scholarly journals Characterization of Neutron Detection Systems with Confidence via Statistical Parameter Correction

2020 ◽  
Vol 225 ◽  
pp. 07010
Author(s):  
Phillip Boone
Keyword(s):  
Signal To Noise Ratio ◽  
Statistical Parameter ◽  
Neutron Detection ◽  
Correction Method ◽  
Point Estimate ◽  
Data Sets ◽  
Detection Systems ◽  
Automated Method ◽  
Calculated Correction ◽  
Accurate Comparison

Neutron detection systems utilize statistical alarm techniques where a measured false alarm rate (FAR) can vary drastically from the FAR predicted by a theoretical model. The ability to set an alarm threshold that results in a practically controlled FAR is crucial to characterize detector sensitivity with both accuracy and precision. A generalized and automated method is presented to statistically evaluate FAR performance by assuming that the FAR itself is not deterministic, but a normal stochastic process over a specific parameter to be corrected that will hereafter be referred to as the correction. In this manner, a specific correction results in not only a point estimate of FAR, but also a confidence interval. The central objective is focused exclusively on characterization assuming that experiments are executed in a tightly controlled environment so that an accurate comparison is enabled across detectors. Once a correction is calculated, the estimated FAR is only assumed accurate in a similar environment for sensitivity evaluation. Initially, the calculated correction factor was used to compare FARs across various distributions including normal, corrected normal, Poisson, and a simplified normal distribution. Later verification data sets were used to empirically demonstrate the rate of containment of measured confidence coefficients using two detectors of different technology. A second application uses the correction method to improve the signal-to-noise ratio metric to agree more with dynamic sensitivity results. Finally, a third application studies the effect of altering the duration of background acquisition on FAR performance.

Absolute calibration of neutron detection systems on TFTR and accurate comparison of source strength measurements to transport simulations (invited)

1990 ◽  
Vol 61 (10) ◽  
pp. 3151-3156 ◽  
Author(s):  
Cris W. Barnes ◽  
M. G. Bell ◽  
H. W. Hendel ◽  
D. L. Jassby ◽  
D. Mikkelsen ◽  
...  
Keyword(s):  
Neutron Detection ◽  
Absolute Calibration ◽  
Source Strength ◽  
Detection Systems ◽  
Accurate Comparison

scholarly journals Automotive antenna diversity system for satellite radio with high phase accuracy in low SNR-scenarios

2018 ◽  
Vol 10 (5-6) ◽  
pp. 578-586 ◽  
Author(s):  
Simon Senega ◽  
Ali Nassar ◽  
Stefan Lindenmeier
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Multipath Fading ◽  
Correction Method ◽  
Antenna System ◽  
Antenna Diversity ◽  
Phase Detection ◽  
Low Snr ◽  
Single Antenna ◽  
Noise Correction

AbstractFor a fast scan-phase satellite radio antenna diversity system a noise correction method is presented for a significant improvement of audio availability at low signal-to-noise ratio (SNR) conditions. An error analysis of the level and phase detection within the diversity system in the presence of noise leads to a correction method based on a priori knowledge of the system's noise floor. This method is described and applied in a hardware example of a satellite digital audio radio services antenna diversity circuit for fast fading conditions. Test drives, which have been performed in real fading scenarios, are described and results are analyzed statistically. Simulations of the scan-phase antenna diversity system show higher signal amplitudes and availabilities. Measurement results of dislocated antennas as well as of a diversity antenna set on a single mounting position are presented. A comparison of a diversity system with noise correction, the same system without noise correction, and a single antenna system with each other is performed. Using this new method in fast multipath fading driving scenarios underneath dense foliage with a low SNR of the antenna signals, a reduction in audio mute time by one order of magnitude compared with single antenna systems is achieved with the diversity system.

Automated phase attribute-based picking applied to reflection seismics

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. V141-V150 ◽  
Author(s):  
Emanuele Forte ◽  
Matteo Dossi ◽  
Michele Pipan ◽  
Anna Del Ben
Keyword(s):  
Signal To Noise Ratio ◽  
Phase Method ◽  
Data Sets ◽  
Reflection Seismic ◽  
Instantaneous Phase ◽  
Whole Process ◽  
Specific Analysis ◽  
Reflection Seismics ◽  
Selection Of ◽  
Energy Package

We have applied an attribute-based autopicking algorithm to reflection seismics with the aim of reducing the influence of the user’s subjectivity on the picking results and making the interpretation faster with respect to manual and semiautomated techniques. Our picking procedure uses the cosine of the instantaneous phase to automatically detect and mark as a horizon any recorded event characterized by lateral phase continuity. A patching procedure, which exploits horizon parallelism, can be used to connect consecutive horizons marking the same event but separated by noise-related gaps. The picking process marks all coherent events regardless of their reflection strength; therefore, a large number of independent horizons can be constructed. To facilitate interpretation, horizons marking different phases of the same reflection can be automatically grouped together and specific horizons from each reflection can be selected using different possible methods. In the phase method, the algorithm reconstructs the reflected wavelets by averaging the cosine of the instantaneous phase along each horizon. The resulting wavelets are then locally analyzed and confronted through crosscorrelation, allowing the recognition and selection of specific reflection phases. In case the reflected wavelets cannot be recovered due to shape-altering processing or a low signal-to-noise ratio, the energy method uses the reflection strength to group together subparallel horizons within the same energy package and to select those satisfying either energy or arrival time criteria. These methods can be applied automatically to all the picked horizons or to horizons individually selected by the interpreter for specific analysis. We show examples of application to 2D reflection seismic data sets in complex geologic and stratigraphic conditions, critically reviewing the performance of the whole process.

scholarly journals АНАЛИЗ И ПРОГНОЗИРОВАНИЕ ЭФФЕКТИВНОСТИ ФИЛЬТРАЦИИ ОДНОМЕРНЫХ СИГНАЛОВ НА ОСНОВЕ ДИСКРЕТНОГО КОСИНУСНОГО ПРЕОБРАЗОВАНИЯ

2019 ◽  
pp. 19-29
Author(s):  
Сергей Клавдиевич Абрамов ◽  
Виктория Валерьевна Абрамова ◽  
Сергей Станиславович Кривенко ◽  
Владимир Васильевич Лукин
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Statistical Parameter ◽  
Mean Square ◽  
Least Mean Square ◽  
Signal To Noise ◽  
Standard Size ◽  
Filter Parameter ◽  
Filtering Efficiency ◽  
Threshold Setting

The article deals with the analysis of the efficiency and expedience of applying filtering based on the discrete cosine transform (DCT) for one-dimensional signals distorted by white Gaussian noise with a known or a priori estimated variance. It is shown that efficiency varies in wide limits depending upon the input ratio of signal-to-noise and degree of processed signal complexity. It is offered a method for predicting filtering efficiency according to the traditional quantitative criteria as the ratio of mean square error to the variance of additive noise and improvement of the signal-to-noise ratio. Forecasting is performed based on dependences obtained by regression analysis. These dependencies can be described by simple functions of several types parameters of which are determined as the result of least mean square fitting. It is shown that for sufficiently accurate prediction, only one statistical parameter calculated in the DCT domain can be preliminarily evaluated (before filtering), and this parameter can be calculated in a relatively small number of non-overlapping or partially overlapping blocks of standard size (for example, 32 samples). It is analyzed the variations of efficiency criteria variations for a set of realizations; it is studied factors that influence prediction accuracy. It is demonstrated that it is possible to carry out the forecasting of filtering efficiency for several possible values of the DCT-filter parameter used for threshold setting and, then, to recommend the best value for practical use. An example of using such an adaptation procedure for the filter parameter setting for processing the ECG signal that has not been used in the determination of regression dependences is given. As a result of adaptation, the efficiency of filtering can be essentially increased – benefit can reach 0.5-1 dB. An advantage of the proposed procedures of adaptation and prediction is their universality – they can be applied for different types of signals and different ratios of signal-to-noise.

scholarly journals New processing tools for weak and/or spatially overlapped macromolecular diffraction patterns

1999 ◽  
Vol 55 (10) ◽  
pp. 1733-1741 ◽  
Author(s):  
Dominique Bourgeois
Keyword(s):  
Signal To Noise Ratio ◽  
Data Sets ◽  
Signal To Noise ◽  
Weighting Method ◽  
Time Resolved ◽  
Accurate Evaluation ◽  
Profile Fitting ◽  
Integration Program ◽  
Diffraction Patterns ◽  
New Processing

Tools originally developed for the treatment of weak and/or spatially overlapped time-resolved Laue patterns were extended to improve the processing of difficult monochromatic data sets. The integration programPrOWallows deconvolution of spatially overlapped spots which are usually rejected by standard packages. By using dynamically adjusted profile-fitting areas, a carefully built library of reference spots and interpolation of reference profiles, this program also provides a more accurate evaluation of weak spots. In addition, by using Wilson statistics, it allows rejection of non-redundant strong outliers such as zingers, which otherwise may badly corrupt the data. A weighting method for optimizing structure-factor amplitude differences, based on Bayesian statistics and originally applied to low signal-to-noise ratio time-resolved Laue data, is also shown to significantly improve other types of subtle amplitude differences, such as anomalous differences.

AN AUTOMATED METHOD TO DETECT AGE-RELATED MACULAR DEGENERATION FROM OPTICAL COHERENCE TOMOGRAPHIC IMAGES

2021 ◽  
pp. 2150036
Author(s):  
Anju Thomas ◽  
P. M. Harikrishnan ◽  
Varun P. Gopi ◽  
P. Palanisamy
Keyword(s):  
Early Detection ◽  
Macular Degeneration ◽  
The Elderly ◽  
Age Related Macular Degeneration ◽  
Data Sets ◽  
Sigmoid Function ◽  
Data Set ◽  
Automated Method ◽  
Age Related ◽  
Sd Oct

Age-related macular degeneration (AMD) is an eye disease that affects the elderly. AMD’s prevalence is increasing as society’s population ages; thus, early detection is critical to prevent vision loss in the elderly. Arrangement of a comprehensive examination of the eye for AMD detection is a challenging task. This paper suggests a new poly scale and dual path (PSDP) convolutional neural network (CNN) architecture for early-stage AMD diagnosis automatically. The proposed PSDP architecture has nine convolutional layers to classify the input image as AMD or normal. A PSDP architecture is used to enhance classification efficiency due to the high variation in size and shape of perforation present in OCT images. The poly scale approach employs filters of various sizes to extract features from local regions more effectively. Simultaneously, the dual path architecture incorporates features extracted from different CNN layers to boost features in the global regions. The sigmoid function is used to classify images into binary categories. The Mendeley data set is used to train the proposed network and tested on Mendeley, Duke, SD-OCT Noor, and OCTID data sets. The testing accuracy of the network in Mendeley, Duke, SD-OCT Noor, and OCT-ID is 99.73%,96.66%,94.89%,99.61%, respectively. The comparison with alternative approaches showed that the proposed algorithm is efficient in detecting AMD. Despite having been trained on the Mendeley data set, the proposed model exhibited good detection accuracy when tested on other data sets. This shows that the suggested model can distinguish AMD/Normal images from various data sets. As compared to other methods, the findings show that the proposed algorithm is efficient at detecting AMD. Rapid eye scanning for early detection of AMD could be possible with the proposed architecture. The proposed CNN can be applied in real-time due to its lower complexity and less learnable parameters.

Improved Semi-Bit Differential Acquisition Method for Navigation Bit Sign Transition and Code Doppler Compensation in Weak Signal Environment

2020 ◽  
Vol 73 (4) ◽  
pp. 892-911 ◽  
Author(s):  
M. Nezhadshahbodaghi ◽  
M. R. Mosavi ◽  
N. Rahemi
Keyword(s):  
Signal To Noise Ratio ◽  
Integration Time ◽  
Data Sets ◽  
Weak Signal ◽  
Differential Acquisition ◽  
Global Positioning ◽  
Signal Environment ◽  
Correlation Techniques ◽  
Transition Issues ◽  
Acquisition Method

The presence of code Doppler and navigation bit sign transitions means that the acquisition of global positioning system (GPS) signals is difficult in weak signal environments where the output signal-to-noise ratio (SNR) is significantly reduced. Post-correlation techniques are typically utilised to solve these problems. Despite the advantages of these techniques, the post-correlation techniques suffer from problems caused by the code Doppler and the navigation bit sign transitions. We present an improved semi-bit differential acquisition method which can improve the code Doppler and the bit sign transition issues in the post-correlation techniques. In order to overcome the phenomenon of navigation bit sign transitions, the proposed method utilises the properties of the navigation bit. Since each navigation bit takes as long as 20 ms, there would be 10 ms correlations duration integration time between the received signal and the local coarse/acquisition (C/A) code in which the navigation bit sign transitions will not occur. Consequently, this problem can be cancelled by performing 10 ms correlations in even and odd units separately. Compensation of the code Doppler is also accomplished by shifting the code phase of the correlation results. To validate the performance of our suggested method, simulations are performed based on three data sets. The results show that the quantity of required input SNR to detect at least four satellites in the proposed method is − 48·3 dB, compared with − 20 dB and − 9 dB, respectively, in traditional differential and non-coherent methods.

scholarly journals Regime-switching model detection map for direct exoplanet detection in ADI sequences

2020 ◽  
Vol 633 ◽  
pp. A95
Author(s):  
C.-H. Dahlqvist ◽  
F. Cantalloube ◽  
O. Absil
Keyword(s):  
Regime Switching ◽  
Signal To Noise Ratio ◽  
Temporal Structure ◽  
Detection Algorithm ◽  
Data Sets ◽  
Regime Switching Model ◽  
Switching Model ◽  
Residual Noise ◽  
Receiver Operating Characteristic Space ◽  
The Many

Context. Beyond the choice of wavefront control systems or coronographs, advanced data processing methods play a crucial role in disentangling potential planetary signals from bright quasi-static speckles. Among these methods, angular differential imaging (ADI) for data sets obtained in pupil tracking mode (ADI sequences) is one of the foremost research avenues, considering the many observing programs performed with ADI-based techniques and the associated discoveries. Aims. Inspired by the field of econometrics, here we propose a new detection algorithm for ADI sequences, deriving from the regime-switching model first proposed in the 1980s. Methods. The proposed model is very versatile as it allows the use of PSF-subtracted data sets (residual cubes) provided by various ADI-based techniques, separately or together, to provide a single detection map. The temporal structure of the residual cubes is used for the detection as the model is fed with a concatenated series of pixel-wise time sequences. The algorithm provides a detection probability map by considering two possible regimes for concentric annuli, the first one accounting for the residual noise and the second one for the planetary signal in addition to the residual noise. Results. The algorithm performance is tested on data sets from two instruments, VLT/NACO and VLT/SPHERE. The results show an overall better performance in the receiver operating characteristic space when compared with standard signal-to-noise-ratio maps for several state-of-the-art ADI-based post-processing algorithms.

Assisted traveltime picking of crosshole GPR data

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. J35-J48 ◽  
Author(s):  
Bernard Giroux ◽  
Abderrezak Bouchedda ◽  
Michel Chouteau
Keyword(s):  
Time Window ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Information Criterion ◽  
High Signal ◽  
Data Sets ◽  
Signal To Noise ◽  
Arrival Times ◽  
Common Time ◽  
Waveform Similarity

We introduce two new traveltime picking schemes developed specifically for crosshole ground-penetrating radar (GPR) applications. The main objective is to automate, at least partially, the traveltime picking procedure and to provide first-arrival times that are closer in quality to those of manual picking approaches. The first scheme is an adaptation of a method based on cross-correlation of radar traces collated in gathers according to their associated transmitter-receiver angle. A detector is added to isolate the first cycle of the radar wave and to suppress secon-dary arrivals that might be mistaken for first arrivals. To improve the accuracy of the arrival times obtained from the crosscorrelation lags, a time-rescaling scheme is implemented to resize the radar wavelets to a common time-window length. The second method is based on the Akaike information criterion(AIC) and continuous wavelet transform (CWT). It is not tied to the restrictive criterion of waveform similarity that underlies crosscorrelation approaches, which is not guaranteed for traces sorted in common ray-angle gathers. It has the advantage of being automated fully. Performances of the new algorithms are tested with synthetic and real data. In all tests, the approach that adds first-cycle isolation to the original crosscorrelation scheme improves the results. In contrast, the time-rescaling approach brings limited benefits, except when strong dispersion is present in the data. In addition, the performance of crosscorrelation picking schemes degrades for data sets with disparate waveforms despite the high signal-to-noise ratio of the data. In general, the AIC-CWT approach is more versatile and performs well on all data sets. Only with data showing low signal-to-noise ratios is the AIC-CWT superseded by the modified crosscorrelation picker.

Diagnosis for Engine Misfire Fault Based on Torsional Vibration and Neural-Network Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 7240-7246
Author(s):  
Can Yi Du ◽  
Kang Ding ◽  
Zhi Jian Yang ◽  
Cui Li Yang
Keyword(s):  
Neural Network ◽  
Torsional Vibration ◽  
Bp Neural Network ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Correction Method ◽  
Operating Conditions ◽  
Diagnostic Model ◽  
Vibration Signal Analysis

Misfire is a common fault which affects the engine performances. Because the signal-to-noise ratio of torsional vibration signal is high, torsional vibration test and analysis for the engine were performed in a variety of operating conditions, including healthy condition and single-cylinder misfire condition. In order to improve the accuracy of analysis, energy centrobaric correction method was used to correct the amplitude. Taking the corrected amplitude of main order as the fault feature, and then a BP neural-network diagnostic model can be established for misfire diagnosis. The result shows that the method of combining torsional vibration signal analysis and neural-network can diagnose engine misfire fault correctly.

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