Signal Processing Commons^™

Revealing Spatiotemporal Neural Activation Patterns In Electrocorticography Recordings Of Human Speech Production By Mutual Information, Julio Kovacs, Dean Krusienski, Minu Maninder, Willy Wriggers

Mechanical & Aerospace Engineering Faculty Publications

Background

Spatiotemporal mapping of neural activity during continuous speech production has been traditionally approached using correlation coefficient (CC) analysis between cortical signals and speech recordings. A prior study employed this approach using electrocorticography (ECoG) data from participants who underwent invasive intracranial monitoring for epilepsy. However, CC cannot detect nonlinear relationships and is dominated by the correspondence between periods of silence and of non-silence.

New Method

We introduce the mutual information (MI) measure, which can capture both linear and nonlinear dependencies. We validated CC and MI on the sub-second spatiotemporal brain activity recorded during continuous speech tasks. To refine the results, …

Decompositions Of Nonlinear Input-Output Systems To Zero The Output, W. Steven Gray, Kurusch Ebrahimi-Fard, Alexander Schmeding

Electrical & Computer Engineering Faculty Publications

Consider an input–output system where the output is the tracking error given some desired reference signal. It is natural to consider under what conditions the problem has an exact solution, that is, the tracking error is exactly the zero function. If the system has a well defined relative degree and the zero function is in the range of the input–output map, then it is well known that the system is locally left invertible, and thus, the problem has a unique exact solution. A system will fail to have relative degree when more than one exact solution exists. The general goal …

Defending Ai-Based Automatic Modulation Recognition Models Against Adversarial Attacks, Haolin Tang, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Yanxiao Zhao

Engineering Technology Faculty Publications

Automatic Modulation Recognition (AMR) is one of the critical steps in the signal processing chain of wireless networks, which can significantly improve communication performance. AMR detects the modulation scheme of the received signal without any prior information. Recently, many Artificial Intelligence (AI) based AMR methods have been proposed, inspired by the considerable progress of AI methods in various fields. On the one hand, AI-based AMR methods can outperform traditional methods in terms of accuracy and efficiency. On the other hand, they are susceptible to new types of cyberattacks, such as model poisoning or adversarial attacks. This paper explores the vulnerabilities …

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 …

Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray

Electrical & Computer Engineering Theses & Dissertations

Affective computing is an exciting and transformative field that is gaining in popularity among psychologists, statisticians, and computer scientists. The ability of a machine to infer human emotion and mood, i.e. affective states, has the potential to greatly improve human-machine interaction in our increasingly digital world. In this work, an ensemble model methodology for detecting human emotions across multiple subjects is outlined. The Continuously Annotated Signals of Emotion (CASE) dataset, which is a dataset of physiological signals labeled with discrete emotions from video stimuli as well as subject-reported continuous emotions, arousal and valence, from the circumplex model, is used for …

Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw

Electrical & Computer Engineering Theses & Dissertations

Automatic classification of digitally modulated signals is a challenging problem that has traditionally been approached using signal processing tools such as log-likelihood algorithms for signal classification or cyclostationary signal analysis. These approaches are computationally intensive and cumbersome in general, and in recent years alternative approaches that use machine learning have been presented in the literature for automatic classification of digitally modulated signals. This thesis studies deep learning approaches for classifying digitally modulated signals that use deep artificial neural networks in conjunction with the canonical representation of digitally modulated signals in terms of in-phase and quadrature components. Specifically, capsule networks are …

On The Use Of High-Frequency Surface Wave Oceanographic Research Radars As Bistatic Single-Frequency Oblique Ionospheric Sounders, Stephen R. Kaeppler, Ethan S. Miller, Daniel Cole, Teresa Updyke

CCPO Publications

We demonstrate that bistatic reception of high-frequency oceanographic radars can be used as single-frequency oblique ionospheric sounders. We develop methods that are agnostic of the software-defined radio system to estimate the group range from the bistatic observations. The group range observations are used to estimate the virtual height and equivalent vertical frequency at the midpoint of the oblique propagation path. Uncertainty estimates of the virtual height and equivalent vertical frequency are presented. We apply this analysis to observations collected from two experiments run at two locations in different years, but utilizing similar software-defined radio data collection systems. In the first …

Real-Time Cavity Fault Prediction In Cebaf Using Deep Learning, Md. M. Rahman, K. Iftekharuddin, A. Carptenter, T. Mcguckin, C. Tennant, L. Vidyaratne, Sandra Biedron (Ed.), Evgenya Simakov (Ed.), Stephen Milton (Ed.), Petr M. Anisimov (Ed.), Volker R.W. Schaa (Ed.)

Electrical & Computer Engineering Faculty Publications

Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply …

A Primer On Software Defined Radios, Dimitrie C. Popescu, Rolland Vida

Electrical & Computer Engineering Faculty Publications

The commercial success of cellular phone systems during the late 1980s and early 1990 years heralded the wireless revolution that became apparent at the turn of the 21st century and has led the modern society to a highly interconnected world where ubiquitous connectivity and mobility are enabled by powerful wireless terminals. Software defined radio (SDR) technology has played a major role in accelerating the pace at which wireless capabilities have advanced, in particular over the past 15 years, and SDRs are now at the core of modern wireless communication systems. In this paper we give an overview of SDRs that …

Matlab Modeling Of Ofdm Modulation Technique Across A 24 Khz, 48 Khz, And 3 Mhz Bandwidth In The High-Frequency Radio Band (3-30) Mhz, Josiah Myer, Tyler Collins, Sarah Taylor, Natalia Anglero

Faculty-Sponsored Student Research & Capstones

The goal of this project is to use MATLAB to model orthogonal frequency division multiplexing (OFDM) modulation technique across 24 kHz, 48 kHz, and 3 MHz bandwidths in the high frequency (HF) radio band (3-30 MHz). The purpose of our design is to make HF long distance communication faster and more reliable so that every part of the world, including the most remote parts, will have access to high speed, long distance wireless communication. Our MATLAB model will allow us to modify the bandwidth, carrier frequency, modulation type, signal to noise ratio (SNR), and image size to determine which combination …

Fixed-Point Proximity Minimization: A Theoretical Review And Numerical Study, Daniel Weddle, Jianfeng Guo

OUR Journal: ODU Undergraduate Research Journal

This study examines the relatively recent development of a “fixed-point proximity” approach to one type of minimization problem, considers its application to image denoising, and explores convergence and divergence of the iterative algorithm beyond a (previously supplied) theoretically guaranteed convergence bound on one of the parameters (𝜆). While reviewing the fixed-point proximity approach and its application to image denoising, we aim to communicate the concepts and details in a way that will facilitate understanding for undergraduates and for scholars from other subfields. In the latter portion of our study, the numerical experiment provides thought-provoking data on the effects that parameters …

Virtual Satcom, Long Range Broadband Digital Communications, Dennis George Watson

Electrical & Computer Engineering Theses & Dissertations

The current naval strategy is based on a distributed force, networked together with high-speed communications that enable operations as an intelligent, fast maneuvering force. Satellites, the existing network connector, are weak and vulnerable to attack. HF is an alternative, but it does not have the information throughput to meet the distributed warfighting need. The US Navy does not have a solution to reduce dependency on space-based communication systems while providing the warfighter with the required information speed.

Virtual SATCOM is a solution that can match satellite communications (SATCOM) data speed without the vulnerable satellite. It is wireless communication on a …

Deep Cellular Recurrent Neural Architecture For Efficient Multidimensional Time-Series Data Processing, Lasitha S. Vidyaratne

Electrical & Computer Engineering Theses & Dissertations

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in …

Cognitive Resource Management In 5g Networks, Kelvin M. Franco-Argueta

OUR Journal: ODU Undergraduate Research Journal

The 4G LTE network offers a high speed connectivity that is predicated on the construction platform of the 3G network and relies on an Internet Protocol (IP) for data transmission and reception. This platform’s utility is quickly becoming exhausted as the frequency spectrum approaches maximum device connectivity capacity. To improve network capacity, we must expand the bandwidth that our devices operate on. To effectively carry out this task, a self-configurable network must be employed in the development of the 5g network. This article aims to explore the technologies which form the platform for the 5G network and the cognitive resource …

Paper-Based Flexible Electrode Using Chemically-Modified Graphene And Functionalized Multiwalled Carbon Nanotube Composites For Electrophysiological Signal Sensing, Md Faruk Hossain, Jae Sang Heo, John Nelson, Insoo Kim

Bioelectrics Publications

Flexible paper-based physiological sensor electrodes were developed using chemically-modified graphene (CG) and carboxylic-functionalized multiwalled carbon nanotube composites (f@MWCNTs). A solvothermal process with additional treatment was conducted to synthesize CG and f@MWCNTs to make CG-f@MWCNT composites. The composite was sonicated in an appropriate solvent to make a uniform suspension, and then it was drop cast on a nylon membrane in a vacuum filter. A number of batches (0%~35% f@MWCNTs) were prepared to investigate the performance of the physical characteristics. The 25% f@MWCNT-loaded composite showed the best adhesion on the paper substrate. The surface topography and chemical bonding of the proposed CG-f@MWCNT …

Demonstration Of Visible And Near Infrared Raman Spectrometers And Improved Matched Filter Model For Analysis Of Combined Raman Signals, Alexander Matthew Atkinson

Electrical & Computer Engineering Theses & Dissertations

Raman spectroscopy is a powerful analysis technique that has found applications in fields such as analytical chemistry, planetary sciences, and medical diagnostics. Recent studies have shown that analysis of Raman spectral profiles can be greatly assisted by use of computational models with achievements including high accuracy pure sample classification with imbalanced data sets and detection of ideal sample deviations for pharmaceutical quality control. The adoption of automated methods is a necessary step in streamlining the analysis process as Raman hardware becomes more advanced. Due to limits in the architectures of current machine learning based Raman classification models, transfer from pure …

Classification Of Digital Communication Signal Modulation Schemes In Multipath Environments Using Higher Order Statistics, Meena Sreekantamurthy

Electrical & Computer Engineering Theses & Dissertations

Automatic identification and classification of modulation schemes in communication signals and decoding of information from the captured signals has assumed great importance recently in the wireless communication industry. Advancements in communications have introduced a large variety of modulation schemes in the transmitted signals; consequently, reliable detection of the modulation scheme in the intercepted signal has become an important issue in communications. It is the aim of this thesis to address this issue of reliable detection. Therefore, this research is focused on modeling and simulation of an automatic modulation classifier and, in particular, on the development of algorithms to use higher …

Generator Polynomial Formulation For Parallel Counters With Applications, Lee A. Belfore Ii

Electrical & Computer Engineering Faculty Publications

Parallel counters have been studied for several decades as a component in high speed multipliers and multi-operand adder circuits. Using a generator polynomial as a formalism for describing parallel counters in the general case, parallel counter properties can be derived and inferred. Furthermore, the structure and decomposition of the generator polynomial can suggest different implementation strategies. These include simple implementations of (7,3) and (15,4) parallel counters. By grouping factors, the design of a fast (7,3) parallel counter is presented. Finally, the generator polynomial is extended to permit factors of different weights. This extension provides a means for describing the design …

Impact Of Primary User Activity On The Performance Of Energy-Based Spectrum Sensing In Cognitive Radio Systems, Sara L. Macdonald

Electrical & Computer Engineering Theses & Dissertations

Increasing numbers of wireless devices and mobile data requirements have led to a spectrum shortage. However spectrum utilization percentages are often low due to the current static spectrum allocation process where primary users (PUs) are given exclusive use to spectrum. Several mechanisms to increase spectrum utilization have been proposed including opportunistic spectrum access (OSA). Cognitive Radio (CR) is an emerging concept in wireless communication systems that aims to enable OSA in licensed frequencies by secondary users (SUs). CR systems are expected to sense the spectrum in order to determine if the PU is transmitting. Therefore OSA performance relies on the …

Advanced Water Vapor Lidar Detection System, Hani Elsayed-Ali

Electrical & Computer Engineering Faculty Publications

In the present water vapor lidar system, the detected signal is sent over long cables to a waveform digitizer in a CAMAC crate. This has the disadvantage of transmitting analog signals for a relatively long distance, which is subjected to pickup noise, leading to a decrease in the signal to noise ratio.

Generally, errors in the measurement of water vapor with the DIAL method arise from both random and systematic sources. Systematic errors in DIAL measurements are caused by both atmospheric and instrumentation effects. The selection of the on-line alexandrite laser with a narrow linewidth, suitable intensity and high spectral …

Solving The Vehicle Re-Identification Problem By Using Neural Networks, Tanweer Rashid

Computational Modeling & Simulation Engineering Theses & Dissertations

Vehicle re-identification is the process by which vehicle attributes measured at one point on a road network are compared to vehicle attributes measured at another point in an effort to match vehicles without using any unique identifiers such as license plate numbers. A match is made if the two measurements are estimated to belong to the same vehicle. Vehicle attributes can be sensor readings such as loop induction signatures, or they can also be actual vehicle characteristics such as length, weight, number of axles, etc. This research makes use of vehicle length, travel time, axle spacing and axle weights for …

Embedding Gps Data Into Speech Signal, Kagan Can

Electrical & Computer Engineering Theses & Dissertations

Communication between deployed troops is very important on a battlefield. The real time knowledge of the location of the communicating patter is also very important in many situations because if you do not know the exact position of your units you cannot give correct orders. A minor error in an order can cause fatal results or lead to defeat. In many cases the position of the units is given through the speech channel by simply speaking the coordinates of the location. This can cause misunderstandings due to misinterpretation of the speech and makes it unsafe to rely on such communications. …

A Novel Digital Audio Watermarking Approach By Embedding Coefficients In Discrete Cosine Transform Domain, Erol Duymaz

Electrical & Computer Engineering Theses & Dissertations

Watermarking is a basic secure communication method. It is used for embedding a recognizable pattern in media in such a manner that modification of the media also modifies the pattern, thus making it easy to detect the modification. This technique and its variants have many practical applications pertaining to secure communications, media verification, etc. Digital audio watermarking is a technique for embedding data within an audio signal in such a way that the original and the modified audio signals are essentially identical. The embedded data can be used for various purposes such as secure communication in military applications, owner identification …

A Robust Method To Detect Concealed Weapons, Anand Gone

Electrical & Computer Engineering Theses & Dissertations

Concealed weapons detection is a large problem that is faced by the Police Department nowadays. There are many disasters caused by poor detection of the weapons. Since public safety is at risk there is a need to design an efficient detector that can detect the weapons hidden under the clothing. This thesis presents a novel method for detecting concealed weapons under clothing using image processing techniques. In this thesis IR imagery is used to capture an image which works on the principle of law of black body radiation. Image thresholding is performed on the captured data using Sauvola's adaptive thresholding …

Dispersion Of Water For Impulse Propagation, Shu Xiao, Karl H. Schoenbach

Bioelectrics Publications

This paper calculates the dispersive loss of water when a Gaussian impulse, or a step function impulse travels over a distance. The minimum risetime t_mr was calculated.

Gaussian Mixture Models And Neural Networks For Automatic Speaker Identification, Usha Gayatri Chalkapally

Electrical & Computer Engineering Theses & Dissertations

Automatic Speaker Recognition is the process of automatically recognizing who is speaking on the basis of individual information contained in speech signals. This technique of Automatic Speaker Recognition makes it possible to use the speaker's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.

In this thesis, the techniques of Gaussian Mixture Models and Neural Networks for Automatic Speaker Identification are presented. Algorithms for Speaker Identification using Gaussian Mixture Models were developed, …

Electromagnetic Propagation Prediction Inside Aircraft Cabins, Genevieve Hankins

Electrical & Computer Engineering Theses & Dissertations

Electromagnetic propagation models for signal strength prediction within aircraft cabins are essential for evaluating and designing a wireless communication system to be implemented onboard aircraft. There are many commercially available software packages for predicting wireless system performance in conventional indoor environments. It is of interest to examine the available software to determine if the aircraft's electromagnetic environment (EME) can be modeled successfully without developing an aircraft specific prediction tool. EnterprisePlanner®, a registered product of Wireless Valley Communications, Incorporated, was selected for the present effort. The performance of the prediction model was evaluated through a comparison with field measurements taken on …

A Multiplier-Less Architecture For High Speed Computation Of Multi-Dimensional Convolution, Ming Zhu Zhang

Electrical & Computer Engineering Theses & Dissertations

One of the most computationally intensive operations in digital image/video processing systems is multi-dimensional convolution. Every image/video processor needs the convolution module in its pre-processing stage. Fast and efficient design of the convolution module in an application specific system is a great challenge in VLSI (Very Large Scale Integration) design. Convolution operator requires a large set of multipliers and accumulators. A high precision multiplier takes enormous amount of VLSI area and it consumes more power. Hence reduction of the number of multipliers is another important challenge in VLSI design. A multiplier-less architecture for the design of a multi-dimensional convolution module …

Electromagnetic Wave Propagation Prediction For Wireless Networks Inside Boeing Fuselages, Mennatoallah Youssef

Electrical & Computer Engineering Theses & Dissertations

Commercial grade software is intended for electromagnetic predictions within office buildings; it was used to develop models to analyze propagation inside airplane fuselages. This study shows that Wireless XGTD and Insite software can accurately predict power propagation within airplane fuselages. Current work uses fuselage models, which contain additional internal components. A comparison was made between empty and full fuselage to examine the effects of internal components. Two propagation model types [Fast 3D and Full 3D] were also compared for accuracy to experimental study. It was concluded that completed fuselages are suggested for further simulation study as well as that the …

A Computer-Based Articulation Training Aid For Short Words (Cata), Mukund Devarajan

Electrical & Computer Engineering Theses & Dissertations

Several improvements in the vowel articulation training aid (VATA) are described, as well as the efforts to extend the visual feedback system to operate with short words in the form of consonant, vowel and consonant (CVC). The extended version of the visual feedback system is referred to as CATA (Computer-based Articulation Training Aid); the vowel version of the aid (VATA) only operates with ten American English monopthong vowels. Improvements in VATA include the use of a neural network (NN) recognizer method to prune a large database of vowel recordings to eliminate noisy and/or mispronounced tokens. The spectral jitter problem, previously …