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Search Results: 1 - 10 of 559 matches for " Mojtaba Lotfizad "
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Modified Clipped LMS Algorithm
Mojtaba Lotfizad,Hadi Sadoghi Yazdi
EURASIP Journal on Advances in Signal Processing , 2005, DOI: 10.1155/asp.2005.1229
Abstract: A new algorithm is proposed for updating the weights of an adaptive filter. The proposed algorithm is a modification of an existing method, namely, the clipped LMS, and uses a three-level quantization (+1,0, ¢ ’1) scheme that involves the threshold clipping of the input signals in the filter weight update formula. Mathematical analysis shows the convergence of the filter weights to the optimum Wiener filter weights. Also, it can be proved that the proposed modified clipped LMS (MCLMS) algorithm has better tracking than the LMS algorithm. In addition, this algorithm has reduced computational complexity relative to the unmodified one. By using a suitable threshold, it is possible to increase the tracking capability of the MCLMS algorithm compared to the LMS algorithm, but this causes slower convergence. Computer simulations confirm the mathematical analysis presented.
Duct Modeling Using the Generalized RBF Neural Network for Active Cancellation of Variable Frequency Narrow Band Noise
Hadi Sadoghi Yazdi,Javad Haddadnia,Mojtaba Lotfizad
EURASIP Journal on Advances in Signal Processing , 2007, DOI: 10.1155/2007/41679
Abstract: We have shown that duct modeling using the generalized RBF neural network (DM_RBF), which has the capability of modeling the nonlinear behavior, can suppress a variable-frequency narrow band noise of a duct more efficiently than an FX-LMS algorithm. In our method (DM_RBF), at first the duct is identified using a generalized RBF network, after that N stage of time delay of the input signal to the N generalized RBF network is applied, then a linear combiner at their outputs makes an online identification of the nonlinear system. The weights of linear combiner are updated by the normalized LMS algorithm. We have showed that the proposed method is more than three times faster in comparison with the FX-LMS algorithm with 30% lower error. Also the DM_RBF method will converge in changing the input frequency, while it makes the FX-LMS cause divergence.
Duct Modeling Using the Generalized RBF Neural Network for Active Cancellation of Variable Frequency Narrow Band Noise
Yazdi Hadi Sadoghi,Haddadnia Javad,Lotfizad Mojtaba
EURASIP Journal on Advances in Signal Processing , 2007,
Abstract: We have shown that duct modeling using the generalized RBF neural network (DM_RBF), which has the capability of modeling the nonlinear behavior, can suppress a variable-frequency narrow band noise of a duct more efficiently than an FX-LMS algorithm. In our method (DM_RBF), at first the duct is identified using a generalized RBF network, after that stage of time delay of the input signal to the generalized RBF network is applied, then a linear combiner at their outputs makes an online identification of the nonlinear system. The weights of linear combiner are updated by the normalized LMS algorithm. We have showed that the proposed method is more than three times faster in comparison with the FX-LMS algorithm with 30% lower error. Also the DM_RBF method will converge in changing the input frequency, while it makes the FX-LMS cause divergence.
Clipped Input RLS Applied to Vehicle Tracking
Hadi Sadoghi Yazdi,Mojtaba Lotfizad,Ehsanollah Kabir,Mahmood Fathy
EURASIP Journal on Advances in Signal Processing , 2005, DOI: 10.1155/asp.2005.1221
Abstract: A new variation to the RLS algorithm is presented. In the clipped RLS algorithm (CRLS), proposed in updating the filter weights and computation of the inverse correlation matrix, the input signal is quantized into three levels. The convergence of the CRLS algorithm to the optimum Wiener weights is proved. The computational complexity and signal estimation error is lower than that of the RLS algorithm. The CRLS algorithm is used in the estimation of a noisy chirp signal and in vehicles tracking. Simulation results in chirp signal detection shows that this algorithm yields considerable error reduction and less computation time in comparison to the conventional RLS algorithm. In the presence of strong noise, also using the proposed algorithm in tracking of 59 vehicles shows an average of 3.06% reduction in prediction error variance relative to conventional RLS algorithm.
Validation and Coupling of the SWAN Wave Prediction Model by WRF for the Persian Gulf  [PDF]
Mojtaba Zoljoodi
Open Journal of Marine Science (OJMS) , 2017, DOI: 10.4236/ojms.2017.71003
Abstract: Generation of waves is affected by forces that exerted constantly in the oceans. The most obvious reason for the appearance of surface-waves is a process of interaction between atmosphere and sea surface that results in wind generation. Wave predictions are usually issued for a maximum of a few days for using in different fields such as shipping, fishing, oil industry, tourism, and to increase the safety of seafarers and beach habitants, maintaining economic assets and optimal utilization of natural resources. In this study, SWAN model has been run for this research over the Oman sea and the Persian Gulf. For implementation of SWAN, another dynamic model with prediction ability of 99-hours also has been used. In this example, wind field is obtained from the outputs of the WRF model converted to the required format for SWAN model. The computational network of SWAN model has been set to spatial grid points of 6 minutes with 1-hour temporal scale. Standard validation ways, including experimental verification, Multiplicative Bias, Mean Error and Root Mean Square Error are used in this study by comparing together for evaluation of accuracy of the model outputs. The results show that the prediction of wave heights by the model for 9 to 24-hour prediction could be the most accurate.
RETRACTED: Wave Run-Up and Surface Stress on a Permeable Coastal Bed  [PDF]
Mojtaba Zoljoodi
Open Journal of Marine Science (OJMS) , 2017, DOI: 10.4236/ojms.2017.71001
Abstract: Short Retraction Notice

The paper is withdrawn from \"Open Journal of Marine Science\" due to personal reasons from the corresponding author of this paper.

This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's Retraction Guidelines. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.

Editor guiding this retraction: Prof. David Alberto Salas-de-León (EiC of OJMS)

The full retraction notice in PDF is preceding the original paper, which is marked \"RETRACTED\".

RETRACTED: The Effects of Internal Waves on Propagation Behavior of Sound in the Sea  [PDF]
Mojtaba Zoljoodi
Open Journal of Marine Science (OJMS) , 2017, DOI: 10.4236/ojms.2017.71002
Abstract: Short Retraction Notice

The paper is withdrawn from \"Open Journal of Marine Science\" due to personal reasons from the corresponding author of this paper.

This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's Retraction Guidelines. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.

Editor guiding this retraction: Prof. David Alberto Salas-de-León (EiC of OJMS)

The full retraction notice in PDF is preceding the original paper, which is marked \"RETRACTED\".

A Family of Adaptive Filter Algorithms in Noise Cancellation for Speech Enhancement
Sayed. A. Hadei,M. lotfizad
Computer Science , 2011,
Abstract: In many application of noise cancellation, the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms, which converge rapidly. Least Mean Squares (LMS) and Normalized Least Mean Squares (NLMS) adaptive filters have been used in a wide range of signal processing application because of its simplicity in computation and implementation. The Recursive Least Squares (RLS) algorithm has established itself as the "ultimate" adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. Recently adaptive filtering was presented, have a nice tradeoff between complexity and the convergence speed. This paper describes a new approach for noise cancellation in speech enhancement using the two new adaptive filtering algorithms named fast affine projection algorithm and fast Euclidean direction search algorithms for attenuating noise in speech signals. The simulation results demonstrate the good performance of the two new algorithms in attenuating the noise.
Extension of Range of MINRES-CN Algorithm  [PDF]
Mojtaba Ghasemi Kamalvand
Applied Mathematics (AM) , 2011, DOI: 10.4236/am.2011.212205
Abstract: MINRES-CN is an iterative method for solving systems of linear equations with conjugate-normal coefficient matrices whose conspectra are located on algebraic curves of a low degree. This method was proposed in a previous publication of author and KH. D. Ikramov. In this paper, the range of applicability of MINRES-CN is extended in new direction. These are conjugate normal matrices that are low rank perturbations of Symmetric matrices. Examples are given that demonstrate a higher efficiency of MINRES-CN for this class of systems compared to the well-known algorithm GMRES.
Evaluation of cloud seeding project in Yazd Province of Iran using historical regression method (case study: Yazd 1 cloud seeding project, 1999)  [PDF]
Mojtaba Zoljoodi, Ali Didevarasl
Natural Science (NS) , 2013, DOI: 10.4236/ns.2013.59124
Abstract:

In this research, the result of the cloud seeding over Yazd province during three months of February, March and April in 1999 has been evaluated using the historical regression method. Hereupon, the rain-gages in Yazd province as the target stations and the rain-gages of the neighboring provinces as the control stations have been selected. The rainfall averages for the three aforementioned months through 25 years (1973-1997) in all control and target stations have been calculated. In the next step, the correlations between the rainfalls of control and target stations have been estimated about 75%, which indicates a good consistency in order to use the historical regression. Then, through the obtained liner correlation equation between the control and target stations the precipitation amount for February, March and April in 1999, over the target region (Yazd province) was estimated about 27.57 mm, whiles the observed amount was 34.23 mm. In fact the precipitation increasing around 19.5% over Yazd province confirmed the success of this cloud seeding project.

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