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Mainlobe Jammer Nulling via TSI Finders: A Space Fast-Time Adaptive Processor  [cached]
Madurasinghe Dan,Shaw Andrew P
EURASIP Journal on Advances in Signal Processing , 2006,
Abstract: An algorithm based on a space fast-time adaptive processor is presented for nulling the mainlobe jammer when the jammer and the target of interest share the same bearing. The computational load involved in the conventional processor, which blindly looks for the terrain-scattered interference (TSI), is required to stack a large number of consecutive range cell returns to form the space fast-time data snapshot making it almost impossible to implement in real time. This issue is resolved via the introduction of a preprocessor (a TSI finder which detects the presence of the minute levels of multipath components of the mainlobe jammer and associated time delays) which directs the STAP processor to select only two desired range returns in order to form the space fast-time data snapshot. The end result is a computationally extremely fast processor. Also a new space fast-time adaptive processor based on the super-resolution approach (eigenvector-based) is presented.
Automatic generation of gene finders for eukaryotic species
Kasper Munch, Anders Krogh
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-263
Abstract: We present a procedure, Agene, that automatically generates a species-specific gene predictor from a set of reliable mRNA sequences and a genome. We apply a Hidden Markov model (HMM) that implements explicit length distribution modelling for all gene structure blocks using acyclic discrete phase type distributions. The state structure of the each HMM is generated dynamically from an array of sub-models to include only gene features represented in the training set.Acyclic discrete phase type distributions are well suited to model sequence length distributions. The performance of each individual gene predictor on each individual genome is comparable to the best of the manually optimised species-specific gene finders. It is shown that species-specific gene finders are superior to gene finders trained on other species.Hidden Markov models (HMMs) have been extensively used for modelling genes. Ab initio HMM gene finders for eukaryotes include Genscan [1], Augustus [2], HMMgene [3,4], GeneMark.HMM-E [5], Genie [6], TigrScan and GlimmerHMM [7], Unveil and Exonomy [8], SNAP [9], and others. Examples of non-HMM approaches are GeneID [10,11], GlimmerM [12], and MZEF [13]. GenelD applies Markov models to score sequence content and signal in a hierarchical manner. GlimmerM uses decision trees and Interpolated Markov Models. MZEF uses quadratic discriminant analysis to predict internal exons. These predictors all use a single genomic sequence. Examples of approaches using two genomic sequences are SLAM [14], SGP-2 [15], TWINSCAN [16], and DoubleScan [17]. These use homology information in alignments that improves prediction accuracy relative to single genome predictors. EHMM [18], Phylo-HMM [19], and N-Scan [20] use more than two genomic sequences, taking advantage of the fact that the molecular evolution of a sequence position is governed by its function. Gene finders using multiple genomes have a higher accuracy but training sets with species of an appropriate evolutionary dis
Gene finding by integrating gene finders  [PDF]
Yudong Cai, Zhisong He, Lele Hu, Bin Li, Yi Zhou, Han Xiao, Zhiwen Wang, Kairui Feng, Lin Lu, Kaiyan Feng, Haipeng Li
Journal of Biomedical Science and Engineering (JBiSE) , 2010, DOI: 10.4236/jbise.2010.311137
Abstract: Gene finding, the accurate annotation of genomic DNA, has become one of the central topics in biological research. Although various computational methods (gene finders) have been proposed and developed, they all have their own limitations in gene findings. In this paper, we introduce an integrating gene finder, which combines the results of several existing gene finders together, to improve the accuracy of gene finding. Four integration schemes, based on majority voting, are developed for the analysis of two datasets – the basic dataset and the testing dataset. The basic dataset consists of 1500 DNA sequences and the testing dataset consists of 103 DNA sequences. It is demonstrated that a simple integration (a simple voting for each nucleotide) can significantly improve the finding performance, and removing confusing gene finders, caused by poor performance or redundant results, is important for a further improvement of the integration. The best prediction results are obtained using weighted majority voting, aided by the mRMR (Minimum Redundancy Maximum Relevance) (Peng, 2005) method for the gene finder selection. The prediction accuracies are 84.16% and 90.06% for the basic dataset and testing dataset respectively, which are better than any individual gene finding software in our research.
An Antenna Array Location Method and Its Simulation Study

Deng Ping,Zhu Zhong-liang,

电子与信息学报 , 2005,
Abstract: In order to overcome the near-far problem encountered in CDMA mobile location estimation, in this paper a mobile location method to utilize an antenna array and TSOA/AOA hybrid location technique is proposed, the GDOP distribution of different location methods are analyzed and TSOA/AOA hybrid location algorithm are provided. Simulation results under different condition show that as far as AOA measurements is relatively accurate, mobile can be exactly located by this method.
Study of 2D DOA Estimation for Uniform Circular Array in Wireless Location System  [cached]
Ping TAN,Pian WANG,Ye LUO,Yufeng ZHANG
International Journal of Computer Network and Information Security , 2010,
Abstract: in this paper, the use of a uniform circular antenna arrays (UCA) for high resolution of two dimensional (2D) direction of arrivals (DOAs) estimation in wireless location system is investigated. Performance of 2D DOA estimation based on the real-valued unitary transformation MUSIC algorithm for UCA is presented, especially focusing on DOA estimation of multiple correlated signals. The determination of the number of incident signals on an antenna array is addressed in the condition of colored noise and coherent signal sources. Selected method for estimating the number of these sources is formulated based on the modified eigenvectors of the covariance matrix of the received signal at the antenna array. The calibration procedure is also presented for UCA especially. Simulation results are presented to confirm the performance analysis of algorithm, then the validations of Unitary Transformation MUSIC algorithm are performed based on the measurement data in a wireless location system.
Comparison between LDG-network and GERESS-array with respect to regional detection and location results
H. P. Harjes,B. Massinon,Y. Ménéchal,H. Schulte-Theis
Annals of Geophysics , 1994, DOI: 10.4401/ag-4214
Abstract: The design of a global seismic system to monitor compliance with a ban on underground nuclear testing considerably deviates from previous concepts of international seismic data exchange. The new concept relies on centralized processing of continuous data from a fixed station network ( alpha stations) which provides the primary detection and location capability. This alpha station network is augmented by additional stations ( beta stations) which send data on request to refine the hypocentres of events which were detected by the alpha network. To test this concept we have used the GERESS array in Germany as a prototype alpha station and investigated its regional detection and location capability for events in France and surrounding areas. For this region, data from the national French network operated by LDG provide an excellent reference data base. Within a 5 degree distance, GERESS showed an excellent performance in terms of detection and location down to magnitude M(LDG) = 3. Between a 5 degree and 10 degree distance, the detection capability is still high but very often it is not sufficient to locate events below M(LDG) = 4. Generalizing these results, we can conclude that either the maximum distance between alpha stations should be 10 degrees or the contribution of beta stations has to play a significant role in a future monitoring system.
Comparison of potential ASKAP HI survey source finders  [PDF]
Attila Popping,Russell Jurek,Tobias Westmeier,Paolo Serra,Lars Floer,Martin Meyer,Baerbel Koribalski
Physics , 2012, DOI: 10.1071/AS11067
Abstract: The large size of the ASKAP HI surveys DINGO and WALLABY necessitates automated 3D source finding. A performance difference of a few percent corresponds to a significant number of galaxies being detected or undetected. As such, the performance of the automated source finding is of paramount importance to both of these surveys. We have analysed the performance of various source finders to determine which will allow us to meet our survey goals during the DINGO and WALLABY design studies. Here we present a comparison of the performance of five different methods of automated source finding. These source finders are Duchamp, the Gamma-finder, CNHI, a 2D-1D Wavelet Reconstruction and S+C finder, a sigma clipping method. Each source finder was applied on the same three-dimensional data cubes containing (a) point sources with a Gaussian velocity profile and (b) spatially extended model-galaxies with inclinations and rotation profiles. We focus on the completeness and reliability of each algorithm when comparing the performance of the different source finders.
Efficient decoding algorithms for generalized hidden Markov model gene finders
William H Majoros, Mihaela Pertea, Arthur L Delcher, Steven L Salzberg
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-16
Abstract: As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN.In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques.Generalized Hidden Markov Models have seen wide use in recent years in the field of computational gene prediction. A number of ab initio gene-finding programs are now available which utilize this mathematical framework internally for the modeling and evaluation of gene structure [1-6], and newer systems are now emerging which expand this framework by simultaneously modeling two genomes at once, in order to harness the mutually informative signals present in homologous gene structures from recently diverged species. As greater numbers of such genomes become available, it is tempting to consider the possibility of integrating all this information into increasingly complex models of gene structure and evolution.Notwithstanding our eagerness to utilize this expected flood of genomic data, methods have yet to be demonstrated which can perform such large-scale parallel analyses without requiring inordinate computational resources. In the case of Generalized Pair HMMs (GPHMMs), for example, the only systems in existence of which we are familiar make
The Murchison Widefield Array: the Square Kilometre Array Precursor at low radio frequencies  [PDF]
S. J. Tingay,R. Goeke,J. D. Bowman,D. Emrich,S. M. Ord,D. A. Mitchell,M. F. Morales,T. Booler,B. Crosse,D. Pallot,A. Wicenec,W. Arcus,D. Barnes,G. Bernardi,F. Briggs,S. Burns,J. D. Bunton,R. J. Cappallo,T. Colegate,B. E. Corey,A. Deshpande,L. deSouza,B. M. Gaensler,L. J. Greenhill,J. Hall,B. J. Hazelton,D. Herne,J. N. Hewitt,M. Johnston-Hollitt,D. L. Kaplan,J. C. Kasper,B. B. Kincaid,R. Koenig,E. Kratzenberg,C. J. Lonsdale,M. J. Lynch,B. McKinley,S. R. McWhirter,E. Morgan,D. Oberoi,J. Pathikulangara,T. Prabu,R. A. Remillard,A. E. E. Rogers,A. Roshi,J. E. Salah,R. J. Sault,N. Udaya-Shankar,F. Schlagenhaufer,K. S. Srivani,J. Stevens,R. Subrahmanyan,S. Tremblay,R. B. Wayth,M. Waterson,R. L. Webster,A. R. Whitney,A. Williams,C. L. Williams,J. S. B. Wyithe
Physics , 2012, DOI: 10.1017/pasa.2012.007
Abstract: The Murchison Widefield Array (MWA) is one of three Square Kilometre Array Precursor telescopes and is located at the Murchison Radio-astronomy Observatory in the Murchison Shire of the mid-west of Western Australia, a location chosen for its extremely low levels of radio frequency interference. The MWA operates at low radio frequencies, 80-300 MHz, with a processed bandwidth of 30.72 MHz for both linear polarisations, and consists of 128 aperture arrays (known as tiles) distributed over a ~3 km diameter area. Novel hybrid hardware/software correlation and a real-time imaging and calibration systems comprise the MWA signal processing backend. In this paper the as-built MWA is described both at a system and sub-system level, the expected performance of the array is presented, and the science goals of the instrument are summarised.
An empirical analysis of training protocols for probabilistic gene finders
William H Majoros, Steven L Salzberg
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-206
Abstract: We decided to investigate the utility of applying a more systematic optimization approach to the tuning of global parameter structure by implementing a global discriminative training procedure for our GHMM-based gene finder. Our results show that significant improvement in prediction accuracy can be achieved by this method.We conclude that training of GHMM-based gene finders is best performed using some form of discriminative training rather than simple maximum likelihood estimation at the submodel level, and that generalized gradient ascent methods are suitable for this task. We also conclude that partitioning of training data for the twin purposes of maximum likelihood initialization and gradient ascent optimization appears to be unnecessary, but that strict segregation of test data must be enforced during final gene finder evaluation to avoid artificially inflated accuracy measurements.The number of generalized hidden Markov model (GHMM) gene finders reported in the literature has increased fairly dramatically of late [1-8], and the community is now contemplating various ways to extend this attractive framework in order to incorporate homology information, with a handful of such systems having already been built (e.g., [9-12]). GHMMs offer a number of clear advantages which would seem to explain this growth in popularity. Chief among these is the fact that the GHMM framework, being (in theory) purely probabilistic, allows for principled approaches to constructing, utilizing, and extending models for accurate prediction of gene structures.While the decoding problem for GHMM gene finders is arguably well understood, being a relatively straightforward extension of the same problem for traditional HMMs and amenable to a Viterbi-like solution (albeit a more complex one), methods for optimally training a GHMM gene finder have received scant attention in the gene-finding literature to date. What information is available (e.g., [2,4]) seems to indicate that the common pr
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