List of Articles Mohammad Reza Karami-Mollaei

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  1 - Speech Intelligibility Improvement in Noisy Environments for Near-End Listening Enhancement
  Peyman Goli Mohammad Reza Karami-Mollaei
  10.7508/jist.2016.01.004
  A new speech intelligibility improvement method for near-end listening enhancement in noisy environments is proposed. This method improves speech intelligibility by optimizing energy correlation of one-third octave bands of clean speech and enhanced noisy speech without More

  A new speech intelligibility improvement method for near-end listening enhancement in noisy environments is proposed. This method improves speech intelligibility by optimizing energy correlation of one-third octave bands of clean speech and enhanced noisy speech without power increasing. The energy correlation is determined as a cost function based on frequency band gains of the clean speech. Interior-point algorithm which is an iterative procedure for the nonlinear optimization is used to determine the optimal points of the cost function because of nonlinearity and complexity of the energy correlation function. Two objective intelligibility measures, speech intelligibility index and short-time objective intelligibility measure, are employed to evaluate the noisy enhanced speech intelligibility. Furthermore, the speech intelligibility scores are compared with unprocessed speech and a baseline method under various noisy conditions. The results show large intelligibility improvements with the proposed method over the unprocessed noisy speech. Manuscript profile
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  2 - Online Signature Verification: a Robust Approach for Persian Signatures
  Mohamamd Esmaeel Yahyatabar Yasser  Baleghi Mohammad Reza Karami-Mollaei
  10.7508/jist.2015.02.007
  In this paper, the specific trait of Persian signatures is applied to signature verification. Efficient features, which can discriminate among Persian signatures, are investigated in this approach. Persian signatures, in comparison with other languages signatures, have More

  In this paper, the specific trait of Persian signatures is applied to signature verification. Efficient features, which can discriminate among Persian signatures, are investigated in this approach. Persian signatures, in comparison with other languages signatures, have more curvature and end in a specific style. Usually, Persian signatures have special characteristics, in terms of speed, acceleration and pen pressure, during drawing curves. An experiment has been designed to determine the function indicating the most robust features of Persian signatures. Results obtained from this experiment are then used in feature extraction stage. To improve the performance of verification, a combination of shape based and dynamic extracted features is applied to Persian signature verification. To classify these signatures, Support Vector Machine (SVM) is applied. The proposed method is examined on two common Persian datasets, the new proposed Persian dataset in this paper (Noshirvani Dynamic Signature Dataset) and an international dataset (SVC2004). For three Persian datasets EER value are equal to 3, 3.93, 4.79, while for SVC2004 the EER value is 4.43. Manuscript profile
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  3 - A new Sparse Coding Approach for Human Face and Action Recognition
  Mohsen Nikpoor Mohammad Reza Karami-Mollaei Reza Ghaderi
  10.7508/jist.2017.17.003
  Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image, video and etc. In the cases where we have some similar images from the different classes, using the sparse coding method the images may be classified into More

  Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image, video and etc. In the cases where we have some similar images from the different classes, using the sparse coding method the images may be classified into the same class and devalue classification performance. In this paper, we propose an Affine Graph Regularized Sparse Coding approach for resolving this problem. We apply the sparse coding and graph regularized sparse coding approaches by adding the affinity constraint to the objective function to improve the recognition rate. Several experiments has been done on well-known face datasets such as ORL and YALE. The first experiment has been done on ORL dataset for face recognition and the second one has been done on YALE dataset for face expression detection. Both experiments have been compared with the basic approaches for evaluating the proposed method. The simulation results show that the proposed method can significantly outperform previous methods in face classification. In addition, the proposed method is applied to KTH action dataset and the results show that the proposed sparse coding approach could be applied for action recognition applications too. Manuscript profile
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  4 - SQP-based Power Allocation Strategy for Target Tracking in MIMO Radar Network with Widely Separated Antennas
  Mohammad  Akhondi Darzikolaei Mohammad Reza Karami-Mollaei Maryam Najimi
  10.52547/jist.9.36.21
  20.1001.1.23221437.2021.9.36.9.0
  MIMO radar with widely separated antennas enhances detection and estimation resolution by utilizing the diversity of the propagation path. Each antenna of this type of radar can steer its beam independently towards any direction as an independent transmitter. However, t More

  MIMO radar with widely separated antennas enhances detection and estimation resolution by utilizing the diversity of the propagation path. Each antenna of this type of radar can steer its beam independently towards any direction as an independent transmitter. However, the joint processing of signals for transmission and reception differs this radar from the multistatic radar. There are many resource optimization problems which improve the performance of MIMO radar. But power allocation is one of the most interesting resource optimization problems. The power allocation finds an optimum strategy to assign power to transmit antennas with the aim of minimizing the target tracking errors under specified transmit power constraints. In this study, the performance of power allocation for target tracking in MIMO radar with widely separated antennas is investigated. Therefore, a MIMO radar with distributed antennas is configured and a target motion model using the constant velocity (CV) method is modeled. Then Joint Cramer Rao bound (CRB) for target parameters (joint target position and velocity) estimation error is calculated. This is utilized as a power allocation problem objective function. Since the proposed power allocation problem is nonconvex. Therefore, a SQP-based power allocation algorithm is proposed to solve it. In simulation results, the performance of the proposed algorithm in various conditions such as a different number of antennas and antenna geometry configurations is examined. Results affirm the accuracy of the proposed algorithm. Manuscript profile
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  5 - A New Power Allocation Optimization for One Target Tracking in Widely Separated MIMO Radar
  Mohammad Akhondi Darzikolaei Mohammad Reza Karami-Mollaei Maryam Najimi
  10.61186/jist.39531.11.44.294
  In this paper, a new power allocation scheme for one target tracking in MIMO radar with widely dispersed antennas is designed. This kind of radar applies multiple antennas which are deployed widely dispersed from each other. Therefore, a target is observed simultaneousl More

  In this paper, a new power allocation scheme for one target tracking in MIMO radar with widely dispersed antennas is designed. This kind of radar applies multiple antennas which are deployed widely dispersed from each other. Therefore, a target is observed simultaneously from different uncorrelated angles and it offers spatial diversity. In this radar, a target’s radar cross section (RCS) is different in each transmit-receive path. So, a random complex Gaussian RCS is supposed for one target. Power allocation is used to allocate the optimum power to each transmit antenna and avoid illuminating the extra power in the environment and hiding it from interception. This manuscript aims to minimize the target tracking error with constraints on total transmit power and the power of each transmit antenna. For calculation of target tracking error, the joint Cramer Rao bound for a target velocity and position is computed and this is assumed as an objective function of the problem. It should be noted that a target RCS is also considered as unknown parameter and it is estimated along with target parameters. This makes a problem more similar to real conditions. After the investigation of the problem convexity, the problem is solved by particle swarm optimization (PSO) and sequential quadratic programming (SQP) algorithms. Then, various scenarios are simulated to evaluate the proposed scheme. The simulation results validate the accuracy and the effectiveness of the power allocation structure for target tracking in MIMO radar with widely separated antennas. Manuscript profile