فهرست مطالب

Iranian Journal of Electrical and Electronic Engineering
Volume:4 Issue: 1, Jan 2008

  • تاریخ انتشار: 1386/02/15
  • تعداد عناوین: 5
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  • M. Eghtedarim., H. Kahaei Page 1
    In this paper, the nonlinear lattice-Hammerstein filter and its properties are derived. It is shown that the error signals are orthogonal to the input signal and also backward errors of different stages are orthogonal to each other. Numerical results confirm all the theoretical properties of the lattice-Hammerstein structure.
  • A. Falahatim., R. Ardestani Page 10
    A low complexity dynamic subcarrier and power allocation methodology for downlink communication in an OFDM-based multiuser environment is developed. The problem of maximizing overall capacity with constraints on total power consumption, bit error rate and data rate proportionality among users requiring different QOS specifications is formulated. Assuming perfect knowledge of the instantaneous channel gains for all users, a new simple algorithm is developed to solve the mentioned problem. We compare the sum capacity, proportionality, and computational complexity of the proposed algorithm with the one presented by Wong et al. Numerical results demonstrate that the proposed algorithm offers a performance comparable with Wong’s algorithm, yet complexity remains low and proportionality constraint will be tightly satisfied. As well, the proposed algorithm can provide a flexible trade-off between complexity, capacity and proportionality constraint
  • Sh. Mahmoudi, Barmas, Sh. Kasaei Page 17
    Image registration is a crucial step in most image processing tasks for which the final result is achieved from a combination of various resources. In general, the majority of registration methods consist of the following four steps: feature extraction, feature matching, transform modeling, and finally image resampling. As the accuracy of a registration process is highly dependent to the feature extraction and matching methods, in this paper, we have proposed a new method for extracting salient edges from satellite images. Due to the efficiency of multiresolution data representation, we have considered four state-of-the-art multiresolution transforms –namely, wavelet, curvelet, complex wavelet and contourlet transform- in the feature extraction step of the proposed image registration method. Experimental results and performance comparison among these transformations showed the high performance of the contourlet transform in extracting efficient edges from satellite images. Obtaining salient, stable and distinguishable features increased the accuracy of the proposed registration process.
  • A., R. Zirak, M. Khademim., S. Mahloji Page 35
    We present an efficient method for the reduction of model equations in the linearized diffuse optical tomography (DOT) problem. We first implement the maximum a posteriori (MAP) estimator and Tikhonov regularization, which are based on applying preconditioners to linear perturbation equations. For model reduction, the precondition is split into two parts: the principal components are considered as reduced size preconditioners applied to linear perturbation equations while the less important components are marginalized as noise. Simulation results illustrate that the new proposed method improves the image reconstruction performance and localizes the abnormal section well with a better computational efficiency.
  • H. Miar, Naimi, P. Davari Page 46
    In this paper, a new Hidden Markov Model (HMM)-based face recognition system is proposed. As a novel point despite of five-state HMM used in pervious researches, we used 7-state HMM to cover more details. Indeed we add two new face regions, eyebrows and chin, to the model. As another novel point, we used a small number of quantized Singular Values Decomposition (SVD) coefficients as features describing blocks of face images. This makes the system very fast. The system has been evaluated on the Olivetti Research Laboratory (ORL) face database. In order to additional reduction in computational complexity and memory consumption the images are resized to 64×64 jpeg format. Before anything, an order-statistic filter is used as a preprocessing operation. Then a top-down sequence of overlapping sub-image blocks is considered. Using quantized SVD coefficients of these blocks, each face is considered as a numerical sequence that can be easily modeled by HMM. The system has been examined on 400 face images of the Olivetti Research Laboratory (ORL) face database. The experiments showed a recognition rate of 99%, using half of the images for training. The system has been evaluated on 64×64 jpeg resized YALE database too. This database contains 165 face images with 231×195 pgm format. Using five training image, we obtained 97.78% recognition rate where for six training images the recognition rate was 100%, a record in the literature. The proposed method is compared with the best researches in the literature. The results show that the proposed method is the fastest one, having approximately 100% recognition rate.