Open Access. Powered by Scholars. Published by Universities.®
Digital Communications and Networking Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Technological University Dublin (10)
- Old Dominion University (9)
- Air Force Institute of Technology (7)
- Association of Arab Universities (7)
- California Polytechnic State University, San Luis Obispo (6)
-
- Purdue University (6)
- The University of Akron (5)
- Embry-Riddle Aeronautical University (4)
- Clemson University (3)
- University of South Carolina (3)
- City University of New York (CUNY) (2)
- Kennesaw State University (2)
- San Jose State University (2)
- University of Nebraska - Lincoln (2)
- American University in Cairo (1)
- Boise State University (1)
- Dartmouth College (1)
- Institute of Business Administration (1)
- Louisiana State University (1)
- Marshall University (1)
- Michigan Technological University (1)
- Ministry of Higher and Secondary Specialized Education of the Republic of Uzbekistan (1)
- Murray State University (1)
- New Jersey Institute of Technology (1)
- Syracuse University (1)
- Tashkent State Technical University (1)
- The University of Southern Mississippi (1)
- Universitas Indonesia (1)
- University at Albany, State University of New York (1)
- University of Kentucky (1)
- Keyword
-
- Deep Learning (5)
- Wireless communication systems (5)
- Machine learning (4)
- Deep learning (3)
- Digital communications (3)
-
- Internet of Things (3)
- Machine Learning (3)
- Signal Processing (3)
- VoIP (3)
- Agriculture (2)
- Audio (2)
- Automation (2)
- Broadband antennas (2)
- Classification (2)
- Cloud Computing (2)
- Detectors (2)
- Dielectric (2)
- Dipole antennas (2)
- Fading (2)
- Moisture (2)
- Multimedia (2)
- Neural networks (2)
- Permittivity (2)
- Radio frequency (2)
- Random forest (2)
- Sensor networks (2)
- Signal processing (2)
- Soil (2)
- Steganography (2)
- Underground communication (2)
- Publication Year
- Publication
-
- Electrical & Computer Engineering Theses & Dissertations (8)
- Faculty Publications (8)
- Theses and Dissertations (8)
- Future Computing and Informatics Journal (7)
- Articles (5)
-
- Williams Honors College, Honors Research Projects (5)
- Conference papers (4)
- Master's Theses (3)
- All Dissertations (2)
- Computer Engineering (2)
- Department of Computer Electronics and Engineering: Dissertations, Theses, and Student Research (2)
- Electrical Engineering (2)
- Publications (2)
- Symposium of Student Scholars (2)
- 2024 Fall Honors Capstone Projects - Archive (1)
- AFIT Patents (1)
- Acta of Turin Polytechnic University in Tashkent (1)
- All Theses (1)
- Annual ADFSL Conference on Digital Forensics, Security and Law (1)
- Boise State University Theses and Dissertations (1)
- Chemical Technology, Control and Management (1)
- Computational Modeling & Simulation Engineering Theses & Dissertations (1)
- Discovery Day - Daytona Beach (1)
- Dissertations (1)
- Dissertations, Master's Theses and Master's Reports (1)
- Doctoral (1)
- ENGS 89/90 Reports (1)
- Electrical & Computer Engineering Technical Reports (1)
- Electronic Theses & Dissertations (2024 - present) (1)
- Electronic Theses and Dissertations (1)
- Publication Type
- File Type
Articles 91 - 93 of 93
Full-Text Articles in Digital Communications and Networking
Detection For A Statistically-Known, Time-Varying Dispersive Channel, David W. Matolak, S. G. Wilson
Detection For A Statistically-Known, Time-Varying Dispersive Channel, David W. Matolak, S. G. Wilson
Faculty Publications
Detection for the statistically known channel (SKC) is aimed at obtaining good performance in situations where our statistical knowledge of a time-varying channel is good, and where other equalization/detection schemes are either too complex to implement, or their performance is limited due to the rapidity of channel fading, or where we are simply unable to perform channel estimation. By using a statistical characterization of the channel, we develop a new detector that performs maximum-likelihood sequence estimation (MLSE) (given the channel model) on blocks of N symbols. Both symbol-spaced and fractionally spaced samples are used, to obtain two different detectors, that …
Visual Speech Training Aid For The Deaf, Subhashri Venkat
Visual Speech Training Aid For The Deaf, Subhashri Venkat
Electrical & Computer Engineering Theses & Dissertations
A computer-based vowel articulation training aid has been developed. A "continuous" acoustic-phonetic transformation is performed to map speech parameters to a lower dimensionality display space. There are two possible approaches to this transformation problem. The transformation could be either linear or a combination nonlinear/linear. The nonlinear transformation is performed using a multi-layered feedforward neural network with linear output layers. Speech parameters are extracted either from an analog filter bank arrangement (band energies) or by a digital signal processing procedure (Discrete Cosine Transform Coefficients). The speech parameters obtained from both methods correspond to the spectral envelope of the speech signals. The …
An Investigation To Improve Linear Predictive Vocoder Pulse/Noise Excitation Models, Elizabeth Annella Martina Effer
An Investigation To Improve Linear Predictive Vocoder Pulse/Noise Excitation Models, Elizabeth Annella Martina Effer
Electrical & Computer Engineering Theses & Dissertations
The quality of synthetic speech from Linear Predictive (LP) vocoders is known to be degraded due to the lack of detail in the commonly used pulse/noise excitation model. In this investigation, it was hypothesized that this degradation is due to the lack of precise timing information in the pulses and to the constraint that each short-time segment of excitation be either an impulse train or white noise. Accordingly, more complex excitation models were implemented using precise timing from peaks in the residual and a mixture of pulses and noise. Since the LP residual is known to be the perfect excitation …