Department of Information Systems and Technologies

Permanent URI for this communityhttps://hdl.handle.net/11693/115471

Browse

Browsing Department of Information Systems and Technologies by Author "Ali, S. A."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Exact expression and tight bound on pairwise error probability for performance analysis of turbo codes over Nakagami-m fading channels
    (IEEE, 2007) Ali, S. A.; Kambo, N. S.; İnce, E. A.
    This letter presents derivation for an exact and efficient expression on pairwise error probability over fully interleaved Nakagami-m fading channels under ideal channel state information at the decoder. As an outcome, this derivation also leads to a tight upper bound on pairwise error probability which is close to the exact expression. Pairwise error probability plots for different values of Nakagami parameter m along with an already existing numerically computable expression are provided. As an application of pairwise error probability, average union upper bounds for turbo codes having (1, 7/5, 7/5) and (1, 5/7, 5/7) generator polynomials employing transfer function approach are presented to illustrate the usefulness of the new efficient results. © 2007 IEEE.
  • Thumbnail Image
    ItemOpen Access
    Rule based segmentation and subject identification using fiducial features and subspace projection methods
    (Academy Publisher, 2007) Ince, E. A.; Ali, S. A.
    This paper describes a framework for carrying out face recognition on a subset of standard color FERET database using two different subspace projection methods, namely PCA and Fisherfaces. At first, a rule based skin region segmentation algorithm is discussed and then details about eye localization and geometric normalization are given. The work achieves scale and rotation invariance by fixing the inter ocular distance to a selected value and by setting the direction of the eye-to-eye axis. Furthermore, the work also tries to avoid the small sample space (S3) problem by increasing the number of shots per subject through the use of one duplicate set per subject. Finally, performance analysis for the normalized global faces, the individual extracted features and for a multiple component combination are provided using a nearest neighbour classifier with Euclidean and/or Cosine distance metrics.