scholarly journals Performance analysis of the Karhunen–Loève Transform for artificial and astrophysical transmissions: denoizing and detection

2020 ◽  
Vol 494 (1) ◽  
pp. 69-83
Author(s):  
Matteo Trudu ◽  
Maura Pilia ◽  
Gregory Hellbourg ◽  
Pierpaolo Pari ◽  
Nicolò Antonietti ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Signal To Noise Ratio ◽  
Computation Time ◽  
Real Data ◽  
Detection Techniques ◽  
Standard Signal ◽  
Novel Method ◽  
Study Case ◽  
Karhunen Loeve Transform ◽  
Level Reduction

ABSTRACT In this work, we propose a new method of computing the Karhunen–Loève Transform (KLT) applied to complex voltage data for the detection and noise level reduction in astronomical signals. We compared this method with the standard KLT techniques based on the Toeplitz correlation matrix and we conducted a performance analysis for the detection and extraction of astrophysical and artificial signals via Monte Carlo (MC) simulations. We applied our novel method to a real data study-case: the Voyager 1 telemetry signal. We evaluated the KLT performance in an astrophysical context: our technique provides a remarkable improvement in computation time and MC simulations show significant reconstruction results for signal-to-noise ratio (SNR) down to −10 dB and comparable results with standard signal detection techniques. The application to artificial signals, such as the Voyager 1 data, shows a notable gain in SNR after the KLT.

Instantaneous phase variation for seismic velocity monitoring from ambient noise at the exploration scale

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. Q37-Q44 ◽  
Author(s):  
Margherita Corciulo ◽  
Philippe Roux ◽  
Michel Campillo ◽  
Dominique Dubucq
Keyword(s):  
Seismic Noise ◽  
Seismic Velocity ◽  
Phase Measurement ◽  
Signal To Noise Ratio ◽  
Phase Variation ◽  
Computation Time ◽  
Real Data ◽  
Velocity Change ◽  
Data Set ◽  
Instantaneous Phase

Recent studies in geophysics have investigated the use of seismic-noise correlations to measure weak-velocity variations from seismic-noise recordings. However, classically, the existing algorithms used to monitor medium velocities need extensive efforts in terms of computation time. This implies that these techniques are not appropriate at smaller scales in an exploration context when continuous data sets on dense arrays of sensors have to be analyzed. We applied a faster technique that allows the monitoring of small velocity changes from the instantaneous phase measurement of the seismic-noise crosscorrelation functions. We performed comparisons with existing algorithms using synthetic signals. The results we have obtained for a real data set show that the statistical distribution of the velocity-change estimates provides reliable measurements, despite the low signal-to-noise ratio obtained from the noise-correlation process.

scholarly journals Evaluating the Potentiality of Sentinel-2 for Change Detection Analysis Associated to LULUCF in Wallonia, Belgium

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 55
Author(s):  
Odile Close ◽  
Sophie Petit ◽  
Benjamin Beaumont ◽  
Eric Hallot
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Change Detection ◽  
Signal To Noise Ratio ◽  
Land Conversion ◽  
Sustainable Resources ◽  
Detection Techniques ◽  
Detection Analysis ◽  
Conversion Rates ◽  
Sentinel 2

Land Use/Cover changes are crucial for the use of sustainable resources and the delivery of ecosystem services. They play an important contribution in the climate change mitigation due to their ability to emit and remove greenhouse gas from the atmosphere. These emissions/removals are subject to an inventory which must be reported annually under the United Nations Framework Convention on Climate Change. This study investigates the use of Sentinel-2 data for analysing lands conversion associated to Land Use, Land Use Change and Forestry sector in the Wallonia region (southern Belgium). This region is characterized by one of the lowest conversion rates across European countries, which constitutes a particular challenge in identifying land changes. The proposed research tests the most commonly used change detection techniques on a bi-temporal and multi-temporal set of mosaics of Sentinel-2 data from the years 2016 and 2018. Our results reveal that land conversion is a very rare phenomenon in Wallonia. All the change detection techniques tested have been found to substantially overestimate the changes. In spite of this moderate results our study has demonstrated the potential of Sentinel-2 regarding land conversion. However, in this specific context of very low magnitude of land conversion in Wallonia, change detection techniques appear to be not sufficient to exceed the signal to noise ratio.

scholarly journals Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target

2021 ◽  
Vol 11 (2) ◽  
pp. 582
Author(s):  
Zean Bu ◽  
Changku Sun ◽  
Peng Wang ◽  
Hang Dong
Keyword(s):  
Simulated Data ◽  
Real Data ◽  
Calibration Method ◽  
Multiple Sensors ◽  
Triangular Pyramid ◽  
World Coordinate System ◽  
Flash Lidar ◽  
Novel Method ◽  
3D Information ◽  
Incremental Validation

Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.

One-Class-Based Intelligent Classifier for Detecting Anomalous Situations During the Anesthetic Process

2020 ◽  
Author(s):  
Alberto Leira ◽  
Esteban Jove ◽  
Jose M Gonzalez-Cava ◽  
José-Luis Casteleiro-Roca ◽  
Héctor Quintián ◽  
...  
Keyword(s):  
Side Effects ◽  
Anomaly Detection ◽  
General Anesthesia ◽  
Closed Loop ◽  
Real Data ◽  
Successful Performance ◽  
Final Model ◽  
Detection Techniques ◽  
Adverse Side Effects ◽  
Operative Recovery

Abstract Closed-loop administration of propofol for the control of hypnosis in anesthesia has evidenced an outperformance when comparing it with manual administration in terms of drug consumption and post-operative recovery of patients. Unlike other systems, the success of this strategy lies on the availability of a feedback variable capable of quantifying the current hypnotic state of the patient. However, the appearance of anomalies during the anesthetic process may result in inaccurate actions of the automatic controller. These anomalies may come from the monitors, the syringe pumps, the actions of the surgeon or even from alterations in patients. This could produce adverse side effects that can affect the patient postoperative and reduce the safety of the patient in the operating room. Then, the use of anomaly detection techniques plays a significant role to avoid this undesirable situations. This work assesses different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining real data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final model presents successful performance.

scholarly journals Delayed improvement local search

2021 ◽  
Author(s):  
Heber F. Amaral ◽  
Sebastián Urrutia ◽  
Lars M. Hvattum
Keyword(s):  
Local Search ◽  
Heuristic Algorithms ◽  
Travelling Salesman Problem ◽  
Computation Time ◽  
The Novel ◽  
Local Optima ◽  
New Strategy ◽  
Max Cut Problem ◽  
Novel Method ◽  
Similar Solutions

AbstractLocal search is a fundamental tool in the development of heuristic algorithms. A neighborhood operator takes a current solution and returns a set of similar solutions, denoted as neighbors. In best improvement local search, the best of the neighboring solutions replaces the current solution in each iteration. On the other hand, in first improvement local search, the neighborhood is only explored until any improving solution is found, which then replaces the current solution. In this work we propose a new strategy for local search that attempts to avoid low-quality local optima by selecting in each iteration the improving neighbor that has the fewest possible attributes in common with local optima. To this end, it uses inequalities previously used as optimality cuts in the context of integer linear programming. The novel method, referred to as delayed improvement local search, is implemented and evaluated using the travelling salesman problem with the 2-opt neighborhood and the max-cut problem with the 1-flip neighborhood as test cases. Computational results show that the new strategy, while slower, obtains better local optima compared to the traditional local search strategies. The comparison is favourable to the new strategy in experiments with fixed computation time or with a fixed target.

Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo
Keyword(s):  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Critical Role ◽  
Real Data ◽  
Monitoring Method ◽  
Frequency Spectra ◽  
Arrival Times ◽  
Time Frequency ◽  
Fracturing Process ◽  
Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

scholarly journals A Novel Method for Performance Analysis of Compartmentalized Reservoirs

2015 ◽  
Vol 71 (3) ◽  
pp. 38 ◽  
Author(s):  
Mohammad Sadeq Shahamat ◽  
Hamidreza Hamdi ◽  
Louis Mattar ◽  
Roberto Aguilera
Keyword(s):  
Performance Analysis ◽  
Novel Method

scholarly journals Detection of Anomalies in Water Networks by Functional Data Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Laura Millán-Roures ◽  
Irene Epifanio ◽  
Vicente Martínez
Keyword(s):  
Data Analysis ◽  
Outlier Detection ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Real Data ◽  
Water Networks ◽  
Archetypal Analysis ◽  
Detection Techniques ◽  
Second Stage ◽  
Two Phases

A functional data analysis (FDA) based methodology for detecting anomalous flows in urban water networks is introduced. Primary hydraulic variables are recorded in real-time by telecontrol systems, so they are functional data (FD). In the first stage, the data are validated (false data are detected) and reconstructed, since there could be not only false data, but also missing and noisy data. FDA tools are used such as tolerance bands for FD and smoothing for dense and sparse FD. In the second stage, functional outlier detection tools are used in two phases. In Phase I, the data are cleared of anomalies to ensure that data are representative of the in-control system. The objective of Phase II is system monitoring. A new functional outlier detection method is also proposed based on archetypal analysis. The methodology is applied and illustrated with real data. A simulated study is also carried out to assess the performance of the outlier detection techniques, including our proposal. The results are very promising.

An Improved ECG Steganography Using Integer Wavelet Transform and Offset Coefficient with LSB Substitution

2021 ◽  
Author(s):  
Ching-Yu Yang ◽  
Wen-Fong Wang
Keyword(s):  
Wavelet Transform ◽  
Personal Information ◽  
Signal To Noise Ratio ◽  
Computation Time ◽  
Integer Wavelet Transform ◽  
Wearable Electronics ◽  
Least Significant Bit ◽  
Hiding Capacity ◽  
Offset Coefficient ◽  
Integer Wavelet

Abstract In this work, we present an improved steganography for electrocardiogram (ECG) hosts to solve the issues of existing ECG steganographic methods, which have less hiding capacity and insufficient signal-to-noise ratio (SNR)/ peak SNR (PSNR). Based on the integer wavelet transform (IWT) domain, sensitive (or private) data such as patients’ data and personal information can be efficiently embedded in an ECG host via the IWT coefficient adjustment and the least significant bit (LSB) technique. Simulations confirmed that the SNR/ PSNR, and payload of the proposed method outperform those of existing techniques. In addition, the proposed method is capable of resisting attacks, such as cropping, Gaussian noise-addition inversion, scaling, translation, and truncation attacks from third parties (or adversaries). Due to the fast computation time, the proposed method can be employed in portable biometric devices or wearable electronics.

A Robust Procedure to Implement Dynamic Stall Models Into Actuator Line Methods for the Simulation of Vertical-Axis Wind Turbines

2021 ◽  
Author(s):  
Pier Francesco Melani ◽  
Francesco Balduzzi ◽  
Alessandro Bianchini
Keyword(s):  
Flow Field ◽  
Wind Turbines ◽  
Signal To Noise Ratio ◽  
Computational Cost ◽  
Vertical Axis ◽  
Dynamic Stall ◽  
Lumped Parameter ◽  
Low Pass ◽  
Unsteady Rans ◽  
Novel Method

Abstract The Actuator Line Method (ALM), combining a lumped-parameter representation of the rotating blades with the CFD resolution of the turbine flow field, stands out among the modern simulation methods for wind turbines as probably the most interesting compromise between accuracy and computational cost. Being however a method relying on tabulated coefficients for modeling the blade-flow interaction, the correct implementation of the sub-models to account for higher order aerodynamic effects is pivotal. Inter alia, the introduction of a dynamic stall model is extremely challenging: first, it is important to extrapolate a correct value of the angle of attack (AoA) from the solved flow field; second, the AoA history needed to calculate the rate of dynamic variation of the angle itself is characterized by a low signal-to-noise ratio, leading to severe numerical oscillations of the solution. The study introduces a robust procedure to improve the quality of the AoA signal extracted from an ALM simulation. It combines a novel method for sampling the inflow velocity from the numerical flow field with a low-pass filtering of the corresponding AoA signal based on Cubic Spline Smoothing. Such procedure has been implemented in the Actuator Line module developed by the authors for the commercial ANSYS® FLUENT® solver. To verify the reliability of the methodology, two-dimensional unsteady RANS simulations of a test 2-blade Darrieus H-rotor, for which high-fidelity experimental and numerical blade loading data were available, have been performed for a selected unstable operation point.

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