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Early algorithm development efforts for the National Ecological Observatory Network Airborne Observation Platform imaging spectrometer and waveform lidar instruments

Early algorithm development efforts for the National Ecologi...

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Conference on Imaging Spectrometry XVI

作者： Krause, Keith S. Kuester, Michele A. Johnson, Brian R. McCorkel, Joel Kampe, Thomas U. Natl Ecol Observ Network Boulder CO 80301 USA

ISBN: (纸本)9780819487681

The National Ecological Observatory Network (NEON) will be the first observatory network of its kind designed to detect and enable forecasting of ecological change at continental scales over multiple decades. NEON will collect data at sites distributed at 20 ecoclimatic domains across the United States on the impacts of climate change, land use change, and invasive species on natural resources and biodiversity. The NEON Airborne Observation Platform (AOP) is an aircraft platform carrying remote sensing instrumentation designed to achieve sub-meter to meter scale ground resolution, bridging the scales from organisms and individual stands to satellite-based remote sensing. AOP instrumentation consists of a VIS/SWIR imaging spectrometer, a scanning small-footprint waveform LiDAR, and a high resolution airborne digital camera. AOP data will provide quantitative information on land use change and changes in ecological structure and chemistry including the presence and effects of invasive species. A Pathfinder Flight Campaign was conducted over a two week period during late August to early September 2010 in order to collect representative AOP data over one NEON domain site. NASA JPL flew the AVIRIS imaging spectrometer and NCALM flew an Optech Gemini waveform LiDAR over the University of Florida Ordway-Swisher Biological Station and Donaldson tree plantation near Gainesville Florida. The pathfinder data are discussed in detail along with how the data are being used for early algorithm and product development prototyping activities. The data collected during the campaign and prototype products are openly available to scientists to become more familiar with representative NEON AOP data.

关键词： Imaging spectrometer waveform lidar algorithm development airborne remote sensing

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Manual physical balance assistance of therapists during gait training of stroke survivors: characteristics and predicting the timing

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2017年第1期14卷 125-125页

作者： Haarman, Juliet A. M. Maartens, Erik van der Kooij, Herman Buurke, Jaap H. Reenalda, Jasper Rietman, Johan S. Roessingh Res & Dev Roessinghsbleekweg 33b NL-7522 AH Enschede Netherlands Univ Twente Dept Biomech Engn Drienerlolaan 5 NL-7522 NB Enschede Netherlands Roessingh Res & Dev Roessinghsbleekweg 33bPOB 310 NL-7500 AH Enschede Netherlands

Background: During gait training, physical therapists continuously supervise stroke survivors and provide physical support to their pelvis when they judge that the patient is unable to keep his balance. This paper is the first in providing quantitative data about the corrective forces that therapists use during gait training. It is assumed that changes in the acceleration of a patient's COM are a good predictor for therapeutic balance assistance during the training sessions Therefore, this paper provides a method that predicts the timing of therapeutic balance assistance, based on acceleration data of the sacrum. Methods: Eight sub-acute stroke survivors and seven therapists were included in this study. Patients were asked to perform straight line walking as well as slalom walking in a conventional training setting. Acceleration of the sacrum was captured by an Inertial Magnetic Measurement Unit. Balance-assisting corrective forces applied by the therapist were collected from two force sensors positioned on both sides of the patient's hips. Measures to characterize the therapeutic balance assistance were the amount of force, duration, impulse and the anatomical plane in which the assistance took place. Based on the acceleration data of the sacrum, an algorithm was developed to predict therapeutic balance assistance. To validate the developed algorithm, the predicted events of balance assistance by the algorithm were compared with the actual provided therapeutic assistance. Results: The algorithm was able to predict the actual therapeutic assistance with a Positive Predictive Value of 87% and a True Positive Rate of 81%. Assistance mainly took place over the medio-lateral axis and corrective forces of about 2% of the patient's body weight (15.9 N (11), median (IQR)) were provided by therapists in this plane. Median duration of balance assistance was 1.1 s (0.6) (median (IQR)) and median impulse was 9.4Ns (8.2) (median (IQR)). Although therapists were specifically ins

关键词： Stroke rehabilitation Gait training Technical requirements Balance-assisting characteristics algorithm development Behavior prediction Correction forces

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An efficient repeating signal detector to investigate earthquake swarms

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JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 2016年第8期121卷 5880-5897页

作者： Skoumal, Robert J. Brudzinski, Michael R. Currie, Brian S. Miami Univ Dept Geol & Environm Earth Sci Oxford OH 45056 USA

Repetitive earthquake swarms have been recognized as key signatures in fluid injection induced seismicity, precursors to volcanic eruptions, and slow slip events preceding megathrust earthquakes. We investigate earthquake swarms by developing a Repeating Signal Detector (RSD), a computationally efficient algorithm utilizing agglomerative clustering to identify similar waveforms buried in years of seismic recordings using a single seismometer. Instead of relying on existing earthquake catalogs of larger earthquakes, RSD identifies characteristic repetitive waveforms by rapidly identifying signals of interest above a low signal-to-noise ratio and then grouping based on spectral and time domain characteristics, resulting in dramatically shorter processing time than more exhaustive autocorrelation approaches. We investigate seismicity in four regions using RSD: (1) volcanic seismicity at Mammoth Mountain, California, (2) subduction-related seismicity in Oaxaca, Mexico, (3) induced seismicity in Central Alberta, Canada, and (4) induced seismicity in Harrison County, Ohio. In each case, RSD detects a similar or larger number of earthquakes than existing catalogs created using more time intensive methods. In Harrison County, RSD identifies 18 seismic sequences that correlate temporally and spatially to separate hydraulic fracturing operations, 15 of which were previously unreported. RSD utilizes a single seismometer for earthquake detection which enables seismicity to be quickly identified in poorly instrumented regions at the expense of relying on another method to locate the new detections. Due to the smaller computation overhead and success at distances up to similar to 50km, RSD is well suited for real-time detection of low-magnitude earthquake swarms with permanent regional networks.

关键词： earthquake swarms earthquake detection algorithm development induced seismicity

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Cy-preds: An algorithm and a web service for the analysis and prediction of cysteine reactivity

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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2016年第2期84卷 278-291页

作者： Soylu, Inanc Marino, Stefano M. Akdeniz Univ Dept Agr Biotechnol TR-07058 Antalya Turkey

Cysteine (Cys) is a critically important amino acid, serving a variety of functions within proteins including structural roles, catalysis, and regulation of function through post-translational modifications. Predicting which Cys residues are likely to be reactive is a very sought after feature. Few methods are currently available for the task, either based on evaluation of physicochemical features (e.g., pKa and exposure) or based on similarity with known instances. In this study, we developed an algorithm (named HAL-Cy) which blends previous work with novel implementations to identify reactive Cys from nonreactive. HAL-Cy present two major components: (i) an energy based part, rooted on the evaluation of H-bond network contributions and (ii) a knowledge based part, composed of different profiling approaches (including a newly developed weighting matrix for sequence profiling). In our evaluations, HAL-Cy provided significantly improved performances, as tested in comparisons with existing approaches. We implemented our algorithm in a web service (Cy-preds), the ultimate product of our work;we provided it with a variety of additional features, tools, and options: Cy-preds is capable of performing fully automated calculations for a thorough analysis of Cys reactivity in proteins, ranging from reactivity predictions (e.g., with HAL-Cy) to functional characterization. We believe it represents an original, effective, and very useful addition to the current array of tools available to scientists involved in redox biology, Cys biochemistry, and structural bioinformatics.

关键词： cysteine biochemistry redox biology prediction of reactivity algorithm development web service structural bioinformatics

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Learning Overcomplete Representations from Distributed Data: A Brief Review 5

Learning Overcomplete Representations from Distributed Data:...

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Conference on Compressive Sensing V - From Diverse Modalities to Big Data Analytics

作者： Raja, Haroon Bajwa, Waheed U. Rutgers State Univ Dept Elect & Comp Engn Piscataway NJ 08854 USA

ISBN: (纸本)9781510600980

Most of the research on dictionary learning has focused on developing algorithms under the assumption that data is available at a centralized location. But often the data is not available at a centralized location due to practical constraints like data aggregation costs, privacy concerns, etc. Using centralized dictionary learning algorithms may not be the optimal choice in such settings. This motivates the design of dictionary learning algorithms that consider distributed nature of data as one of the problem variables. Just like centralized settings, distributed dictionary learning problem can be posed in more than one way depending on the problem setup. Most notable distinguishing features are the online versus batch nature of data and the representative versus discriminative nature of the dictionaries. In this paper, several distributed dictionary learning algorithms that are designed to tackle different problem setups are reviewed. One of these algorithms is cloud K-SVD, which solves the dictionary learning problem for batch data in distributed settings. One distinguishing feature of cloud K-SVD is that it has been shown to converge to its centralized counterpart, namely, the K-SVD solution. On the other hand, no such guarantees are provided for other distributed dictionary learning algorithms. Convergence of cloud K-SVD to the centralized K-SVD solution means problems that are solvable by K-SVD in centralized settings can now be solved in distributed settings with similar performance. Finally, cloud K-SVD is used as an example to show the advantages that are attainable by deploying distributed dictionary algorithms for real world distributed datasets.

关键词： Associative arrays Clouds Optimization (mathematics) Chemical species Data modeling algorithm development Data processing Nickel Stochastic processes Image processing

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CALIBRATION/VALIDATION OF LANDSAT-DERIVED OCEAN COLOUR PRODUCTS IN BOSTON HARBOUR 23

CALIBRATION/VALIDATION OF LANDSAT-DERIVED OCEAN COLOUR PRODU...

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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Pahlevan, Nima Sheldon, Patrick Peri, Francesco Wei, Jianwei Shang, Zhehai Sun, Qingsong Chen, Robert F. Lee, Zhongping Schaaf, Crystal B. Schott, John R. Loveland, Thomas NASA Goddard Space Flight Ctr 8800 Greenbelt Rd Greenbelt MD 20771 USA Sci Syst & Applicat Inc 10210 Greenbelt Rd Lanham MD 20706 USA Univ Massachusetts 100 Morrissey Blvd Boston MA 02125 USA Rochester Inst Technol 54 Lomb Mem Dr Rochester NY 14623 USA US Geol Survey EROS Ctr 47914 252nd St Sioux Falls SD 57030 USA

The Landsat data archive provides a unique opportunity to investigate the long-term evolution of coastal ecosystems at fine spatial scales that cannot be resolved by ocean colour (OC) satellite sensors. Recognizing Landsat's limitations in applications over coastal waters, we have launched a series of field campaigns in Boston Harbor and Massachusetts Bay (MA, USA) to validate OC products derived from Landsat-8. We will provide a preliminary demonstration on the calibration/validation of the existing OC algorithms (atmospheric correction and in-water optical properties) to enhance monitoring efforts in Boston Harbor. To do so, Landsat optical images were first compared against ocean colour products over high-latitude regions. The in situ cruise data, including optical data (remote sensing reflectance) and water samples were analyzed to obtain insights into the optical and biogeochemical properties of near-surface waters. Along with the cruise data, three buoys were deployed in three locations across the Harbor to complement our database of concentrations of chlorophyll a, total suspended solids (TSS), and absorption of colour dissolved organic matter (CDOM). The data collected during the first year of the project are used to develop and/or tune OC algorithms. The data will be combined with historic field data to map in-water constituents back to the early 1990's. This paper presents preliminary analysis of some of the data collected under Landsat-8 overpasses.

关键词： Landsat ocean colour coastal waters algorithm development water constituents

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C-arm cone beam CT perfusion imaging using the SMART-RECON algorithm to improve temporal sampling density and temporal resolution

C-arm cone beam CT perfusion imaging using the SMART-RECON a...

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Li, Yinsheng Niu, Kai Li, Ke Schafer, Sebastian Royalty, Kevin Strother, Charles Chen, Guang-Hong Univ Wisconsin Sch Med & Publ Hlth Dept Med Phys Madison WI 53705 USA Siemens Med Solut Hoffman Estates IL 60192 USA Univ Wisconsin Dept Radiol Sch Med & Publ Hlth Madison WI 53792 USA

ISBN: (纸本)9781510600188

In this work, a newly developed reconstruction algorithm, Synchronized MultiArtifact Reduction with Tomographic RECONstruction (SMART-RECON), was applied to C-arm cone beam CT perfusion (CBCTP) imaging. This algorithm contains a special rank regularizer, designed to reduce limited-view artifacts associated with super short scan reconstructions. As a result, high temporal sampling and temporal resolution image reconstructions were achieved using an interventional C-arm x-ray system. The algorithm was evaluated in terms of the fidelity of the dynamic contrast update curves and the accuracy of perfusion parameters through numerical simulation studies. Results shows that, not only were the dynamic curves accurately recovered (relative root mean square error epsilon [3%, 5%] compared with [13%, 22%] for FBP), but also the noise in the final perfusion maps was dramatically reduced. Compared with filtered backprojection, SMART-RECON generated CBCTP maps with much improved capability in differentiating lesions with perfusion deficits from the surrounding healthy brain tissues.

关键词： Reconstruction algorithms Imaging systems Temporal resolution Tissues X-ray computed tomography Blood Computed tomography algorithm development Brain Brain mapping Computer simulations Denoising Image restoration In vivo imaging Tomography

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Convolutional Neural Networks for Synthetic Aperture Radar Classification 23

Convolutional Neural Networks for Synthetic Aperture Radar C...

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Conference on algorithms for Synthetic Aperture Radar Imagery XXIII

作者： Profeta, Andrew Rodriguez, Andres Clouse, H. Scott Wright State Univ Dayton OH 45435 USA Intel Corp Hillsboro OR 97124 USA Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781510600843

For electro-optical object recognition, convolutional neural networks (CNNs) are the state-of-the-art. For large datasets, CNNs are able to learn meaningful features used for classification. However, their application to synthetic aperture radar (SAR) has been limited. In this work we experimented with various CNN architectures on the MSTAR SAR dataset. As the input to the CNN we used the magnitude and phase (2 channels) of the SAR imagery. We used the deep learning toolboxes CAFFE and Torch7. Our results show that we can achieve 93% accuracy on the MSTAR dataset using CNNs.

关键词： Synthetic aperture radar Radar Neural networks Convolutional neural networks algorithm development Machine learning Automatic target recognition Network architectures Detection and tracking algorithms Speckle

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Analysis of analytic nonresonant background removal algorithm for MCARS spectra 17

Analysis of analytic nonresonant background removal algorith...

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17th Meeting of the Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing Conference met as part of the SPIE Defense + Commercial Sensing (DCS) Symposium

作者： Roberson, Stephen D. Pilkington, Sherrie Bowman Pellegrino, Paul M. US Army Res Lab 2800 Powder Mill Rd Adelphi MD 20783 USA

ISBN: (纸本)9781510600652

Multiplex Coherent Anti Stokes Raman Spectroscopy (MCARS) has been shown to generate a complete Raman spectrum of a material on a millisecond time scale which allows for rapid identification of a wide variety of molecular targets. Along with the desired resonant spectrum due to the vibrational Raman spectroscopy of the analyte, MCARS is known to simultaneously generate a nonresonant spectrum that can obscure the desired Raman spectrum which hinders detection. Extracting the desired resonant Raman signal analytically from the overall MCARS signal has proven difficult without having prior knowledge of the analyte. We have developed an algorithm that utilizes a combination of the maximum entropy method in conjunction with advanced Fourier filtering to analytically remove the nonresonant background from our MCARS spectra without having prior knowledge of the vibrational spectrum of the analyte. In this report, we will report on the theoretical background for this algorithm as well as our experimental work testing this algorithm under various nonresonant spectra conditions for a number of analytes. We will systematically vary the amount of nonresonant background generated in the sample by changing the temporal overlap of the two beams necessary to generate the MCARS signal. Additionally, we place the analyte into increasing concentrations of water to generate increasing amounts of nonresonant background spectra to test the algorithm's effectiveness. Finally, we compare the analyte vibrational spectral output from the algorithm to the Raman spectrum measured with the spontaneous Raman system in the laboratory of the same sample in an effort to ascertain accuracy of the output spectra.

关键词： Raman spectroscopy Optical filters Fourier transforms Molecules Microscopes Spectroscopy algorithm development algorithms Analytical research Molecular spectroscopy

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Supervised hub-detection for brain connectivity

Supervised hub-detection for brain connectivity

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Conference on Medical Imaging - Image Processing

作者： Kasenburg, Niklas Liptrot, Matthew Reislev, Nina Linde Garde, Ellen Nielsen, Mads Feragen, Aasa Univ Copenhagen Dept Comp Sci DK-1168 Copenhagen Denmark DTU Compute Copenhagen Denmark Copenhagen Univ Hosp Hvidovre Ctr Funct & Diagnost Imaging & Res Danish Res Ctr Magnet Resonance Copenhagen Denmark

ISBN: (纸本)9781510600195

A structural brain network consists of physical connections between brain regions. Brain network analysis aims to find features associated with a parameter of interest through supervised prediction models such as regression. Unsupervised preprocessing steps like clustering are often applied, but can smooth discriminative signals in the population, degrading predictive performance. We present a novel hub-detection optimized for supervised learning that both clusters network nodes based on population level variation in connectivity and also takes the learning problem into account. The found hubs are a low-dimensional representation of the network and are chosen based on predictive performance as features for a linear regression. We apply our method to the problem of finding age-related changes in structural connectivity. We compare our supervised hub-detection (SHD) to an unsupervised hub-detection and a linear regression using the original network connections as features. The results show that the SHD is able to retain regression performance, while still finding hubs that represent the underlying variation in the population. Although here we applied the SHD to brain networks, it can be applied to any network regression problem. Further development of the presented algorithm will be the extension to other predictive models such as classification or non-linear regression.

关键词： Brain Neuroimaging algorithm development Machine learning Diffusion weighted imaging Data modeling Distortion Network security Statistical analysis Visualization

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