scholarly journals Fast comparison of IS radar code sequences for lag profile inversion

2008 ◽  
Vol 26 (8) ◽  
pp. 2291-2301 ◽  
Author(s):  
M. S. Lehtinen ◽  
I. I. Virtanen ◽  
J. Vierinen
Keyword(s):  
Pulse Length ◽  
Real Data ◽  
Estimation Accuracy ◽  
Fast Method ◽  
Incoherent Scatter ◽  
Ground Clutter ◽  
Phase Code ◽  
Short Code ◽  
Profile Inversion ◽  
Code Searches

Abstract. A fast method for theoretically comparing the posteriori variances produced by different phase code sequences in incoherent scatter radar (ISR) experiments is introduced. Alternating codes of types 1 and 2 are known to be optimal for selected range resolutions, but the code sets are inconveniently long for many purposes like ground clutter estimation and in cases where coherent echoes from lower ionospheric layers are to be analyzed in addition to standard F-layer spectra. The method is used in practice for searching binary code quads that have estimation accuracy almost equal to that of much longer alternating code sets. Though the code sequences can consist of as few as four different transmission envelopes, the lag profile estimation variances are near to the theoretical minimum. Thus the short code sequence is equally good as a full cycle of alternating codes with the same pulse length and bit length. The short code groups cannot be directly decoded, but the decoding is done in connection with more computationally expensive lag profile inversion in data analysis. The actual code searches as well as the analysis and real data results from the found short code searches are explained in other papers sent to the same issue of this journal. We also discuss interesting subtle differences found between the different alternating codes by this method. We assume that thermal noise dominates the incoherent scatter signal.

scholarly journals Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Leah L. Weber ◽  
Mohammed El-Kebir
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Evolutionary Process ◽  
Treatment Decision ◽  
Real Data ◽  
Current Data ◽  
Fast Method ◽  
Sequencing Data ◽  
Evolutionary Trajectory ◽  
Cancer Types

Abstract Background Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched. Results We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach. Conclusion Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.

syris: a flexible and efficient framework for X-ray imaging experiments simulation

2017 ◽  
Vol 24 (6) ◽  
pp. 1283-1295 ◽  
Author(s):  
Tomáš Faragó ◽  
Petr Mikulík ◽  
Alexey Ershov ◽  
Matthias Vogelgesang ◽  
Daniel Hänschke ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Data Processing ◽  
Graphics Processing Units ◽  
High Speed ◽  
Real Data ◽  
Estimation Accuracy ◽  
Processing Parameter ◽  
X Ray ◽  
X Ray Imaging ◽  
Imaging Conditions

An open-source framework for conducting a broad range of virtual X-ray imaging experiments,syris, is presented. The simulated wavefield created by a source propagates through an arbitrary number of objects until it reaches a detector. The objects in the light path and the source are time-dependent, which enables simulations of dynamic experiments,e.g.four-dimensional time-resolved tomography and laminography. The high-level interface ofsyrisis written in Python and its modularity makes the framework very flexible. The computationally demanding parts behind this interface are implemented in OpenCL, which enables fast calculations on modern graphics processing units. The combination of flexibility and speed opens new possibilities for studying novel imaging methods and systematic search of optimal combinations of measurement conditions and data processing parameters. This can help to increase the success rates and efficiency of valuable synchrotron beam time. To demonstrate the capabilities of the framework, various experiments have been simulated and compared with real data. To show the use case of measurement and data processing parameter optimization based on simulation, a virtual counterpart of a high-speed radiography experiment was created and the simulated data were used to select a suitable motion estimation algorithm; one of its parameters was optimized in order to achieve the best motion estimation accuracy when applied on the real data.syriswas also used to simulate tomographic data sets under various imaging conditions which impact the tomographic reconstruction accuracy, and it is shown how the accuracy may guide the selection of imaging conditions for particular use cases.

scholarly journals Phase-coded pulse aperiodic transmitter coding

2009 ◽  
Vol 27 (7) ◽  
pp. 2799-2811 ◽  
Author(s):  
I. I. Virtanen ◽  
J. Vierinen ◽  
M. S. Lehtinen
Keyword(s):  
Correlation Time ◽  
Pulse Repetition Frequency ◽  
Pulse Length ◽  
Simulated Data ◽  
Qualitative Evaluation ◽  
Weather Radar ◽  
Estimation Accuracy ◽  
Time Lags ◽  
F Region ◽  
D Region

Abstract. Both ionospheric and weather radar communities have already adopted the method of transmitting radar pulses in an aperiodic manner when measuring moderately overspread targets. Among the users of the ionospheric radars, this method is called Aperiodic Transmitter Coding (ATC), whereas the weather radar users have adopted the term Simultaneous Multiple Pulse-Repetition Frequency (SMPRF). When probing the ionosphere at the carrier frequencies of the EISCAT Incoherent Scatter Radar facilities, the range extent of the detectable target is typically of the order of one thousand kilometers – about seven milliseconds – whereas the characteristic correlation time of the scattered signal varies from a few milliseconds in the D-region to only tens of microseconds in the F-region. If one is interested in estimating the scattering autocorrelation function (ACF) at time lags shorter than the F-region correlation time, the D-region must be considered as a moderately overspread target, whereas the F-region is a severely overspread one. Given the technical restrictions of the radar hardware, a combination of ATC and phase-coded long pulses is advantageous for this kind of target. We evaluate such an experiment under infinitely low signal-to-noise ratio (SNR) conditions using lag profile inversion. In addition, a qualitative evaluation under high-SNR conditions is performed by analysing simulated data. The results show that an acceptable estimation accuracy and a very good lag resolution in the D-region can be achieved with a pulse length long enough for simultaneous E- and F-region measurements with a reasonable lag extent. The new experiment design is tested with the EISCAT Tromsø VHF (224 MHz) radar. An example of a full D/E/F-region ACF from the test run is shown at the end of the paper.

scholarly journals Low-Altitude and Slow-Speed Small Target Detection Based on Spectrum Zoom Processing

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xuwang Zhang ◽  
Songtao Lu ◽  
Jinping Sun ◽  
Wei Shangguan
Keyword(s):  
Target Detection ◽  
Doppler Frequency ◽  
Real Data ◽  
Detection Algorithm ◽  
Clutter Suppression ◽  
Slow Speed ◽  
Small Target Detection ◽  
Ground Clutter ◽  
Radar Systems ◽  
Low Altitude

This paper proposes a spectrum zoom processing based target detection algorithm for detecting the weak echo of low-altitude and slow-speed small (LSS) targets in heavy ground clutter environments, which can be used to retrofit the existing radar systems. With the existing range-Doppler frequency images, the proposed method firstly concatenates the data from the same Doppler frequency slot of different images and then applies the spectrum zoom processing. After performing the clutter suppression, the target detection can be finally implemented. Through the theoretical analysis and real data verification, it is shown that the proposed algorithm can obtain a preferable spectrum zoom result and improve the signal-to-clutter ratio (SCR) with a very low computational load.

scholarly journals Ground clutter cancellation in incoherent radars: solutions for EISCAT Svalbard radar

2000 ◽  
Vol 18 (9) ◽  
pp. 1242-1247 ◽  
Author(s):  
T. Turunen ◽  
J. Markkanen ◽  
A. P. van Eyken
Keyword(s):  
Subtraction Method ◽  
Free Electrons ◽  
Simple Manner ◽  
Radio Science ◽  
Ionospheric Physics ◽  
Incoherent Scatter ◽  
Ground Clutter ◽  
Incoherent Scatter Radars ◽  
Instruments And Techniques ◽  
Physics Signal

Abstract. Incoherent scatter radars measure ionosphere parameters using modified Thomson scatter from free electrons in the target (see e.g. Hagfors, 1997). The integrated cross section of the ionospheric scatterers is extremely small and the measurements can easily be disturbed by signals returned by unwanted targets. Ground clutter signals, entering via the antenna side lobes, can render measurements at the nearest target ranges totally impossible. The EISCAT Svalbard Radar (ESR), which started measurements in 1996, suffers from severe ground clutter and the ionosphere cannot be measured in any simple manner at ranges less than about 120–150 km, depending on the modulation employed. If the target and clutter signals have different, and clearly identifiable, properties then, in principle, there are always ways to eliminate the clutter. In incoherent scatter measurements, differences in the coherence times of the wanted and unwanted signals can be used for clutter cancellation. The clutter cancellation must be applied to all modulations, usually alternating codes in modern experiments, used for shorter ranges. Excellent results have been obtained at the ESR using a simple pulse-to-pulse clutter subtraction method, but there are also other possibilities.Key words: Radio science (ionospheric physics; signal processing; instruments and techniques)

scholarly journals Modern Subsampling Methods for Large-Scale Least Squares Regression

2020 ◽  
Vol 2 (2) ◽  
pp. 1-28
Author(s):  
Tao Li ◽  
Cheng Meng
Keyword(s):  
Least Squares ◽  
Large Scale ◽  
Computing Time ◽  
Real Data ◽  
Optimality Criteria ◽  
Estimation Accuracy ◽  
Least Squares Regression ◽  
Large Scale Data ◽  
Coefficient Estimation ◽  
Sampling Probability

Subsampling methods aim to select a subsample as a surrogate for the observed sample. As a powerful technique for large-scale data analysis, various subsampling methods are developed for more effective coefficient estimation and model prediction. This review presents some cutting-edge subsampling methods based on the large-scale least squares estimation. Two major families of subsampling methods are introduced: the randomized subsampling approach and the optimal subsampling approach. The former aims to develop a more effective data-dependent sampling probability while the latter aims to select a deterministic subsample in accordance with certain optimality criteria. Real data examples are provided to compare these methods empirically, respecting both the estimation accuracy and the computing time.

scholarly journals Estimation Method of SAR Doppler Frequency Rate Based on FrFT

2020 ◽  
Vol 38 (6) ◽  
pp. 1352-1360
Author(s):  
Ying Chen ◽  
Tao Chen ◽  
Kewei Li ◽  
Julan Xiao ◽  
Hongli Liu
Keyword(s):  
Present Method ◽  
Pulse Compression ◽  
Estimation Method ◽  
Doppler Frequency ◽  
Real Data ◽  
Estimation Accuracy ◽  
Optimal Order ◽  
Maximum Point ◽  
Frequency Rate ◽  
Strong Scattering

Due to the problem that the existing Doppler frequency rate estimation method is limited by the estimation accuracy, a novel estimation method of Doppler frequency rate is proposed. The present method searches the frequency rate according to the characteristic of the chirp signal in the FrFT domain. Firstly, dechirp is performed on several strong scattering points extracted from the data domain after pulse compression, and a frequency domain focused image is obtained after FFT. Then the maximum point of each distance unit is extracted. The energy of the maximum point is selected by using the window processing. After that, IFFT is performed and the dechirp conjugate reference function is multiplied by using the selected points. FrFT is performed according to the preset orders. The entropy is used to evaluate whether the order of FRFT is optimal or not. The Doppler frequency rate is calculated by using the optimal order. The simulation and real data are processed and analyzed. The present method can estimate the Doppler frequency rate accurately. A well-focused SAR image is obtained after azimuth matching filtering.

scholarly journals The One Standard Error Rule for Model Selection: Does It Work?

Stats ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 868-892
Author(s):  
Yuchen Chen ◽  
Yuhong Yang
Keyword(s):  
Variable Selection ◽  
Standard Error ◽  
Cross Validation ◽  
Housing Prices ◽  
Regression Function ◽  
Real Data ◽  
Estimation Accuracy ◽  
Regression Estimation ◽  
Large Sample Size ◽  
Estimation Formula

Previous research provided a lot of discussion on the selection of regularization parameters when it comes to the application of regularization methods for high-dimensional regression. The popular “One Standard Error Rule” (1se rule) used with cross validation (CV) is to select the most parsimonious model whose prediction error is not much worse than the minimum CV error. This paper examines the validity of the 1se rule from a theoretical angle and also studies its estimation accuracy and performances in applications of regression estimation and variable selection, particularly for Lasso in a regression framework. Our theoretical result shows that when a regression procedure produces the regression estimator converging relatively fast to the true regression function, the standard error estimation formula in the 1se rule is justified asymptotically. The numerical results show the following: 1. the 1se rule in general does not necessarily provide a good estimation for the intended standard deviation of the cross validation error. The estimation bias can be 50–100% upwards or downwards in various situations; 2. the results tend to support that 1se rule usually outperforms the regular CV in sparse variable selection and alleviates the over-selection tendency of Lasso; 3. in regression estimation or prediction, the 1se rule often performs worse. In addition, comparisons are made over two real data sets: Boston Housing Prices (large sample size n, small/moderate number of variables p) and Bardet–Biedl data (large p, small n). Data guided simulations are done to provide insight on the relative performances of the 1se rule and the regular CV.

scholarly journals Investigation of the emit source coordinates estimation accuracy using reflections from the ground clutter

2020 ◽  
Vol 1499 ◽  
pp. 012014
Author(s):  
D V Dubinin ◽  
A A Mescheryakov ◽  
V P Denisov ◽  
A M Rahimova ◽  
L A Bazarbay ◽  
...  
Keyword(s):  
Estimation Accuracy ◽  
Ground Clutter

scholarly journals Towards multi-purpose IS radar experiments

2008 ◽  
Vol 26 (8) ◽  
pp. 2281-2289 ◽  
Author(s):  
I. I. Virtanen ◽  
M. S. Lehtinen ◽  
J. Vierinen
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Large Range ◽  
Time Lag ◽  
Estimation Accuracy ◽  
Inversion Method ◽  
Real Time Monitoring ◽  
F Region ◽  
D Region ◽  
Profile Inversion

Abstract. The EISCAT incoherent scatter radars routinely perform simultaneous measurements of E- and F-regions of the ionosphere. In addition several experiments exist for measuring pulse-to-pulse correlations from the D-region. However, the D-region experiments have quite limited range extents and the short lags suffer from F-region echoes, which are difficult to properly handle with standard decoding methods. In this paper it is demonstrated with real data how D-region experiments can be designed to produce continuous lag profiles extending above the F-region maximum. The large range coverage is attained for all lags shorter than the longest transmission pulse and it allows one to properly include the F-region echoes in the analysis. The large coverage is not needed for pulse-to-pulse lags because E- and F-regions do not have this long correlation times. The lag profiles with large range extent also provide a useful measurement of the upper parts of the ionosphere. The experiments utilise new kind of phase coding technique, which has estimation accuracy comparable to that of alternating codes though the code sequence is very short. No special decoding method is applied to the codes, because the lag profile inversion method automatically adapts to any kind of transmission codes provided transmission samples are available. The computing resources needed for real-time lag profile inversion with two different kinds of goals are also discussed here: 1) real-time monitoring of the results and 2) use of inverted lag profiles as a way to permanently store the data. While it was possible to accomplish real-time monitoring with a standard high-end desktop workstation, the higher resolution requirement for permanent data storage purposes is a much more critical task, requiring the use of larger-scale parallel processing.

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