Multilevel Inverters (MLIs) have drawn increasing attention in numerous applications, especially in drives, distributed energy resources area, utility etc. MLIs have the ability to synthesize a near sinusoidal output ...
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Multilevel Inverters (MLIs) have drawn increasing attention in numerous applications, especially in drives, distributed energy resources area, utility etc. MLIs have the ability to synthesize a near sinusoidal output voltage wave with minimal Total Harmonic Distortion (THD) in low frequency switching. Even though they offer lower THD, the presence of lower order harmonics is objectionable and harmonics elimination in Multilevel Inverters (MLIs) has been receiving immense attention for the past few decades. Existing Selective Harmonic Elimination (SHE) techniques can eliminate the objectionable lower order voltage harmonics with low switching frequency by solving the Fourier non-linear transcendental equations of the output voltage. The line current harmonics has a direct role to play on the magneto-motive force and results in increase of mismatching of air-gap permeance, vibrations, acoustic noise etc. This study proposes normalizedleastmeansquares (NLMS) algorithm based scheme to eliminate the selected dominant harmonics in load current using only the knowledge of the frequencies to be eliminated. The algorithm is simulated using MATLAB/SIMULINK tool for a three-phase VSI to eliminate the fifth and seventh harmonics. The informative simulation results verify the validity and effectiveness of the proposed algorithm. The system performance is analyzed based on the simulation results considering Total Harmonic Distortion (THD), magnitude of eliminated harmonics and frequency spectrum.
A channel-estimate-based decision feedback equalizer (CEB DFE) robust under impulsive noise is presented for single-input multiple-output (SIMO) underwater acoustic communications. Channel estimation is performed via ...
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ISBN:
(纸本)9781479975785
A channel-estimate-based decision feedback equalizer (CEB DFE) robust under impulsive noise is presented for single-input multiple-output (SIMO) underwater acoustic communications. Channel estimation is performed via the improvedproportionate M-estimate affine projection algorithm (IPMAPA), a linear complexity algorithm which is robust against impulsive interference and exploits channel sparseness. The superiority of IPMAPA to the normalized sign algorithm (NSA) and the normalizedleast-mean-square (NLMS) algorithm is demonstrated by processing data transmitted at 9 kbps over a 1.2km shallow water environment contaminated by snapping shrimp noise.
A channel equalization technique based on the affine projection algorithms (APA) with set-membership (SM) filtering for updating filters of a decision feedback equalizer (DFE) is proposed for time-varying wireless cha...
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ISBN:
(纸本)9781467326209;9781467326193
A channel equalization technique based on the affine projection algorithms (APA) with set-membership (SM) filtering for updating filters of a decision feedback equalizer (DFE) is proposed for time-varying wireless channels. The performance of the proposed approach is analyzed through simulations. It is shown in the results that this scheme offers advantages of having substantial fewer number of weight updating operations at moderate SNR with almost similar convergence as normal APA based equalizer. The introduced equalizer converges faster with a marginal increase in complexity than the set-membership normalizedleastmeansquare (NLMS) algorithm based equalizer.
Functional near infrared spectroscopy (fNIRS) is non-invasive brain imaging techniques that detects the cortical activity by measuring the change in the concentration of oxyhemoglobin and de-oxy hemoglobin. It uses ne...
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ISBN:
(纸本)9781467348843;9781467348850
Functional near infrared spectroscopy (fNIRS) is non-invasive brain imaging techniques that detects the cortical activity by measuring the change in the concentration of oxyhemoglobin and de-oxy hemoglobin. It uses near infrared light of two wave lengths, 760 nm and 830 nm. NIRS is emerging neuro imaging modality with high temporal resolution. The advantage of NIRS system over other neuro imaging modalities is low cost, portable, safe and somehow results in short period of time. The scalp remains intact throughout the experiment. In this study we present a method for identification of brain activity by using fNIRS data. The general linear model has been used in study with predicted blood oxygen level dependent (BOLD) response signal and its delayed versions. The normalizedleastmeansquare (NLMS) algorithm has been used for identification of unknown parameters in the model recursively. A one way t-test has been performed for the significance of results.
A multiple-input multiple-output (MIMO) channel equalization scheme for time-varying wireless channels is proposed. The proposed equalization technique uses set-membership affine projection (SM-AP) algorithm for updat...
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ISBN:
(纸本)9781467348232;9781467348225
A multiple-input multiple-output (MIMO) channel equalization scheme for time-varying wireless channels is proposed. The proposed equalization technique uses set-membership affine projection (SM-AP) algorithm for updating filter weights of a decision feedback equalizer (DFE). The performance of the proposed scheme is investigated through simulations using wireless channel models recommended by International Telecommunication Union for Radiocommunication Sector (ITU-R). The results show that the proposed scheme achieves a fast convergence speed. The convergence performance of this scheme has also been studied for different projection order and compared with the convergence performance of set-membership normalizedleastmean-square (SM-NLMS) algorithm based MIMO-DFE. The proposed equalizer has low complexity and works well even in frequency-selective channels.
A channel equalization technique based on the affine projection algorithms (APA) with set-membership (SM) filtering for updating filters of a decision feedback equalizer (DFE) is proposed for time-varying wireless cha...
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ISBN:
(纸本)9781467326193
A channel equalization technique based on the affine projection algorithms (APA) with set-membership (SM) filtering for updating filters of a decision feedback equalizer (DFE) is proposed for time-varying wireless channels. The performance of the proposed approach is analyzed through simulations. It is shown in the results that this scheme offers advantages of having substantial fewer number of weight updating operations at moderate SNR with almost similar convergence as normal APA based equalizer. The introduced equalizer converges faster with a marginal increase in complexity than the set-membership normalizedleastmeansquare (NLMS) algorithm based equalizer.
The paper proposes a nonlinear system identification method that uses a combination of adaptive linear, Volterra and power filters. Adaptation of the kernels is made using a normalized least mean square algorithm. The...
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ISBN:
(纸本)9781457702013
The paper proposes a nonlinear system identification method that uses a combination of adaptive linear, Volterra and power filters. Adaptation of the kernels is made using a normalized least mean square algorithm. The method is applied in echo cancellation, where several sources of nonlinearities exist: the overdriven amplifier, the small loudspeaker at high volume, the room with different absorbent walls. Functions with nonlinear characteristics are chosen to model these distortions. The evaluation is made in terms of Echo Return Loss Enhancement. Results show that the overall convex combination approach performs better or at least as well as the best single adaptive filter.
In this letter, we propose a variable step-size normalizedleastmeansquare (NLMS) algorithm. We study the relationship among the NLMS, recursive leastsquare and Kalman filter algorithms. Based on the relationship, ...
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In this letter, we propose a variable step-size normalizedleastmeansquare (NLMS) algorithm. We study the relationship among the NLMS, recursive leastsquare and Kalman filter algorithms. Based on the relationship, we derive an equation to determine the step-size of NLMS algorithm at each time instant. In steady state, the convergence of the proposed algorithm is verified by using the equation, which describes the relationship among the mean-square error, excess mean-square error, and measurement noise variance. Through computer simulation results, we verify the performance of the proposed algorithm and the change in the variable step-size over iterations. (C) 2010 Elsevier B.V. All rights reserved.
High bit rates optical communication systems pose the challenge of their tolerance to linear and nonlinear fiber impairments. Coherent optical receivers using digital signal processing techniques can mitigate the fibe...
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High bit rates optical communication systems pose the challenge of their tolerance to linear and nonlinear fiber impairments. Coherent optical receivers using digital signal processing techniques can mitigate the fiber impairments in the optical transmission system, including the chromatic dispersion equalization with digital filters. In this paper, an adaptive finite impulse response filter employing normalized least mean square algorithm is developed for compensating the chromatic dispersion in a 112-Gbit/s polarization division multiplexed quadrature phase shift keying coherent communication system, which is established in the VPI Simulation platform. The principle of the adaptive normalized least mean square algorithm for signal equalization is analyzed theoretically, and at the meanwhile, the taps number and the tap weights in the adaptive finite impulse response filter for compensating a certain fiber chromatic dispersion are also investigated by numerical simulation. The chromatic dispersion compensation performance of the adaptive filter is analyzed by evaluating the behavior of the bit-error-rate versus the optical signal-to-noise ratio, and the compensation results are also compared with other present digital filters. (C) 2009 Elsevier B.V. All rights reserved.
A normalized-least-mean-square (NLMS) algorithm is used to adapt to an unknown system by minimizing the error between the desired signal and the signal resulting from the adaptive filter. A small positive constant val...
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ISBN:
(纸本)9788955191356
A normalized-least-mean-square (NLMS) algorithm is used to adapt to an unknown system by minimizing the error between the desired signal and the signal resulting from the adaptive filter. A small positive constant value is used to ameliorate the problem of its diverging when the input power is small. Moreover, it can be a variable, where Generalized normalized Gradient Descent (GNGD) algorithm can be used to update the value based on the a priori error. The Steady-State meansquare Error (SSMSE) performance of the algorithm is expected to improve by using a posteriori error instead of the a priori error. In this paper, a GNGD algorithm based on estimated a posteriori error is derived for use in coefficient update using NLMS algorithm. With simulation results, it is shown that using a posteriori error for updating the small constant value within channel equalizer coefficient update process decreases the algorithm's SSMSE performance sensitivity to varying initial step size.
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