Engineering Commons^™

Frameworks To Investigate Robustness And Disease Characterization/Prediction Utility Of Time-Varying Functional Connectivity State Profiles Of The Human Brain At Rest, Anees Abrol

Electrical and Computer Engineering ETDs

Neuroimaging technologies aim at delineating the highly complex structural and functional organization of the human brain. In recent years, several unimodal as well as multimodal analyses of structural MRI (sMRI) and functional MRI (fMRI) neuroimaging modalities, leveraging advanced signal processing and machine learning based feature extraction algorithms, have opened new avenues in diagnosis of complex brain syndromes and neurocognitive disorders. Generically regarding these neuroimaging modalities as filtered, complimentary insights of brain’s anatomical and functional organization, multimodal data fusion efforts could enable more comprehensive mapping of brain structure and function.

Large scale functional organization of the brain is often studied by …

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 …

On Designing An Ecg-Based Intelligent System: Utilizing The Heart’S Electrical Activity To Recognize Humans And Detect Arrhythmia, Sara Saeed Abdeldayem

Graduate Theses, Dissertations, and Problem Reports

The electrocardiogram (ECG) signal is the bioelectrical signal that reflects the heart's activity. It has been extensively used as a diagnostic tool since it holds information about the cardiac health condition. However, recent researches have shown that it exhibits an inter-subject variability property. Therefore, it can be used as a biometric-based modality for either identification or verification purposes. Nevertheless, some of the challenges are faced while employing such a signal. For instance, ECG signal is prone to noise, accordingly, noise filters should be designed to remove the noise while keeping the signal properties. Moreover, factors such as medications, health condition, …