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This paper proposes a novel enhancement to Histogram Equalization technique by modifying the probability density function with a padding fac- tor to improve background contrast. The proposed method also gives better enhancement results yielding a higher level of detail in the images.
In the mobile environment, however, these assumptions are rarely valid or appropriate. In the scenario where clients or both the clients and server are in mobility, there is a requirement of development of mobile distributed client-server architecture. This paper presents a new paradigm of extended client-server architecture.
In XCM, we have concentrated on hierarchy of multiple independent sub-projects in project development phase. XCM merges through the subprojects binding them at their Integration Testing Phases. Hence combining binary modules at each stage into a new module through chains of X till the final level in the hierarchy is implemented. X-CM results in reducing the time and effort for software developer. In addition to this, X-CM also rules out the dependency errors between the modules at each stage of combination.
Processing of large quantities of stream data requires an efficient, real time system. We needed to process multiple queries on a large graph depicting a map with linked nodes. The graph would have to be updated in real time with data from sources like sensors with information depicting traffic, diversions, etc. This graph would then be used to service queries from remote clients regarding an optimal path from a source to a destination. This required a computing framework which could efficiently update and process multiple queries on large datasets in parallel. We explore a method to do so in this thesis.