Loading...
Search for: reza-veisi--mohsen
0.074 seconds

    , M.Sc. Thesis Sharif University of Technology Reza Veisi, Mohsen (Author) ; Ghotbi, Siroos (Supervisor) ; Ayatollahi, Shahabedin (Co-Advisor)
    Abstract
    In water drive or waterflooded reservoirs, a significant amount of oil is remained in the reservoir as the residual oil. A process known as gas assisted gravity drainage, in its tertiary mode, has been applied in a number of conventional reservoirs to recover this residual oil. Gravity drainage is the main mechanism of oil recovery in the gas assisted gravity drainage enhanced oil recovery process. Previous investigations has shown that gravity force, spreading coefficient, three phase capillary pressure and reservoir wettability have significant effect on oil recovered via this process. The feasibility of using such a process in a naturally fractured reservoir is investigated in this work... 

    A Combined Molecular Dynamics-Coarse Graining Technique for Modeling the Mechanical Behavior of Crystalline Nano-Structures

    , M.Sc. Thesis Sharif University of Technology Mohammad Reza, Kimia (Author) ; Khoei, Amir Reza (Supervisor) ; Jahanshahi, Mohsen (Co-Supervisor)
    Abstract
    In the area of material studies, the atom structure models are the basis of all simulations and methods. With improvements in computers power, these models have become more consistent with experimental results. New theoretical methods combined with supercomputers assist to an understanding with detail and accuracy of material behavior at the atomic scale that leads to develop of the Computational Materials Science. Recently, developments in fields such as quantum mechanics, statistical physics, solid-state physics, quantum chemistry, computer science and graphics, allowed for faster computing which leads a powerful tool for material calculations and designs. New computer applications allow... 

    Experimental and Analytical Investigation of Elastic-Plastic Behavior of Glass Fiber Reinforced Polymer Composites

    , M.Sc. Thesis Sharif University of Technology Sajadi, Banafshe (Author) ; Naghdabadi, Reza (Supervisor) ; Asghari, Mohsen (Supervisor)
    Abstract
    In this thesis, we aim to study the elastic-plastic behavior of short fiber reinforced composites experimentally and analytically. The experimental investigation is applied to glass fiber-polypropylene composites (PP/GF). The samples are produced by injection process with maximum temperature of 190C. Fibers are randomly oriented in this procedure of fabrication. The samples are tested in tension, and the mechanical strength is measured by changes in fiber content. The Young's modulus is also investigated in these experiments.Also in this thesis, an analytical model for calculation of Young’s modulus of fiber reinforced composite materials is proposed. In this analysis a displacement field is... 

    A comparative study on single-channel noise estimation methods for speech enhancement

    , Article International Conference on Intelligent Systems Design and Applications, ISDA ; 2012 , Pages 645-650 ; 21647143 (ISSN) ; 9781467351188 (ISBN) Veisi, H ; Sameti, H ; Sharif University of Technology
    2012
    Abstract
    This paper studies a number of well-known noise estimation techniques and provides a comparative performance analysis of them in speech enhancement platform. Two types of evaluation data that simulate consistent and inconsistent noisy conditions are prepared in the presence of six noise types at different SNR levels. The performance of speech enhancement systems and the spectrum distance of the estimated and original noise spectrums are used as evaluation criteria. The evaluations indicate that a simple VAD method outperforms noise estimation methods in most of the consistent noisy conditions  

    Hidden-Markov-model-based voice activity detector with high speech detection rate for speech enhancement

    , Article IET Signal Processing ; Volume 6, Issue 1 , February , 2012 , Pages 54-63 ; 17519675 (ISSN) Veisi, H ; Sameti, H ; Sharif University of Technology
    2012
    Abstract
    A new voice activity detection (VAD) algorithm with soft decision output in Mel-frequency domain is developed based on hidden Markov model (HMM) and is incorporated in an HMM-based speech enhancement system. The proposed VAD uses a two-state ergodic HMM representing speech presence and speech absence. The states are constructed from noisy speech and noise HMMs used in the speech enhancement system. This composite model provides a robust detection of speech segments in the presence of noise and obviates the need for extra modeling in HMM-based speech enhancement applications. As the main purpose of the proposed VAD is to detect speech segments accurately, a hang-over mechanism is proposed and... 

    The integration of principal component analysis and cepstral mean subtraction in parallel model combination for robust speech recognition

    , Article Digital Signal Processing: A Review Journal ; Volume 21, Issue 1 , 2011 , Pages 36-53 ; 10512004 (ISSN) Veisi, H ; Sameti, H ; Sharif University of Technology
    2011
    Abstract
    This paper addresses the problem of automatic speech recognition in real applications in which the speech signal is altered by various noises. Feature compensation and model compensation robustness methods are studied. Parallel model combination (PMC) and its recent advances are reviewed and a novel algorithm called PC-PMC is proposed. This algorithm utilizes cepstral mean subtraction (CMS) normalization ability and principal component analysis (PCA) compression and de-correlation capability in the combination with PMC model transformation method. PC-PMC algorithm takes the advantages of additive noise compensation ability of PMC and convolutional noise removal capability of CMS and PCA. In... 

    Cepstral-domain HMM-based speech enhancement using vector Taylor series and parallel model combination

    , Article 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012, 2 July 2012 through 5 July 2012 ; July , 2012 , Pages 298-303 ; 9781467303828 (ISBN) Veisi, H ; Sameti, H ; Sharif University of Technology
    2012
    Abstract
    Speech enhancement problem using hidden Markov model (HMM) and minimum mean square error (MMSE) in cepstral domain is studied. This noise reduction approach can be considered as weighted-sum filtering of the noisy speech signal in which the filters weights are estimated using the HMM of noisy speech. To have an accurate estimation of the noisy speech HMM, vector Taylor series (VTS) is proposed and compared with the parallel model combination (PMC) technique. Furthermore, proposed cepstral-domain HMM-based speech enhancement systems are compared with the renowned autoregressive HMM (AR-HMM) approach. The evaluation results confirm the superiority of the cepstral domain approach in comparison... 

    A parallel cepstral and spectral modeling for HMM-based speech enhancement

    , Article 17th DSP 2011 International Conference on Digital Signal Processing, Proceedings, 6 July 2011 through 8 July 2011, Corfu ; 2011 ; 9781457702747 (ISBN) Veisi, H ; Sameti, H ; Sharif University of Technology
    2011
    Abstract
    An HMM-based speech enhancement in Mel-frequency domain is introduced and improved. It is shown that hidden Markov modeling in the Mel-frequency domain is beneficial due to its effective representation of the speech spectrum; however, speech enhancement in this domain requires an inversion from the Mel-frequency to the spectral domain which introduces distortion artifacts for spectrum estimation. To reduce the distortion effects of the inversion and employ the advantages of robustness modeling in the Mel-frequency domain, a parallel cepstral and spectral (PCS) modeling is proposed. In PCS, a concurrent modeling in both cepstral and spectral domains is performed. The performances of the... 

    Speech enhancement using hidden Markov models in Mel-frequency domain

    , Article Speech Communication ; Volume 55, Issue 2 , 2013 , Pages 205-220 ; 01676393 (ISSN) Veisi, H ; Sameti, H ; Sharif University of Technology
    2013
    Abstract
    Hidden Markov model (HMM)-based minimum mean square error speech enhancement method in Mel-frequency domain is focused on and a parallel cepstral and spectral (PCS) modeling is proposed. Both Mel-frequency spectral (MFS) and Mel-frequency cepstral (MFC) features are studied and experimented for speech enhancement. To estimate clean speech waveform from a noisy signal, an inversion from the Mel-frequency domain to the spectral domain is required which introduces distortion artifacts in the spectrum estimation and the filtering. To reduce the corrupting effects of the inversion, the PCS modeling is proposed. This method performs concurrent modeling in both cepstral and magnitude spectral... 

    An improved parallel model combination method for noisy speech recognition

    , Article Proceedings of the 2009 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2009 ; 2009 , Pages 237-242 ; 9781424454792 (ISBN) Veisi, H ; Sameti, H ; Sharif University of Technology
    2009
    Abstract
    In this paper a novel method, called PC-PMC, is proposed to improve the performance of automatic speech recognition systems in noisy environments. This method is based on the parallel model combination (PMC) technique and uses the Cepstral Mean Subtraction (CMS) normalization ability and Principal Component Analysis (PCA) compression and decorrelation capabilities. It takes the advantages of both additive noise compensation of PMC and convolutive noise removal ability of CMS and PCA. The first problem to be solved in the realizing of PC-PMC is that PMC algorithm requires invertible modules in the front-end of the system while CMS normalization is not an invertible process. Also, it is... 

    A complexity-based approach in image compression using neural networks

    , Article World Academy of Science, Engineering and Technology ; Volume 35 , 2009 , Pages 684-694 ; 2010376X (ISSN) Veisi, H ; Jamzad, M ; Sharif University of Technology
    2009
    Abstract
    In this paper we present an adaptive method for image compression that is based on complexity level of the image. The basic compressor/de-compressor structure of this method is a multilayer perceptron artificial neural network. In adaptive approach different Back-Propagation artificial neural networks are used as compressor and de-compressor and this is done by dividing the image into blocks, computing the complexity of each block and then selecting one network for each block according to its complexity value. Three complexity measure methods, called Entropy, Activity and Pattern-based are used to determine the level of complexity in image blocks and their ability in complexity estimation... 

    Investigation of the equivalent material properties and failure stress of the re-entrant composite lattice structures using an analytical model

    , Article Composite Structures ; 2020 Veisi, H ; Farrokhabadi, A ; Sharif University of Technology
    Elsevier Ltd  2020
    Abstract
    In the present study, a novel theoretical model is developed, based on classical laminate theory, to predict the equivalent mechanical properties of the re-entrant lattice structures, which composed of continuous fiber reinforced composite struts. Three main mechanism of stretching, flexing and hinging are considered and a general closed-form formulation is derived to estimate the auxetic honeycomb's elastic and shear modulus as well as Poisson's ratios. In spite of previous studies in which the response of honeycomb structures is modeled using beam theory, here, each strut of unit cell is expressed as a composite laminate with orthotropic mechanical properties and classical laminate theory... 

    Improving the performance of speech recognition systems using fault-tolerant techniques

    , Article 2008 9th International Conference on Signal Processing, ICSP 2008, Beijing, 26 October 2008 through 29 October 2008 ; 2008 , Pages 579-582 ; 9781424421794 (ISBN) Veisi, H ; Sameti, H ; Sharif University of Technology
    2008
    Abstract
    In this paper, using of fault tolerant techniques are studied and experimented in speech recognition systems to make these systems robust to noise. Recognizer redundancy is implemented to utilize the strengths of several recognition methods that each one has acceptable performance in a specific condition. Duplication-with-comparison and NMR methods are experimented with majority and plurality voting on a telephony Persian speech-enabled IVR system. Results of evaluations present two promising outcomes, first, it improves the performance considerably; second, it enables us to detect the outputs with low confidence. © 2008 IEEE  

    Noise and speaker robustness in a persian continuous speech recognition system

    , Article 2007 9th International Symposium on Signal Processing and its Applications, ISSPA 2007, Sharjah, 12 February 2007 through 15 February 2007 ; 2007 ; 1424407796 (ISBN); 9781424407798 (ISBN) Veisi, H ; Sameti, H ; Sharif University of Technology
    2007
    Abstract
    In this paper VTLN speaker normalization, MLLR and MAP adaptation methods are investigated in a Persian HMM-based speaker independent large vocabulary continuous speech recognition system. Speaker and environmental noise robustness are achieved in real world applications for this system. A search-based method is used in VTLN to find speaker relative warping factors. The warping factors are applied to signal's spectrum to normalize the variation effect of VTL between speakers. In the MLLR framework, Gaussian mean and covariance transformations in global and full adaptation are experienced. In this method, regression tree based adaptation in batch-supervised fashion is used. Also the standard... 

    Image compression with neural networks using complexity level of images

    , Article ISPA 2007 - 5th International Symposium on Image and Signal Processing and Analysis, Istanbul, 27 September 2007 through 29 September 2007 ; 2007 , Pages 282-287 ; 9789531841160 (ISBN) Veisi, H ; Jamzad, M ; Sharif University of Technology
    2007
    Abstract
    This paper presents a complexity-based image compression method using neural networks. In this method, different multi-layer perceptron ANNs are used as compressor and de-compressor. Each image is divided into blocks, complexity of each block is computed using complexity measure methods and one network is selected for each block according to its complexity value. Three complexity measure methods, called Entropy, Activity and Pattern-based are used to determine the level of complexity in image blocks and their ability are evaluated and compared together. Selection of a network for each image block is based on its complexity value or the Best-SNR criterion. Best-SNR chooses one of the trained... 

    The combination of CMS with PMC for improving robustness of speech recognition systems

    , Article 13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008, Kish Island, 9 March 2008 through 11 March 2008 ; Volume 6 CCIS , 2008 , Pages 825-829 ; 18650929 (ISSN); 3540899847 (ISBN); 9783540899846 (ISBN) Veisi, H ; Sameti, H ; Sharif University of Technology
    2008
    Abstract
    This paper addresses the robustness problem of automatic speech recognition systems for real applications in presence of noise. PMCC algorithm is proposed for combining PMC technique with CMS method. The proposed algorithm utilizes the CMS normalization ability in PMC method to takes the advantages of these methods to compensate the effect of both additive and convolutional noises. Also, we have investigated VTLN for speaker normalization and MLLR and MAP for speaker and acoustic adaptation. Different combinations of these methods are used to achieve robustness and making the system usable in real applications. Our evaluations are done on 4 different real noisy tasks on Nevisa recognition... 

    Speech Enhancement Based on Statistical Methods

    , Ph.D. Dissertation Sharif University of Technology Veisi, Hadi (Author) ; Sameti, Hossein (Supervisor)
    Abstract
    Signle-channel speech enhancement using hidden Markov model (HMM) based on minimum mean square error (MMSE) estimator is focused on and an HMM-based speech enhancement in Mel-frequency domain is proposed. The MMSE estimator results in a weighted sum filtering of the noisy signal in which accurate estimation of the filter values and filter weights comprise the main challenges. The cepstral domain modeling for speech enhancement is motivated by accurate filter selection in this domain. In the propsed framework, Mel-frequency spectral (MFS) and Mel-frequency cepstral (MFC) features are studied and experimented. In addition to the spectrum estimator, magnitude spectrum, log-magnitude spectrum... 

    Biopolymeric based Interpenetrating Polymer Network for Extending the Microextraction Techniques

    , M.Sc. Thesis Sharif University of Technology Veisi, Nazila (Author) ; Bagheri, Habib (Supervisor)
    Abstract
    In this study, gelatin/polyvinylpyrrolidone semi interpenetrating polymer network scaffold was synthesized in presence of glutaraldehyde as a cross linker and a porous structure was obtained by freeze drying method. The biocompatible network with properties like huge porosity, good thermal stability and establishing different interactions, was employed as an extractive phase in needle trap device and used for evaluation of benzene, toluene, ethylbenzene and xylene (BTEX) in conjugation with gas chromatography-flame ionization detector. For investigating semi interpenetrating polymer network efficiency, Different ratios of gelatin and polyvinylpyrrolidone (gelatin/polyvinylpyrrolidone: 1:0,... 

    Preparation of Polyethersulfone/Polyamide (PES/PA)Thin Film Composite Membranes based Modified Nanoclays for Forward Osmosis-Disalination

    , M.Sc. Thesis Sharif University of Technology Veisi, Vahab (Author) ; Bagherzadeh, Mojtaba (Supervisor)
    Abstract
    In this study, two layer nanocomposite membrane of polyethersulfone (support layer) and polyamide (active layer) were prepared for the purpose of desalination of water by forward osmosis method. In order to take more advantage of this membrane, montmorillonite nanoparticles which had been modified by graphene oxide were placed in it. To bind the nanoparticle components together, at first, montmorillonite was functionalized by (3-Aminopropyl) triethoxysilane to increase its nucleophilicity, and for graphene oxide to increase its electrophilicity, thionyl chloride was added. The two compounds were mixed and stirred for 24 hours in the presence of triethylamine base by magnetic stirrer at 30°C... 

    Removal of toxic heavy metal ions from waste water by functionalized magnetic core–zeolitic shell nanocomposites as adsorbents

    , Article Environmental Science and Pollution Research ; Volume 20, Issue 6 , June , 2013 , pp. 3900-3909 ; 1614-7499 Padervand, M. (Mohsen) ; Gholami, M. R. (Mohammad Reza) ; Sharif University of Technology
    2013
    Abstract
    Functionalized magnetic core–zeolitic shell nanocomposites were prepared via hydrothermal and precipitation methods. The products were characterized by vibrating sample magnetometer, X-ray powder diffraction, Fourier transform infrared spectroscopy, nitrogen adsorption–desorption isotherms, and transmission electron microscopy analysis. The growth of mordenite nanocrystals on the outer surface of silica-coated magnetic nanoparticles at the presence of organic templates was well approved. The removal performance and the selectivity of mixed metal ions (Pb2+ and Cd2+) in aqueous solution were investigated via the sorption process. The batch method was employed to study the sorption kinetic,...