As the size and complexity of a software system increases, the design problem goes beyond the algorithms and data structures of the computation. Designing and specifying the overall system structure -- or software architecture ...

Code optimization or code transformation is a complex function of a compiler involving analyses and modifications with the entire program as its scope. In spite of its complexity, hardly any tools exist to support this ...

A wireless sensor network (WSN) is a collection of sensor nodes distributed over a geographical region to obtain the environmental data. It can have different types of applications ranging from low data rate event driven ...

Software components expose Application Programming Interfaces (APIs) as a means to access their functionality, and facilitate reuse. Developers use APIs supplied by programming languages to access the core data structures ...

In recent times there has been an explosion of online user-generated video content. This has generated significant research interest in video analytics. Human users understand videos based on high-level semantic ...

This thesis introduces a new mathematical object: collection of arithmetic progressions with elements satisfying the inverse property, \j-th terms of i-th and (i+1)-th progressions are multiplicative inverses of each other ...

In this thesis, we consider stochastic systems which arise in different real-world application contexts. The first problem we consider is based on product adoption and pricing. A monopolist selling a product has to appropriately ...

Image processing pipelines are ubiquitous. Every image captured by a camera and every image uploaded on social networks like Google+or Facebook is processed by a pipeline. Applications in a wide range of domains like ...

Optimization problems involving uncertainties are common in a variety of engineering disciplines such as transportation systems, manufacturing, communication networks, healthcare and finance. The large number of input ...

In recent years, deep neural network models have shown to outperform many state of the art algorithms. The reason for this is, unsupervised pretraining with multi-layered deep neural networks have shown to learn better ...