Mohammad Shams Esfand Abadi

Mohammad Shams Esfand Abadi
Shahid Rajaee Teacher Training University | SRTTU · Department of Electronic Engineering

Doctor of Engineering

About

64
Publications
6,660
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659
Citations

Publications

Publications (64)
Article
This paper utilizes the family of affine projection algorithms (APAs) for distributed estimation in the adaptive diffusion networks. The diffusion AP algorithm (DAPA), the diffusion selective partial update (SPU) APA (DSPU-APA), the diffusion selective regressor (SR) APA (DSR-APA), and the diffusion dynamic selection (DS) APA (DDS-APA) are introduc...
Article
This paper presents a novel diffusion subband adaptive filtering algorithm for distributed estimation over networks. To achieve the low computational load, the signed regressor (SR) approach is applied to normalized subband adaptive filter (NSAF) and two algorithms for diffusion networks are established. The diffusion SR-NSAF (DSR-NSAF) and modifie...
Article
This paper solves the problem of distributed estimation in the diffusion networks based on the family of normalized subband adaptive filters (NSAFs). The diffusion NSAF (DNSAF), the diffusion selective partial update NSAF (DSPU-NSAF), the diffusion fix selection NSAF (DFS-NSAF), and the diffusion dynamic selection NSAF (DDS-NSAF) are established ba...
Conference Paper
Many industrial machines are used in travertine stone industry. But classification of this type of stone in terms of quality and appearance is generally done by human experts. Using of human experts for classification has a lot of errors, times, and costs. The reason of choosing of travertine stone is the large variety and increasing use of this st...
Article
Two-dimensional (2D) adaptive filtering is a technique that can be applied to many images, and signal processing applications. This paper extends the one-dimensional adaptive filter algorithms to 2D structures and the novel 2D adaptive filters are established. Based on this extension, the 2D selective partial update NLMS (2D-SPU-NLMS), the 2D selec...
Article
This paper presents a new variable step-size normalized subband adaptive filter (VSS-NSAF) algorithm. In the proposed VSS-NSAF, the step-size changes in order to have largest decrease in the mean square deviation (MSD) for sequential iterations. To reduce the computational complexity of VSS-NSAF, the variable step-size selective partial update norm...
Article
This paper presents the problem of distributed estimation in an incremental network based on the family of affine projection (AP) adaptive algorithms. The distributed selective partial update normalized least mean squares (dSPU-NLMS), the distributed SPU-AP algorithm (dSPU-APA), the distributed selective regressor APA (dSR-APA), the distributed dyn...
Conference Paper
In this paper we apply Set-Membership (SM) adaptive algorithm over distributed networks based on incremental strategy. The distributed SM normalized least mean squares (dSM-NLMS) algorithm is introduced which has high convergence speed, low steady-state mean square error and low computational complexity features. The good performance of dSM-NLMS is...
Conference Paper
This paper presents a new variable step-size normalized subband adaptive filter (VSS-NSAF) algorithm with dynamic selection (DS) of subband filters. The selection strategy is performed to achieve the largest decrease between the successive mean square deviations at every iteration. The proposed VSS-DS-NSAF has fast convergence speed and low steady-...
Conference Paper
This paper presents the problem of distributed estimation in an adaptive incremental network based on the selective partial update affine projection algorithm (SPU-APA). In dSPU-APA, the weight coefficients are partially updated at each node during the adaptation. The distributed SPU-APA (dSPU-APA) has low computational complexity feature and close...
Conference Paper
In this paper, we present the distributed selective partial update affine projection algorithm (dSPU-APA), the distributed dynamic selection affine projection algorithm (dDS-APA), and dSPU-DS-APA to solve distributed estimation problem over adaptive diffusion networks. In dSPU-APA, the filter coefficients are partially updated during the adaptation...
Article
Selective partial update (SPU) strategy in adaptive filter algorithms is used to reduce the computational complexity. In this paper we apply the SPU normalized least mean squares algorithm (SPU-NLMS) for distributed estimation problem in an incremental network. The distributed SPU-NLMS (dSPU-NLMS) has close convergence speed to dNLMS, low steady-st...
Conference Paper
Selective partial update (SPU) strategy in adaptive filter algorithms is used to reduce the computational complexity. In this paper we apply the SPU Normalized Least Mean Squares algorithms (SPU-NLMS) for distributed estimation problem based on incremental strategy in a incremental network. The distributed SPU-NLMS (dSPUNLMS) reduces the computatio...
Conference Paper
Selective partial update (SPU) approach is applied to adaptive filter algorithms to reduce the computational complexity. In this paper we extend the SPU method to affine projection with dynamic selection of input vectors (DS-APA) to establish the SPU-DS-APA. In the following, we analyze the mean-square performance of SPU-DS-APA in stationary and no...
Article
In this paper, we extend the set-membership (SM) adaptive filtering approach to the various affine projection (AP) adaptive filter algorithms to propose the computationally efficient algorithms. Based on this, the SM-APA, SM selective regressor APA (SM-SR-APA), SM dynamic selection APA (SM-DS-APA) and SM selective partial update APA (SM-SPU-APA) ar...
Article
Full-text available
This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection...
Article
In this paper, we present an interferometry method for refractive index determination in membranes of fuel cells. This technique is based on the use of an improved laser heterodyne interferometer. The photocurrents of the avalanche photodiodes, resulting from reflected beams of the optical head, are led to the signal conditioner and digital signal...
Article
Full-text available
Two-dimensional (2D) adaptive filtering is a technique that can be applied to many image and signal processing applications. This paper extends the one-dimensional adaptive filter algorithms to 2D structure and the novel 2D adaptive filters are established. Based on this extension, the 2D variable step-size normalized least mean squares (2D-VSS- NL...
Article
Full-text available
In this paper, the concept of proportionate adaptation is extended to the normalized subband adaptive filter (NSAF), and seven proportionate normalized subband adaptive filter algorithms are established. The proposed algorithms are proportionate normalized subband adaptive filter (PNSAF), μ‐law PNSAF (MPNSAF), improved PNSAF (IPNSAF), the improved...
Article
Full-text available
We present the general framework formean-square performance analysis of the selective partial update affine projection algorithm (SPU-APA) and the family of SPU normalized least mean-squares (SPU-NLMS) adaptive filter algorithms in nonstationary environment. Based on this the tracking performance of Max-NLMS, N-Max NLMS and the various types of SPU...
Article
In this study the concepts of selective partial updates (SPU) and selective regressors (SR) in the affine projection adaptive filtering algorithm are combined and the family of affine projection algorithms (APAs) with SPU and SR features are established. These algorithms are computationally efficient. The mean-square performance of the presented al...
Conference Paper
In this paper the new two-dimensional (TD) adaptive filter algorithms are introduced. The presented algorithms are TD variable step-size (VSS) normalized least mean squares (TD-VSS-NLMS) and TD-VSS affine projection algorithms (TD-VSS-APA). In these algorithms, the step-size changes during the adaptation which leads to the low steady-state mean squ...
Article
This paper presents a new way of computing the weights for combining multiple neural network classifiers based on particle swarm optimization, PSO. The weights are obtained so that they minimize the total classification error rate of the ensemble system. In order to evaluate the effectiveness of the proposed method, we have carried out some experim...
Article
Laser heterodyne interferometer is one kind of nano-metrology systems which has been widely used in industry for high-accuracy displacement measurements. The accuracy of the nano-metrology systems based on the laser heterodyne interferometers can be effectively limited by the periodic nonlinearity. In this paper, we present the nonlinearity modelin...
Article
In this article, the concept of proportionate adaptation is extended to the selective partial update (SPU) and set-membership (SM) normalized subband adaptive filters (NSAFs), and three proportionate normalized subband adaptive filter algorithms are established. The proposed algorithms are the improved proportionate NSAF (IPNSAF), the SPU improved...
Conference Paper
Laser heterodyne interferometer is one kind of nano-metrology systems which has been widely used in industry for high-accuracy displacement measurements. The accuracy of the nano-metrology systems based on the laser heterodyne interferometers can be effectively limited by the periodic nonlinearity. In this paper, we present the nonlinearity modelin...
Article
This paper presents a family of Variable Step-Size (VSS) AAne Projection (AP) adaptive ltering algorithms with Selective Partial Updates (SPU) and Selective Regressors (SR). The presented algorithms have good convergence speed, low steady state Mean Square Error (MSE), and low computa-tional complexity features. The stability bounds of the family o...
Conference Paper
The periodic nonlinearity in the nanometrology systems based on the laser heterodyne interferometers mainly arises from imperfect laser source and misalignment of their optical setup. The accuracy of the nanometric displacement measurements can be effectively limited by the periodic nonlinearity. In this paper, we model the periodic nonlinearity in...
Conference Paper
This paper presents a family variable step-size (VSS) affine projection (AP) adaptive filtering algorithms with selective partial updates (SPU). The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms thr...
Article
In this paper we present a general formalism for the establishment and mean-square performance analysis of the family of selective partial update affine projection (SPU-AP), selective regressor affine projection (SR-AP), and selective partial update subband adaptive filter (SPU-SAF) algorithms. This analysis is based on energy conservation argument...
Article
In the context of sample rate conversion (SRC) filter design; this paper makes two key contributions. Firstly, a practical factorization algorithm has been formulated that iteratively assigns rate change factors to filtering stages of a proposed multi-standard ...
Conference Paper
In this paper the concepts of selective partial updates (SPU) and selective regressors (SR) in the affine projection (AP) adaptive filtering algorithm are combined and the family of affine projection algorithms with SPU and SR features are established. These algorithms are computationally efficient. We demonstrate the performance of the presented a...
Article
Employing a recently introduced unified adaptive filter theory, we show how the performance of a large number of important adaptive filter algorithms can be predicted within a unified way. This approach is based on energy conservation arguments and does not need to assume the specific models for the regressors. This general performance analysis can...
Article
In this paper we present a general formalism for the establishment of the family of selective regressor affine projection algorithms (SR-APA). The SR-APA, the SR regularized APA (SR-R-APA), the SR partial rank algorithm (SR-PRA), the SR binormalized data reusing least mean squares (SR-BNDR-LMS), and the SR nor-malized LMS with orthogonal correction...
Article
In this paper, the concept of proportionate adaptation is extended to the normalized subband adaptive filters (NSAFs) and four proportionate adaptive filters are established. The proposed algorithms are proportionate normalized subband adaptive filter (PNSAF), μ-law PNSAF (MPNSAF), improved PNSAF (IPNSAF), and improved IPNSAF (IIPNSAF) which are su...
Article
The aim of the minimization analysis of network attack graphs (NAGs) is to nd a minimum critical set of exploits so that by preventing them an intruder cannot reach his goal using any attack scenario. This problem is, in fact, a constrained optimization problem. In this paper, a binary particle swarm optimization algorithm, called SwarmNAG, is pres...
Article
In this paper, we present a general formalism for the mean-square performance analysis of selective partial update subband adaptive filter (SPU-SAF) algorithms. This analysis is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. We demonstrate through simulations that the results...
Article
This paper presents three efficient subband adaptive filter (SAF) algorithms featuring low computational complexity. In the first algorithm, which is called selective partial update SAF (SPU-SAF), the filter coefficients are partially updated in each subband rather than the entire filter at every adaptation. In the second one, the concept of set-me...
Article
In this paper we present a general formalism for the family of adaptive filter algorithms with selective partial updates. Based on this, the mean-square performance analysis of this family of adaptive filters is presented in a unified way. This analysis is based on energy conservation arguments and does not need to assume a Gaussian or white distri...
Article
A streamlined theory is presented for adaptive filters within which the major adaptive filter algorithms can be seen as special cases. The algorithm development part of the theory involves three ingredients: a preconditioned Wiener Hopf equation, its simplest possible iterative solution through the Richardson iteration, and an estimation strategy f...
Article
Employing a recently introduced framework within which a large number of classical and modern adaptive filter algorithms can be viewed as special cases, we extend this framework to cover block normalized LMS (BNLMS) and normalized data reusing LMS (NDRLMS) adaptive filter algorithms. Accordingly, we develop a generic variable step-size adaptive fil...
Conference Paper
In this paper, the concept of set-membership (SM) adaptive filtering is extended to the subband adaptive filters (SAFs) and a novel SM-SAF algorithm is presented. The proposed algorithm exhibits superior performance with significant reduction in the overall computational complexity compared with the ordinary SAF.
Article
Employing a recently introduced framework, within which a large number of classical and modern adaptive filter algorithms can be viewed as special cases, a generic, variable step-size adaptive filter has been presented. Variable Step-Size (VSS) Normalized Least Mean Square (VSSNLMS) and VSS Affine Projection, Algorithms (VSSAPA) are particular exam...
Conference Paper
The LMS adaptive filter algorithm can be viewed as the application of a simple iterative linear equation solver to an estimated time variant Wiener-Hopf equation. With this interpretation we can utilize the "bag-of-tricks" available in numerical linear algebra in devising new adaptive filter algorithms. Making use of the preconditioning paradigm, w...
Article
Fast Euclidean Direction Search (FEDS) and Recursive Adaptive Matching Pursuit (RAMP) are two recently introduced algorithms for adaptive filtering characterized by low computational complexity, good convergence, and numerical robustness. While conceived from quite different perspectives, we point out, that both algorithms are closely related and c...
Article
The independence assumptions are widely used conditions in the performance analysis of adaptive filters. Although not valid in general, because of the tapped-delay-line structure of the regression data in most filter implementations, its value lies in the simplifications, it introduces into the analysis. Another approach to study the performance of...
Conference Paper
The block LMS algorithms can constitute a major branch in the adaptive algorithms family. In this paper we introduce the new variable step size block least mean square (VSSBLMS) adaptive filter algorithm. The proposed algorithm exhibits fast convergence and lower steady state mean square error when compared to the ordinary BLMS algorithm
Article
Full-text available
Intruders often combine exploits against multiple vulnerabilities in order to break into the system. Each attack scenario is a sequence of exploits launched by an intruder that leads to an undesirable state such as access to a database, service disruption, etc. The collection of possible attack scenarios in a computer network can be represented by...
Conference Paper
Full-text available
Least mean square (LMS) adaptive filters have been used in a wide range of one-dimensional signal processing applications. Recently adaptive filtering are presented that are based on the Euclidean Direction Search (EDS) method of optimization. The fast version of this class is called the Fast-EDS or FEDS algorithm. The FEDS based algorithms have a...
Conference Paper
Employing a recently introduced framework within which a large number of classical and modern adaptive filter algorithms can be viewed as special cases, we develop a generic variable step size adaptive filter. Variable step-size (VSS) least mean square (VSSLMS), VSS normalized LMS (VSSNLS) and VSS affine projection algorithms (VSSAPA) are particula...
Conference Paper
Fast Euclidean direction search (FEDS) (T. Bose et al., 2002) and recursive adaptive matching pursuit (RAMP) (J.H. Husoy, 2003) are two recently introduced algorithms for adaptive filtering characterized by low computational complexity, good convergence, and numerical robustness. While conceived from quite different perspectives, we point out in th...
Conference Paper
Employing a recently introduced framework in which a large number of adaptive filter algorithms can be viewed as special cases, we present a common framework for the analysis of their transient behavior. An important implication of this is that while the theoretical analysis can be performed for a generic filter coefficient update equation, the res...
Conference Paper
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. Ra...
Article
Employing a recently introduced framework in which a large number of adaptive filter algorithms can be viewed as special cases, we present a generalized transient analysis. An important implication of this is that while the theoretical analysis is performed for a generic filter coefficient update equation the results are directly applicable to a la...
Article
Employing a recently introduced unified adaptive filter theory, we show how the performance of a large number of important adaptive filter algorithms can be predicted within a general framework in nonstationary environment. This approach is based on energy con-servation arguments and does not need to assume a Gaussian or white distribution for the...
Article
known to be capable of performing much better than the LMS algorithm but practical implementations of this algorithm are often associated with high computational complexity and/or poor numerical properties [1], [2] ,[3]. It is well known that two of most frequently applied algorithms for noise cancellation [1], [4], [5] are normalized least mean sq...

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