Robust least squares for quantized data matrices

Becker, Stephen; Clancy, Richard J

doi:10.1016/j.sigpro.2020.107711

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…The temperature change can also make deformation of the substrate to make SAW propagation change which further causes the time delay change. This WP-SAW reflective delay line temperature and pressure sensor has the following regulations based on our previous work [30]. In time domain, phase differences of the response signals reflected by the three reflectors from the sensor node have linear relationships with testing temperature and pressure changes, which can be shown in (6) and (7).…”

Section: Wp-saw Water Temperature and Pressure Sensormentioning

confidence: 99%

“…Researchers made efforts on reduction of sensing errors caused by these interferences. Some algorithms were developed by previous researchers [29], such as least squares [30], polynomial fitting [31], and interpolation [32], etc., but these methods do not reflect real-time output data and cannot be used for real-time monitoring tasks. This disadvantage limits their usage scenarios.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Iteration Enhanced 2P-SMA Method for Improved Error Reduction on a WP-SAW Water Temperature and Pressure Sensor

Tang

Gao

et al. 2021

IEEE Access

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Due to the instability of the characteristics of materials, fabrication processes and user handling, newly designed and fabricated wireless passive surface acoustic wave (WP-SAW) sensor nodes have inconsistent sensing performance. Furthermore, ambient environmental interferences aggravate inconsistences under complex working conditions. In this paper, a multi-iteration enhanced two-point simple moving average (MI-2P-SMA) method is proposed for sensing error reduction of a WP-SAW reflective delay line water temperature and pressure sensor. This method is improved from the traditional 2P-SMA method for better performance on error reduction. The results show: the MI-2P-SMA method does not change the original characteristics of experimental data; it can reduce relative errors of the WP-SAW reflective delay line water temperature and pressure sensor and has better performance than a traditional 2P-SMA method; it reduces the number of data points and the extent of this reduction is dependent on iteration time.

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Section: Wp-saw Water Temperature and Pressure Sensormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Iteration Enhanced 2P-SMA Method for Improved Error Reduction on a WP-SAW Water Temperature and Pressure Sensor

Tang

Gao

et al. 2021

IEEE Access

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show abstract

“…Efforts have been made to move away from Gaussian noise through robust optimization where the goal is to generate estimates impervious to perturbations in the observed data. Many robust optimization problems are cast in a minimax form [5,6,7,8,9]. Typically, an uncertainty set U and objective function f are specified, then the aim is to solve min x {max U ∈U f (x, U )}.…”

Section: Introductionmentioning

confidence: 99%

Approximate maximum likelihood estimators for linear regression with design matrix uncertainty

Clancy¹,

Becker²

2021

Preprint

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In this paper we consider regression problems subject to arbitrary noise in the operator or design matrix. This characterization appropriately models many physical phenomena with uncertainty in the regressors. Although the problem has been studied extensively for ordinary/total least squares, and via models that implicitly or explicitly assume Gaussianity, less attention has been paid to improving estimation for regression problems under general uncertainty in the design matrix. To address difficulties encountered when dealing with distributions of sums of random variables, we rely on the saddle point method to estimate densities and form an approximate log-likelihood to maximize. We show that the proposed method performs favorably against other classical methods.

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Approximate maximum likelihood estimators for linear regression with independent component-wise design matrix uncertainty

Clancy,

Becker

2024

Math. Prog. Comp.

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Robust least squares for quantized data matrices

Cited by 4 publications

References 21 publications

A Multi-Iteration Enhanced 2P-SMA Method for Improved Error Reduction on a WP-SAW Water Temperature and Pressure Sensor

A Multi-Iteration Enhanced 2P-SMA Method for Improved Error Reduction on a WP-SAW Water Temperature and Pressure Sensor

Approximate maximum likelihood estimators for linear regression with design matrix uncertainty

Approximate maximum likelihood estimators for linear regression with independent component-wise design matrix uncertainty

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