scholarly journals Weak lensing magnification reconstruction with the modified internal linear combination method

2021 ◽  
Vol 21 (10) ◽  
pp. 247
Author(s):  
Shu-Tong Hou ◽  
Yu Yu ◽  
Peng-Jie Zhang
Keyword(s):  
Linear Combination ◽  
Signal Reconstruction ◽  
Combination Method ◽  
Weak Lensing ◽  
Systematic Bias ◽  
Statistical Uncertainty ◽  
Galaxy Distribution ◽  
Reconstruction Performance

Abstract Measuring weak lensing cosmic magnification signal is very challenging due to the overwhelming intrinsic clustering in the observed galaxy distribution. In this paper, we modify the Internal Linear Combination (ILC) method to reconstruct the lensing signal with an extra constraint to suppress the intrinsic clustering. To quantify the performance, we construct a realistic galaxy catalogue for the LSST-like photometric survey, covering 20 000 deg2 with mean source redshift at zs ∼ 1. We find that the reconstruction performance depends on the width of the photo-z bin we choose. Due to the correlation between the lensing signal and the source galaxy distribution, the derived signal has smaller systematic bias but larger statistical uncertainty for a narrower photo-z bin. We conclude that the lensing signal reconstruction with the Modified ILC method is unbiased with a statistical uncertainty <5% for bin width Δ zP = 0.2.

scholarly journals Filtering-Based Regularized Sparsity Variable Step-Size Matching Pursuit and Its Applications in Vehicle Health Monitoring

2021 ◽  
Vol 11 (11) ◽  
pp. 4816
Author(s):  
Haoqiang Liu ◽  
Hongbo Zhao ◽  
Wenquan Feng
Keyword(s):  
Health Monitoring ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Superior Performance ◽  
Sparse Signal ◽  
Variable Step Size ◽  
Step Size ◽  
Signal Sampling ◽  
Reconstruction Performance ◽  
Sparsity Level

Recent years have witnessed that real-time health monitoring for vehicles is gaining importance. Conventional monitoring scheme faces formidable challenges imposed by the massive signals generated with extremely heavy burden on storage and transmission. To address issues of signal sampling and transmission, compressed sensing (CS) has served as a promising solution in vehicle health monitoring, which performs signal sampling and compression simultaneously. Signal reconstruction is regarded as the most critical part of CS, while greedy reconstruction has been a research hotspot. However, the existing approaches either require prior knowledge of the sparse signal or perform with expensive computational complexity. To exploit the structure of the sparse signal, in this paper, we introduce an initial estimation approach for signal sparsity level firstly. Then, a novel greedy reconstruction algorithm that relies on no prior information of sparsity level while maintaining a good reconstruction performance is presented. The proposed algorithm integrates strategies of regularization and variable adaptive step size and further performs filtration. To verify the efficiency of the algorithm, typical voltage disturbance signals generated by the vehicle power system are taken as trial data. Preliminary simulation results demonstrate that the proposed algorithm achieves superior performance compared to the existing methods.

scholarly journals Wide range and highly linear signal processed systematic humidity sensor array using Methylene Blue and Graphene composite

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Umair Khan ◽  
Gul Hassan ◽  
Rayyan Ali Shaukat ◽  
Qazi Muhammad Saqib ◽  
Mahesh Y. Chougale ◽  
...  
Keyword(s):  
Methylene Blue ◽  
Linear Combination ◽  
Linear Function ◽  
Humidity Sensor ◽  
Sensor Array ◽  
Combination Method ◽  
Humidity Sensing ◽  
Sensing Applications ◽  
Wide Range ◽  
Graphene Flakes

AbstractThis paper proposes a signal processed systematic 3 × 3 humidity sensor array with all range and highly linear humidity response based on different particles size composite inks and different interspaces of interdigital electrodes (IDEs). The fabricated sensors are patterned through a commercial inkjet printer and the composite of Methylene Blue and Graphene with three different particle sizes of bulk Graphene Flakes (BGF), Graphene Flakes (GF), and Graphene Quantum Dots (GQD), which are employed as an active layer using spin coating technique on three types of IDEs with different interspaces of 300, 200, and 100 µm. All range linear function (0–100% RH) is achieved by applying the linear combination method of nine sensors in the signal processing field, where weights for linear combination are required, which are estimated by the least square solution. The humidity sensing array shows a fast response time (Tres) of 0.2 s and recovery time (Trec) of 0.4 s. From the results, the proposed humidity sensor array opens a new gateway for a wide range of humidity sensing applications with a linear function.

scholarly journals The Application of the BSSD Iono-Free Linear Combination Method in the Processing of Aircraft Positioning

2019 ◽  
Vol 26 (3) ◽  
pp. 15-21
Author(s):  
Janusz Ćwiklak ◽  
Marek Grzegorzewski ◽  
Kamil Krasuski
Keyword(s):  
Linear Combination ◽  
Experimental Test ◽  
Combination Method ◽  
Dual Frequency ◽  
Chi Square ◽  
Internal Reliability ◽  
Navigation Data ◽  
Positioning Method ◽  
Gps System ◽  
Positioning Algorithm

Abstract The article presents the results of research into the use of the differentiation technique of BSSD (Between Satellite Single Difference) observations for the Iono-Free LC combination (Linear Combination) in the GPS system for the needs of aircraft positioning. Within the conducted investigations, a positioning algorithm for the BSSD Iono-Free LC positioning method was presented. In addition, an experimental test was conducted, in which raw observational data and GPS navigation data were exploited in order to recover the aircraft position. The examination was conducted for the Cessna 172 and the on-board dual-frequency receiver Topcon HiperPro. The experimental test presents the results of average errors of determining the position of the Cessna 172 in the XYZ geocentric frame and in the ellipsoidal BLh frame. Furthermore, the article presents the results of DOP (Dilution of Precision) coefficients, the test of the Chi square internal reliability test and the HPL and VPL confidence levels in GNSS precision approach (PA) in air transport. The calculations were performed in the original APS software (APS Aircraft Positioning Software) developed in the Department of Air Navigation of the Faculty of Aeronautics at the Polish Air Force University.

Component-scaled signal reconstruction for enhanced noise filtration

2022 ◽  
pp. 107754632110514
Author(s):  
Aryan Singh ◽  
Keegan J Moore
Keyword(s):  
Linear Combination ◽  
Signal Reconstruction ◽  
Gaussian White Noise ◽  
Three Dimensional ◽  
Noise Removal ◽  
Noise Signal ◽  
Image Correlation ◽  
Intrinsic Mode Functions ◽  
Optimal Linear Combination ◽  
Optimal Linear

This research introduces a procedure for signal denoising based on linear combinations of intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). The method, termed component-scaled signal reconstruction, employs the standard EMD algorithm, with no enhancements to decompose the signal into a set of IMFs. The problem of mode mixing is leveraged for noise removal by constructing an optimal linear combination of the potentially mixed IMFs. The optimal linear combination is determined using an optimization routine with an objective function that maximizes and minimizes the information and noise, respectively, in the denoised signal. The method is demonstrated by applying it to a computer-generated voice sample and the displacement response of a cantilever beam with local stiffness nonlinearity. In the first application, the noise is introduced into the sample manually by adding a Gaussian white-noise signal to the signal. In the second application, the response of the entire beam is filmed using two 1-megapixel cameras, and the three-dimensional displacement field is extracted using digital image correlation. The noise in this application arises entirely from the images captured. The proposed method is compared to existing EMD, ensemble EMD, and LMD based denoising approaches and is found to perform better.

scholarly journals Optimal void finders in weak lensing maps

2020 ◽  
Vol 500 (2) ◽  
pp. 2417-2439
Author(s):  
Christopher T Davies ◽  
Enrique Paillas ◽  
Marius Cautun ◽  
Baojiu Li
Keyword(s):  
Large Scale ◽  
Large Scale Structure ◽  
Matter Field ◽  
Line Of Sight ◽  
Weak Lensing ◽  
Signal To Noise ◽  
Dominant Source ◽  
Good Compromise ◽  
Galaxy Distribution ◽  
Total Matter

ABSTRACT Cosmic voids are a key component of the large-scale structure that contain a plethora of cosmological information. Typically, voids are identified from the underlying galaxy distribution, which is a biased tracer of the total matter field. Previous works have shown that 2D voids identified in weak lensing (WL) maps – WL voids – correspond better to true underdense regions along the line of sight. In this work, we study how the properties of WL voids depend on the choice of void finder, by adapting several popular void finders. We present and discuss the differences between identifying voids directly in the convergence maps, and in the distribution of WL peaks. Particular effort has been made to test how these results are affected by galaxy shape noise (GSN), which is a dominant source of noise in WL observations. By studying the signal-to-noise ratios (S/N) for the tangential shear profile of each void finder, we find that voids identified directly in the convergence maps have the highest S/N but are also the ones most affected by GSN. Troughs are least affected by noise, but also have the lowest S/N. The tunnel algorithm, which identifies voids in the distribution of WL peaks, represents a good compromise between finding a large tangential shear S/N and mitigating the effect of GSN.

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