To standardize parameters used in seismometer testing and calibration and to make these algorithms accessible to the seismological community, we have developed a new seismometer testing software package called Albuque...
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To standardize parameters used in seismometer testing and calibration and to make these algorithms accessible to the seismological community, we have developed a new seismometer testing software package called Albuquerque Seismological Laboratory (ASL) Sensor Test Suite. This software is written in Java and makes use of Seismological Exchange for Earthquake Data (SEED) format. Our goal is not to be all-inclusive but instead to focus on a few of the instrumentation tests we view as critical when verifying a sensor's performance. The tests include self-noise, relative azimuth, relative gain, and estimation of the poles and zeros. For the self-noise and the relative azimuth, we also include three-component versions of these tests to allow for the case of sensors with potentially different orientations (e.g., boreholes). The software has been made available on GitHub with the hope that it will be useful for other seismologists who need to quickly verify various sensor parameters without having to write their own versions of the algorithms. Furthermore, by using a common platform and processing algorithms, it becomes possible to compare results among different tests with similar processing methods being used for both.
Ground Penetrating Radar(GPR) is one of a number of technologies that have been used to improve landmine detection efficiency. The clutter environment within the first few cm of the soil where landmines are buried, ex...
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ISBN:
(纸本)9781457718359
Ground Penetrating Radar(GPR) is one of a number of technologies that have been used to improve landmine detection efficiency. The clutter environment within the first few cm of the soil where landmines are buried, exhibits strong reflections with highly non-stationary statistics. An antipersonnel mine(AP) can have a diameter as low as 2cm whereas many soils have very high attenuation frequencies above 3GHZ. The landmine detection problem can be solved by carrying out system level analysis of the issues involved to synthesise an image which people can readily understand. The SIMCA ('SIMulated Correlation algorithm') is a technique that carries out correlation between the actual GPR trace that is recorded at the field and the ideal trace which is obtained by carrying out GPR simulation. The SIMCA algorithm firstly calculates by forward modelling a synthetic point spread function of the GPR by using the design parameters of the radar and soil properties to carry out radar simulation. This allows the derivation of the correlation kernel. The SIMCA algorithm then filters these unwanted components or clutter from the signal to enhance landmine detection. The clutter removed GPR B scan is then correlated with the kernel using the Pearson correlation coefficient. This results in a image which emphasises the target features and allows the detection of the target by looking at the brightest spots. Raising of the image to an odd power > 2 enhances the target/background separation. To validate the algorithm, the length of the target in some cases and the diameter of the target in other cases, along with the burial depth obtained by the SIMCA system are compared with the actual values used during the experiments for the burial depth and those of the dimensions of the actual target. Because, due to the security intelligence involved with landmine detection and most authors work in collaboration with the national government military programs, a database of landmine signatures is
The architecture and processing algorithm of mainlobe interference suppression method is described for nulling the signal from mainlobe electronic jammer and multiple sidelobe electronic jammers while maintaining mono...
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The architecture and processing algorithm of mainlobe interference suppression method is described for nulling the signal from mainlobe electronic jammer and multiple sidelobe electronic jammers while maintaining monopulse angle estimation accuracy on the target. The existing mainlobe interference suppression method is based on monopulse Wiener filtering. However, the Wiener filtering is more suitable to remove static interference. Therefore, the authors present the dynamic mainlobe interference suppression method with Gray Model Kalman filter to resolve this problem. The simulations verify the proposed method.
The search for new exoplanets by direct imaging is a very active research topic in astronomy. The detection is particularly challenging because of the very high contrast between the host star and the companions. They ...
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ISBN:
(数字)9781510619609
ISBN:
(纸本)9781510619609
The search for new exoplanets by direct imaging is a very active research topic in astronomy. The detection is particularly challenging because of the very high contrast between the host star and the companions. They thus remain hidden by a nonstationary background displaying strong spatial correlations. We propose a new algorithm named PACO (for PAtch COvariances) for reduction of differential imaging datasets. Contrary to existing approaches, we model the background correlations using a local Gaussian distribution that locally captures the spatial correlations at the scale of a patch of a few tens of pixels. The decision in favor of the presence or the absence of an exoplanet in then performed by a binary hypothesis test. The method is completely parameter-free and produces both stationary and statistically grounded detection maps so that the false alarm rate, the probability of detection and the contrast can be directly assessed without postprocessing and/or Monte-Carlo simulations. We describe in a forthcoming paper its detailed principle and implementation. In this paper, we recall the principle of the PACO algorithm and we give new illustrations of its bene fits in terms of detection capabilities on datasets from the VLT/SPHERE-IRDIS instrument. We also apply our algorithm on multi-spectral datasets from the VLT/SPHERE-IFS spectrograph. The performance of PACO is compared to state-of-the-art algorithms such as TLOCI and KLIP-PCA.
The report describes algorithms for calculating the durations of phases, determining the peak values in each phase, implemented on the basis of the step phase program results. The algorithms results of the operation f...
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ISBN:
(纸本)9781538643402
The report describes algorithms for calculating the durations of phases, determining the peak values in each phase, implemented on the basis of the step phase program results. The algorithms results of the operation for the processing of experimental data are presented. The interval size of passive and active phases for the double step is presented as a percentage, and the moment of the least stable position of the body is determined. The presented algorithms in the article will be used in the future to identify diseases or abnormalities in the work of the locomotor system.
Change detection has a wide range of applications in hurricane damage assessment, urban growth monitoring, etc. It is well-known that synthetic aperture radar (SAR) can penetrate clouds and can work well under all wea...
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ISBN:
(纸本)9781728138862
Change detection has a wide range of applications in hurricane damage assessment, urban growth monitoring, etc. It is well-known that synthetic aperture radar (SAR) can penetrate clouds and can work well under all weather conditions. However, SAR images also contain a lot of speckle noise that seriously affects change detection performance. In this paper, we focus on change detection using SAR images. In particular, we propose a new change detection algorithm that is applicable to both optical and SAR images. The detection performance of the algorithm was compared with a number of existing algorithms in the literature. Preliminary results using actual SAR images are encouraging. Most importantly, it was observed that effective change detection using SAR images require good denoising and post-processing algorithms in order to achieve decent performance.
The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are ...
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The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are referred to as data ingesting subsystem (DIS) and product generation subsystem (PGS). The primary purpose of DIS is to record and catalogue GF-3 raw data with a transferring format, and PGS is to produce slant range or geocoded imagery from the signal data. This paper presents a brief introduction of the GF-3 data processor, including descriptions of the system architecture, the processing algorithms and its output format.
Ultrasonic systems are widely used in imaging applications for non-destructive evaluation, quality assurance and medical diagnosis. These applications require large volumes of data to be processed, stored and/or trans...
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Ultrasonic systems are widely used in imaging applications for non-destructive evaluation, quality assurance and medical diagnosis. These applications require large volumes of data to be processed, stored and/or transmitted in real-time. Therefore it is essential to compress the acquired ultrasonic radio frequency (RF) signal without inadvertently degrading desirable signal features. In this paper, two algorithms for ultrasonic signal compression are analysed based on: sub-band elimination using discrete wavelet transform;and decimation/interpolation using time-shift property of Fourier transform. Both algorithms offer high signal reconstruction quality with a peak signal-to-noise ratio (PSNR) between 36 to 39 dB for minimum 80% compression. The computational loads and signal reconstruction quality are examined in order to determine the best compression method in terms of the choice of DWT kernel, sub-band decomposition architecture and computational efficiency. Furthermore, for compressing a large amount of volumetric information, three-dimensional (3D) compression algorithms are designed by utilising the temporal and spatial correlation properties of the ultrasonic RF signals. The performance analysis indicates that the 3D compression algorithm presented in this paper offers an overall 3D compression ratio of 95% with a minimum PSNR of 27 dB.
This paper develops a batch processing algorithm that can be used to track a constant velocity surface target. The purpose of this algorithm is to facilitate passive tracking when sensor-target geometry is poor, which...
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ISBN:
(纸本)9781457705564
This paper develops a batch processing algorithm that can be used to track a constant velocity surface target. The purpose of this algorithm is to facilitate passive tracking when sensor-target geometry is poor, which can prevent the convergence of a recursive estimator. The target's position is considered to be the output of an ordinary differential equation having unknown parameters to be estimated. This contrasts with the model used for the design of recursive estimators such as a Kalman filter where the target's position is the output of a dynamic system driven by white noise. Batch processing of all sensor measurements and Iterated Least-Squares (ILS) are used to estimate the target model parameters. Numerical integration is used to propagate the target's position and the Jacobian needed by ILS. Simulation results are shown for a maritime surveillance mission.
The deluge of networked big data motivates the development of computation- and communication-efficient network information processing algorithms. In this paper, we propose two data-adaptive censoring strategies that s...
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ISBN:
(纸本)9781509041183
The deluge of networked big data motivates the development of computation- and communication-efficient network information processing algorithms. In this paper, we propose two data-adaptive censoring strategies that significantly reduce the computation and communication costs of the distributed recursive least-squares (D-RLS) algorithm. Through introducing a cost function that underrates the importance of those observations with small innovations, we develop the first censoring strategy based on the alternating minimization algorithm and the stochastic Newton method. It saves computation when a datum is censored. The computation and communication costs are further reduced by the second censoring strategy, which prohibits a node updating and transmitting its local estimate to neighbors when its current innovation is less than a threshold. For both strategies, a simple criterion for selecting the threshold of innovation is given so as to reach a target ratio of data reduction. The proposed censored D-RLS algorithms guarantee convergence to the optimal argument in the mean-square deviation sense. Numerical experiments validate the effectiveness of the proposed algorithms.
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