Publications of IIUC Family

Permanent URI for this communityhttp://dspace.iiuc.ac.bd/handle/88203/9

Browse

Filters

2015 - 20162

Settings

Author: search.filters.author.Khaliluzzaman, Md.Subject: search.filters.subject.SNR

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Evaluation of SNR for AWGN, Rayleigh and Rician Fading Channels Under DPSK Modulation Scheme with Constant BER
    (International Journal of Wireless Communications and Mobile Computing, 2015-02-06) Chy., Deepak Kumar; Khaliluzzaman, Md.
    With the growing demand in modern communication, it has become necessary to give better and efficient service to users by using better technique. Technique such as Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK), Phase Shift Keying (PSK), Differential Phase shift Keying (DPSK) and Quadrature Amplitude Modulation (QAM) are very important parts of the implementation of modern communications systems in which DPSK is the simplest and most robust of all techniques. In this paper, evaluation of SNR in terms of constant bit error rate is performed on AWGN, Rayleigh and Rician fading channels. Among these channels, Rician is showing better performance as compared to AWGN and Rayleigh
  • No Thumbnail Available
    Item
    Analyzing Wavelet and Bidimensional Empirical Mode Decomposition of MRI Segmentation using Fuzzy C-Means Clustering
    (Rajshahi University Journal of Science & Engineering, 2016) Chuwdhury, Gulam Sarwar; Khaliluzzaman, Md.; Al-Mahfuz, Rashed-
    Image segmentation is a vital step in medical image processing. Magnetic resonance imaging (MRI) is used for brain tissues extraction in white and gray matter. These tissues extraction help in image segmentation applications such as radiotherapy planning, clinical diagnosis, treatment planning. This paper presents utilization of fuzzy C-means (FCM) clustering by using wavelet and bidimensional empirical mode decomposition (BEMD) to improve the quality of noisy MR images. The signal to noise ratio (SNR) value is calculated from FCM clustering data to examine the best segmentation technique. The experiment with synthetic Brain Web images has demonstrated the efficiency and robustness of the appropriate approach in segmenting medical MRI.