College of Engineering, Pune

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

Super Resolution Using Neural Network

Abstract: Super resolution imaging refers to inferring the missing high resolution image from low resolution image(s). Super resolution methods are generally classified into reconstruction based and learning based methods. The learning based methods fully learn the intensity prior between all bands of images. For most of these, some fake information can be inevitably introduced into synthesized high resolution image though the image may offer visually good effects. For medical image analysis, it might deteriorate the image analysis and diagnosis performance. In this paper we propose a better learning using real image training set that enhances the high frequency information. The method exploits richness of real-world images. The training set is preprocessed images so as to extract the structural correlation. The technique learns the fine details that correspond to different image structures seen at a low-resolution and then uses those learned relationships to predict fine details in other images.

Sonochemical decolorization of wastewaters containing Rhodamine 6G using ultrasonic bath at an operating capacity of 2 L

Abstract: In the present work, the sonochemical decolorization of wastewater containing a basic dye, Rhodamine 6G (Rh 6G) has been investigated using an ultrasonic bath at an operating capacity of 2 L. Effect of initial concentration, pH, and use of different additives, such as CCl4, H2O2, air, and UV light in combination with ultrasound on the extent of decolorization has been investigated. The experimental results show that an increase in dye concentration leads to a decrease in the decolorization rate, whereas basic conditions favor the decolorization of the pollutant. Among the use of different additives, maximum effect was observed for the use of hydrogen peroxide where the extent of decolorization was 77.8%. Use of air and CCl4 as an process intensifying additive leads to a moderate increase in the extent of decolorization whereas combination of ultrasound and ultraviolet irradiations resulted in lower effectiveness as compared to the combination of ultrasound and hydrogen peroxide. Kinetic studies ha...

Biometric user authentication using brain waves

Abstract: Authentication has become an essential part of our everyday lives which is used at almost every place from banks to experimental labs, from car automation to home automation. This authentication is generally provided through systems like passwords, PIN codes, card readers. At some places biometrics like fingerprint and retina scans are used. All designed with one purpose; to confirm a person's identity. Brain wave based authentication is another addition to the wide range of authentication systems, which has many advantages over other authentication systems. With a standard password someone can watch or ‘shoulder-surf’ what others type, but no one ca n watch thoughts. Cards and keys can be lost, but the brain wave is always present. Differently abled persons can't use systems which uses fingerprints or retina scans but they can use system using brain-waves. This clears that using brain waves as biometric to provide authentication is very beneficial. A system is designed and implemented which allows user to set a pattern of brain waves which must be provided as an unlock pattern to get the access. This pattern can be any combination of eye blink, attention and various brain rhythms like Alpha, Beta, Theta and Delta. The system described in this paper provides two-level authentication. First level of which is brain waves. Once the correct pattern of brain signal is provide the system will ask for a pass key as a second level of authentication. This paper describes the design and implementation of the system.

Fusion classification of multispectral and panchromatic image using improved decision tree algorithm

Abstract: In this paper, efforts are made to detect the areas such as vegetation, water, soil, built-up area etc. from the satellite images. Landsat 7 ETM+ satellite is used for data set of images. It gives multispectral image with low resolution and panchromatic image with high resolution. For detecting the features of the urban area we require both spatial and spectral information in image. Hence these both images are first fused using different methods. Resultant fused image is then used for classification in various areas. Decision tree algorithm is used to divide the image in various classes. Various indexes such as NDVI, NDWI, SAVI and NDBI are used as decision tree rules. Some modification is done in the NDBI formula. The final results of decision tree algorithm using original and modified NDBI are compared and it was found that the decision tree algorithm using modified NDBI gives more accurate results.