scholarly journals Examining signal estimation methods by including missing signal parts

2020 ◽  
Vol 2020 (0) ◽  
pp. 49-53
Author(s):  
Kohei Okishio ◽  
Yoshiharu Koya
Keyword(s):  
Estimation Methods ◽  
Signal Estimation

scholarly journals GPS Sparse Multipath Signal Estimation Based on Compressive Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guodong He ◽  
Maozhong Song ◽  
Shanshan Zhang ◽  
Huiping Qin ◽  
Xiaojuan Xie
Keyword(s):  
Compressive Sensing ◽  
Estimation Error ◽  
Estimation Method ◽  
Reconstruction Algorithm ◽  
Estimation Methods ◽  
Signal Estimation ◽  
Reconstruction Algorithms ◽  
Modified Newton Method ◽  
Reconstruction Performance ◽  
Multipath Signal

A GPS sparse multipath signal estimation method based on compressive sensing is proposed. A new 0 norm approximation function is designed, and the parameter of the approximate function is gradually reduced to realize the approximation of 0 norm. The sparse signal is reconstructed by a modified Newton method. The reconstruction performance of the proposed algorithm is better than several commonly reconstruction algorithms at different sparse numbers and noise intensities. The GPS sparse multipath signal model is established, and the sparse multipath signal is estimated by the proposed reconstruction algorithm in this paper. Compared with several commonly used estimation methods, the estimation error of the proposed method is lower.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

Femtocell Selection Scheme for Reducing Unnecessary Handover and Enhancing Downlink QoS in Cognitive Femtocell Networks

2017 ◽  
Author(s):  
Hoang Nhu Dong ◽  
Hoang Nam Nguyen ◽  
Hoang Trong Minh ◽  
Takahiko Saba
Keyword(s):  
Cellular Networks ◽  
Estimation Methods ◽  
Mobility Prediction ◽  
Capacity Estimation ◽  
Femtocell Networks ◽  
Selection Scheme ◽  
Indoor Communications ◽  
Performance Results ◽  
Cognitive Femtocell

Femtocell networks have been proposed for indoor communications as the extension of cellular networks for enhancing coverage performance. Because femtocells have small coverage radius, typically from 15 to 30 meters, a femtocell user (FU) walking at low speed can still make several femtocell-to-femtocell handovers during its connection. When performing a femtocell-to-femtocell handover, femtocell selection used to select the target handover femtocell has to be able not only to reduce unnecessary handovers and but also to support FU’s quality of service (QoS). In the paper, we propose a femtocell selection scheme for femtocell-tofemtocell handover, named Mobility Prediction and Capacity Estimation based scheme (MPCE-based scheme), which has the advantages of the mobility prediction and femtocell’s available capacity estimation methods. Performance results obtained by computer simulation show that the proposed MPCE-based scheme can reduce unnecessary femtocell-tofemtocell handovers, maintain low data delay and improve the throughput of femtocell users. DOI: 10.32913/rd-ict.vol3.no14.536

Biorefinery implementation for recovery debottlenecking at existing pulp mills- Part II: Techno-economic evaluation

TAPPI Journal ◽  
2012 ◽  
Vol 11 (8) ◽  
pp. 17-24 ◽  
Author(s):  
HAKIM GHEZZAZ ◽  
LUC PELLETIER ◽  
PAUL R. STUART
Keyword(s):  
Cost Estimation ◽  
Process Model ◽  
Black Liquor ◽  
Pulp Mill ◽  
Value Added ◽  
Economic Assessment ◽  
Extraction Process ◽  
Estimation Methods ◽  
Precipitation Process

The evaluation and process risk assessment of (a) lignin precipitation from black liquor, and (b) the near-neutral hemicellulose pre-extraction for recovery boiler debottlenecking in an existing pulp mill is presented in Part I of this paper, which was published in the July 2012 issue of TAPPI Journal. In Part II, the economic assessment of the two biorefinery process options is presented and interpreted. A mill process model was developed using WinGEMS software and used for calculating the mass and energy balances. Investment costs, operating costs, and profitability of the two biorefinery options have been calculated using standard cost estimation methods. The results show that the two biorefinery options are profitable for the case study mill and effective at process debottlenecking. The after-tax internal rate of return (IRR) of the lignin precipitation process option was estimated to be 95%, while that of the hemicellulose pre-extraction process option was 28%. Sensitivity analysis showed that the after tax-IRR of the lignin precipitation process remains higher than that of the hemicellulose pre-extraction process option, for all changes in the selected sensitivity parameters. If we consider the after-tax IRR, as well as capital cost, as selection criteria, the results show that for the case study mill, the lignin precipitation process is more promising than the near-neutral hemicellulose pre-extraction process. However, the comparison between the two biorefinery options should include long-term evaluation criteria. The potential of high value-added products that could be produced from lignin in the case of the lignin precipitation process, or from ethanol and acetic acid in the case of the hemicellulose pre-extraction process, should also be considered in the selection of the most promising process option.

Position and Speed Estimation Methods of SPMSMs using a Hall Effect Sensor

2008 ◽  
Vol 128 (2) ◽  
pp. 125-130
Author(s):  
Kan Akatsu ◽  
Nobuhiro Mitomo ◽  
Shinji Wakui
Keyword(s):  
Hall Effect ◽  
Estimation Methods ◽  
Speed Estimation ◽  
Hall Effect Sensor

Estimation Methods of Short Circuit Current using Normal PMU Measurements and Field Testing

2013 ◽  
Vol 133 (1) ◽  
pp. 37-44
Author(s):  
Suresh Chand Verma ◽  
Yoshiki Nakachi ◽  
Yoshihiko Wazawa ◽  
Yoko Kosaka ◽  
Takenori Kobayashi ◽  
...  
Keyword(s):  
Short Circuit ◽  
Short Circuit Current ◽  
Field Testing ◽  
Estimation Methods

A Spectrum-Adaptive Decomposition Method for Effective Atomic Number Estimation Using Dual Energy CT

2020 ◽  
Vol 2020 (14) ◽  
pp. 293-1-293-7
Author(s):  
Ankit Manerikar ◽  
Fangda Li ◽  
Avinash C. Kak
Keyword(s):  
Atomic Number ◽  
Decomposition Method ◽  
Traditional Approach ◽  
Effective Atomic Number ◽  
Dual Energy ◽  
Performance Comparison ◽  
Estimation Methods ◽  
Dual Energy Ct ◽  
Projection Data ◽  
X Ray

Dual Energy Computed Tomography (DECT) is expected to become a significant tool for voxel-based detection of hazardous materials in airport baggage screening. The traditional approach to DECT imaging involves collecting the projection data using two different X-ray spectra and then decomposing the data thus collected into line integrals of two independent characterizations of the material properties. Typically, one of these characterizations involves the effective atomic number (Zeff) of the materials. However, with the X-ray spectral energies typically used for DECT imaging, the current best-practice approaches for dualenergy decomposition yield Zeff values whose accuracy range is limited to only a subset of the periodic-table elements, more specifically to (Z < 30). Although this estimation can be improved by using a system-independent ρe — Ze (SIRZ) space, the SIRZ transformation does not efficiently model the polychromatic nature of the X-ray spectra typically used in physical CT scanners. In this paper, we present a new decomposition method, AdaSIRZ, that corrects this shortcoming by adapting the SIRZ decomposition to the entire spectrum of an X-ray source. The method reformulates the X-ray attenuation equations as direct functions of (ρe, Ze) and solves for the coefficients using bounded nonlinear least-squares optimization. Performance comparison of AdaSIRZ with other Zeff estimation methods on different sets of real DECT images shows that AdaSIRZ provides a higher output accuracy for Zeff image reconstructions for a wider range of object materials.

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