Khawza I. Ahmed

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Pair-wise error probability (PEP) is analyzed in the presence of channel estimation error (CEE) for group linear constellation precoded orthogonal frequency division multiplexing (GLCP-OFDM). It is observed that the CEE does not reduce the diversity order but contributes to a loss of coding gain. Furthermore, the proposed optimal power allocation scheme(More)
In this paper, an improved target detection algorithm for MIMO airborne radar has been proposed namely, Compressive Parametric Generalized Likelihood Ratio Test (CP-GLRT). The Parametric Generalized Likelihood Ratio Test (P-GLRT) and Generalized likelihood ratio test (GLRT) are also studied considering the availability of secondary data. The signal is(More)
This paper features the spatial characteristics of the brain towards brain-computer interface (BCI) research. A study on motor imagery (MI) based BCI has been carried out and important implications are identified. Common Spatial Pattern (CSP) is applied to the EEG signals before proceeding to the classification. The primary focus of this research is to(More)
This paper considers the performance enhancement of orthogonal space time block coded (OSTBC) multiple-input multiple-output system based on an optimal training strategy. Pairwise error probability (PEP) based performance analysis provides us the training design tools as well as a generic expression for the performance improvement due to optimal training(More)
Pair-wise error probability (PEP) is analyzed in the presence of channel estimation error (CEE) for orthogonal frequency division multiplexing (OFDM) in a quasi-static Rayleigh fading channel. Subcarriers in OFDM are grouped in an equi-spaced manner with the number of subcarriers in a group equal to the number of channel taps. One group is dedicated for(More)
We present a novel approach for abnormal breast mass classification from digitized mammography images. The proposed framework exploits rotation invariant uniform Local Binary Pattern (LBP) as texture feature. These features are classified using Support Vector Machine (SVM). In addition, we take advantage of the breast mammograms taken from multiple views or(More)