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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 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 .

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