Rambabu Busi

The performances of the Proportional-Integral controller, Fuzzy Logic controller, and Subtractive Fuzzy Clustering-based PI controller (SFC-PI) have been investigated for regulating the level in a Quadruple Tank System with Dead Time (QTSWDT). The QTSWDT is an ideal benchmark to test various control approaches since it has nonlinear dynamics and complicated interactions between tanks. While classic PI controllers are successful in controlling linear systems, they face difficulties in regulating the nonlinearities and cross-couplings inherent in QTSWDT. Fuzzy logic controllers offer extended adaptation to nonlinearities but require substantial tuning. The SFC-PI controller, which offers subtractive fuzzy clustering to instinctively generate fuzzy rules, surpasses the other techniques by significantly reducing ISE, IAE, settling time, and peak overshoot. Simulation outputs disclose that the SFC-PI controller has the best overall performance, making it a competent choice for complex nonlinear control applications

A nonlinear optimization based decentralized PI controller for Two Input Two Output (TITO) is presented in this paper. Modelling of Quadruple tank minimum phase system with time delay is introduced here. The basic principles of nonlinear optimization are utilized to design the proposed PI controller in which the overshoot is bounded with constraints on the maximum closed-loop amplitude ratio, maximum closed loop width, gain and angle bounds. Besides, the control algorithm is designed for decoupled systems to reduce the loop interactions. Further, the first order plus dead time (FOPDT) model is derived for each of the decoupled subsystems to design the control law. The robust stability is analyzed by using Hurwitz and Upper triangular matrix of interrelated Kharitonov polynomials. The performance of the proposed control strategy is verified by MATLAB/SIMULINK software. The performance specifications of the simulation indicate that the suggested control strategy is more efficient.

One of the prevalent, life-threatening disorders that has been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. However, it takes time and presents difficulties for the specialists to evaluate all of the slide images. Automated, reliable, and objective methods are required for accurately evaluating ultrasound images. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) techniques that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present deep learning applications for thyroid cancer detection and discussed the study's goals. We provided an overview of latest developments in the deep learning techniques for thyroid cancer diagnosis and addressed some of the difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.

This paper presents a novel architecture of Femto power-delay super threshold voltage level shifter for network on chip voltage control, and developed using feedback topology for error aware interconnections data transmission. The proposed Level Shifter (LS) utilize eight MOS transistors with low aspect ratios for level up or level down. The developed LS can able to shift as low as 0.12 V to 1.25 V with tremendously reduction in delay and power consumptions. Implemented in 65 nm CMOS technology and the post-layout simulation substantiate the achievement of voltage translation. The proposed LS incurs energy per cycle 44 fJ during up shift, average level up and level down static power consumption is of 3.61 nW while VDDL is 0.3 V and VDDH is 1.2 V at the frequency of 1 MHz. The layout area of the proposed LS is 3.21 µm × 2.13 µm.

A correction to this paper has been published: https://doi.org/10.1007/s42452-021-04542-3