The coefficient reuse strategy is able to improve the steady-state performance of adaptive filter algo-rithms, especially in very challenging low signal-to-noise scenarios. This paper advances deterministic and stocha...
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The coefficient reuse strategy is able to improve the steady-state performance of adaptive filter algo-rithms, especially in very challenging low signal-to-noise scenarios. This paper advances deterministic and stochastic models that predict various learning characteristics of the lms algorithm with coefficient reuse. First-order and second-order analyzes are derived for the sufficient order case and then extended for the tracking and deficient length scenarios. An exact expectation analysis, which does not employ the ubiquitous independence assumption, is presented for a particular configuration of the algorithm, and its results suggest that, except in the first phase of the learning process, the decay in mean-square deviation of the coefficient vector is governed by an almost-sure theoretical analysis. The simulation results confirm the equations obtained in the theoretical analysis. (c) 2022 Elsevier B.V. All rights reserved.
The issue of sparsity adaptive channel reconstruction in time-varying cooperative communication networks through the amplify-and-forward transmission scheme is studied. A new sparsity adaptive system identification me...
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The issue of sparsity adaptive channel reconstruction in time-varying cooperative communication networks through the amplify-and-forward transmission scheme is studied. A new sparsity adaptive system identification method is proposed, namely reweighted lp norm (0lms) algorithm. The main idea of the algorithm is to add a lp norm penalty of sparsity into the cost function of the lms algorithm. By doing so, the weight factor becomes a balance parameter of the associated lp norm adaptive sparse system identification. Subsequently, the steady state of the coefficient misalignment vector is derived theoretically, with a performance upper bounds provided which serve as a sufficient condition for the lms channel estimation of the precise reweighted lp norm. With the upper bounds, the authors prove that the lp (0algorithm has a better convergence speed and better steady-state behaviour than other lms algorithms.
Mandibular movement is complex and individual due to variations in the temporomandibular joint (TMJ). Consequently, patient-centered dentistry should incorporate patients' specific anatomy and condylar function in...
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Mandibular movement is complex and individual due to variations in the temporomandibular joint (TMJ). Consequently, patient-centered dentistry should incorporate patients' specific anatomy and condylar function in treatment planning. Real-time magnetic resonance imaging (rt-MRI) visualizes relevant structures and tracks mandibular movement. However, current assessments rely on qualitative observations or time-consuming manual tracking, lacking reliability. This study developed an automatic tracking algorithm for mandibular movement in rt-MRI using least mean square registration (lms) and compared it to manual tracking (MT) during mouth opening. Ten participants with skeletal class I underwent rt-MRI (10 frames/s). The same operator tracked the condylar pathway for the two methods, setting 2000 landmarks (2 landmarks x100 frames x10 participants) for MT and 210 landmarks (3 landmarks x7 frames x10 participants) for lms. Time required, superimposition error, and the distance between tracked condylar pathways were compared between methods. lms tracking was 76% faster and showed significantly better superimposition (0.0289 +/- 0.0058) than MT (0.059 +/- 0.0145) (p = 0.002). During one-third of the movement, the pathways tracked by both methods were more than 1 mm and 1 degrees apart. These findings highlight the benefits of automatic condylar movement tracking in rt-MRI, laying the groundwork for more objective and quantitative observation of TMJ function.
The least mean squares (lms) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of O(2N) where N is the number of taps. The drawback of the lms algorithm...
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The least mean squares (lms) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of O(2N) where N is the number of taps. The drawback of the lms algorithm is that its performance is sensitive to the scaling of the input. The normalized lms (Nlms) algorithm solves this problem on the lms algorithm by normalizing with the sliding-window power of the input;however, this normalization increases the computational cost to O(3N) per iteration. In this work, we derive a new formula to strictly perform the Nlms algorithm at a computational complexity of O(2N), that is referred to as the C-Nlms algorithm. The derivation of the C-Nlms algorithm uses the H-infinity framework presented previously by one of the authors for creating a unified view of adaptive filtering algorithms. The validity of the C-Nlms algorithm is verified using simulations.
This paper studies the stochastic behavior of the lms and Nlms algorithms in a system identification framework for a cyclostationary white input without assuming a Gaussian distribution for the input. The input cyclos...
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This paper studies the stochastic behavior of the lms and Nlms algorithms in a system identification framework for a cyclostationary white input without assuming a Gaussian distribution for the input. The input cyclostationary signal is modeled by a white random process with periodically time-varying power. The system parameters vary according to a random-walk. Mathematical models are derived for the mean and mean-square-deviation behavior of the adaptive weights as a function of the input cyclostationarity. Analytical models are first derived for the lms and Nlms algorithms for cyclostationary white inputs. These models show the dependence of the two algorithms upon the kurtosis of the input. Significant differences are found between the behaviors of the two algorithms when the analysis is applied to non-Gaussian cases. Monte Carlo simulations provide strong support for the theory.
To improve the convergence performance of an integer-order active noise control algorithm and achieve the noise cancellation of a train electric traction system fan, in this research, an active noise control algorithm...
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ISBN:
(纸本)9781665434980
To improve the convergence performance of an integer-order active noise control algorithm and achieve the noise cancellation of a train electric traction system fan, in this research, an active noise control algorithm based on a fractional-order variable step size was proposed. This algorithm introduced fractional calculus to control the iteration of the filter weights and to optimize the variable step size in order to improve the algorithm convergence process. The simulation results showed that the algorithm was superior to the conventional algorithm in terms of the performance of the convergence speed, steady-state error, and error attenuation. Consistent with the simulation results, this algorithm showed good performance in the half physical simulation. The results showed that this algorithm had a good noise-cancellation effect on the fan noise of the train electric traction system.
Although combinations of filters improve the performance of individual adaptive filters, they increase the computational cost by operating multiple simultaneous filters. In this paper, we propose the undermodeling of ...
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ISBN:
(纸本)9781728157672
Although combinations of filters improve the performance of individual adaptive filters, they increase the computational cost by operating multiple simultaneous filters. In this paper, we propose the undermodeling of the fast filter in a combination to reduce complexity and also obtain improved performance. As an inadequate design of the undermodeled fast filter may affect the combination performance, we describe how the length and the step-size of this filter should be designed, based on analytical models of transient and steady-state performance. We present simulations that evidence the improvement achieved by using this method, also including nonstationary scenarios as well as the application of the proposed method for OFDM wireless communications.
The paper analyzes the non-linear distortion effects of the Low Noise Amplifier (LNA) in the radio frequency direct-sampling receiver (DRF). A novel lookup table (LUT)-based linearization scheme was proposed. In this ...
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ISBN:
(数字)9781728154718
ISBN:
(纸本)9781728154718
The paper analyzes the non-linear distortion effects of the Low Noise Amplifier (LNA) in the radio frequency direct-sampling receiver (DRF). A novel lookup table (LUT)-based linearization scheme was proposed. In this scheme, the LNA nonlinearity is estimated by a specialized training-circuit. During the training process, an internally-generated signal with different power levels ranging from the LNA linear region to the saturated region is fed to the training circuit. An lms algorithm is used to invert the LNA nonlinear coefficients. The estimated coefficients and the corresponding power level of the input signals are stored in a LUT, accessed during the receiving mode, to remove the distortion. The effectiveness of the proposed method is evaluated by a Matlab simulation with four QPSK channels. The simulation results show that the proposed solution significantly enhances the amplification linearity. The Adjacent Channel Power Ratio (ACPR) increases by 40dB, accordingly the spectra of distorted components reduce close to the receiver's background noise level.
We proposed and analyze the use of adaptive feedforward controllers based on discrete-time FIR filters to enhance the performance of voltage and current class-D amplifiers to be used in protective relay testing. The f...
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ISBN:
(纸本)9781665442312
We proposed and analyze the use of adaptive feedforward controllers based on discrete-time FIR filters to enhance the performance of voltage and current class-D amplifiers to be used in protective relay testing. The feedforward controller compensates for inherent frequency response distortions, allowing the compound system to conduct tests with signal components from DC up to the 50th harmonic. We investigate the use of both, the lms and the RLS algorithms to estimate the parameters of the feedforward controller for different relay burdens. Results concerning convergence rates of the controller parameters and temporal response for ATP-like transient waveforms for use in protective relay tests are presented.
This work focuses on the development of a new family of decision-making algorithms for adaptation and learning, which are specifically tailored to decision problems and are constructed by building up on first principl...
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This work focuses on the development of a new family of decision-making algorithms for adaptation and learning, which are specifically tailored to decision problems and are constructed by building up on first principles from decision theory. A key observation is that estimation and decision problems are struc-turally different and, therefore, algorithms that have proven successful for the former need not perform well when adjusted for the latter. Exploiting classical tools from quickest detection, we propose a tailored version of Page's test, referred to as BLLR (barrier log-likelihood ratio) test, and demonstrate its applica-bility to real-data from the COVID-19 pandemic in Italy. The results illustrate the ability of the design tool to track the different phases of the outbreak.(c) 2021 Published by Elsevier B.V.
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