The electroencephalogram (EEG) is the frequently used signal to detect epileptic seizures in the brain. For a successful epilepsy surgery, it is very essential to localize epileptogenic area in the brain. The signals from the epileptogenic area are focal signals and signals from other area of the brain region nonfocal signals. Hence, the classification of focal and nonfocal signals is important for locating the epileptogenic area for epilepsy surgery. In this article, we present a computer aided automatic detection and classification method for focal and nonfocal EEG signal. The EEG signal is decomposed by Dual Tree Complex Wavelet Transform (DT-CWT) and the features are computed from the decomposed coefficients. These features are trained and classified using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The proposed system achieves 98% sensitivity, 100% specificity, and 99% accuracy for EEG signal classification. The experimental results are presented to show the effectiveness of the proposed classification method to classify the focal and nonfocal EEG signals.
In this paper, a unit cell of a single-negative metamaterial structure loaded with a meander line and defected ground structure (DGS) is investigated as the principle radiating element of an antenna. The unit cell antenna causes even or odd mode resonances similar to the unit cell structure depending on the orientation of the microstrip feed used to excite the unit cell. However, the orientation which gives low-frequency resonance is considered here. The unit cell antenna is then loaded with a meander line which is parallel to the split bearing side and connects the other two sides orthogonal to the split bearing side. This modified structure excites another mode of resonance at high frequency when a meander line defect is loaded on the metallic ground plane. Specific parameters of the meander line structure, the DGS shape, and the unit cell are optimized to place these two resonances at different frequencies with proper frequency intervals to enhance the bandwidth. Finally, the feed is placed in an offset position for better impedance matching without affecting the bandwidth The compact dimension of the antenna is 0.25 λL × 0.23 λL × 0.02 λL, where λL is the free space wavelength with respect to the center frequency of the impedance bandwidth. The proposed antenna is fabricated and measured. Experimental results reveal that the modified design gives monopole like radiation patterns which achieves a fractional operating bandwidth of 26.6%, from 3.26 to 4.26 GHz for |S11|<−10 dB and a pick gain of 1.26 dBi is realized. In addition, the simulated and measured crosspolarization levels are both less than −15 dB in the horizontal plane.
In wireless communication networks, the necessity for high-speed data rates has increased in emerging 5G application areas. The Power Amplifier (PA) topologies reported to date achieved desired Power Added Efficiency (PAE) and linearity. However, these harmonically tuned switching PAs are less appealing for broadband applications as they are restricted to narrow bandwidth (BW). Therefore, to meet the 5G requirements, the challenge of designing a PA with improved efficiency and linearity for a dynamic range of BW becomes critical for PA designers. Recently developed Class-J PA topology can obtain good efficiency while maintaining linearity for wide BW applications. This research work presents a methodology to design a 5 GHz Class-J mode PA topology using Silterra 0.13 μm CMOS technology. This research's main objectives are to determine the Ropt of the transistor and design a proper Output Matching Network (OMN) for obtaining Class-J PA operation to make it suitable for 5G wireless applications. The simulation results represent that the designed Class-J PA provides 27 dBm of maximum power output with a maximum power gain of 13.7 dB and the small-signal gain of 17 dB for a BW of around 500 MHz with a 5 V power supply into a 50Ω load.
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