Gogte Institute of Technology

About: Gogte Institute of Technology is a based out in . It is known for research contribution in the topics: Wireless sensor network & Feature extraction. The organization has 438 authors who have published 608 publications receiving 4595 citations.

A theoretical model to predict the effect of localized defect on vibrations associated with ball bearing

Abstract: The paper presents an analytical model for predicting the effect of a localized defect on the ball bearing vibrations. In the analytical formulation, the contacts between the ball and the races are considered as non-linear springs. The contact force is calculated using the Hertzian contact deformation theory. A computer program is developed to simulate the defect on the raceways with the results presented in the time domain and frequency domain. The model yields both the frequency and the acceleration of vibration components of the bearing. The effect of the defect size and its location has been investigated. Numerical results for 6305 deep groove ball bearing have been obtained and discussed. The results obtained from the experiments have also been presented.

Diagnosis and classification of grape leaf diseases using neural networks

Abstract: Plant diseases cause significant damage and economic losses in crops. Subsequently, reduction in plant diseases by early diagnosis results in substantial improvement in quality of the product. Erroneous diagnosis of disease and its severity leads to inappropriate use of pesticides. The goal of proposed work is to diagnose the disease using image processing and artificial intelligence techniques on images of grape plant leaf. In the proposed system, grape leaf image with complex background is taken as input. Thresholding is deployed to mask green pixels and image is processed to remove noise using anisotropic diffusion. Then grape leaf disease segmentation is done using K-means clustering. The diseased portion from segmented images is identified. Best results were observed when Feed forward Back Propagation Neural Network was trained for classification.

An electrochemical sensor for clozapine at ruthenium doped TiO2 nanoparticles modified electrode

Abstract: A new modified sensor based on carbon paste electrode (CPE) was developed using ruthenium doped titanium dioxide nanoparticles (Ru-TiO2). In this work, we demonstrated the analytical behavior and applications of CPE modified with Ru-TiO2 (Ru-TiO2/CPE) for the determination of Clozapine by employing different voltammetric techniques. The characterization of synthesized ruthenium doped titanium dioxide nanoparticles was accomplished by utilizing X-ray diffraction (XRD), scanning electron microscope (SEM), Energy-dispersive X-ray spectroscopy (EDX) and transmission electron microscope (TEM). The morphology of the sensing surface was analyzed by atomic force microscope (AFM). Modification of working electrode increases the electro-oxidation of clozapine with increased current intensity. The influence of parameters like scan rate, pH, accumulation time, amount of the modifier and concentration on the peak current of the drug were studied. A probable electro-oxidation mechanism was proposed based on the obtained experimental results. The effect of CLZ concentration variation was studied using square wave voltammetric (SWV) technique in the range of 9.0 × 10−7 M to 4.0 × 10−5 M with the detection limit of 0.43 nM. The prepared Ru-TiO2 modified sensor was employed for the determination of clozapine in pharmaceutical formulations and human urine.