This paper analyses the Proportional-Integral-Derivative (PID) controller's performance for quadruple tank process. The selection of controlling the flow ratios in quadruple tank process act as Minimum and Non-minimum phase system. Its performance can be affected when system is shifted from minimum to non-minimum phase configuration and vice versa. This paper mainly focuses on searching the optimal controller structure by increasing the controllers' performance criteria. A comparative study on different controllers' structures responses are in the presence of peak overshoot. A simulation study of PID controller and Modified PID controller structures have been designed and to analyzed the different controllers' performance for the minimum and non-minimum phase system. The simulation results show that the PI-PD controller structure is provides enhanced performance for the set point tracking with nonappearance of peak overshoot.

This paper addresses the position control of antenna azimuth using proportional and integral (PI) controller and lead compensators. The fractional order calculus plays an important role for designing the robust control. The fractional order lead compensator is proposed for enhancing the closed loop performance of azimuth position control of antenna system. From the comparison of the closed loop responses, the proposed lead compensator delivers a superior closed loop performance when compared with PI controller and lead compensator.

Glaucoma is a disease where the optic nerve of the eyes is smashed up due to the building up of pressure inside the vision point. This has no symptoms at the initial stages, and hence, patients with this disease cannot identify them at the beginning stage. It is explained as if the pressure in the eye increases, then it will hurt the optic nerve which sends images to the brain. This will lead to permanent vision loss or total blindness. The existing method used for the detection of glaucoma includes k-nearest neighbour and support vector machine algorithms. The k-nearest neighbour algorithm and support vector machine algorithm are the machine learning methods for both categorization and degeneration problems. The drawback in using these algorithms is that we can get accuracy level only up to 80%. The proposed methods in this study focus on the convolution neural network for the recognition of glaucoma. In this study, 2 architectures of VGG, Inception method, AlexNet, GoogLeNet, and ResNet architectures which provide accuracy levels up to 100% are presented.

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