131,006 research outputs found
STAP
No full text<p>The comparative analysis of ss-rRNA sequences is one of the most powerful approaches for studying phylogenetic relationships among all organisms. Our ss-rRNA Taxonomy Assigning Pipeline (STAP) combines publicly available packages such as, PHYML, BLASTN, and CLUSTALW with our own automatic alignment masking and tree parsing programs. STAP makes automatic taxonomic assignments for ss-rRNAs based on neighbor-joining or maximum likelihood phylogenetic trees rather than on the top BLASTN hits, and thus its results are more reliable and accurate. Most importantly, the automation yields results comparable to those achievable by manual analysis, yet offers speed and capacity that are unattainable by manual efforts.</p>
<p>First, ss-rRNA sequences obtained either by PCR of environmental samples or by metagenomic shotgun sequencing are searched against our ss-rRNA database by BLASTN to select a related data set. STAP then automatically generates, masks, and trims the multiple sequence alignments. Next, it builds a phylogenetic tree by either the maximum likelihood or neighbor-joining method. Automated analysis of the tree yields taxonomic assignments for each query sequence.</p>
<p>A paper describing STAP has been published (7/2/08) in PLoS One.</p>
<p>• Wu D, Hartman A, Ward N, Eisen JA (2008) An Automated Phylogenetic Tree-Based Small Subunit rRNA Taxonomy and Alignment Pipeline (STAP). PLoS ONE 3(7): e2566. doi:10.1371/ journal.pone.0002566</p>
<p> </p>
<p>There is only one file (STAP.zip) in this package, unzip it and the README.pdf file explains how to install STAP.</p
Knowledge-aided STAP in heterogeneous clutter using a hierarchical bayesian algorithm
No full textThis paper addresses the problem of estimating the covariance matrix of a primary vector from heterogeneous samples and some prior knowledge, under the framework of knowledge-aided space-time adaptive processing (KA-STAP). More precisely, a Gaussian scenario is considered where the covariance matrix of the secondary data may differ from the one of interest. Additionally, some knowledge on the primary data is supposed to be available and summarized into a prior matrix. Two KA-estimation schemes are presented in a Bayesian framework whereby the minimum mean square error (MMSE) estimates are derived. The first scheme is an extension of a previous work and takes into account the non-homogeneity via an original relation. {In search of simplicity and to reduce the computational load, a second estimation scheme, less complex, is proposed and omits the fact that the environment may be heterogeneous.} Along the estimation process, not only the covariance matrix is estimated but also some parameters representing the degree of \emph{a priori} and/or the degree of heterogeneity. Performance of the two approaches are then compared using STAP synthetic data. STAP filter shapes are analyzed and also compared with a colored loading technique
Clutter Suppression for Wideband Radar STAP
No full textTraditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or ``zero-bandwidth,'' where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets
Target DOA estimation based on robust deterministic STAP
No full textS.271-274A novel approach for target direction of arrival (DOA) estimation based on deterministic space time adaptive processing (STAP) is presented. Deterministic STAP [1] represents a valid alternative to stochastic STAP in fast varying interference scenarios, due to its intrinsic single snapshot interference cancellation characteristics. On the other hand, in its classical derivation, detection performances of deterministic STAP are severely deteriorated in case of uncertainty in the knowledge of the exact target DOA. Reformulating the deterministic STAP problem in terms of convex optimization, a robust deterministic STAP technique has been derived in [2] which takes into account a possible mismatch between the nominal and the actual target DOA. In this paper, the robust deterministic STAP filter is applied to retrieve an accurate estimation of the actual target DOA based on a closed form formula. Such target DOA estimates may serve in tracking scenarios to keep updated the target parameters knowledge in successive coherent processing intervals (CPIs)
Metrizable TAP, HTAP and STAP groups
No full textAbstractIn a recent paper by D. Shakhmatov and J. Spěvák [D. Shakhmatov, J. Spěvák, Group-valued continuous functions with the topology of pointwise convergence, Topology Appl. 157 (2010) 1518–1540] the concept of a TAP group is introduced and it is shown in particular that NSS groups are TAP. We define the classes of STAP and HTAP groups and show that in general one has the inclusions NSS ⊂ STAP ⊂ HTAP ⊂ TAP. We show that metrizable STAP groups are NSS and that Weil-complete metrizable TAP groups are NSS as well. We prove that an abelian TAP group is HTAP, while, as recently proved by D. Dikranjan and the above mentioned authors, there are nonabelian metrizable TAP groups which are not HTAP. A remarkable characterization of pseudocompact spaces obtained in the above mentioned paper asserts: a Tychonoff space X is pseudocompact if and only if Cp(X,R) has the TAP property. We show that for no infinite Tychonoff space X, the group Cp(X,R) has the STAP property. We also show that a metrizable locally balanced topological vector group is STAP iff it does not contain a subgroup topologically isomorphic to Z(N)
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
STAP: Sequencing Task-Agnostic Policies
No full textAdvances in robotic skill acquisition have made it possible to build
general-purpose libraries of learned skills for downstream manipulation tasks.
However, naively executing these skills one after the other is unlikely to
succeed without accounting for dependencies between actions prevalent in
long-horizon plans. We present Sequencing Task-Agnostic Policies (STAP), a
scalable framework for training manipulation skills and coordinating their
geometric dependencies at planning time to solve long-horizon tasks never seen
by any skill during training. Given that Q-functions encode a measure of skill
feasibility, we formulate an optimization problem to maximize the joint success
of all skills sequenced in a plan, which we estimate by the product of their
Q-values. Our experiments indicate that this objective function approximates
ground truth plan feasibility and, when used as a planning objective, reduces
myopic behavior and thereby promotes long-horizon task success. We further
demonstrate how STAP can be used for task and motion planning by estimating the
geometric feasibility of skill sequences provided by a task planner. We
evaluate our approach in simulation and on a real robot. Qualitative results
and code are made available at https://sites.google.com/stanford.edu/stap.Comment: Video:
https://drive.google.com/file/d/1zp3qFeZLACNPsGLLP7p6q9X1tuA_PGEo/view.
Project page: https://sites.google.com/stanford.edu/stap. 12 pages, 7
figures. In proceedings of the IEEE International Conference on Robotics and
Automation (ICRA) 2023. The first two authors contributed equall
Exploiting robust direct data domain STAP for GMTI in very high resolution SAR
No full textS.348-353In this paper we exploit the robust direct data domain STAP (RD3-STAP) in the SAR case. This is extremely interesting especially for very high resolution SAR systems. In fact, in this case due to the strong heterogeneous characteristics of the clutter over range, stochastic STAP might fail due to the possible lack of enough homogeneous secondary data for adequate clutter covariance matrix estimation. In the proposed approach, the RD3-STAP filter is applied in the Doppler domain independently for every different Doppler bin, leading to a factorization of the space-time cancellation problem into independent space only cancellation sub-problems. The different spectral clutter components decoupling resulting from long SAR coherent processing intervals ensures that clutter cancellation capabilities are not reduced by this factorization. A nice property of the RD3-STAP filter ensures together with the clutter cancellation also a compensation of the moving target phase history. A possible integration of the RD3-STAP with the remainder SAR processing is presented and finally the effectiveness of the integrated technique is tested against a simulated dataset
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Track-before-detect strategies for STAP radars
No full textIn this correspondence we propose track-before-detect (TBD) strategies for space-time adaptive processing (STAP) radars. As a preliminary step we introduce the target and noise models in discrete-time form. Then, resorting to generalized likelihood ratio test (GLRT)-based and ad hoc procedures we derive detectors for two different scenarios (a point better clarified in the body of the correspondence). The preliminary performance assessment, conducted resorting to Monte Carlo simulation, shows that the proposed procedures might be viable means to implement early detection and track initiation of weak moving targets
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