Proceedings of the ... Annual Hawaii International Conference on System Sciences, 2019
Cyber Systems and associated analytics will enable a future where secure, cognitive technologies ... more Cyber Systems and associated analytics will enable a future where secure, cognitive technologies anticipate long-and short-term information needs, perceptively coordinate and adapt distributed sensors, and deliver timely and accurate information and recommendations to humans and machines. Effective designs will require machine-to-human, human-to-machine, and machine-to-machine collaboration. This minitrack invites original, technical research in the subject area.
Proceedings of the 51st Hawaii International Conference on System Sciences, 2018
The stable distribution has been shown to more accurately model some aspects of network traffic t... more The stable distribution has been shown to more accurately model some aspects of network traffic than alternative distributions. In this work, we quantitatively examine aspects of the modeling performance of the stable distribution as envisioned in a statistical network cyber event detection system. We examine the flexibility and robustness of the stable distribution, extending previous work by comparing the performance of the stable distribution against alternatives using three different, public network traffic data sets with a mix of traffic rates and cyber events. After showing the stable distribution to be the overall most accurate for the examined scenarios, we use the Hellinger metric to investigate the ability of the stable distribution to reduce modeling error when using small data windows and counting periods. For the selected case and metric, the stable model is compared to a Gaussian model and is shown to produce the best overall fit as well as the best (or at worst, equivalent) fit for all counting periods. Additionally, the best stable fit occurs at a counting period that is five times shorter than the best Gaussian case. These results imply that the stable distribution can provide a more robust and accurate model than Gaussian-based alternatives in statistical network anomaly detection implementations while also facilitating faster system detection and response.
Proceedings of the 52nd Hawaii International Conference on System Sciences, 2019
Cyber Systems and associated analytics will enable a future where secure, cognitive technologies ... more Cyber Systems and associated analytics will enable a future where secure, cognitive technologies anticipate long-and short-term information needs, perceptively coordinate and adapt distributed sensors, and deliver timely and accurate information and recommendations to humans and machines. Effective designs will require machine-to-human, human-to-machine, and machine-to-machine collaboration. This minitrack invites original, technical research in the subject area.
Night vision sensors, such as image-intensifier (II) tubes in night vision goggles and forward lo... more Night vision sensors, such as image-intensifier (II) tubes in night vision goggles and forward looking infrared sensors (FLIR) are routinely used by U.S. naval personnel for night operations. The quality of imagery from these devices however, can be extremely poor. Since these sensors exploit different regions of the electromagnetic spectrum, the information they provide is often complimentary, and therefore, improvements are possible with the enhancement and subsequent fusion of this information into a single presentation. Such processing can maximize scene content by incorporating information from both images as well as increase contrast and dynamic range. This thesis introduces a new algorithm, which produces such an enhanced/fused image. It performs adaptive enhancement of both the low-light visible (II) and thermal infrared imagery (IR) inputs, followed by a data fusion for combining the two images into a composite image. The methodology for visual testing of the algorithm for comparison of fused and original II and IR imagery is also presented and a discussion of the results is included. Tests confirmed that the fusion algorithm resulted in significant improvement over either single-band image.
Public reporting burden for this collection of information is estimated to average 1 hour per res... more Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215
Proceedings of the 54th Hawaii International Conference on System Sciences | 2021The article of r... more Proceedings of the 54th Hawaii International Conference on System Sciences | 2021The article of record as published may be found at https://doi.org/10.24251/HICSS.2021.844Cyber security is a multi-functionary area of practice; effective solutions are difficult because of the diverse range of expertise required and the involvement of fallible humans. The impact and number of successful attacks grows every year even while cyber security spending grows at a double-digit annual rate. To fundamentally improve the state of cyber security, research must consider cross-disciplinary techniques and investigate novel paths; incremental progress is unlikely to fundamentally improve the state of the practice
Proceedings of the 51st Hawaii International Conference on System Sciences, 2018
With urbanization and cellular subscribership rising sharply, cellular use in urban locales has b... more With urbanization and cellular subscribership rising sharply, cellular use in urban locales has become a normative behavior for the majority of the world's population. As the research community pushes the limits of what is possible in the next generation cellular arena, it is prudent to simultaneously hold in tension the responsibility to provide appropriate protections to the ultimate end users of such technology. To this end, this research illustrates a location-based attack in modern cellular networks. This attack leverages control information sent over the radio access network without the benefit of encryption. We show how this attack is particularly potent in urban localization where it is important to infer location in three dimensions. We quantify the efficacy of such an attack, and therefore the associated location privacy, through simulation both in a generic cellular environment and in an environment modeled after downtown Honolulu. Our results show that accuracy on the order of 15 meters is possible.
Proceedings of the Annual Hawaii International Conference on System Sciences, 2019
Since the revelations of interference in the 2016 US Presidential elections, the UK's Brexit refe... more Since the revelations of interference in the 2016 US Presidential elections, the UK's Brexit referendum, the Catalan independence vote in 2017 and numerous other major political discussions by malicious online actors and propaganda bots, there has been increasing interest in understanding how to detect and characterize such threats. We focus on some of the recent research in algorithms for detection of propaganda botnets and metrics by which their impact can be measured.
Proceedings of the 50th Hawaii International Conference on System Sciences (2017), 2017
Location privacy is an oft-overlooked, but exceedingly important niche of the overall privacy mac... more Location privacy is an oft-overlooked, but exceedingly important niche of the overall privacy macrocosm. An ambition of this work is to raise awareness of concerns relating to location privacy in cellular networks. To this end, we will demonstrate how user location information is leaked through a vulnerability, viz. the timing advance (TA) parameter, in the Long Term Evolution (LTE) signaling plane and how the position estimate that results from that parameter can be refined through a previously introduced method called Cellular Synchronization Assisted Refinement (CeSAR) [1]. With CeSAR, positioning accuracies that meet or exceed the FCC's E-911 mandate are possible making CeSAR simultaneously a candidate technology for meeting the FCC's wireless localization requirements and a demonstration of the alarming level of location information sent over the air. We also introduce a geographically diverse data set of TAs collected from actual LTE network implementations utilizing different cell phone chipsets. With this data set we show the appropriateness of modeling the error associated with a TA as normally distributed.
2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 2019
Hyperspectral imagery (HSI) cubes are high-dimensional datasets that lend themselves well to deep... more Hyperspectral imagery (HSI) cubes are high-dimensional datasets that lend themselves well to deep learning approaches for classification. Deep learning approaches, specifically generative adversarial networks (GANs), have been shown to be very effective in classification and generation of accurate synthetic data in computer vision problems. This work proposes an extension of an existing GAN training scheme, called extended semi-supervised learning (ESSL), metrics for evaluating GAN training performance, and demonstrates the effectiveness of the proposed training scheme to improve classification of HSI. Using ESSL with GAN, we have been able to achieve approximately 0.8% increase in classification accuracy over convolutional neural networks as well as generate extremely accurate synthetic imagery.
2016 19th International Conference on Information Fusion (FUSION), 2016
In this paper, we propose a scheme for classification of maritime targets through fusion of image... more In this paper, we propose a scheme for classification of maritime targets through fusion of images collected from dissimilar sensors with an objective to improve maritime domain awareness. Low- and medium-level fusion methods are applied to three types of image data-visual, thermal, multi-spectral-using features obtained from the speeded-up robust features algorithm. The goal was to implement the classification scheme using machine learning techniques. Results indicate that multi-spectral images from low-level fusion yielded the best classification performance. Artificial neural networks are used to derive the classification results and demonstrate the ability to obtain results in a timely manner that could accommodate near real-time classification.
2018 26th European Signal Processing Conference (EUSIPCO), 2018
In this paper, we propose a novel method for embedding one-dimensional, periodic time-series data... more In this paper, we propose a novel method for embedding one-dimensional, periodic time-series data into higherdimensional topological spaces to support robust recovery of signal features via topological data analysis under noisy sampling conditions. Our method can be considered an extension of the popular time delay embedding method to a larger class of linear operators. To provide evidence for the viability of this method, we analyze the simple case of sinusoidal data in three steps. First, we discuss some of the drawbacks of the time delay embedding framework in the context of periodic, sinusoidal data. Next, we show analytically that using the Hilbert transform as an alternative embedding function for sinusoidal data overcomes these drawbacks. Finally, we provide empirical evidence of the viability of the Hilbert transform as an embedding function when the parameters of the sinusoidal data vary over time.
2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018
Location-based services have seen a boon in data production recently which has simultaneously sto... more Location-based services have seen a boon in data production recently which has simultaneously stoked the research community to better understand this type of information. Traditional methods in analyzing such data require significant a priori understanding of the organization of the data. We submit that the nascent field of topological data analysis (TDA) may be able to contribute new insights to analysis of such data without the aforementioned requirement. To this end, we propose two novel methods of embedding such data in order to leverage the expressive power of TDA. To demonstrate its effectiveness we apply the embeddings to maritime automated information system data.
In this paper, we develop and test a three-stage algorithm for performing unsupervised segmentati... more In this paper, we develop and test a three-stage algorithm for performing unsupervised segmentation of hyperspectral imagery. Each stage of the algorithm leverages modified clustering methods which incorporate both the spatial and spectral information present within a hyperspectral scene. The ultimate output of the process is a classification map which groups spatially adjoining pixels into a number of distinct regions, each of which can be well-approximated by a multivariate normal distribution. This output is expected to be useful in various target detection scenarios which require accurate estimates of background statistics. Execution time and memory metrics are provided for each stage of the algorithm, along with segmentation proficiency scores as measured against random synthetic data sets. Segmentation results of real-world hyperspectral images are also provided for qualitative analysis. We conclude from the experiments that the algorithm usually performs well in typical overhead views with multiple, spectrally diverse objects to segment, but suffers from reduced accuracy in scenarios where individual regions are exceptionally large or small.
2016 10th International Conference on Signal Processing and Communication Systems (ICSPCS), 2016
Wireless geo location is an increasingly relevant area of research as cellular technology becomes... more Wireless geo location is an increasingly relevant area of research as cellular technology becomes ever more ubiquitous. In this work we consider the timing advance parameter in Long Term Evolution cellular networks to this end. We also evaluate a previously introduced method of position estimation augmentation called Cellular Synchronization Assisted Refinement (CeSAR). We first develop the concept of geometric dilution of precision and the Cramer-Rao Lower Bound (CRLB) to motivate an intuition for positioning accuracy. We then derive an exact maximum likelihood estimator (MLE) for TA-specific position estimation. While the exact MLE proves to be computationally difficult, it is shown to be equivalent to the MLE in the general case of Gaussian noise. This equivalency is then used to propose an approximate MLE (AMLE). Through simulation, the AMLE is shown to meet the CRLB. Real-world data that includes urban, suburban, line-of-sight, and non-line-of-sight channels are also presented and used to build a realistic model of the TA-specific channel. This model is then used to evaluate the performance of the AMLE and CeSAR in non-line-of-sight and line-of-sight conditions. Our results suggest that the proposed method may be appropriate for meeting the Federal Communication Commission's E-911 requirements.
IEEE Transactions on Information Forensics and Security, 2017
Location privacy is an ever increasing concern as the pervasiveness of computing becomes more ubi... more Location privacy is an ever increasing concern as the pervasiveness of computing becomes more ubiquitous. This is especially apparent at the intersection of privacy, convenience, and quality of service in cellular networks. In this paper, we show the long term evolution (LTE) signaling plane to be vulnerable to location-based attacks via the timing advance (TA) parameter. To this end, we adapt the Cramér-Rao lower bound for timing advance-based estimation and show the associated estimator to be efficient. The analysis is complemented with numerical studies that feature synthetic and real-world data collected in existing LTE network deployments. Additionally, the Cellular Synchronization Assisted Refinement algorithm, a method of TA-based attack augmentation is examined. We show how it can simultaneously improve location resolution and negate the effects of poor network infrastructure geometry. The analysis and simulation demonstrate that a localization attack can yield resolution as high as 40 m.
Public reporting burden for this collection of information is estimated to average 1 hour per res... more Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to
Proceedings of the ... Annual Hawaii International Conference on System Sciences, 2019
Cyber Systems and associated analytics will enable a future where secure, cognitive technologies ... more Cyber Systems and associated analytics will enable a future where secure, cognitive technologies anticipate long-and short-term information needs, perceptively coordinate and adapt distributed sensors, and deliver timely and accurate information and recommendations to humans and machines. Effective designs will require machine-to-human, human-to-machine, and machine-to-machine collaboration. This minitrack invites original, technical research in the subject area.
Proceedings of the 51st Hawaii International Conference on System Sciences, 2018
The stable distribution has been shown to more accurately model some aspects of network traffic t... more The stable distribution has been shown to more accurately model some aspects of network traffic than alternative distributions. In this work, we quantitatively examine aspects of the modeling performance of the stable distribution as envisioned in a statistical network cyber event detection system. We examine the flexibility and robustness of the stable distribution, extending previous work by comparing the performance of the stable distribution against alternatives using three different, public network traffic data sets with a mix of traffic rates and cyber events. After showing the stable distribution to be the overall most accurate for the examined scenarios, we use the Hellinger metric to investigate the ability of the stable distribution to reduce modeling error when using small data windows and counting periods. For the selected case and metric, the stable model is compared to a Gaussian model and is shown to produce the best overall fit as well as the best (or at worst, equivalent) fit for all counting periods. Additionally, the best stable fit occurs at a counting period that is five times shorter than the best Gaussian case. These results imply that the stable distribution can provide a more robust and accurate model than Gaussian-based alternatives in statistical network anomaly detection implementations while also facilitating faster system detection and response.
Proceedings of the 52nd Hawaii International Conference on System Sciences, 2019
Cyber Systems and associated analytics will enable a future where secure, cognitive technologies ... more Cyber Systems and associated analytics will enable a future where secure, cognitive technologies anticipate long-and short-term information needs, perceptively coordinate and adapt distributed sensors, and deliver timely and accurate information and recommendations to humans and machines. Effective designs will require machine-to-human, human-to-machine, and machine-to-machine collaboration. This minitrack invites original, technical research in the subject area.
Night vision sensors, such as image-intensifier (II) tubes in night vision goggles and forward lo... more Night vision sensors, such as image-intensifier (II) tubes in night vision goggles and forward looking infrared sensors (FLIR) are routinely used by U.S. naval personnel for night operations. The quality of imagery from these devices however, can be extremely poor. Since these sensors exploit different regions of the electromagnetic spectrum, the information they provide is often complimentary, and therefore, improvements are possible with the enhancement and subsequent fusion of this information into a single presentation. Such processing can maximize scene content by incorporating information from both images as well as increase contrast and dynamic range. This thesis introduces a new algorithm, which produces such an enhanced/fused image. It performs adaptive enhancement of both the low-light visible (II) and thermal infrared imagery (IR) inputs, followed by a data fusion for combining the two images into a composite image. The methodology for visual testing of the algorithm for comparison of fused and original II and IR imagery is also presented and a discussion of the results is included. Tests confirmed that the fusion algorithm resulted in significant improvement over either single-band image.
Public reporting burden for this collection of information is estimated to average 1 hour per res... more Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215
Proceedings of the 54th Hawaii International Conference on System Sciences | 2021The article of r... more Proceedings of the 54th Hawaii International Conference on System Sciences | 2021The article of record as published may be found at https://doi.org/10.24251/HICSS.2021.844Cyber security is a multi-functionary area of practice; effective solutions are difficult because of the diverse range of expertise required and the involvement of fallible humans. The impact and number of successful attacks grows every year even while cyber security spending grows at a double-digit annual rate. To fundamentally improve the state of cyber security, research must consider cross-disciplinary techniques and investigate novel paths; incremental progress is unlikely to fundamentally improve the state of the practice
Proceedings of the 51st Hawaii International Conference on System Sciences, 2018
With urbanization and cellular subscribership rising sharply, cellular use in urban locales has b... more With urbanization and cellular subscribership rising sharply, cellular use in urban locales has become a normative behavior for the majority of the world's population. As the research community pushes the limits of what is possible in the next generation cellular arena, it is prudent to simultaneously hold in tension the responsibility to provide appropriate protections to the ultimate end users of such technology. To this end, this research illustrates a location-based attack in modern cellular networks. This attack leverages control information sent over the radio access network without the benefit of encryption. We show how this attack is particularly potent in urban localization where it is important to infer location in three dimensions. We quantify the efficacy of such an attack, and therefore the associated location privacy, through simulation both in a generic cellular environment and in an environment modeled after downtown Honolulu. Our results show that accuracy on the order of 15 meters is possible.
Proceedings of the Annual Hawaii International Conference on System Sciences, 2019
Since the revelations of interference in the 2016 US Presidential elections, the UK's Brexit refe... more Since the revelations of interference in the 2016 US Presidential elections, the UK's Brexit referendum, the Catalan independence vote in 2017 and numerous other major political discussions by malicious online actors and propaganda bots, there has been increasing interest in understanding how to detect and characterize such threats. We focus on some of the recent research in algorithms for detection of propaganda botnets and metrics by which their impact can be measured.
Proceedings of the 50th Hawaii International Conference on System Sciences (2017), 2017
Location privacy is an oft-overlooked, but exceedingly important niche of the overall privacy mac... more Location privacy is an oft-overlooked, but exceedingly important niche of the overall privacy macrocosm. An ambition of this work is to raise awareness of concerns relating to location privacy in cellular networks. To this end, we will demonstrate how user location information is leaked through a vulnerability, viz. the timing advance (TA) parameter, in the Long Term Evolution (LTE) signaling plane and how the position estimate that results from that parameter can be refined through a previously introduced method called Cellular Synchronization Assisted Refinement (CeSAR) [1]. With CeSAR, positioning accuracies that meet or exceed the FCC's E-911 mandate are possible making CeSAR simultaneously a candidate technology for meeting the FCC's wireless localization requirements and a demonstration of the alarming level of location information sent over the air. We also introduce a geographically diverse data set of TAs collected from actual LTE network implementations utilizing different cell phone chipsets. With this data set we show the appropriateness of modeling the error associated with a TA as normally distributed.
2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 2019
Hyperspectral imagery (HSI) cubes are high-dimensional datasets that lend themselves well to deep... more Hyperspectral imagery (HSI) cubes are high-dimensional datasets that lend themselves well to deep learning approaches for classification. Deep learning approaches, specifically generative adversarial networks (GANs), have been shown to be very effective in classification and generation of accurate synthetic data in computer vision problems. This work proposes an extension of an existing GAN training scheme, called extended semi-supervised learning (ESSL), metrics for evaluating GAN training performance, and demonstrates the effectiveness of the proposed training scheme to improve classification of HSI. Using ESSL with GAN, we have been able to achieve approximately 0.8% increase in classification accuracy over convolutional neural networks as well as generate extremely accurate synthetic imagery.
2016 19th International Conference on Information Fusion (FUSION), 2016
In this paper, we propose a scheme for classification of maritime targets through fusion of image... more In this paper, we propose a scheme for classification of maritime targets through fusion of images collected from dissimilar sensors with an objective to improve maritime domain awareness. Low- and medium-level fusion methods are applied to three types of image data-visual, thermal, multi-spectral-using features obtained from the speeded-up robust features algorithm. The goal was to implement the classification scheme using machine learning techniques. Results indicate that multi-spectral images from low-level fusion yielded the best classification performance. Artificial neural networks are used to derive the classification results and demonstrate the ability to obtain results in a timely manner that could accommodate near real-time classification.
2018 26th European Signal Processing Conference (EUSIPCO), 2018
In this paper, we propose a novel method for embedding one-dimensional, periodic time-series data... more In this paper, we propose a novel method for embedding one-dimensional, periodic time-series data into higherdimensional topological spaces to support robust recovery of signal features via topological data analysis under noisy sampling conditions. Our method can be considered an extension of the popular time delay embedding method to a larger class of linear operators. To provide evidence for the viability of this method, we analyze the simple case of sinusoidal data in three steps. First, we discuss some of the drawbacks of the time delay embedding framework in the context of periodic, sinusoidal data. Next, we show analytically that using the Hilbert transform as an alternative embedding function for sinusoidal data overcomes these drawbacks. Finally, we provide empirical evidence of the viability of the Hilbert transform as an embedding function when the parameters of the sinusoidal data vary over time.
2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018
Location-based services have seen a boon in data production recently which has simultaneously sto... more Location-based services have seen a boon in data production recently which has simultaneously stoked the research community to better understand this type of information. Traditional methods in analyzing such data require significant a priori understanding of the organization of the data. We submit that the nascent field of topological data analysis (TDA) may be able to contribute new insights to analysis of such data without the aforementioned requirement. To this end, we propose two novel methods of embedding such data in order to leverage the expressive power of TDA. To demonstrate its effectiveness we apply the embeddings to maritime automated information system data.
In this paper, we develop and test a three-stage algorithm for performing unsupervised segmentati... more In this paper, we develop and test a three-stage algorithm for performing unsupervised segmentation of hyperspectral imagery. Each stage of the algorithm leverages modified clustering methods which incorporate both the spatial and spectral information present within a hyperspectral scene. The ultimate output of the process is a classification map which groups spatially adjoining pixels into a number of distinct regions, each of which can be well-approximated by a multivariate normal distribution. This output is expected to be useful in various target detection scenarios which require accurate estimates of background statistics. Execution time and memory metrics are provided for each stage of the algorithm, along with segmentation proficiency scores as measured against random synthetic data sets. Segmentation results of real-world hyperspectral images are also provided for qualitative analysis. We conclude from the experiments that the algorithm usually performs well in typical overhead views with multiple, spectrally diverse objects to segment, but suffers from reduced accuracy in scenarios where individual regions are exceptionally large or small.
2016 10th International Conference on Signal Processing and Communication Systems (ICSPCS), 2016
Wireless geo location is an increasingly relevant area of research as cellular technology becomes... more Wireless geo location is an increasingly relevant area of research as cellular technology becomes ever more ubiquitous. In this work we consider the timing advance parameter in Long Term Evolution cellular networks to this end. We also evaluate a previously introduced method of position estimation augmentation called Cellular Synchronization Assisted Refinement (CeSAR). We first develop the concept of geometric dilution of precision and the Cramer-Rao Lower Bound (CRLB) to motivate an intuition for positioning accuracy. We then derive an exact maximum likelihood estimator (MLE) for TA-specific position estimation. While the exact MLE proves to be computationally difficult, it is shown to be equivalent to the MLE in the general case of Gaussian noise. This equivalency is then used to propose an approximate MLE (AMLE). Through simulation, the AMLE is shown to meet the CRLB. Real-world data that includes urban, suburban, line-of-sight, and non-line-of-sight channels are also presented and used to build a realistic model of the TA-specific channel. This model is then used to evaluate the performance of the AMLE and CeSAR in non-line-of-sight and line-of-sight conditions. Our results suggest that the proposed method may be appropriate for meeting the Federal Communication Commission's E-911 requirements.
IEEE Transactions on Information Forensics and Security, 2017
Location privacy is an ever increasing concern as the pervasiveness of computing becomes more ubi... more Location privacy is an ever increasing concern as the pervasiveness of computing becomes more ubiquitous. This is especially apparent at the intersection of privacy, convenience, and quality of service in cellular networks. In this paper, we show the long term evolution (LTE) signaling plane to be vulnerable to location-based attacks via the timing advance (TA) parameter. To this end, we adapt the Cramér-Rao lower bound for timing advance-based estimation and show the associated estimator to be efficient. The analysis is complemented with numerical studies that feature synthetic and real-world data collected in existing LTE network deployments. Additionally, the Cellular Synchronization Assisted Refinement algorithm, a method of TA-based attack augmentation is examined. We show how it can simultaneously improve location resolution and negate the effects of poor network infrastructure geometry. The analysis and simulation demonstrate that a localization attack can yield resolution as high as 40 m.
Public reporting burden for this collection of information is estimated to average 1 hour per res... more Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to
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Papers by James Scrofani