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)
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)
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)
Diabetes mellitus is a chronic disease and its prolonged existence may cause proliferation of diverse abnormalities in human physiological system. Maintaining a healthy lifestyle can improve the condition of a diabetic patient. However, continuous monitoring of diabetes level is necessary for adapting diets and others for healthy life, which requires(More)
Major Depressive Disorder (MDD) is a serious mental disorder that if untreated not only affects physical health but also has a high risk of suicide. While the neurophysiological phenomena that contribute to the formation of Suicidal Ideation (SI) are still ill-defined, clear links between MDD and cardiovascular disease have been reported. The aim of this(More)
Localizing event-related cortical sources is a key factor while developing a computationally efficient Brain Computer Interface (BCI). This paper proposes a unified application of wavelet-based Maximum Entropy on the Mean (wMEM), as a channel selection method, for classifying two motor imagery (MI) tasks using optimal electroencephalography (EEG) sources.(More)