College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Sliding mode control & Control theory. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

Smart Electronic Stick for Visually Impaired using Android Application and Google’s Cloud Vision

Abstract: Presently, the visually impaired use a simple stick, which is insufficient to help them perform their daily activities independently. To resolve this issue, the paper proposes a smart and effective solution in the form of an e-stick module integrated with voice controlled android application. The primary aim is to provide a simple and affordable solution for the visually impaired by keeping the stick structurally similar to the traditional stick used by them today, that is thin, lightweight and easy to handle. All these functionalities are being provided at a lower cost and using efficient Natural Language processing (NLP) features. Thus, the proposed method attempts to help the visually impaired to lead a normal life.

MRI denoising using BM3D equipped with noise invalidation denoising technique and VST for improved contrast

Abstract: Denoising of medical scanned images such as X-ray, MRI etc. is important stage in the medical use. To remove the noise from “magnetic resonance images” (MRI) is the attention of researchers to generate the MR images with high “signal-to-noise ratio” as well as with high spatial resolution. In this denoising technique, block-matching and 3-dimensional filtering (BM3D) method is used to denoise the MR images. Main steps used in BM3D are grouping, 3-dimensional discrete wavelet transformation and wavelet shrinkage. In the proposed method, noise invalidation denoising technique (NIDe) is used rather than hard thresholding. NIDe gives the threshold value automatically based on the data and noise characteristics and threshold value changes according to the characteristics of data i.e. wavelet coefficient of image. Before denoising MR images, variance stabilization transform (VST) discard the noise variance dependency of the MRI intensities. Combining block-matching and 3-dimensional filtering technique and VST make able the use of the BM3D technique for Magnetic Resonance Image denoising. After BM3D i.e. final denoised MR image, “contrast limited adaptive histogram equalization” technique is applied to increase the contrast of MR images which are denoised. Performance metrics such as “Peak Signal to Noise ratio”, “Root Mean Square Error”, “Mutual Information”, “Edge Entropy” and “Structural Similarity Index Method” are found out for “T1 weighted”, “T2 weighted” and “PD weighted” magnetic resonance images.

Monitoring of transformer parameters using Internet of Things in Smart Grid

Abstract: Electricity plays important role in day-to-day life. Energy industry always look forward to improve the performance of the power system. The traditional power system comprises of generation, transmission and distribution which is unidirectional in nature. This power system is required to be monitored and controlled in real time. Smart Grid is a concept which integrates the entire power system right from generation to end user in one system. By using modern technology, Smart Grid can be build on the existing power system. In this concept, things being monitored for the better performance of the substation and grid. This paper presents an idea on real time monitoring of the distribution transformer in order to make the system more reliable. Different parameter of the distribution transformer are monitored and demonstrated through Internet of Things platform. System data has been monitored and analysed.

Application of Machine Learning on Remote Sensing Data for Sugarcane Crop Classification: A Review

Abstract: Sugarcane is a major contributing component in the economy of tropical and subtropical countries like India, Brazil and China. Sugarcane agriculture is empowered with the advancements in the remote sensing technology because of its timely, non invasive, and labor and cost effective capability. Remote sensing data with machine learning algorithms like Support Vector Machine, Artificial Neural Network and Random Forest are proven to be suitable in sugarcane agriculture. The aim of this paper is to present a review of studies that implemented various machine learning algorithms based on remote sensing data in sugarcane crop mapping and classification.

A Scale Adaptive Tracker Using Hybrid Color Histogram Matching Scheme

Abstract: In this paper we propose a computationally efficient scale adaptive tracking method using a hybrid color histogram matching scheme. Firstly, we report an important property of the Chi-squared measure- It outperforms Bhattacharyya measure in the task of histogram matching from a few significantly similar multimodal histograms. Also, Bhattacharyya measure performs better while selecting matches from a varied dataset. We employ these results to develop a hybrid histogram matching procedure using the two measures. This method is used for a patch matching algorithm in real time tracking. We first calculate a color histogram of the target which is then compared with histograms of patches in the neighborhood in subsequent frames using this hybrid procedure to obtain the best match. We devise a systematic scale adaptive tracking method which is robust to rapid changes in the target size. It is also robust to partial occlusion of the target. Extensive experimental proof based on real life and test datasets is presented which demonstrates the excellent tracking accuracy achieved by our algorithm at real time.