List of Articles Equalization

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  1 - Achieving Better Performance of S-MMA Algorithm in the OFDM Modulation
  Saeed Ghazi-Maghrebi Babak Haji Bagher Naeeni Mojtaba Lotfizad
  10.7508/jist.2013.01.002
  Effective algorithms in modern digital communication systems provide a fundamental basis for increasing the efficiency of the application networks which are in many cases neither optimized nor very close to their practical limits. Equalizations are one of the preferred More

  Effective algorithms in modern digital communication systems provide a fundamental basis for increasing the efficiency of the application networks which are in many cases neither optimized nor very close to their practical limits. Equalizations are one of the preferred methods for increasing the efficiency of application systems such as orthogonal frequency division multiplexing (OFDM). In this paper, we study the possibility of improving the OFDM modulation employing sliced multi-modulus algorithm (S-MMA) equalization. We compare applying the least mean square (LMS), multi modulus algorithm (MMA) and S-MMA equalizations to the per tone equalization in the OFDM modulation. The paper contribution lies in using the S-MMA technique, for weight adaptation, to decreasing the BER in the OFDM multicarrier modulation. For more efficiency, it is assumed that the channel impulse response is longer than the cyclic prefix (CP) length and as a result, the system will be more efficient but at the expense of the high intersymbol interference (ISI) impairment existing. Both analysis and simulations demonstrate better performance of the S-MMA compared to LMS and MMA algorithms, in standard channels with additive white Gaussian noise (AWGN) and ISI impairment simultanously. Therefore, the S-MMA equalization is a good choice for high speed and real-time applications such as OFDM based systems. Manuscript profile
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  2 - A Study on Clustering for Clustering Based Image De-noising
  Hossein Bakhshi Golestani Mohsen Joneidi Mostafa Sadeghi
  10.7508/jist.2014.04.001
  In this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. In the present paper, we suggest a method based on global clustering of i More

  In this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. In the present paper, we suggest a method based on global clustering of image constructing blocks. As the type of clustering plays an important role in clustering-based de-noising methods, we address two questions about the clustering. The first, which parts of the data should be considered for clustering? The second, what data clustering method is suitable for de-noising? Then clustering is exploited to learn an over complete dictionary. By obtaining sparse decomposition of the noisy image blocks in terms of the dictionary atoms, the de-noised version is achieved. Experimental results show that our dictionary learning framework outperforms its competitors in terms of de-noising performance and execution time. Manuscript profile
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  3 - A Two-Stage Multi-Objective Enhancement for Fused Magnetic Resonance Image and Computed Tomography Brain Images
  Leena Chandrashekar A Sreedevi Asundi
  10.29252/jist.8.30.93
  Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a diff More

  Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a different characteristic of the brain. Therefore, experts have to analyze each of the images independently. This requires more expertise by doctors and delays the detection and diagnosis time. Multimodal Image Fusion is a process of generating image of high visual quality, by fusing different images. However, it introduces blocking effect, noise and artifacts in the fused image. Most of the enhancement techniques deal with contrast enhancement, however enhancing the image quality in terms of edges, entropy, peak signal to noise ratio is also significant. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a widely used enhancement technique. The major drawback of the technique is that it only enhances the pixel intensities and also requires selection of operational parameters like clip limit, block size and distribution function. Particle Swarm Optimization (PSO) is an optimization technique used to choose the CLAHE parameters, based on a multi objective fitness function representing entropy and edge information of the image. The proposed technique provides improvement in visual quality of the Laplacian Pyramid fused MRI and CT images. Manuscript profile
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  4 - Automatic Reference Image Selecting for Histogram Matching in Image Enhancement
  N. Samadiani H. Hassanpour
  In this paper, a method is proposed to automatically select reference image in histogram matching. Histogram matching is one of the simplest spatial image enhancement methods which improves contrast of the initial image based on histogram of the reference image. In the More

  In this paper, a method is proposed to automatically select reference image in histogram matching. Histogram matching is one of the simplest spatial image enhancement methods which improves contrast of the initial image based on histogram of the reference image. In the conventional histogram matching methods, user should perform several experiments on various images to find a suitable reference image. This paper presents a new method to automatically select the reference image. In this method, images are converted from RGB to HSV, and the illumination (V) components are considered to select the reference image. The appropriate reference image is selected using a similarity measure via measuring the similarity between the histograms of the initial image and histograms of the images in the data base. Indeed, an image with similar histogram to the histogram of the original images is more appropriate to choose as the reference image for histogram matching. Results in this research indicate superiority of the proposed approach, compared to other existing approaches, in image enhancement via histogram matching. In addition, the user would have no concern in selecting an appropriate reference image for histogram matching in the proposed approach. This approach is applicable to both RGB and gray scale images. Manuscript profile
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  5 - Joint Blind Equalization and Decoding over Frequency Selective Channels in OFDM Systems Using Particle Filtering Joint Blind Equalization and Decoding over Frequency Selective Channels in OFDM Systems Using Particle Filtering
  N. Ghasemi M. F. Sabahi A. R. Forouzan
  In this paper a sequential algorithm is proposed for joint blind channel equalization and decoding for orthogonal frequency-division multiplexing (OFDM) in frequency selective channels. This algorithm offers a recursive method to sequentially calculate the posterior pro More

  In this paper a sequential algorithm is proposed for joint blind channel equalization and decoding for orthogonal frequency-division multiplexing (OFDM) in frequency selective channels. This algorithm offers a recursive method to sequentially calculate the posterior probability for maximum a posteriori (MAP) detection. Recursive calculations are done along the indexes in each OFDM symbol using a particle filter. By defining an appropriate importance function, and a proper prior probability distribution function for the channel tap coefficients (and marginalizing it), an efficient method is presented for joint equalization and channel decoding in OFDM based systems. Performance of the proposed detector is evaluated using computer simulations and its bit error rate is compared with the trained turbo equalizer and a conventional particle filter-based method. The results show that the proposed method outperforms the previously presented particle filter-based method without a need for training data. Manuscript profile
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  6 - Performance Assessment of a WCDMA Based Radio-over-Fiber System with Near-Far Effect: Uplink Scenario
  G. Baghersalimi
  In this research the effect of a radio-over-fiber (RoF) subsystem on the total degradation (TD) performance of uplink wideband code division multiple access (WCDMA) is assessed in the presence of near-far effect. The study considers the use of pilot-aided channel estima More

  In this research the effect of a radio-over-fiber (RoF) subsystem on the total degradation (TD) performance of uplink wideband code division multiple access (WCDMA) is assessed in the presence of near-far effect. The study considers the use of pilot-aided channel estimation to neutralize the optical subsystem nonlinearities for different channel conditions, estimation intervals, and near-far factors (NFF). The results demonstrate that the proposed equalization technique almost compensates for the joint impact of the optical subsystem nonlinearity and the near-far effect irrespective of spreading factor, large signal distortion, estimation interval, and user number. Manuscript profile