O'Neil Smith - Academia.edu (original) (raw)

Papers by O'Neil Smith

2020 IEEE 6th World Forum on Internet of Things (WF-IoT), 2020

Recent advances in embedded system technology have created opportunities for alleviating or elimi... more Recent advances in embedded system technology have created opportunities for alleviating or eliminating common Big Data problems, by providing the resources necessary to perform AI/ML algorithms on-board edge devices. This has led to the emergence of a sub-discipline of Measurements and Signal Intelligence (MASINT) known as Cyber-Physical MASINT, wherein analysts can receive and exploit data directly from cyber-physical devices, and execute algorithms on-board, without the need to transfer to cloud servers. This type of edge analytics can decrease latency, improve security, decrease the amount of data transferred out of the device, and increase the quality of the data being transferred. With this motivation, we approach the task of environmental sound classification, a task which has seen a substantial amount of research in recent years, but which has had very limited implementation at the edge. In this work, we design and deploy an application on a mobile device to perform event detection and sound classification using a novel ensemble of deep neural networks optimized for a mobile environment, capable of classifying six common office sounds with high accuracy and low latency. We provide an accuracy and performance analysis at varying levels of optimization.

2018 International Conference on Computational Science and Computational Intelligence (CSCI), 2018

In this paper, we propose a novel method for estimating the density-difference (DD) between two d... more In this paper, we propose a novel method for estimating the density-difference (DD) between two distributions represented in a wavelet basis expansion. This new Regularized Wavelet-based Density-Difference (RWDD) method directly estimates the DD using the l2 distance between two distributions (expanded in a wavelet basis) without an explicit need to reconstruct the individual probabilities. We develop a regularized objective function that is balanced using both l2 and l1 norm penalties. Experimental evaluations on simulated noisy datasets, from complex multimodal to skewed distributions, clearly showed the superior performance of the proposed RWDD in comparison to other competing techniques.

Disruptive Technologies in Information Sciences, 2018

In this work, we investigate and compare centrality metrics on several datasets. Many real-world ... more In this work, we investigate and compare centrality metrics on several datasets. Many real-world complex systems can be addressed using a graph-based analytical approach, where nodes represent the components of the system and edges are the interactions or relationships between them. Different systems such as communication networks and critical infrastructure are known to exhibit common characteristics in their behavior and structure. Infrastructure networks such as power girds, communication networks and natural gas are interdependent. These systems are usually coupled such that failures in one network can propagate and affect the entire system. The purpose of this analysis is to perform a metric analysis on synthetic infrastructure data. Our view of critical infrastructure systems holds that the function of each system, and especially continuity of that function, is of primary importance. In this work, we view an infrastructure as a collection of interconnected components that work together as a system to achieve a domain-specific function. The importance of a single component within an infrastructure system is based on how it contributes, which we assess with centrality metrics.

ArXiv, 2020

It is abundantly clear that time dependent data is a vital source of information in the world. Th... more It is abundantly clear that time dependent data is a vital source of information in the world. The challenge has been for applications in machine learning to gain access to a considerable amount of quality data needed for algorithm development and analysis. Modeling synthetic data using a Generative Adversarial Network (GAN) has been at the heart of providing a viable solution. Our work focuses on one dimensional times series and explores the few shot approach, which is the ability of an algorithm to perform well with limited data. This work attempts to ease the frustration by proposing a new architecture, Time Series GAN (TSGAN), to model realistic time series data. We evaluate TSGAN on 70 data sets from a benchmark time series database. Our results demonstrate that TSGAN performs better than the competition both quantitatively using the Frechet Inception Score (FID) metric, and qualitatively when classification is used as the evaluation criteria.

2017 International Conference on Computational Science and Computational Intelligence (CSCI), 2017

Here we consider a novel approach for the categorization of time series data, called Classificati... more Here we consider a novel approach for the categorization of time series data, called Classification by Discriminative Interpolation with Sparsity (CDIS), that circumvents the need for feature extraction as in traditional machine learning techniques. During training, the wavelet representations of functions in the same class are warped to become more similar to each other while moving away from functions in different classes. This process—termed discriminative interpolation—leads to gerrymandering of functional neighborhoods in service of supervised learning. We detail a full multiresolution wavelet expansion, incorporated with sparsity, for the functional data. The utility of the proposed CDIS method was experimentally validated on several data sets, thus demonstrating its competitiveness against contemporary and state-of-the-art feature-based methods.

Discrimination between different rocket types is an important application for utilizing infrasoun... more Discrimination between different rocket types is an important application for utilizing infrasound in event monitoring within a range of 0-100 km. This is in contrast to traditional nuclear weapons monitoring which leverages infrasound propagation over thousands of kilometers. The motivation of this research is to demonstrate the utilization of deep neural network architectures to discriminate infrasonic signals produced by rocket launches and collected by an near-field infrasound sensor array. The data collection contains three space bound rocket classes: Delta IV, Atlas V, and Falcon 9. In particular, we investigate the classification accuracy of a multi-class convolutional neural network (CNN) and a deep neural network (DNN) on various feature representations, such as neural network derived features, spectrograms, and wavelet scattering transform coefficients. Our experiments validate the viability of a CNN and DNN framework for near-field infrasonic applications, with our propos...

The number of Internet users had grown rapidly enticing companies and cooperations to make full u... more The number of Internet users had grown rapidly enticing companies and cooperations to make full use of recommendation infrastructures. Consequently, online advertisement companies emerged to aid us in the presence of numerous items and users. Even as a user, you may find yourself drowned in a set of items that you think you might need, but you are not sure if you should try them. Those items could be online services, products, places or even a person for a friendship. Therefore, we need recommender systems that pave the way and help us making good decisions. This paper provides a review on traditional recommendation systems, recommendation system evaluations and metrics, context-aware recommendation systems, and social-based recommendation systems. While it is hard to include all the information in a brief review paper, we try to have an introductory review over the essentials of recommendation systems. More detailed information on each chapter will be found in the corresponding ref...

Density-Difference (DD) estimation is an important unsupervised learning procedure that proceeds ... more Density-Difference (DD) estimation is an important unsupervised learning procedure that proceeds many regression methods. The present work details a novel method for estimating the Difference of Densities (DoD) between two distributions. This new method directly calculates the DD, in the form of a wavelet expansion, without the need for explicitly reconstructing individual distributions. Furthermore, the method applies a regularization technique that utilizes both l2 and l1 norm penalties to robustly estimate the coefficients of the wavelet expansion. Optimizing the regularized objective is accomplished via a Proximal Gradient Descent (PGD) approach. Thus, we term our method Regularized Wavelet-based Density-Difference (RWDD) with PGD. On extensive simulated datasets, from complex multimodal to skewed distributions, our method demonstrated superior performance in comparison to other contemporary techniques.

Supervised dimensionality reduction has emerged as an important theme in the last decade. Despite... more Supervised dimensionality reduction has emerged as an important theme in the last decade. Despite the plethora of models and formulations, there is a lack of a simple model which aims to project the set of patterns into a space defined by the classes (or categories). To this end, we set up a model in which each class is represented as a 1D subspace of the vector space formed by the features. Assuming the set of classes does not exceed the cardinality of the features, the model results in multi-class supervised learning in which the features of each class are projected into the class subspace. Class discrimination is automatically guaranteed via the imposition of orthogonality of the 1D class sub-spaces. The resulting optimization problem - formulated as the minimization of a sum of quadratic functions on a Stiefel manifold - while being non-convex (due to the constraints), nevertheless has a structure for which we can identify when we have reached a global minimum. After formulating...

Here, we present a novel algorithm for detecting changes in a continuous time series stream based... more Here, we present a novel algorithm for detecting changes in a continuous time series stream based on the \({\ell }_{2}\) distance between two distributions. The distributions are non-parametrically modeled using wavelet expansions, inspiring the name of our method: Wavelet-based Least Squares Density–Difference (WLSDD). Using the least squares method, we show that the \({\ell }_{2}\) distance between two wavelet expanded densities results in a closed-form expression of their coefficients. This circumvents the need to evaluate the densities and, instead, allows us to work directly with the differences between the corresponding scaling and wavelet coefficients. The method demonstrated superior change detection performance on both synthetic and real datasets, stationary or nonstationary, in comparison to other competing techniques.

Lecture Notes in Computer Science, 2015

Among the multitude of probabilistic tracking techniques, the Continuously Adaptive Mean Shift CA... more Among the multitude of probabilistic tracking techniques, the Continuously Adaptive Mean Shift CAMSHIFT algorithm has been one of the most popular. Though several modifications have been proposed to the original formulation of CAMSHIFT, limitations still exist. In particular the algorithm underperforms when tracking textured and patterned objects. In this paper we generalize CAMSHIFT for the purposes of tracking such objects in non-stationary backgrounds. Our extension introduces a novel object modeling technique, while retaining a probabilistic back projection stage similar to the original CAMSHIFT algorithm, but with considerably more discriminative power. The object modeling now evolves beyond a single probability distribution to a more generalized joint density function on localized color patterns. In our framework, multiple co-occurrence density functions are estimated using information from several color channel combinations and these distributions are combined using an intuitive Bayesian approach. We validate our approach on several aerial tracking scenarios and demonstrate its improved performance over the original CAMSHIFT algorithm and one of its most successful variants.

Advanced Environmental, Chemical, and Biological Sensing Technologies XI, 2014

ABSTRACT We discuss a robust method for optimal oil probe path planning inspired by medical imagi... more ABSTRACT We discuss a robust method for optimal oil probe path planning inspired by medical imaging. Horizontal wells require three-dimensional steering made possible by the rotary steerable capabilities of the system, which allows the hole to intersect multiple target shale gas zones. Horizontal "legs" can be over a mile long; the longer the exposure length, the more oil and natural gas is drained and the faster it can flow. More oil and natural gas can be produced with fewer wells and less surface disturbance. Horizontal drilling can help producers tap oil and natural gas deposits under surface areas where a vertical well cannot be drilled, such as under developed or environmentally sensitive areas. Drilling creates well paths which have multiple twists and turns to try to hit multiple accumulations from a single well location. Our algorithm can be used to augment current state of the art methods. Our goal is to obtain a 3D path with nodes describing the optimal route to the destination. This algorithm works with BIG data and saves cost in planning for probe insertion. Our solution may be able to help increase the energy extracted vs. input energy.

Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIX, 2018

Infrasound propagation through various atmospheric conditions and interaction with environmental ... more Infrasound propagation through various atmospheric conditions and interaction with environmental factors in- duce highly non-linear and non-stationary effects that make it difficult to extract reliable attributes for classi- fication. We present featureless classification results on the Library of Typical Infrasonic Signals using several deep learning techniques, including long short-term memory, self-normalizing, and fully convolutional neural net- works with statistical analysis to establish significantly superior models. In general, the deep classifiers achieve near-perfect classification accuracies on the four classes of infrasonic events including mountain associated waves, microbaroms, auroral infrasonic waves, and volcanic eruptions. Our results provide evidence that deep neural network architectures be considered the leading candidate for classifying infrasound waveforms which can directly benefit applications that seek to identify infrasonic events such as severe weather forecasting, natural disaster early warning systems, and nuclear weapons monitoring.

Harris describes a novel way to autonomously inpaint missing data into high resolution single ref... more Harris describes a novel way to autonomously inpaint missing data into high resolution single reflective surfaces utilizing a variant of the Navier-Stokes equations. One product of this process is a high-resolution bare earth Digital Elevation Model (DEM) with the same resolution as the input data. Inpainting allows generation of high resolution bare earth DEMs in both high and low frequency terrain environments for urban 3-D modeling. Having this bare earth DEM accounts for a dramatic increase in accuracy in all other steps of the urban 3-D modeling process. The LiteSite™ toolkit has the capability to automatically extract buildings and vegetation from an urban scene. The resulting DEM from this step of the process acts as input to the inpainting process. The expected building and vegetation base heights can then be inpainted into the area of extraction where data is now missing. The inpainting process maintains building and vegetation base height consistency in the inpainted regio...

Laser Radar Technology and Applications XIX; and Atmospheric Propagation XI, 2014

ABSTRACT A novel approach using a support vector machine (SVM) is proposed to classify bare earth... more ABSTRACT A novel approach using a support vector machine (SVM) is proposed to classify bare earth points in LiDAR point clouds. Using graph based segmentation, the LiDAR point cloud is segmented into a set of topological components. Several features establishing relationships from those components to their neighboring components are formulated. The SVM is then trained on the segment features to establish a model for the classification of bare earth and non bare earth points. Quantitative results are presented for training and testing the proposed SVM classifier on the ISPRS data set. Using the ISPRS data set as a training set, qualitative results are presented by testing the proposed SVM classifier on data downloaded from Open Topography; which covers a variety of different landscapes and building structures in Frazier Park, California. Despite the data being captured from different sensors, and collected from scenes with different terrain types and building structures, the results shown were processed with no parameter changes. Furthermore, a confidence value is returned indicating how well the unforeseen data fits the SVM’s trained model for bare earth recognition.

Laser Radar Technology and Applications XVIII, 2013

ABSTRACT

Laser Radar Technology and Applications XVIII, 2013

ABSTRACT

Advanced Environmental, Chemical, and Biological Sensing Technologies XI, 2014

ABSTRACT We discuss a robust method for optimal oil probe path planning inspired by medical imagi... more ABSTRACT We discuss a robust method for optimal oil probe path planning inspired by medical imaging. Horizontal wells require three-dimensional steering made possible by the rotary steerable capabilities of the system, which allows the hole to intersect multiple target shale gas zones. Horizontal "legs" can be over a mile long; the longer the exposure length, the more oil and natural gas is drained and the faster it can flow. More oil and natural gas can be produced with fewer wells and less surface disturbance. Horizontal drilling can help producers tap oil and natural gas deposits under surface areas where a vertical well cannot be drilled, such as under developed or environmentally sensitive areas. Drilling creates well paths which have multiple twists and turns to try to hit multiple accumulations from a single well location. Our algorithm can be used to augment current state of the art methods. Our goal is to obtain a 3D path with nodes describing the optimal route to the destination. This algorithm works with BIG data and saves cost in planning for probe insertion. Our solution may be able to help increase the energy extracted vs. input energy.

2020 IEEE 6th World Forum on Internet of Things (WF-IoT), 2020

Recent advances in embedded system technology have created opportunities for alleviating or elimi... more Recent advances in embedded system technology have created opportunities for alleviating or eliminating common Big Data problems, by providing the resources necessary to perform AI/ML algorithms on-board edge devices. This has led to the emergence of a sub-discipline of Measurements and Signal Intelligence (MASINT) known as Cyber-Physical MASINT, wherein analysts can receive and exploit data directly from cyber-physical devices, and execute algorithms on-board, without the need to transfer to cloud servers. This type of edge analytics can decrease latency, improve security, decrease the amount of data transferred out of the device, and increase the quality of the data being transferred. With this motivation, we approach the task of environmental sound classification, a task which has seen a substantial amount of research in recent years, but which has had very limited implementation at the edge. In this work, we design and deploy an application on a mobile device to perform event detection and sound classification using a novel ensemble of deep neural networks optimized for a mobile environment, capable of classifying six common office sounds with high accuracy and low latency. We provide an accuracy and performance analysis at varying levels of optimization.

2018 International Conference on Computational Science and Computational Intelligence (CSCI), 2018

In this paper, we propose a novel method for estimating the density-difference (DD) between two d... more In this paper, we propose a novel method for estimating the density-difference (DD) between two distributions represented in a wavelet basis expansion. This new Regularized Wavelet-based Density-Difference (RWDD) method directly estimates the DD using the l2 distance between two distributions (expanded in a wavelet basis) without an explicit need to reconstruct the individual probabilities. We develop a regularized objective function that is balanced using both l2 and l1 norm penalties. Experimental evaluations on simulated noisy datasets, from complex multimodal to skewed distributions, clearly showed the superior performance of the proposed RWDD in comparison to other competing techniques.

Disruptive Technologies in Information Sciences, 2018

In this work, we investigate and compare centrality metrics on several datasets. Many real-world ... more In this work, we investigate and compare centrality metrics on several datasets. Many real-world complex systems can be addressed using a graph-based analytical approach, where nodes represent the components of the system and edges are the interactions or relationships between them. Different systems such as communication networks and critical infrastructure are known to exhibit common characteristics in their behavior and structure. Infrastructure networks such as power girds, communication networks and natural gas are interdependent. These systems are usually coupled such that failures in one network can propagate and affect the entire system. The purpose of this analysis is to perform a metric analysis on synthetic infrastructure data. Our view of critical infrastructure systems holds that the function of each system, and especially continuity of that function, is of primary importance. In this work, we view an infrastructure as a collection of interconnected components that work together as a system to achieve a domain-specific function. The importance of a single component within an infrastructure system is based on how it contributes, which we assess with centrality metrics.

ArXiv, 2020

It is abundantly clear that time dependent data is a vital source of information in the world. Th... more It is abundantly clear that time dependent data is a vital source of information in the world. The challenge has been for applications in machine learning to gain access to a considerable amount of quality data needed for algorithm development and analysis. Modeling synthetic data using a Generative Adversarial Network (GAN) has been at the heart of providing a viable solution. Our work focuses on one dimensional times series and explores the few shot approach, which is the ability of an algorithm to perform well with limited data. This work attempts to ease the frustration by proposing a new architecture, Time Series GAN (TSGAN), to model realistic time series data. We evaluate TSGAN on 70 data sets from a benchmark time series database. Our results demonstrate that TSGAN performs better than the competition both quantitatively using the Frechet Inception Score (FID) metric, and qualitatively when classification is used as the evaluation criteria.

2017 International Conference on Computational Science and Computational Intelligence (CSCI), 2017

Here we consider a novel approach for the categorization of time series data, called Classificati... more Here we consider a novel approach for the categorization of time series data, called Classification by Discriminative Interpolation with Sparsity (CDIS), that circumvents the need for feature extraction as in traditional machine learning techniques. During training, the wavelet representations of functions in the same class are warped to become more similar to each other while moving away from functions in different classes. This process—termed discriminative interpolation—leads to gerrymandering of functional neighborhoods in service of supervised learning. We detail a full multiresolution wavelet expansion, incorporated with sparsity, for the functional data. The utility of the proposed CDIS method was experimentally validated on several data sets, thus demonstrating its competitiveness against contemporary and state-of-the-art feature-based methods.

Discrimination between different rocket types is an important application for utilizing infrasoun... more Discrimination between different rocket types is an important application for utilizing infrasound in event monitoring within a range of 0-100 km. This is in contrast to traditional nuclear weapons monitoring which leverages infrasound propagation over thousands of kilometers. The motivation of this research is to demonstrate the utilization of deep neural network architectures to discriminate infrasonic signals produced by rocket launches and collected by an near-field infrasound sensor array. The data collection contains three space bound rocket classes: Delta IV, Atlas V, and Falcon 9. In particular, we investigate the classification accuracy of a multi-class convolutional neural network (CNN) and a deep neural network (DNN) on various feature representations, such as neural network derived features, spectrograms, and wavelet scattering transform coefficients. Our experiments validate the viability of a CNN and DNN framework for near-field infrasonic applications, with our propos...

The number of Internet users had grown rapidly enticing companies and cooperations to make full u... more The number of Internet users had grown rapidly enticing companies and cooperations to make full use of recommendation infrastructures. Consequently, online advertisement companies emerged to aid us in the presence of numerous items and users. Even as a user, you may find yourself drowned in a set of items that you think you might need, but you are not sure if you should try them. Those items could be online services, products, places or even a person for a friendship. Therefore, we need recommender systems that pave the way and help us making good decisions. This paper provides a review on traditional recommendation systems, recommendation system evaluations and metrics, context-aware recommendation systems, and social-based recommendation systems. While it is hard to include all the information in a brief review paper, we try to have an introductory review over the essentials of recommendation systems. More detailed information on each chapter will be found in the corresponding ref...

Density-Difference (DD) estimation is an important unsupervised learning procedure that proceeds ... more Density-Difference (DD) estimation is an important unsupervised learning procedure that proceeds many regression methods. The present work details a novel method for estimating the Difference of Densities (DoD) between two distributions. This new method directly calculates the DD, in the form of a wavelet expansion, without the need for explicitly reconstructing individual distributions. Furthermore, the method applies a regularization technique that utilizes both l2 and l1 norm penalties to robustly estimate the coefficients of the wavelet expansion. Optimizing the regularized objective is accomplished via a Proximal Gradient Descent (PGD) approach. Thus, we term our method Regularized Wavelet-based Density-Difference (RWDD) with PGD. On extensive simulated datasets, from complex multimodal to skewed distributions, our method demonstrated superior performance in comparison to other contemporary techniques.

Supervised dimensionality reduction has emerged as an important theme in the last decade. Despite... more Supervised dimensionality reduction has emerged as an important theme in the last decade. Despite the plethora of models and formulations, there is a lack of a simple model which aims to project the set of patterns into a space defined by the classes (or categories). To this end, we set up a model in which each class is represented as a 1D subspace of the vector space formed by the features. Assuming the set of classes does not exceed the cardinality of the features, the model results in multi-class supervised learning in which the features of each class are projected into the class subspace. Class discrimination is automatically guaranteed via the imposition of orthogonality of the 1D class sub-spaces. The resulting optimization problem - formulated as the minimization of a sum of quadratic functions on a Stiefel manifold - while being non-convex (due to the constraints), nevertheless has a structure for which we can identify when we have reached a global minimum. After formulating...

Here, we present a novel algorithm for detecting changes in a continuous time series stream based... more Here, we present a novel algorithm for detecting changes in a continuous time series stream based on the \({\ell }_{2}\) distance between two distributions. The distributions are non-parametrically modeled using wavelet expansions, inspiring the name of our method: Wavelet-based Least Squares Density–Difference (WLSDD). Using the least squares method, we show that the \({\ell }_{2}\) distance between two wavelet expanded densities results in a closed-form expression of their coefficients. This circumvents the need to evaluate the densities and, instead, allows us to work directly with the differences between the corresponding scaling and wavelet coefficients. The method demonstrated superior change detection performance on both synthetic and real datasets, stationary or nonstationary, in comparison to other competing techniques.

Lecture Notes in Computer Science, 2015

Among the multitude of probabilistic tracking techniques, the Continuously Adaptive Mean Shift CA... more Among the multitude of probabilistic tracking techniques, the Continuously Adaptive Mean Shift CAMSHIFT algorithm has been one of the most popular. Though several modifications have been proposed to the original formulation of CAMSHIFT, limitations still exist. In particular the algorithm underperforms when tracking textured and patterned objects. In this paper we generalize CAMSHIFT for the purposes of tracking such objects in non-stationary backgrounds. Our extension introduces a novel object modeling technique, while retaining a probabilistic back projection stage similar to the original CAMSHIFT algorithm, but with considerably more discriminative power. The object modeling now evolves beyond a single probability distribution to a more generalized joint density function on localized color patterns. In our framework, multiple co-occurrence density functions are estimated using information from several color channel combinations and these distributions are combined using an intuitive Bayesian approach. We validate our approach on several aerial tracking scenarios and demonstrate its improved performance over the original CAMSHIFT algorithm and one of its most successful variants.

Advanced Environmental, Chemical, and Biological Sensing Technologies XI, 2014

ABSTRACT We discuss a robust method for optimal oil probe path planning inspired by medical imagi... more ABSTRACT We discuss a robust method for optimal oil probe path planning inspired by medical imaging. Horizontal wells require three-dimensional steering made possible by the rotary steerable capabilities of the system, which allows the hole to intersect multiple target shale gas zones. Horizontal "legs" can be over a mile long; the longer the exposure length, the more oil and natural gas is drained and the faster it can flow. More oil and natural gas can be produced with fewer wells and less surface disturbance. Horizontal drilling can help producers tap oil and natural gas deposits under surface areas where a vertical well cannot be drilled, such as under developed or environmentally sensitive areas. Drilling creates well paths which have multiple twists and turns to try to hit multiple accumulations from a single well location. Our algorithm can be used to augment current state of the art methods. Our goal is to obtain a 3D path with nodes describing the optimal route to the destination. This algorithm works with BIG data and saves cost in planning for probe insertion. Our solution may be able to help increase the energy extracted vs. input energy.

Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIX, 2018

Infrasound propagation through various atmospheric conditions and interaction with environmental ... more Infrasound propagation through various atmospheric conditions and interaction with environmental factors in- duce highly non-linear and non-stationary effects that make it difficult to extract reliable attributes for classi- fication. We present featureless classification results on the Library of Typical Infrasonic Signals using several deep learning techniques, including long short-term memory, self-normalizing, and fully convolutional neural net- works with statistical analysis to establish significantly superior models. In general, the deep classifiers achieve near-perfect classification accuracies on the four classes of infrasonic events including mountain associated waves, microbaroms, auroral infrasonic waves, and volcanic eruptions. Our results provide evidence that deep neural network architectures be considered the leading candidate for classifying infrasound waveforms which can directly benefit applications that seek to identify infrasonic events such as severe weather forecasting, natural disaster early warning systems, and nuclear weapons monitoring.

Harris describes a novel way to autonomously inpaint missing data into high resolution single ref... more Harris describes a novel way to autonomously inpaint missing data into high resolution single reflective surfaces utilizing a variant of the Navier-Stokes equations. One product of this process is a high-resolution bare earth Digital Elevation Model (DEM) with the same resolution as the input data. Inpainting allows generation of high resolution bare earth DEMs in both high and low frequency terrain environments for urban 3-D modeling. Having this bare earth DEM accounts for a dramatic increase in accuracy in all other steps of the urban 3-D modeling process. The LiteSite™ toolkit has the capability to automatically extract buildings and vegetation from an urban scene. The resulting DEM from this step of the process acts as input to the inpainting process. The expected building and vegetation base heights can then be inpainted into the area of extraction where data is now missing. The inpainting process maintains building and vegetation base height consistency in the inpainted regio...

Laser Radar Technology and Applications XIX; and Atmospheric Propagation XI, 2014

ABSTRACT A novel approach using a support vector machine (SVM) is proposed to classify bare earth... more ABSTRACT A novel approach using a support vector machine (SVM) is proposed to classify bare earth points in LiDAR point clouds. Using graph based segmentation, the LiDAR point cloud is segmented into a set of topological components. Several features establishing relationships from those components to their neighboring components are formulated. The SVM is then trained on the segment features to establish a model for the classification of bare earth and non bare earth points. Quantitative results are presented for training and testing the proposed SVM classifier on the ISPRS data set. Using the ISPRS data set as a training set, qualitative results are presented by testing the proposed SVM classifier on data downloaded from Open Topography; which covers a variety of different landscapes and building structures in Frazier Park, California. Despite the data being captured from different sensors, and collected from scenes with different terrain types and building structures, the results shown were processed with no parameter changes. Furthermore, a confidence value is returned indicating how well the unforeseen data fits the SVM’s trained model for bare earth recognition.

Laser Radar Technology and Applications XVIII, 2013

ABSTRACT

Laser Radar Technology and Applications XVIII, 2013

ABSTRACT

Advanced Environmental, Chemical, and Biological Sensing Technologies XI, 2014

ABSTRACT We discuss a robust method for optimal oil probe path planning inspired by medical imagi... more ABSTRACT We discuss a robust method for optimal oil probe path planning inspired by medical imaging. Horizontal wells require three-dimensional steering made possible by the rotary steerable capabilities of the system, which allows the hole to intersect multiple target shale gas zones. Horizontal "legs" can be over a mile long; the longer the exposure length, the more oil and natural gas is drained and the faster it can flow. More oil and natural gas can be produced with fewer wells and less surface disturbance. Horizontal drilling can help producers tap oil and natural gas deposits under surface areas where a vertical well cannot be drilled, such as under developed or environmentally sensitive areas. Drilling creates well paths which have multiple twists and turns to try to hit multiple accumulations from a single well location. Our algorithm can be used to augment current state of the art methods. Our goal is to obtain a 3D path with nodes describing the optimal route to the destination. This algorithm works with BIG data and saves cost in planning for probe insertion. Our solution may be able to help increase the energy extracted vs. input energy.