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This paper presents the application of two swarm intelligence techniques, multi-objective artificial bee colony (MOABC) and multi-objective particle swarm optimization (MOPSO), to the optimal design of a complementary metal oxide semiconductor (CMOS) low noise amplifier (LNA) cascode with inductive source degeneration. The aim is to achieve a balanced trade off between voltage gain and noise figure. The optimized LNA circuit operates at 2.4 GHz with a 1.8 V power supply and is implemented in a 180 nm CMOS process. Both optimization algorithms were implemented in MATLAB and evaluated using the ZDT1, ZDT2, and ZDT3 test functions. The optimized designs were then simulated using the advance design system (ADS) simulator. The results showed that the MOABC and MOPSO techniques are practical and effective in optimizing LNA design, resulting in better performance than previously published works, with a gain of 21.2 dB and a noise figure of 0.848 dB.

The main aim of this paper is to present a novel control approach of an induction machine (IM) using an improved space vector modulation based direct torque control (SVM-DTC) on the basis of imaginary swapping instant technique. The improved control strategy is presented to surmount the drawbacks of the classical direct torque control (DTC) and to enhance the dynamic performance of the induction motor. This method requires neither angle identification nor sector determination; the imaginary swapping instant vector is used to fix the effective period in which the power is transferred to the IM. Both the classical DTC method and the suggested adaptive DTC techniques have been carried out in MATLAB/SimulinkTM. Simulation results shows the effectiveness of the enhanced control strategy and demonstrate that torque and flux ripples are massively diminished compared to the conventional DTC (CDTC) which gives a better performance. Finally, the system will also be tested for its robustness against variations in the IM parameters.