant Babaria, Ranajay Mandal, Zhongming Liu
The Summer Undergraduate Research Fellowship (SURF) Symposium
Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are useful tools to analyze brain activities given active stimulation. However, the electromagnetic noise from the MRI distorts the brain signal recording and damages the subject with excessive heat generated on the electrodes attached to the skin. MRI-compatible recording and stimulation systems previously developed at LIBI lab were capable of removing the electromagnetic noise during the imaging process. Previously, the hardware systems had required the integrative software that could control both circuits simultaneously and enable users to easily change recording and stimulation parameters. Graphical user interface (GUI) programmed with computer language informed …
Experimental Testing And Validation Of P-Band Bi-Static Remote Sensing Of Soil Moisture In 137-138mhz Range, Xiangyu Qu, Yao-Cheng Lin, James L. Garrison
Experimental Testing And Validation Of P-Band Bi-Static Remote Sensing Of Soil Moisture In 137-138mhz Range, Xiangyu Qu, Yao-Cheng Lin, James L. Garrison
The Summer Undergraduate Research Fellowship (SURF) Symposium
Remote sensing using readily available communication signal transmitted by ORBCOMM satellites at very high frequency (VHF) range (137-138MHz) is a promising method for detecting the root zone soil moisture content. The radio wave reflectivity of soil is strongly correlated to soil moisture content. Therefore, if we were able to measure the reflectivity, we might be able to estimate the soil moisture content. In this preliminary study, we analyze direct signal data from the satellites to investigate and verify communication channels in frequency range of interest and their characteristics (bandwidth, pattern, etc.). The analysis of direct signal data is also used …
Model-Free Method Of Reinforcement Learning For Visual Tasks, Jeff S. Soldate, Jonghoon Jin, Eugenio Culurciello
Model-Free Method Of Reinforcement Learning For Visual Tasks, Jeff S. Soldate, Jonghoon Jin, Eugenio Culurciello
The Summer Undergraduate Research Fellowship (SURF) Symposium
There has been success in recent years for neural networks in applications requiring high level intelligence such as categorization and assessment. In this work, we present a neural network model to learn control policies using reinforcement learning. It takes a raw pixel representation of the current state and outputs an approximation of a Q value function made with a neural network that represents the expected reward for each possible state-action pair. The action is chosen an \epsilon-greedy policy, choosing the highest expected reward with a small chance of random action. We used gradient descent to update the weights and biases …
Multi-Channel Analysis For Gradient Artifact Removal From Concurrent Eeg-Fmri Studies, Miguel R. Castellanos, Zhongming Liu
Multi-Channel Analysis For Gradient Artifact Removal From Concurrent Eeg-Fmri Studies, Miguel R. Castellanos, Zhongming Liu
The Summer Undergraduate Research Fellowship (SURF) Symposium
Concurrent electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) recordings are susceptible to large amounts of noise due to the static and dynamic magnetic fields present inside the MR scanner. EEG-fMRI studies are conducted to provide better spatial and temporal resolution for each recording, respectively, but the artifacts found in the EEG render the data impossible to interpret. Past studies have focused on signal post-processing techniques which are able to effectively remove noise upon the completion of a study, but there are no techniques able to process the data in real-time without extensive calibration. This research addresses this issue by …