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- 검색키워드
- 저자 : Mahdi Khosravy (검색결과 5 건)
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ICA-based blind MIMO OFDM Receiver with Low-complexity by using Stone BSS
Mahdi Khosravy,Mohammad Reza Alsharif,Hai Lin,Katsumi Yamashita 대한전자공학회 2009 ITC-CSCC :International Technical Conference on Ci Vol.2009 No.7
In order to reduce the complexity of the Blind ICA-based MIMO-OFDM receiver an algorithm of employing blind source separation (BSS) methods has been proposed. The proposed algorithm exploits high performance of kullback leibler ICA as well as low complexity of Stone BSS. Stone BSS reduces the number of iteration required for convergence of KL ICA by initializing the separator matrix. The complexity of KL ICA has been reduced even more by using a NlogN kernel entropy estimation method. The numerical evaluation demonstrates the significant reduction in complexity of the blind MIMO OFDM receiver. In order to reduce the complexity of the Blind ICA-based MIMO-OFDM receiver an algorithm of employing blind source separation (BSS) methods has been proposed. The proposed algorithm exploits high performance of kullback leibler ICA as well as low complexity of Stone BSS. Stone BSS reduces the number of iteration required for convergence of KL ICA by initializing the separator matrix. The complexity of KL ICA has been reduced even more by using a NlogN kernel entropy estimation method. The numerical evaluation demonstrates the significant reduction in complexity of the blind MIMO OFDM receiver.
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A Probabilistic Short-length Linear Predictability Approach to Blind Source Separation
Mahdi Khosravy,Mohammad Reza Alsharif,Katsumi Yamashita 대한전자공학회 2008 ITC-CSCC :International Technical Conference on Ci Vol.2008 No.7
A merit function based on short length linear predictability of signal in an objective probabilistic algorithm is defined and used for blind source separation (BSS) of linear mixtures of signals. In BSS literatures, it has been conjectured that linear mixture of statistically independent source signals will result in a set of signals which each of them has less predictability than (or equal to) that of any of component source signals. We have used this property to extract source signals by finding an un-mixing matrix that maximizes the proposed merit function of predictability for each recovered signal. This method which is called Probabilistic Short-length Linear Predictability BSS (PSLP-BSS), its performance has been driven with many tests performed with mixtures of different kinds (speech, audio, image, constructed mathematical signals like saw tooth and sinusoidal). In all cases, correlation between each of source signals and each of extracted signals shows near-perfect performance of the method. The proposed BSS doesnt require any assumption regarding the probability density function of source signals. It has been demonstrated that PSLP-BSS can separate signal mixtures in which each mixture is a linear combination of source signals with gaussian, super-gaussian and sub-gaussian probability density functions. However, the method is adapted to temporal structure of recovered signals. Since, the un-mixing matrix that is concluded by proposed merit function can be obtained as the solution to a generalized eigenvalue routine, signals can be extracted simultaneously using the fast eigen value.
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( Mahdi Khosravy ),( Kazuaki Nakamura ),( Yuki Hirose ),( Naoko Nitta ),( Noboru Babaguchi ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.3
In a wide range of ML applications, the training data contains privacy-sensitive information that should be kept secure. Training the ML systems by privacy-sensitive data makes the ML model inherent to the data. As the structure of the model has been fine-tuned by training data, the model can be abused for accessing the data by the estimation in a reverse process called model inversion attack (MIA). Although, MIA has been applied to shallow neural network models of recognizers in literature and its threat in privacy violation has been approved, in the case of a deep learning (DL) model, its efficiency was under question. It was due to the complexity of a DL model structure, big number of DL model parameters, the huge size of training data, big number of registered users to a DL model and thereof big number of class labels. This research work first analyses the possibility of MIA on a deep learning model of a recognition system, namely a face recognizer. Second, despite the conventional MIA under the white box scenario of having partial access to the users' non-sensitive information in addition to the model structure, the MIA is implemented on a deep face recognition system by just having the model structure and parameters but not any user information. In this aspect, it is under a semi-white box scenario or in other words a gray-box scenario. The experimental results in targeting five registered users of a CNN-based face recognition system approve the possibility of regeneration of users' face images even for a deep model by MIA under a gray box scenario. Although, for some images the evaluation recognition score is low and the generated images are not easily recognizable, but for some other images the score is high and facial features of the targeted identities are observable. The objective and subjective evaluations demonstrate that privacy cyber-attack by MIA on a deep recognition system not only is feasible but also is a serious threat with increasing alert state in the future as there is considerable potential for integration more advanced ML techniques to MIA.
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Size dependent axial free and forced vibration of carbon nanotube via different rod models
Khosravi, Farshad,Simyari, Mahdi,Hosseini, Seyed A.,Tounsi, Abdelouahed Techno-Press 2020 Advances in nano research Vol.9 No.3
The aim of this present research is the effect of the higher-order terms of the governing equation on the forced longitudinal vibration of a nanorod model and making comparisons of the results with classical nonlocal elasticity theory. For this purpose, the free axial vibration along with forced one under the two various linear and harmonic axial concentrated forces in zigzag Single-Walled Carbon Nanotube (SWCNT) are analyzed dynamically. Three various theories containing the classical theory, which is called Eringen's nonlocal elasticity, along with Rayleigh and Bishop theories (higher-order theories) are established to justify the nonlocal behavior of constitutive relations. The governing equation and the related boundary conditions are derived from Hamilton's principle. The assumed modes method is adopted to solve the equation of motion. For the free axial vibration, the natural frequencies are calculated for the various values of the nonlocal parameter only based on Eringen's theory. The effects of the nonlocal parameter, thickness, length, and ratio of the excitation frequency to the natural frequency over time in dimensional and non-dimensional axial displacements are investigated for the first time.
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A Consideration of Noise Cancellation by using PCA-ICA Method with Delay Estimation
Toshiaki Yokoda,Mohammad Reza Alsharif,Mahdi Khosravy 대한전자공학회 2008 ITC-CSCC :International Technical Conference on Ci Vol.2008 No.7
The noise cancellation technologies are useful for speech recognition and other applications. There are some kind of methods for cancellation of background noise. In this paper, The desired signals are separated from background noise by using proposed PCA-ICA method (Principal Component Analysis, Independent Component Analysis). The proposed PCA-ICA method requires several number of observed signals that is, the same as the number of sources. The noise signal can be removed in the same way as BSS (Blind Source Separation). We have done experiments (two-observation one-source one-noise, with delay) and evaluated the results.
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