Snapshot Data Research Articles

Coherent averaging of the passive fathometer response using short correlation time

The passive fathometer algorithm was applied to data from two drifting array experiments in the Mediterranean, Boundary 2003 and 2004. The passive fathometer response was computed with correlation times from 0.34 to 90 s and, for correlation times less than a few seconds, the observed signal-to-noise ratio (SNR) agrees with a 1 D model of SNR of the passive fathometer response in an ideal waveguide. In the 2004 experiment, the fathometer response showed the array depth varied periodically with an amplitude of 1 m and a period of 7 s consistent with wave driven motion of the array. This introduced a destructive interference, which prevents the SNR growing with increasing correlation time. A peak-tracking algorithm applied to the fathometer response of experimental data was used to remove this motion allowing the coherent passive fathometer response to be averaged over several minutes without destructive interference. Multirate adaptive beamforming, using 90 s correlation time to form adaptive steer vectors which were applied to 0.34 s data snapshots, increases the SNR of the passive fathometer response.

MAPPING ENVIRONMENTAL HAZARDS AND BLACK CARBON MEASUREMENTS IN AN IMMIGRANT COMMUNITY

Background and Aims: The Vietnamese population in the US is a rapidly growing immigrant group. This population shows increasing trends for many chronic health problems with increasing number of years in the US, which may be attributed in part to environmental stressors. The objective of this project is to use qualitative and quantitative methods to identify and characterize neighborhood-level environmental hazards for the Vietnamese population in California. Key to this work is the integrated participation and perspective of Vietnamese community members to identify and prioritize elements of their community that potentially impact their health. Methods: Community members (N=35) from two counties in California were recruited and trained to conduct walking street audits of environmental hazards in neighborhoods where they live and work. Each street segment was approximately one city block long. Participants carried GPS devices, collected black carbon measurements using small real-time personal monitors, conducted five-minute car and truck counts and used Photovoice documentary methods. Results: The walking audits were conducted on 144 street segments, of which 45% were in business areas and 55% in residential areas. The mean five-minute truck count was 2 (range: 0 -10) on business streets and 0.3 (range: 0-4) in residential areas. The median level of black carbon was 1.3 µg BC/m3 (range: 0-41). Observational audit survey data highlighted issues around litter and smoking, as well as traffic-related odors and noise. Qualitative data from Photovoice and written comments from auditors reflected concerns around neighborhood safety. Conclusions: We successfully engaged community members in collecting qualitative and quantitative environmental data on their local neighborhoods. Comparisons between these community-collected snapshot data to governmental monitoring data for traffic and black carbon will help inform how well existing secondary data can capture perceived local environmental hazards and how the community-collected data can be used to enhance existing environmental measurement data.

Mining Data with USA Today's Snapshots

The Snapshots section of USA Today offers quick pictures of intriguing data collected for a specific question. A variety of subject matter is available online at http://www.usatoday.com/news/snapshot.htm, and the graphics change frequently. To use this month's activities, first visit the site to review the current topics and choose what is best for your class. If you do not have class access to the Internet, just download a couple of data snapshots, print them for examples for your class, and let students start mining. If an appropriate fit is not available, adapt or create your own. The focus of the following generic problems and activities allows students to collect their own data, transform problems, and process data in meaningful ways.

Challenges in Archiving Electronic Bioanalytical Data Supporting GLP Studies in a CRO

The purpose of this article is to articulate the fundamental issues of archiving electronic GLP data in a CRO environment. CROs struggle to address the absolute requirement of archiving GLP studies electronically. The difficulty of adhering to this is partly due to the wide variety of systems and types of electronic data. Often the end solution to this complicated issue is printing the data at the end of the study and in turn archiving the paper data, foregoing or ignoring the fact that scientific decisions were made while reviewing electronic data. Paper data and electronic data can be different. For example, chromatographic resolution or being able to focus in on detailed integration of chromatograms can significantly change the perspective of the data. While the core goal of archiving according to GLP principles are met, the reality is much different. Businesses purchasing CRO capabilities and regulatory agencies have been quite clear that when it comes to auditing the data that the electronic record is the preferred and often required source of the information for auditing and review purposes. The fact is that paper data do not always provide the flexibility, sensitivity and complete data context that an electronic record can provide. The ability to adhere to compliance standards due to the electronic data cannot be undervalued and by printing the data after all the acquisition, decisions, scientific judgments and review, do a complete disservice to the multidimensional (such as colors for deactivated or changed data, audit trails, 'zooming-in functionality', direct data links and snapshots) data in an electronic system.

Identification of models of heterogeneous cell populations from population snapshot data

BackgroundMost of the modeling performed in the area of systems biology aims at achieving a quantitative description of the intracellular pathways within a "typical cell". However, in many biologically important situations even clonal cell populations can show a heterogeneous response. These situations require study of cell-to-cell variability and the development of models for heterogeneous cell populations.ResultsIn this paper we consider cell populations in which the dynamics of every single cell is captured by a parameter dependent differential equation. Differences among cells are modeled by differences in parameters which are subject to a probability density. A novel Bayesian approach is presented to infer this probability density from population snapshot data, such as flow cytometric analysis, which do not provide single cell time series data. The presented approach can deal with sparse and noisy measurement data. Furthermore, it is appealing from an application point of view as in contrast to other methods the uncertainty of the resulting parameter distribution can directly be assessed.ConclusionsThe proposed method is evaluated using artificial experimental data from a model of the tumor necrosis factor signaling network. We demonstrate that the methods are computationally efficient and yield good estimation result even for sparse data sets.

An Examination of Snapshot Data in Caterpillar Electronic Control Modules

An Examination of Snapshot Data in Caterpillar Electronic Control Modules

Fast algorithm for coherent source number estimation

In order to estimate the number of coherent sources, a Hankel matrix with the size of half the number of the received arrays is constructed using snapshot data of observed vectors. And the rank of the Hankel matrix is only related with the number of signal sources, no matter the signals are uncorrelated or coherent. We can get the signal and noise eigenvalues by conducting the singular value decomposition (SVD) to the Hankel matrix, the source number can be obtained by calculating the maximum ratio of each eigenvalue pair. The complexity of the algorithm is reduced greatly as only part of the observed data (single snapshot) is used. The Monte-Carlo simulation results demonstrate the feasibility of the algorithm.

Bayesian Learning from Marginal Data in Bionetwork Models

In studies of dynamic molecular networks in systems biology, experiments are increasingly exploiting technologies such as flow cytometry to generate data on marginal distributions of a few network nodes at snapshots in time. For example, levels of intracellular expression of a few genes, or cell surface protein markers, can be assayed at a series of interim time points and assumed steady-states under experimentally stimulated growth conditions in small cellular systems. Such marginal data on a small number of cellular markers will typically carry very limited information on the parameters and structure of dynamic network models, though experiments will typically be designed to expose variation in cellular phenotypes that are inherently related to some aspects of model parametrization and structure. Our work addresses statistical questions of how to integrate such data with dynamic stochastic models in order to properly quantify the information-or lack of information-it carries relative to models assumed. We present a Bayesian computational strategy coupled with a novel approach to summarizing and numerically characterizing biological phenotypes that are represented in terms of the resulting sample distributions of cellular markers. We build on Bayesian simulation methods and mixture modeling to define the approach to linking mechanistic mathematical models of network dynamics to snapshot data, using a toggle switch example integrating simulated and real data as context.

Sparse Learning via Iterative Minimization With Application to MIMO Radar Imaging

Through waveform diversity, multiple-input multiple-output (MIMO) radar can provide higher resolution, improved sensitivity, and increased parameter identifiability compared to more traditional phased-array radar schemes. Existing methods for target estimation, however, often fail to provide accurate MIMO angle-range-Doppler images when there are only a few data snapshots available. Sparse signal recovery algorithms, including many <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$l_{1}$</tex></formula> -norm based approaches, can offer improved estimation in that case. In this paper, we present a regularized minimization approach to sparse signal recovery. Sparse learning via iterative minimization (SLIM) follows an <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$l_{q}$</tex></formula> -norm constraint (for <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$0&lt;q\leq 1$</tex></formula> ), and can thus be used to provide more accurate estimates compared to the <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$l_{1}$</tex></formula> -norm based approaches. We herein compare SLIM, through imaging examples and examination of computational complexity, to several well-known sparse methods, including the widely used CoSaMP approach. We show that SLIM provides superior performance for sparse MIMO radar imaging applications at a low computational cost. Furthermore, we will show that the user parameter <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$q$</tex></formula> can be automatically determined by incorporating the Bayesian information criterion.

Robust DOA Estimation: The Reiterative Superresolution (RISR) Algorithm

A new approach for spatial direction-of-arrival (DOA) estimation, denoted as re-iterative superresolution (RISR), is developed based upon a recursive implementation of the minimum mean-square error (MMSE) framework. This recursive strategy alternates between updating an MMSE filter bank according to the previous receive spatial power distribution and then subsequently applying the new filter bank to the received data snapshots to obtain a new estimate of the receive spatial power distribution. Benefits of this approach include robustness to coherent sources such as can occur in multipath environments, operation with very low sample support to enable tracking of sources with rapidly changing DOA (e.g., bistatic pulse chasing), intrinsic determination of model order, and robustness to array modeling errors by exploiting approximate knowledge of array calibration tolerances. From an implementation perspective RISR belongs to a class of recursive algorithms that includes Interior Point methods, the minimum-norm-based FoCal underdetermined system solver (FOCUSS) algorithm, and the iterative reweighted least squares (IRLS) algorithm. However, the structure of RISR also enables the natural inclusion of spatial noise covariance information as well as a mechanism to account for array modeling errors which are known to induce degradation for existing superresolution methods. The inclusion of the latter is also found to facilitate an adaptive form of regularization that establishes a feasible (given model uncertainties) dynamic range for source estimates.

Geometry-Induced Range-Dependence Compensation for Bistatic STAP with Conformal Arrays

Radar space-time adaptive processing (STAP) is a well-suited technique to detect slow-moving targets in the presence of a strong interference background. We consider STAP for a radar operating in a bistatic radar configuration and collecting returns with a conformal antenna array (CAA). The statistics of the secondary data snapshots used to estimate the optimum weight vector are not identically distributed with respect to range, thus preventing the STAP processor from achieving its optimum performance. The compensation of the range-dependence (RD) requires the knowledge of the locus of the clutter signature. We use a new RANSAC-based method for estimating this locus or, equivalently, the flight configuration parameters. Based on this knowledge, we perform an RD compensation of the snapshots to obtain an accurate estimate of the clutter covariance matrix. End-to-end performance analysis in terms of signal-to-inference-plus-noise ratio loss shows that our method yields promising performance.

Analysis of space–time adaptive processing performance using K-means clustering algorithm for normalisation method in non-homogeneity detector process

This study describes the performance analysis of the non-homogeneity detector (NHD) with various normalisation methods for the space–time adaptive processing (STAP) of airborne radar signals under the non-homogeneous clutter environments. The authors can calculate a threshold value from the statistical analysis of generalised inner product (GIP) using the normalisation method using mean, median and the K-means clustering algorithm of training data snapshots in the NHD process. The selected homogeneous data using the threshold value are used to recalculate covariance matrix of the total interference. To evaluate the performance of the covariance matrix, the authors calculated the eigenspectra and signal to interference noise ratio (SINR) loss. The accuracy of the recalculated covariance matrix is verified by the modified sample matrix inversion (MSMI) test statistic for the target detection. Projection statistics (PS) based on GIP is also used to compare the performance of detecting single and multiple targets. The authors’ simulation results demonstrate that the K-means clustering algorithm as a normalisation method for both GIP and GIP-based PS can improve the STAP performance in the severe non-homogeneous clutter environment even under the multiple targets scenarios, compared to the other normalisation methods.

How Spacing of Data Collection May Impact Estimates of Substance Use Trajectories

The goal of this study is to provide an empirical example using longitudinal cigarette smoking data that compares results of growth mixture trajectory models on the basis of contiguous and snapshot measurements. Data were drawn from an intensive longitudinal study of college freshman (N = 905) with a previous history of smoking. Participants provided weekly smoking reports for 35 consecutive weeks. We found that using contiguous weekly data (35 waves) or 6-wave or 4-wave snapshot data provided similar trajectory curves and proportions. However, there were notable differences in individual trajectory assignments on the basis of contiguous and snapshot measurements.

Robust Adaptive Beamforming for Multiple Signals of Interest with Cycle Frequency Error

This paper deals with the problem of robust adaptive array beamforming by exploiting the signal cyclostationarity. Recently, a novel cyclostationarity-exploiting beamforming method has been proposed by J.-H. Lee and C.-C. Huang (2009) for dealing with the situation of multiple signals of interest (SOI) based on the LS-SCORE algorithm. This method is referred to as the multiple LS-SCORE (MLS-SCORE) algorithm. However, the MLS-SCORE algorithm suffers from severe performance degradation even if there is a small mismatch in the cycle frequencies of the SOIs. In this paper, we evaluate the performance of the MLS-SCORE algorithm in the presence of cycle frequency error (CFE). The output SINR of an adaptive beamforming using the MLS-SCORE algorithm deteriorates like a function as the number of data snapshots increases. To tackle this difficulty, we present an efficient method to find an appropriate estimate for each of the cycle frequencies of the SOIs iteratively to achieve robust adaptive beamforming against the CFE. Simulation results for showing the effectiveness of the proposed method are provided.

MetNetAPI: A flexible method to access and manipulate biological network data from MetNet

BackgroundConvenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server.ResultsMetNetAPI is a versatile Application Programming Interface (API) to the MetNetDB database. It abstracts, captures and retains operations away from a biological network repository and website. A range of database functions, previously only available online, can be immediately (and independently from the website) applied to a dataset of interest. Data is available in four layers: molecular entities, localized entities (linked to a specific organelle), interactions, and pathways. Navigation between these layers is intuitive (e.g. one can request the molecular entities in a pathway, as well as request in what pathways a specific entity participates). Data retrieval can be customized: Network objects allow the construction of new and integration of existing pathways and interactions, which can be uploaded back to our server. In contrast to webservices, the computational demand on the host server is limited to processing data-related queries only.ConclusionsAn API provides several advantages to a systems biology software platform. MetNetAPI illustrates an interface with a central repository of data that represents the complex interrelationships of a metabolic and regulatory network. As an alternative to data-dumps and webservices, it allows access to a current and "live" database and exposes analytical functions to application developers. Yet it only requires limited resources on the server-side (thin server/fat client setup). The API is available for Java, Microsoft.NET and R programming environments and offers flexible query and broad data- retrieval methods. Data retrieval can be customized to client needs and the API offers a framework to construct and manipulate user-defined networks. The design principles can be used as a template to build programmable interfaces for other biological databases. The API software and tutorials are available at http://www.metnetonline.org/api.

Additional flux arising from unresolved scales in eddying ocean models

An analysis is presented of snapshot data (eastward and northward velocity components: u and v; tracer such as potential temperature: τ) from an eddy-resolving (Rgrid: 1/12°) ocean model experiment, in order to explore a method for improving eddy-permitting model performance. Horizontal 3 × 3 R-grid averages give the eddy-permitting grid (P-grid: 1/4°) variables: 〈u〉, 〈v〉, and 〈τ〉, where 〈〉 denotes the spatial P-grid scale average. The difference between the horizontal tracer flux across the boundary face of a P-grid and that across the corresponding faces of R-grids is estimated as F2E. It is found that the correlations among the gradients of u, v, and τ give a good approximation F2C to the estimated flux F2E. The approximated flux is a function of these gradients and the grid size. A method is presented for implementing the F2C for density to an eddying ocean model as an additional advection. Practical experiments were conducted with a realistic configuration. It is shown that the zonal mean isotherms in the Kuroshio extension region are more flattened in the run using the proposed method than in another run using the conventional horizontal biharmonic operator, suggesting that the additional flux correction leads to an enhancement of sub-basin scale mixing.

Status of Biodiversity in the Baltic Sea

The brackish Baltic Sea hosts species of various origins and environmental tolerances. These immigrated to the sea 10,000 to 15,000 years ago or have been introduced to the area over the relatively recent history of the system. The Baltic Sea has only one known endemic species. While information on some abiotic parameters extends back as long as five centuries and first quantitative snapshot data on biota (on exploited fish populations) originate generally from the same time, international coordination of research began in the early twentieth century. Continuous, annual Baltic Sea-wide long-term datasets on several organism groups (plankton, benthos, fish) are generally available since the mid-1950s. Based on a variety of available data sources (published papers, reports, grey literature, unpublished data), the Baltic Sea, incl. Kattegat, hosts altogether at least 6,065 species, including at least 1,700 phytoplankton, 442 phytobenthos, at least 1,199 zooplankton, at least 569 meiozoobenthos, 1,476 macrozoobenthos, at least 380 vertebrate parasites, about 200 fish, 3 seal, and 83 bird species. In general, but not in all organism groups, high sub-regional total species richness is associated with elevated salinity. Although in comparison with fully marine areas the Baltic Sea supports fewer species, several facets of the system's diversity remain underexplored to this day, such as micro-organisms, foraminiferans, meiobenthos and parasites. In the future, climate change and its interactions with multiple anthropogenic forcings are likely to have major impacts on the Baltic biodiversity.

Spreading code phase measurement technique for snapshot GNSS receiver

Driven by the expanding demand of location based services (LBS), the integration of satellite navigation receiver into mobile devices has received much attention in the last years. Snapshot positioning technique has turned out to be a very suitable candidate in LBS applications for its predominance of low cost and low power dissipation. However, the limited snapshot length is a major hurdle that limits the availability and accuracy of receivers. A code phase measurement technique is developed and used for snapshot receivers. By utilizing the features of the modernized global navigation satellite system (GNSS) signals and the characteristic of snapshot positioning, it uses a novel dual update-rate tracking loop with a non-causal smoothing estimator to obtain a relatively accurate estimation from a second or less snapshot data. Experiments with simulated data show that the proposed method has better estimation accuracy than the conventional code tracking loop in confined data length snapshot receivers.

약 신호 환경의 AGPS를 위한 잔여주파수 추정기의 주파수 영역 성능 분석

In AGPS method, user position can be obtained even in the shadow region by improving signal sensitivity. A hybrid long integration scheme employing both coherent and non-coherent integration method is commonly used in AGPS receivers. Because coherent loss increases as residual frequency become large, residual frequency should be minimized to maximize coherent integration gain. This paper presents performance analysis of residual frequency estimator using FFT in fine-time assistance AGPS method. Considering the hardware complexity and the estimation accuracy, optimal length of FFT is proposed for GPS L1 C/A signal. Signal sensitivity for estimating the residual frequency is also analysed. By field experimental results, it is found that the residual frequency can be successfully estimated using 1 second snap-shot data when GPS signal strength is larger than -150 dBm and its RMS error is 3Hz.

Statistical space-time adaptive processing algorithm

For the slowly changed environment-range-dependent non-homogeneity, a new statistical space-time adaptive processing algorithm is proposed, which uses the statistical methods, such as Bayes or likelihood criterion to estimate the approximative covariance matrix in the non-homogeneous condition. According to the statistical characteristics of the space-time snapshot data, via defining the aggregate snapshot data and corresponding events, the conditional probability of the space-time snapshot data which is the effective training data is given, then the weighting coefficients are obtained for the weighting method. The theory analysis indicates that the statistical methods of the Bayes and likelihood criterion for covariance matrix estimation are more reasonable than other methods that estimate the covariance matrix with the use of training data except the detected outliers. The last simulations attest that the proposed algorithms can estimate the covariance in the non-homogeneous condition exactly and have favorable characteristics.