Compressed sensing based cyclic feature spectrum sensing for cognitive radios

Spectrum sensing is currently one of the most challenging design problems in cognitive radio. A robust spectrum sensing technique is important in allowing implementation of a practical dynamic spectrum access in noisy and interference uncertain environments. In addition, it is desired to minimize the sensing time, while meeting the stringent cognitive radio application requirements. To cope with this challenge, cyclic spectrum sensing techniques have been proposed. However, such techniques require very high sampling rates in the wideband regime and thus are costly in hardware implementation and power consumption. In this thesis the concept of compressed sensing is applied to circumvent this problem by utilizing the sparsity of the two-dimensional cyclic spectrum. Compressive sampling is used to reduce the sampling rate and a recovery method is developed for re- constructing the sparse cyclic spectrum from the compressed samples. The reconstruction solution used, exploits the sparsity structure in the two-dimensional cyclic spectrum do-main which is different from conventional compressed sensing techniques for vector-form sparse signals. The entire wideband cyclic spectrum is reconstructed from sub-Nyquist-rate samples for simultaneous detection of multiple signal sources. After the cyclic spectrum recovery two methods are proposed to make spectral occupancy decisions from the recovered cyclic spectrum: a band-by-band multi-cycle detector which works for all modulation schemes, and a fast and simple thresholding method that works for Binary Phase Shift Keying (BPSK) signals only. In addition a method for recovering the power spectrum of stationary signals is developed as a special case. Simulation results demonstrate that the proposed spectrum sensing algorithms can significantly reduce sampling rate without sacrifcing performance. The robustness of the algorithms to the noise uncertainty of the wireless channel is also shown

Electroencephalographic Findings, Antiepileptic Drugs and Risk Factors of 433 Individuals Referred to a Tertiary Care Hospital in Ethiopia

Background: Little is known about the characteristics of electroencephalogram (EEG) findings in epileptic patients in Ethiopia. The objective of this  study was to characterize the EEG patterns, indications, antiepileptic drugs (AEDs), and epilepsy risk factors.Methods: A retrospective observational review of EEG test records of 433 patients referred to our electrophysiology unit between July 01, 2020 and  December 31, 2021.Results: The age distribution in the study participants was right skewed unipolar age distribution for both sexes and the mean age of 33.8 (SD=15.7) years. Male accounted for 51.7%. Generalized tonic clonic seizure was the most common seizure type. The commonest indication for EEG was  abnormal body movement with loss of consciousness (35.2%). Abnormal EEG findings were observed in 55.2%; more than half of them were Interictal epileptiform discharges, followed by focal/or generalized slowing. Phenobarbitone was the commonest AEDs. A quarter (20.1%) of the  patients were getting a combination of two AEDs and 5.2% were on 3 different AEDs. Individuals taking the older AEDs and those on 2 or more AEDs  tended to have abnormal EEG findings. A cerebrovascular disorder (27.4%) is the prevalent risk factor identified followed by brain tumor, HIV  infection, and traumatic head injury respectively.Conclusion: High burden of abnormal EEG findings among epileptic patients referred to our unit. The proportion of abnormal EEG patterns was  higher in patients taking older generation AEDs and in those on 2 or more AEDs. Stroke, brain tumor, HIV infection and traumatic head injury were  the commonest identified epilepsy risk factors