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Articles 1 - 3 of 3
Full-Text Articles in Signal Processing
Automotive Collision Warning System Retrofit, Ethan Clark Najmy
Automotive Collision Warning System Retrofit, Ethan Clark Najmy
Electrical Engineering
In the early 2000s, few automakers began implementing forward collision warning systems in their cars. As technology advanced this system became available on more and more luxury cars. In recent years, this technology has spread to more affordable vehicles driven every day. However, as this technology has only recently advanced to less expensive, more economical cars, older vehicles of the same model may not have this advanced and important safety feature. This project investigates and creates a preliminary design for an affordable, easy-to-install, forward collision warning system that can be retrofitted to vehicles without the system currently installed. Using a …
On The Fly Audio Processing For The Vocal Conditioning Unit, Tim Lester
On The Fly Audio Processing For The Vocal Conditioning Unit, Tim Lester
Honors College
The Vocal Conditioning Unit was a device designed, constructed, and programmed as a senior design project in Electrical and Computer Engineering by Tim Lester and Grady White. The device’s intended goal was to perform a role similar to Auto-Tune, but as a standalone device similar to effects pedals used by guitarists and other musicians on stage. On-the-fly audio processing, however, was deprioritized in the design of the original device due to other design considerations. In this thesis project, the original design of the Vocal Conditioning Unit is analyzed, and critical functionalities of the device are identified. Then, the device is …
Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner
Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner
Electrical & Computer Engineering Faculty Publications
This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper …