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Difference differential amplifiers (DDA), which were built on FinFET and carbon nanotube FET (CNTFET), are frequently used for signal processing owing to their advantages of low-power dissipation and reduced device dimension. In this work, high-performance DDA was established using CNTFET model parameters as well as FinFET 7 nm and 14 nm technology. The DDA circuit used in this scenario was identically the same to the one used previously. With the use of Verilog AMS code-based Stanford model parameters applied CNTFET and 7 nm and 14 nm FinFETs, schematic capture and simulations of the DDA were carried out in the Symica environment. The mostly used measurements for assessing the performance of operational amplifiers were also adopted for DDA. The CNTFET-based difference differential amplifiers have slew rates of 10.8 V/femtosecond and 11.2 mV/femtosecond, respectively, with settling times of 0.65 femtosecond and 0.43 femtosecond, respectively. The power supply rejection ratio (PSRR) is 2.53 dB with a dynamic range of 198 mV and 6 mV for CNTFET DDA operating at 0.6 V DC. The incentives of CNTFET appropriateness for DDA designed in this study for any analogue front end were further demonstrated by using CNTFET for DDA with the achievement of open loop differential gain of 116.03 dB with BW of 4 GHz and phase margin of 270 and common mode gain of -28.65 dB with BW of 55.14 MHz and phase margin of 270.

In cognitive radio applications, a robust detector is essentially required for the determination of spectrum sensing. Gerschgorin radii and centers ratio (GRCR) detector is a robust detector for cooperative spectrum sensing techniques. Covariance matrix generates the test statistics for signal established from one or supplementary sources. Though the method is robust against noise uncertainty, it is not suitable for wideband sensing due to the complexity associated with the computation of covariance matrix. To tackle this challenge of extensive communication cost and high processing time complexity, an efficient GRCR detector using compressive sensing with non-reconstruction is proposed here. This method introduces relevance for multiple received signals by using same measurement matrix to all received signals. Computational complexity is analysed and the proposed method is compared with the existing method through ROC simulations, and it shown that the proposed method performs better even in the low SNR range of -20 dB. Throughput analysis is validated through simulations.

Routing in cognitive radio networks (CRNs) faces numerous limitations in misbehaving activities and secure the routing requests and reply messages. In this research work, Secure Distance based Improved LEACH Routing (SDILR) protocol is presented to avoid the primary user emulation attack (PUEA) in CRN. Initially, the nodes in the cognitive radio network are clustered by using distance based improved Low- energy adaptive clustering hierarchy (ILEACH). After the formation of clusters, secure routing is presented using support value based signature authentication to avoid PUEA. The proposed secure ILEACH routing results the secure data sharing through the primary user nodes PUEA.

This paper suggests spectrum sensing method for the kind of noise which fluctuates with time in non-uniform manner. There are many methods available for spectrum sensing, but any of those methods do not show the property of stable false alarm in noisy environment. This shows there is a great need of stable false alarm condition for the sensing method to be robust, and it is achieved by Gershgorin disc method. In this method, the test statistic (GRCR—Gershgorin Radii and Centres Ratio) is the ratio of sum of the radii to the sum of the centres calculated from the covariance matrix of the received signal. A comparative analysis with few existing sensing methodologies is presented. But all the spectrum sensing techniques suffer with a problem called as SNR wall, a SNR at which the detector is not robust, even if many observations are made. In GRCR method, the difference between H0 and H1 becomes poor in lower SNR conditions and they overlap each other, which also happens with all the detectors. In this paper, SNR wall is estimated under correlated scenarios for the GRCR sensing method. The performance of this detector is evaluated using MATLAB simulations.

In this drowsiness detection framework two actions including brain and visual features are utilised to distinguish the various levels of drowsiness. These actions are provided by the EEG and EOG signal brain actions. From the EEG and EOG signals the peculiarities like mean, peak, pitch, maximum, minimum, standard deviation are assessed . In these peculiarities we decide on some best attributes - peak and pitch employing an IPSO strategy that picks up the best threshold esteem. These signals are then offered into the STFT which is employed to discover the signal length, producing a STFT network from the intermittent hamming window,the output of which are energy signals alpha and beta. These energy signals are offered into the MCT to get an alpha mean and a beta mean -the most chosen and outstanding attributes. These are then subjected to fuzzy based classification to give a precise result checking over the maximum values in the alpha and the beta series .