Accelerating the rate of convergence for LMS-like on-line identification and adaptation algorithms. Part 1: Basic ideas

Figwer, J.; Michalczyk, M. I.; Glowka, Teresa

doi:10.1109/mmar.2017.8046851

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Their standard deviation is denoted by

. The supplementary discrete-time filters are tuned—their coefficients are obtained by minimisation of the cost function

—using ideas presented in [ 28 , 29 ]. In this operation, number of samples used N less than

means that a part of the period of the CTMR signal is used; further, number N greater that

means using circular extension of the CTMR signal.…”

Section: Simulated Case Studiesmentioning

confidence: 99%

Correction of Dynamical Properties of Data Acquisition Systems

Figwer

Michalczyk

2023

Sensors

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Accurate and fast measurements are important in many areas of everyday engineering and research activities. This paper proposes a method that gives such measurements, additionally shortening the time in which they are obtained. To achieve this, a supplementary discrete-time filter, estimating values of delayed samples of the measured signal, is attached to the output of the data acquisition system. This filter is identified with the use of classical estimation methods, based on a dynamical model of the data acquisition system. The definition of the cost function minimised during filter identification depends on the nature of the environment in which measurements are acquired. The considerations presented in this paper are illustrated with four corresponding simulated case study examples. They show how, in a very simple way, dynamical properties of data acquisition systems may be corrected, and thus improved, using the corresponding supplementary discrete-time filters. The improvement, measured by the correction quality index introduced in the paper, was from a few times up to more than 100. The paper also raises the issue of obtaining models for tuning of the supplementary discrete-time filter. The considerations presented may be applied to formulate the artificial intelligence of data acquisition systems as well as sensors. Finally, the paper proposes to implement this intelligence as a knowledge base of the expert system.

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“…Their standard deviation is denoted by

. The supplementary discrete-time filters are tuned—their coefficients are obtained by minimisation of the cost function

—using ideas presented in [ 28 , 29 ]. In this operation, number of samples used N less than

means that a part of the period of the CTMR signal is used; further, number N greater that

means using circular extension of the CTMR signal.…”

Section: Simulated Case Studiesmentioning

confidence: 99%

Correction of Dynamical Properties of Data Acquisition Systems

Figwer

Michalczyk

2023

Sensors

View full text Add to dashboard Cite

show abstract

Synthesis and Generation of Random Fields in Nonlinear Environment

Figwer

2020

Advances in Intelligent Systems and Computing

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Channel Estimators for Full-Duplex Communication Using Orthogonal Pilot Sequences

2020

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Full-duplex communication is desirable to maximize the spectral efficiency, despite the challenges it puts forth. The key challenge inhibiting the operation of radios in full-duplex mode is selfinterference. In this paper, we propose a pilot-based channel estimation to estimate both self-interference and communication channels simultaneously at both ends of a full-duplex link using orthogonal sequences. The Cramer-Rao Lower Bound for estimators of both the channels was determined and compared with the halfduplex channel estimator. We performed simulations varying sequence length and channel taps and studied the performance of the estimators. We also studied the effect of synchronization between the sequences on the performance of the estimators. Thus, providing a solution to balance the trade-off between the accuracy of the channel estimation and the overhead added to the transmissions for full-duplex communication. INDEX TERMS Channel estimation, orthogonal m-sequences, full-duplex (FDX), half-duplex (HDX), self-interference (SI), Cramer-Rao Lower Bound (CRLB).

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Accelerating the rate of convergence for LMS-like on-line identification and adaptation algorithms. Part 1: Basic ideas

Cited by 3 publications

References 10 publications

Correction of Dynamical Properties of Data Acquisition Systems

Correction of Dynamical Properties of Data Acquisition Systems

Synthesis and Generation of Random Fields in Nonlinear Environment

Channel Estimators for Full-Duplex Communication Using Orthogonal Pilot Sequences

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