The brain tumour is the mass where some tissues become old or damaged, but they do not die or not leave their space. Mainly brain tumour masses occur due to malignant masses. These tissues must die so that new tissues are allowed to be born and take their place. Tumour segmentation is a complex and time-taking problem due to the tumour’s size, shape, and appearance variation. Manually finding such masses in the brain by analyzing Magnetic Resonance Images (MRI) is a crucial task for experts and radiologists. Radiologists could not work for large volume images simultaneously, and many errors occurred due to overwhelming image analysis. The main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning approaches. This research study proposed an automatic model for tumor segmentation in MRI images. The proposed model has a few significant steps, which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative (NIFTI) volumes into the 3D NumPy array. In the second step, the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated parameters. In the third step, the proposed model uses state-of-the-art Medical Image Computing and Computer-Assisted Intervention (MICCAI) BRATS 2018 dataset with MRI modalities such as T1, T1Gd, T2, and Fluid-attenuated inversion recovery (FLAIR). Tumour types in MRI images are classified according to the tumour masses. Labelling of these masses carried by state-of-the-art approaches such that the first is enhancing tumour (label 4), edema (label 2), necrotic and non-enhancing tumour core (label 1), and the remaining region is label 0 such that edema (whole tumour), necrosis and active. The proposed model is evaluated and gets the Dice Coefficient (DSC) value for High-grade glioma (HGG) volumes for their test set-a, test set-b, and test set-c 0.9795, 0.9855 and 0.9793, respectively. DSC value for the Low-grade glioma (LGG) volumes for the test set is 0.9950, which shows the proposed model has achieved significant results in segmenting the tumour in MRI using deep learning approaches. The proposed model is fully automatic that can implement in clinics where human experts consume maximum time to identify the tumorous region of the brain MRI. The proposed model can help in a way it can proceed rapidly by treating the tumor segmentation in MRI.

Vector control schemes have recently been used to drive linear induction motors (LIM) in high-performance applications. This trend promotes the development of precise and efficient control schemes for individual motors. This research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation (SVPWM) inverters. The framework under consideration is developed in four stages. To begin, MATLAB Simulink was used to develop a detailed mathematical and electromechanical dynamic model. The research presents a modified SVPWM inverter control scheme. By tuning the proportional-integral (PI) controller with a transfer function, optimized values for the PI controller are derived. All the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control scheme. The reference speed values were chosen to evaluate the performance of the respective system, and the developed system’s response was verified using various data sets. For the low-speed range, a reference value of 10 m/s is used, while a reference value of 100 m/s is used for the high-speed range. The speed output response indicates that the motor reached reference speed in a matter of seconds, as the delay time is between 8 and 10 s. The maximum amplitude of thrust achieved is less than 400 N, demonstrating the controller’s capability to control a high-speed LIM with minimal thrust ripple. Due to the controlled speed range, the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.