VTechWorks Repository :: Browsing by Author "Roan, Michael J."

Browsing by Author "Roan, Michael J."

Now showing 1 - 20 of 53

Acoustic Response Validation of a Finite Cylindrical Shell with Multiple Loading Conditions
Gallagher, Chad Taylor (Virginia Tech, 2018-06-25)
Cylindrical shells are used for a variety of engineering applications such as undersea vehicles and aircraft. The models currently used to determine the vibration characteristics of these shells are often approximated by assuming the shell is infinitely long or has shear-diaphragm boundary conditions. These models also ignore complex loading conditions such as plane waves in favor of point forces or free vibration models. This work expands on the capabilities of these models by examining the acoustic response of a finite length cylinder with flat plate endcaps to multiple types of distributed loading conditions. Starting with the Donnell equations of motion for thin cylinders and the classical plate theory equations of motion for the endcaps, spacial domain displacement field solutions for the shell and plates take an assumed form that includes unknown wave propagation coefficients. These solutions are substituted into stress boundary conditions and continuity equations evaluated at the intersections between the shell and plates. An infinite summation is contained within the boundary conditions and continuity equations which is decoupled, truncated, and compiled in matrix form to allow for the propagation coefficients to be found via a convergent sum of vectors. System responses due to a ring loading and multiple cases of plane waves are studied and validated using a finite element analysis of the system. It is shown that the analytical model matches the finite element model well.
Acoustic source localization in 3D complex urban environments
Choi, Bumsuk (Virginia Tech, 2012-04-30)
The detection and localization of important acoustic events in a complex urban environment, such as gunfire and explosions, is critical to providing effective surveillance of military and civilian areas and installations. In a complex environment, obstacles such as terrain or buildings introduce multipath propagations, reflections, and diffractions which make source localization challenging. This dissertation focuses on the problem of source localization in three-dimensional (3D) realistic urban environments. Two different localization techniques are developed to solve this problem: a) Beamforming using a few microphone phased arrays in conjunction with a high fidelity model and b) Fingerprinting using many dispersed microphones in conjunction with a low fidelity model of the environment. For an effective source localization technique using microphone phased arrays, several candidate beamformers are investigated using 2D and corresponding 3D numerical models. Among them, the most promising beamformers are chosen for further investigation using 3D large models. For realistic validation, localization error of the beamformers is analyzed for different levels of uncorrelated noise in the environment. Multiple-array processing is also considered to improve the overall localization performance. The sensitivity of the beamformers to uncertainties that cannot be easily accounted for (e.g. temperature gradient and unmodeled object) is then investigated. It is observed that evaluation in 3D models is critical to assess correctly the potential of the localization technique. The enhanced minimum variance distortionless response (EMVDR) is identified to be the only beamformer that has super-directivity property (i.e. accurate localization capability) and still robust to uncorrelated noise in the environment. It is also demonstrated that the detrimental effect of uncertainties in the modeling of the environment can be alleviated by incoherent multiple arrays. For efficient source localization technique using dispersed microphones in the environment, acoustic fingerprinting in conjunction with a diffused-based energy model is developed as an alternative to the beamforming technique. This approach is much simpler requiring only microphones rather than arrays. Moreover, it does not require an accurate modeling of the acoustic environment. The approach is validated using the 3D large models. The relationship between the localization accuracy and the number of dispersed microphones is investigated. The effect of the accuracy of the model is also addressed. The results show a progressive improvement in the source localization capabilities as the number of microphones increases. Moreover, it is shown that the fingerprints do not need to be very accurate for successful localization if enough microphones are dispersed in the environment.
Adaptive Beamforming using ICA for Target Identification in Noisy Environments
Wiltgen, Timothy Edward (Virginia Tech, 2007-05-09)
The blind source separation problem has received a great deal of attention in previous years. The aim of this problem is to estimate a set of original source signals from a set of linearly mixed signals through any number of signal processing techniques. While many methods exist that attempt to solve the blind source separation problem, a new technique is being used that uniquely separates audio sources as they are received from a microphone array. In this thesis a new algorithm is proposed that that utilizes the ICA algorithm in conjunction with a filtering technique that separates source signals and then removes sources of interference so that a signal of interest can be accurately tracked. Experimental results will compare a common blind source separation technique to the new algorithm and show that the new algorithm can detect a signal of interest and accurately track it as it moves through an anechoic environment.
Adaptive near-field beamforming techniques for sound source imaging
Cho, Yong Thung; Roan, Michael J. (Acoustical Society of America, 2009-02-01)
Phased array signal processing techniques such as beamforming have a long history in applications such as sonar for detection and localization of far-field sound sources. Two sometimes competing challenges arise in any type of spatial processing; these are to minimize contributions from directions other than the look direction and minimize the width of the main lobe. To tackle this problem a large body of work has been devoted to the development of adaptive procedures that attempt to minimize side lobe contributions to the spatial processor output. In this paper, two adaptive beamforming procedures-minimum variance distorsionless response and weight optimization to minimize maximum side lobes-are modified for use in source visualization applications to estimate beamforming pressure and intensity using near-field pressure measurements. These adaptive techniques are compared to a fixed near-field focusing technique (both techniques use near-field beamforming weightings focusing at source locations estimated based on spherical wave array manifold vectors with spatial windows). Sound source resolution accuracies of near-field imaging procedures with different weighting strategies are compared using numerical simulations both in anechoic and reverberant environments with random measurement noise. Also, experimental results are given for near-field sound pressure measurements of an enclosed loudspeaker.
Analysis and Development of Control Methodologies for Semi-active Suspensions
Ghasemalizadeh, Omid (Virginia Tech, 2016-11-14)
Semi-active suspensions have drawn particular attention due to their superior performance over the other types of suspensions. One of their advantages is that their damping coefficient can be controlled without the need for any external source of power. In this study, a handful of control approaches are implemented on a car models using MATLAB/Simulink. The investigated control methodologies are skyhook, groundhook, hybrid skyhook-groundhook, Acceleration Driven Damper, Power Driven Damper, H∞ Robust Control, Fuzzy Logic Controller, and Inverse ANFIS. H∞ Robust Control is an advanced method that guarantees transient performance and rejects external disturbances. It is shown that H∞ with the proposed modification, has the best performance although its relatively high cost of computation could be potentially considered as a drawback. Also, the proposed Inverse ANFIS controller uses the power of fuzzy systems along with neural networks to help improve vehicle ride metrics significantly. In this study, a novel approach is introduced to analyze and fine-tune semi-active suspension control algorithms. In some cases, such as military trucks moving on off-road terrains, it is critical to keep the vehicle ride quality in an acceptable range. Semi-active suspensions are used to have more control over the ride metrics compared to passive suspensions and also, be more cost-effective compared to active suspensions. The proposed methodology will investigate the skyhook-groundhook hybrid controller. This is accomplished by conducting sensitivity analysis of the controller performance to varying vehicle/road parameters. This approach utilizes sensitivity analysis and one-at-a-time methodology to find and reach the optimum point of vehicle suspensions. Furthermore, real-time tuning of the mentioned controller will be studied. The online tuning will help keep the ride quality of the vehicle close to its optimum point while the vehicle parameters are changing. A quarter-car model is used for all simulations and analyses.
Analysis of Jamming-Vulnerabilities of Modern Multi-carrier Communication Systems
Mahal, Jasmin Ara (Virginia Tech, 2018-06-19)
The ever-increasing demand for private and sensitive data transmission over wireless networks has made security a crucial concern in the current and future large-scale, dynamic, and heterogeneous wireless communication systems. To address this challenge, wireless researchers have tried hard to continuously analyze the jamming threats and come up with improved countermeausres. In this research, we have analyzed the jamming-vulnerabilities of the leading multi-carrier communication systems, Orthogonal Frequency Division Multiplexing (OFDM) and Single-Carrier Frequency Division Multiple Access (SC-FDMA). In order to lay the necessary theoretical groundwork, first we derived the analytical BER expressions for BPSK/QPSK and analytical upper and lower bounds for 16-QAM for OFDMA and SC-FDMA using Pilot Symbol Assisted Channel Estimation (PSACE) techniques in Rayleigh slow-fading channel that takes into account channel estimation error as well as pilot-jamming effect. From there we advanced to propose more novel attacks on the Cyclic Prefix (CP) of SC-FDMA. The associated countermeasures developed prove to be very effective to restore the system. We are first to consider the effect of frequency-selectivity and fading correlation of channel on the achievable rates of the legitimate system under pilot-spoofing attack. With respect to jamming mitigation techniques, our approaches are more focused on Anti-Jamming (AJ) techniques rather than Low Probability of Intercept (LPI) methods. The Channel State Information (CSI) of the two transceivers and the CSI between the jammer and the target play critical roles in ensuring the effectiveness of jamming and nulling attacks. Although current literature is rich with different channel estimation techniques between two legitimate transceivers, it does not have much to offer in the area of channel estimation from jammer's perspective. In this dissertation, we have proposed novel, computationally simple, deterministic, and optimal blind channel estimation techniques for PSK-OFDM as well as QAM-OFDM that estimate the jammer channel to the target precisely in high Signal-to-Noise (SNR) environment from a single OFDM symbol and thus perform well in mobile radio channel. We have also presented the feasibility analysis of estimating transceiver channel from jammer's perspective at the transmitter as well as receiver side of the underlying OFDM system.
Anomaly Detection in Heterogeneous Data Environments with Applications to Mechanical Engineering Signals & Systems
Milo, Michael William (Virginia Tech, 2013-11-08)
Anomaly detection is a relevant problem in the field of Mechanical Engineering, because the analysis of mechanical systems often relies on identifying deviations from what is considered "normal". The mechanical sciences are represented by a heterogeneous collection of data types: some systems may be highly dimensional, may contain exclusively spatial or temporal data, may be spatiotemporally linked, or may be non-deterministic and best described probabilistically. Given the broad range of data types in this field, it is not possible to propose a single processing method that will be appropriate, or even usable, for all data types. This has led to human observation remaining a common, albeit costly and inefficient, approach to detecting anomalous signals or patterns in mechanical data. The advantages of automated anomaly detection in mechanical systems include reduced monitoring costs, increased reliability of fault detection, and improved safety for users and operators. This dissertation proposes a hierarchical framework for anomaly detection through machine learning, and applies it to three distinct and heterogeneous data types: state-based data, parameter-driven data, and spatiotemporal sensor network data. In time-series data, anomaly detection results were robust in synthetic data generated using multiple simulation algorithms, as well as experimental data from rolling element bearings, with highly accurate detection rates (>99% detection, <1% false alarm). Significant developments were shown in parameter-driven data by reducing the sample sizes necessary for analysis, as well as reducing the time required for computation. The event-space model extends previous work into a geospatial sensor network and demonstrates applications of this type of event modeling at various timescales, and compares the model to results obtained using other approaches. Each data type is processed in a unique way relative to the others, but all are fitted to the same hierarchical structure for system modeling. This hierarchical model is the key development proposed by this dissertation, and makes both novel and significant contributions to the fields of mechanical analysis and data processing. This work demonstrates the effectiveness of the developed approaches, details how they differ from other relevant industry standard methods, and concludes with a proposal for additional research into other data types.
Anomaly detection in rolling element bearings via two-dimensional Symbolic Aggregate Approximation
Harris, Bradley William (Virginia Tech, 2013-05-26)
Symbolic dynamics is a current interest in the area of anomaly detection, especially in mechanical systems. Symbolic dynamics reduces the overall dimensionality of system responses while maintaining a high level of robustness to noise. Rolling element bearings are particularly common mechanical components where anomaly detection is of high importance. Harsh operating conditions and manufacturing imperfections increase vibration innately reducing component life and increasing downtime and costly repairs. This thesis presents a novel way to detect bearing vibrational anomalies through Symbolic Aggregate Approximation (SAX) in the two-dimensional time-frequency domain. SAX reduces computational requirements by partitioning high-dimensional sensor data into discrete states. This analysis specifically suits bearing vibration data in the time-frequency domain, as the distribution of data does not greatly change between normal and faulty conditions. Under ground truth synthetically-generated experiments, two-dimensional SAX in conjunction with Markov model feature extraction is successful in detecting anomalies (> 99%) using short time spans (< 0.1 seconds) of data in the time-frequency domain with low false alarms (< 8%). Analysis of real-world datasets validates the performance over the commonly used one-dimensional symbolic analysis by detecting 100% of experimental anomalous vibration with 0 false alarms in all fault types using less than 1 second of data for the basis of 'normality'. Two-dimensional SAX also demonstrates the ability to detect anomalies in predicative monitoring environments earlier than previous methods, even in low Signal-to-Noise ratios.
Antifragile Communications
Lichtman, Marc Louis (Virginia Tech, 2016-08-16)
Jamming is an ongoing threat that plagues wireless communications in contested areas. Unfortunately, jamming complexity and sophistication will continue to increase over time. The traditional approach to addressing the jamming threat is to harden radios, such that they sacrifice communications performance for more advanced jamming protection. To provide an escape from this trend, we investigate the previously unexplored area of jammer exploitation. This dissertation develops the concept of antifragile communications, defined as the capability for a communications system to improve in performance due to a system stressor or harsh condition. Antifragility refers to systems that increase in capability, resilience, or robustness as a result of disorder (e.g., chaos, uncertainty, stress). An antifragile system is fundamentally different from one that is resilient (i.e., able to recover from failure) and robust (i.e., able to resist failure). We apply the concept of antifragility to wireless communications through several novel strategies that all involve exploiting a communications jammer. These strategies can provide an increase in throughput, efficiency, connectivity, or covertness, as a result of the jamming attack itself. Through analysis and simulation, we show that an antifragile gain is possible under a wide array of electronic warfare scenarios. Throughout this dissertation we provide guidelines for realizing these antifragile waveforms. Other major contributions of this dissertation include the development of a communications jamming taxonomy, feasibility study of reactive jamming in a SATCOM-type scenario, and a reinforcement learning-based reactive jamming mitigation strategy, for times when an antifragile approach is not practical. Most of the jammer exploitation strategies described in this dissertation fall under the category of jammer piggybacking, meaning the communications system turns the jammer into an unwitting relay. We study this jammer piggybacking approach under a variety of reactive jamming behaviors, with emphasis on the sense-and-transmit type. One piggybacking approach involves transmitting using a specialized FSK waveform, tailored to exploit a jammer that channelizes a block of spectrum and selectively jams active subchannels. To aid in analysis, we introduce a generalized model for reactive jamming, applicable to both repeater-based and sensing-based jamming behaviors. Despite being limited to electronic warfare scenarios, we hope that this work can pave the way for further research into antifragile communications.
Applications of Sensor Fusion to Classification, Localization and Mapping
Abdelbar, Mahi Othman Helmi Mohamed Helmi Hussein (Virginia Tech, 2018-04-30)
Sensor Fusion is an essential framework in many Engineering fields. It is a relatively new paradigm for integrating data from multiple sources to synthesize new information that in general would not have been feasible from the individual parts. Within the wireless communications fields, many emerging technologies such as Wireless Sensor Networks (WSN), the Internet of Things (IoT), and spectrum sharing schemes, depend on large numbers of distributed nodes working collaboratively and sharing information. In addition, there is a huge proliferation of smartphones in the world with a growing set of cheap powerful embedded sensors. Smartphone sensors can collectively monitor a diverse range of human activities and the surrounding environment far beyond the scale of what was possible before. Wireless communications open up great opportunities for the application of sensor fusion techniques at multiple levels. In this dissertation, we identify two key problems in wireless communications that can greatly benefit from sensor fusion algorithms: Automatic Modulation Classification (AMC) and indoor localization and mapping based on smartphone sensors. Automatic Modulation Classification is a key technology in Cognitive Radio (CR) networks, spectrum sharing, and wireless military applications. Although extensively researched, performance of signal classification at a single node is largely bounded by channel conditions which can easily be unreliable. Applying sensor fusion techniques to the signal classification problem within a network of distributed nodes is presented as a means to overcome the detrimental channel effects faced by single nodes and provide more reliable classification performance. Indoor localization and mapping has gained increasing interest in recent years. Currently-deployed positioning techniques, such as the widely successful Global Positioning System (GPS), are optimized for outdoor operation. Providing indoor location estimates with high accuracy up to the room or suite level is an ongoing challenge. Recently, smartphone sensors, specially accelerometers and gyroscopes, provided attractive solutions to the indoor localization problem through Pedestrian Dead-Reckoning (PDR) frameworks, although still suffering from several challenges. Sensor fusion algorithms can be applied to provide new and efficient solutions to the indoor localization problem at two different levels: fusion of measurements from different sensors in a smartphone, and fusion of measurements from several smartphones within a collaborative framework.
Binaural Hearing Effects of Mapping Microphone Array's Responses to a Listener's Head-Related Transfer Functions
Hughet, James (Virginia Tech, 2011-09-29)
This thesis focuses on the mapping of the microphone array’s response to match the characteristics of a human subject’s Head-Related Transfer Function (HRTF). The mapping of the response is first explored with a ‘monaural HRTF matching’ that filters the response independent of the arrival angles. For arbitrary array geometry with the listener external to the acoustic, the monaural HRTF matching did not provide listeners with enough spatial information to precisely localize sound sources. To correct this, a preprocessor control algorithm was added to the HRTF matching, a ‘binaural HRTF matching’ process. The binaural HRTF matching increased the listeners’ performance in perceiving the location of a sound source. With the addition of simulated head movement, the listeners’ perception increased by 20%. An issue with this approach is the use of HRTFs other than the listeners’ measured HRTF, creating a psychoacoustic based error in localization, i.e., front/back confusion.
Biomechanical adaptations of human gait due to external loads
Lee, Minhyung (Virginia Tech, 2008-08-01)
Gait is the method of human locomotion using limbs. Recently, the analysis of human motion, specifically human gait, has received a large amount of research attention. Human gait can contain a wide variety of information that can be used in biometrics, disease diagnosis, injury rehabilitation, and load determination. In this dissertation, the development of a model-based gait analysis technique to classify external loads is presented. Specifically, the effects of external loads on gait are quantified and these effects are then used to classify whether an individual gait pattern is the result of carrying an external load or not. First of all, the reliability of using continuous relative phase as a metric to determine loading condition is quantified by intra-class correlation coefficients (ICC) and the number of required trials is computed. The ICC(2, 1) values showed moderate reliability and 3 trials are sufficient to determine lower body kinematics under two external load conditions. Then, the work was conducted to provide the baseline separability of load carriage conditions into loaded and unloaded categories using several lower body kinematic parameters. Satisfactory classification of subjects into the correct loading condition was achieved by resorting to linear discriminant analysis (LDA). The baseline performance from 4 subjects who were not included in training data sets shows that the use of LDA provides an 88.9% correct classification over two loaded and unloaded walking conditions. Extra weights, however, can be in the form of an external load carried by an individual or excessive body weight carried by an overweight individual. The study now attempts to define the differences in lower body gait patterns caused by either external load carriage, excessive body weight, or a combination of both. It was found significant gait differences due to external load carriage and excessive body weight. Principal Component Analysis (PCA) was also used to analyze the lower body gait patterns for four loading conditions: normal weight unloaded, normal weight loaded, overweight unloaded and overweight loaded. PCA has been shown to be a powerful tool for analyzing complex gait data. In this analysis, it is shown that in order to quantify the effects of external loads for both normal weight and overweight subjects, only two principal components (PCs) are needed. The results in this dissertation suggest that there are gait pattern changes due to external loads, and LDA could be applied successfully to classify the gait patterns with an unknown load condition. Both load carriage and excessive body weight affect lower body kinematics, but it is proved that they are not the same loading conditions. Methods in the current work also give a potential for new medical and clinical ways of investigating gait effects in osteoarthritis patients and/or obese people.
Biomimetic Detection of Dynamic Signatures in Foliage Echoes
Bhardwaj, Ananya (Virginia Tech, 2021-02-05)
Horseshoe bats (family Rhinolophidae) are among the bat species that dynamically deform their reception baffles (pinnae) and emission baffles (noseleaves) during signal reception and emissions, respectively. These dynamics are a focus of prior studies that demonstrated that these effects could introduce time-variance within emitted and received signals. Recent lab based experiments with biomimetic hardware have shown that these dynamics can also inject time-variant signatures into echoes from simple targets. However, complex foliage echoes, which comprise a large portion of the received echoes and contain useful information for these bats, have not been studied in prior research. We used a biomimetic sonarhead which replicated these dynamics, to collect a large dataset of foliage echoes (>55,000). To generate a neuromorphic representation of echoes that was representative of the neural spikes in bat brains, we developed an auditory processing model based on Horseshoe bat physiological data. Then, machine learning classifiers were employed to classify these spike representations of echoes into distinct groups, based on the presence or absence of dynamics' effects. Our results showed that classification with up to 80% accuracy was possible, indicating the presence of these effects in foliage echoes, and their persistence through the auditory processing. These results suggest that these dynamics' effects might be present in bat brains, and therefore have the potential to inform behavioral decisions. Our results also indicated that potential benefits from these effects might be location specific, as our classifier was more effective in classifying echoes from the same physical location, compared to a dataset with significant variation in recording locations. This result suggested that advantages of these effects may be limited to the context of particular surroundings if the bat brain similarly fails to generalize over variation in locations.
A Comprehensive Hamiltonian Atmospheric Sound Propagation Model for Prediction of Wind Turbine Noise
McBride, Sterling M. (Virginia Tech, 2017-12-06)
Wind energy is the world´s fastest-growing renewable energy source. Thus, the amount of people exposed to wind farm noise is increasing. Due to its broadband amplitude modulated characteristic, wind turbine noise (WTN) is more annoying than noise produced by other common community/industrial sources. Aerodynamic noise along the blade span is the dominant noise source of modern large wind turbines. This type of noise propagates through the atmosphere in the proximity of wind farms. However, modelling and simulating WTN propagation over large distances is challenging due to the complexity of atmospheric conditions. Real temperature, wind velocity and relative humidity measurements typically show a characteristic nonlinear behavior. A comprehensive propagation model that addresses this problem while maintaining high accuracy and computational efficiency is necessary. A Hamiltonian Ray tracing (HRT) technique coupled to aerodynamically induced WTN is presented in this work. It incorporates acoustic wave refraction due to spatial speed of sound gradients, a full Doppler Effect formulation resulting from wind velocities in any arbitrary direction, proper acoustic energy dissipation during propagation, and ground reflection. The HRT method averts many of the setbacks presented by other common numerical approaches such as fast field program (FFP), parabolic equation methods (PE), and the standard Eikonal ray tracing (ERT) technique. In addition, it is not bounded to the linearity assumptions made for analytic propagation solutions. A wave phase tracking analysis through inhomogeneous and moving media is performed. Curved ray-paths are numerically computed by solving a non-linear system of coupled first order differential equations. Sound pressure levels through the propagation media are then calculated by using standard ray tubes and performing energy analysis along them. The ray model is validated by comparing a monopole’s ray path results against analytically obtained ones. Sound pressure level predictions are also validated against both FFP and ERT methods. Finally, results for a 5MW modern wind turbine over a flat acoustically soft terrain are provided.
A Data Driven Approach to the Development and Evaluation of Acoustic Electric Vehicle Alerting Systems for Vision Impaired Pedestrians
Roan, Michael J.; Beard, Michael; Neurauter, Luke; Miller, Marty (Safe-D National UTC, 2023-02)
The number of electric vehicles on the road increases exponentially every year. Due to the quieter nature of these vehicles when operating at low speeds, there is significant concern that pedestrians and bicyclists will be at increased risk of vehicle collisions. This research explores the detectability of six electric vehicle acoustic additive sounds produced by two sound dispersion techniques: (1) using the factory approach versus (2) an exciter transducer-based system. Detectability was initially measured using on-road participant tests and was then replicated in a high-fidelity immersive reality lab. Results were analyzed through both mean detection distances and pedestrian probability of detection. This research aims to verify the lab environment in order to allow for a broader range of potential test scenarios, more repeatable tests, and faster test sessions. Along with pedestrian drive-by tests, supplemental experiments were conducted to evaluate stationary vehicle acoustics, 10 and 20 km/h drive by acoustics, and interior acoustics of each additive sound.
Development of Ground-Level Hyperspectral Image Datasets and Analysis Tools, and their use towards a Feature Selection based Sensor Design Method for Material Classification
Brown, Ryan Charles (Virginia Tech, 2018-08-31)
Visual sensing in robotics, especially in the context of autonomous vehicles, has advanced quickly and many important contributions have been made in the areas of target classification. Typical to these studies is the use of the Red-Green-Blue (RGB) camera. Separately, in the field of remote sensing, the hyperspectral camera has been used to perform classification tasks on natural and man-made objects from typically aerial or satellite platforms. Hyperspectral data is characterized by a very fine spectral resolution, resulting in a significant increase in the ability to identify materials in the image. This hardware has not been studied in the context of autonomy as the sensors are large, expensive, and have non-trivial image capture times. This work presents three novel contributions: a Labeled Hyperspectral Image Dataset (LHID) of ground-level, outdoor objects based on typical scenes that a vehicle or pedestrian may encounter, an open-source hyperspectral interface software package (HSImage), and a feature selection based sensor design algorithm for object detection sensors (DLSD). These three contributions are novel and useful in the fields of hyperspectral data analysis, visual sensor design, and hyperspectral machine learning. The hyperspectral dataset and hyperspectral interface software were used in the design and testing of the sensor design algorithm. The LHID is shown to be useful for machine learning tasks through experimentation and provides a unique data source for hyperspectral machine learning. HSImage is shown to be useful for manipulating, labeling and interacting with hyperspectral data, and allows wavelength and classification based data retrieval, storage of labeling information and ambient light data. DLSD is shown to be useful for creating wavelength bands for a sensor design that increase the accuracy of classifiers trained on data from the LHID. DLSD shows accuracy near that of the full spectrum hyperspectral data, with a reduction in features on the order of 100 times. It compared favorably to other state-of-the-art wavelength feature selection techniques and exceeded the accuracy of an RGB sensor by 10%.
Development of High Speed High Dynamic Range Videography
Griffiths, David John (Virginia Tech, 2017-02-09)
High speed video has been a significant tool for unraveling the quantitative and qualitative assessment of phenomena that is too fast to readily observe. It was first used in 1852 by William Henry Fox Talbot to settle a dispute with reference to the synchronous position of a horse's hooves while galloping. Since that time private industry, government, and enthusiasts have been measuring dynamic scenarios with high speed video. One challenge that faces the high speed video community is the dynamic range of the sensors. The dynamic range of the sensor is constrained to the bit depth of the analog to digital converter, the deep well capacity of the sensor site, and baseline noise. A typical high speed camera can span a 60 dB dynamic range, 1000:1, natively. More recently the dynamic range has been extended to about 80 dB utilizing different pixel acquisition methods. In this dissertation a method to extend the dynamic range will be presented and demonstrated to extend the dynamic range of a high speed camera system to over 170 dB, about 31,000,000:1. The proposed formation methodology is adaptable to any camera combination, and almost any needed dynamic range. The dramatic increase in the dynamic range is made possible through an adaptation of the current high dynamic range image formation methodologies. Due to the high cost of a high speed camera, a minimum number of cameras are desired to form a high dynamic range high speed video system. With a reduced number of cameras spanning a significant range, the errors on the formation process compound significantly relative to a normal high dynamic range image. The increase in uncertainty is created from the lack of relevant correlated information for final image formation, necessitating the development of a new formation methodology. In the proceeding text the problem statement and background information will be reviewed in depth. The development of a new weighting function, stochastic image formation process, tone map methodology, and optimized multi camera design will be presented. The proposed methodologies' effectiveness will be compared to current methods throughout the text and a final demonstration will be presented.
Development of Ionic Polymer Metallic Composites as sensors
Griffiths, David John (Virginia Tech, 2008-09-26)
Ionomeric polymer transducers (IPTs) are an exciting new class of smart materials that can serve a dual purpose in engineering or biomedical applications as sensors or actuators. Most commonly they are used for mechanical actuation, as they have the ability to generate large bending strains and moderate stress under low applied voltages. Although the actuation capabilities of IPTs have been extensively studied, the sensing capabilities of these transducers have yet to be fully explored. The work presented herein aims to investigate the fundamental sensing characteristics of these transducers and apply the acquired knowledge toward the development of an electronic stethoscope for digital auscultation. The sensors were characterized both geometrically and electrically to determine their effectiveness in resolving a signal from sub 1 Hz to 2 kHz. Impedance spectroscopy was used to interrogate the sensing mechanism. Following the characterization of the transducer, a bio–acoustic sensor was designed and fabricated. The bio–acoustic sensor was placed over the carotid artery to resolve the arterial pressure waveform in situ and on the thorax to measure the S1 and S2 sounds generated by the heart. The temporal response and spectral content was compared with previously known data and a commercially available electronic stethoscope to prove the acquisition of cardiovascular sounds.
Drone Imagery Applied to Enhance Flood Modeling
Friedman, Brianna (Virginia Tech, 2021-06-01)
Accessible flood modeling for low-resource, data-scarce communities currently does not exist. This paper proposes using drone imagery to compensate for the lack of other flood modeling data (i.e. streamflow measurements). Three flood models were run for Dzaleka Refugee Camp, located in Dowa, Malawi. Two of the models (the Soil and Water Assessment Tool (SWAT) and the Hydrologic Engineering Center River Analysis System (HEC-RAS)) are commonly used hydrological-hydraulic based models. The third model, the Water Caused Erosion Patterns (WCEP) model, was proposed by the author to capitalize on the high-resolution drone imagery using geological-geomorphological information. The drone imagery used in this study has a resolution of 3.5cm and shows erosion patterns throughout the refugee camp. By comparing the erosion patterns to flow direction of the surface, the erosion patterns were determined to be water caused or not water caused, the erosion patterns considered water caused were defined as high-risk flood areas, creating the WCEP model. The three models were compared using locations of collapsed houses throughout the camp. It was found that the WCEP model represents the location of collapsed houses significantly better (misclassification rate below 17%) than the SWAT or HEC-RAS models (misclassification rate below 54%, and 67% respectively). The WCEP model was combined with the best hydrological-hydraulic model (SWAT) to create a hydrogeomorphological model which capitalizes on both the drone imagery and the hydrological process.
Dynamics and Control of a Pressurized Optical Membranes
Tarazaga, Pablo Alberto (Virginia Tech, 2009-08-07)
Optical membranes are currently pursued for their ability to replace the conventional mirrors that are used to correct wave front aberration and space-based telescopes. Among some of the many benefits of using optical membranes, is their ability to considerably reduce the weight of the structure. As a secondary effect, the cost of transportation, which is of great interest in space applications, is reduced as well. Given the low density of these thin-film membranes, the lower end dynamics play a greater significant role than their rigid plate-like counterparts in achieving functional mirrors. Space-based mirrors are subjected to a series of disturbances. Among those encountered are thermal radiation, debris impact, and slewing maneuvers. Thus, dynamic control is essential for the adequate performance of thin-film membrane mirrors. With this in mind, the work described herein aims to improve the performance of optical membranes with an innovative, acoustical control approach to suppress vibration of optical membranes backed by an air cavity. This is achieved by using a centralized acoustic source in the cavity as the method of actuation. The acoustic actuation is of great interest since it does not mass load the membrane in the conventional way, as most methods of actuation would. To achieve this end goal, two structural-acoustic coupled models are developed to describe the dynamics of a pressurized optical membrane system. This is done through an impedance based modeling approach where the subsystems are modeled individually, and then coupled at the interface. The control of the membrane is implemented using a positive position feedback approach. The theory is also extended to positive velocity and positive acceleration feedback. Three experiments are carried out to validate the models previously mentioned. Successful implementation of a control experiment is also accomplished leading to considerable attenuations in the coupled membrane's dynamics.

Browsing by Author "Roan, Michael J."

Results Per Page

Sort Options