Rajib Kar

In order to optimize the synthesis of Asymmetric Time-Modulated Circular Antenna Array (ATMCAA) and Symmetric Time-Modulated Circular Antenna Array (STMCAA), this work presents the Novel Particle Swarm Optimization Algorithm (NPSO). Inter-element spacing and uniform current excitation are maintained by regulating the switching time sequence and progressive phase delay of each element. A distinct cost function is developed for each of the two case studies. Using 20- and 36-element examples, several low side-lobe designs synthesized from ATMCAA and STMCAA are compared with traditional circular arrays. Through the manipulation of switching time sequence and progressive phase delay, the cost function is optimized to simultaneously reduce the side-lobe level (SLL) and directivity in ATMCAA and STMCAA. When it comes to antenna array synthesis, NPSO performs better than other algorithms, such as cat swarm optimization and invasive weed optimization. This study demonstrates how effective NPSO is at optimizing antenna arrays in order to improve higher communication reliability and signal quality.

We first introduce a new approach for optimising a cascaded spline adaptive filter (CSAF) to identify unknown nonlinear systems by using a meta-heuristic o

CMOS analog amplifier circuits are integral components in the signal‐processing stages of consumer electronics. They also find applications in advanced imaging systems, augmented reality devices, smartphone audio applications, power management circuits, etc. The differential and two‐stage operational amplifier circuits must be optimally designed for next‐generation consumer electronics devices, where speed and sizing are important constraints. This nonlinear constrained optimization problem can be resolved using evolutionary optimization techniques. This article proposes an efficient design technique for a CMOS differential amplifier circuit and CMOS two‐stage op‐amplifier. A well‐known optimization problem is the particle swarm optimization (PSO). PSO is a robust optimization method but may suffer from premature convergence and slow convergence speed. The evolutionary technique used here is the modified version of the PSO, named Levy flight, power function, and Singer map utilized PSO (LPSPSO). In PSO, the Levy flight can enhance the exploration capability and improve the convergence speed. The proposed evolutionary technique reduces the individual circuits’ area and design time while satisfying the design constraint. The LPSPSO‐based results are authenticated with SPICE. SPICE results demonstrate that LPSPSO is an efficient evolutionary technique than formerly reported methods in designing analog amplifiers in terms of MOS area, gain, power dissipation, etc.

Objective: Electroencephalogram (EEG) signals are well-known non-stationary brain signals of lower strength. Due to their small amplitude, they attract other biomedical artefacts from the surroundings. This research mainly focuses on removing artefacts from the EEG. Method: The presented work uses the recent metaheuristics to efficiently design a Wiener-based adaptive noise mitigation structure (WANMS). Many powerful evolutionary optimisation algorithms (EOAs) such as particle swarm optimisation algorithm (PSOA), moth flame optimisation algorithm (MFOA), symbiotic organism search optimisation algorithm (SOSOA), and the student psychology-based optimisation algorithm (SPBOA) have been applied for the optimal design of WANMS. The proposed structure is analysed with various noisy signals such as electrooculogram (EOG) and electrocardiogram (ECG) with white Gaussian noise (WGN). Results: Among all the metaheuristic algorithms applied to WANMS, the SPBOA-based WANMS has performed better with improved Signal-to-Noise-Ratio (SNR) and minimal Mean-Squared-Error (MSE) values. Conclusion: The results obtained through the proposed technique ensure its supremacy compared to other state-of-the-art techniques. Significance: Hence, the proposed method can be utilised for EEG signal enhancement.

The potential for nanoscale devices to serve as a platform for the detection of biomolecules is immense. Several challenges have been faced in the device fabrication process. To address these issues, the uniformly doped concept was introduced. This study suggests the use of a vertical tunnel field-effect (VTFET) transistor with a pH base and a gate overlap dielectric modulation for label-free electrical recognition of biomolecules such as cells, deoxyribonucleic acids, proteins, enzymes, and others. In the present paper, two simple approaches of sensitivity analysis are compared, the first one is the dielectric modulated analysis and the second one is the electrolytic-based pH sensing of vertical TFET biosensor. The proposed model incorporates the effects of pH and the dielectric constant to provide a generalised solution applicable to neutral biomolecules. To test the effectiveness of the proposed sensors, a thorough simulation is run using an ATLAS device simulator. The effects of pH changes on device characteristics like the device current (IDS) and surface potential sensitivity are investigated. Additionally, pH sensors measure changes in hydrogen ion concentration in aqueous solutions and shifts in drain current caused by changes in the dielectric constant that have been used to analyse the sensitivity of dielectric modulated biosensors. The drain current sensitivity of the pH-hetero-VTFET biosensor is estimated as 8.8X105 which is higher than the sensitivity (9.1X104) of DM-hetero-VTFET at the cavity length of 30 nm. Since it has been found that the proposed sensor exhibits significant variations in its surface potential and drain current (ION), so this device is preferable as an appropriate sensing transducer.

This paper deals with the optimal design of different VLSI circuits, namely, the CMOS voltage reference circuit and the CMOS ring oscillator (RO). The optimization technique used here is the multiobjective differential evolution algorithm (MDEA). All the circuits are designed for 90nm technology. The main objective of the CMOS voltage reference circuit is to minimize the voltage variation at the output. The targeted value of the reference voltage is 550mV. A CMOS ring oscillator (RO) is designed depending on the performance parameters such as power consumption and phase noise. The optimal transistor sizing of each circuit is obtained from MDEA. Each circuit is implemented in SPICE by taking the optimal dimensions of the transistors, and the performance parameters are achieved. The designed voltage reference circuit achieves a reference voltage of 550mV with 600nW power dissipation. The reference voltage variation of 8.18 is observed due to temperature variation from 40°C to+125°C. The MDEA-based optimal design of RO oscillates at 2.001GHz frequency, has a phase noise of 87dBc/Hz at 1MHz offset frequency, and consumes 71μW power. This work mainly aims to optimize the MOS transistors sizes using MDEA for better circuit performance parameters. SPICE simulation has been carried out by using the optimal values of MOS transistor sizes to exhibit the performance parameters of the circuit. Simulation results establish that design specifications are closely met. SPICE results show that MDEA is a better technique for the optimal design of the above-mentioned VLSI circuits.

In this article, the performance of n-type junctionless (JL) double-gate (DG) MOSFET-based biosensors with and without gate stack (GS) has been studied. Here, the dielectric modulation (DM) method is applied to detect biomolecules in the cavity. The sensitivity of n-type JL-DM-DG-MOSFET and n-type JL-DM-GSDG-MOSFET-based biosensors have also been evaluated. The sensitivity (ΔV_th) improved in JL-DM-GSDG MOSFET/JL-DM-DG-MOSFET-based biosensors for neutral/charged biomolecules is 116.66%/66.66% and 1165.78%/978.94%, respectively, compared with the previously reported results. The electrical detection of biomolecules is validated using the ATLAS device simulator. The noise and analog/RF parameters are compared between both biosensors. A lower threshold voltage is observed in the GSDG-MOSFET-based biosensor. The I_on/I_off ratio is higher for DG-MOSFET-based biosensors. The proposed GSDG-MOSFET-based biosensor demonstrates higher sensitivity than the DG-MOSFET-based biosensor. The GSDG-MOSFET-based biosensor is suitable for low-power, high-speed, and high sensitivity applications.