Mudit Kapoor

The over-exploitation of non-renewable resources for energy demands is a serious issue. Convergence towards renewable resources such as solar energy is need of the day. Solar energy is the cleanest form of energy available on Earth. The objective of this research is to extract the building rooftop from the satellite images using a k-means clustering algorithm to identify the usable area for solar potential assessment. The scenes of WorldView-3 and Google Earth are segmented into nine parts and the algorithm implemented in Matlab is applied to the individual parts for better utilization of computing resources. This approach has been applied to the parts of northern states of India for solar potential assessment in a fast and accurate manner. The Global Horizontal Irradiance (GHI) data obtained from the database of National Renewable Energy Laboratory (NREL), United States have been used in the solar potential assessment. For the validation purpose, the above-mentioned algorithm has been compared with the digitization in QGIS software. The results obtained from the above-mentioned algorithm developed have extracted 85% to 90% of the features in the satellite image. The developed algorithm has given best results with the WorldView-3 (high-resolution image) than the other coarser resolution scenes. The developed approach is helpful in evaluating the feasibility of the large areas for solar potential assessment. This methodology is useful for the implementation of different government’s solar energy generation schemes for rural and hilly areas. It helped in estimating the solar potential of the large hilly area for electricity generation. This approach is useful for a larger area as it computes the usable area by dividing the scenes into smaller parts and applies the algorithm individually to each part of the scene. Keywords: k-means clustering, GHI, rooftop, solar potential, Google Earth, WorldView-3

This paper presents a technical study review of code obfuscation. To address this, we discuss the need and methods of code obfuscation. We analyze the different techniques which are used to thwart reverse engineers and to protect against malicious code injection and attacks. Obfuscation, in software technology, is the deliberate act of creating an obfuscated code, that is difficult for humans to understand. Code obfuscation is a protective mechanism which is used to reduce the attack activities on a software system. It is a behavior preserving program transformation which aims to make a program unintelligible to automated program comprehension tools. Code obfuscation is convenient in situations where depending on cryptographic techniques is not enough; this is normal in remote execution situations where the software is executed on an unforeseen exposed hostile environment, such as the new computing platforms: cloud-computing paradigm and smart phones.

This research demonstrates the important role of the remote sensing in finding out the different parameters behind the agricultural crop change, forest cover and urban 3D models. Standalone software is developed to view and analysis the different factors effecting the change in crop productions. Open-source libraries from the Open Source Geospatial Foundation have been used for the development of the shape-file viewer. Software can be used to get the attribute information, scale, zoom in/out and pan the shapefiles. Environmental changes due to pollution and population that are increasing the urbanisation and decreasing the forest cover on the earth. Satellite imagery such as Landsat 5(1984) to Landsat TRIS/8 (2014), Landsat Data Continuity Mission (LDCM) and NDVI are used to analyse the different parameters that are effecting the agricultural crop production change and forest change. It is advisable for the development of good quality of NDVI and forest cover maps to use data collected from the same processing methods for the complete region. Management practices have been developed from the analysed data for the betterment of the crop and saving the forest cover