Image provides complete detailed information for thing or object. It is considered as an important aspect of analyzing the details of various objects or environments of real life applications. From analyzing or studying images, various techniques come into existence. These include zooming, watermarking, hazing, and compression. Each has its own advantages and disadvantages with respect to various implicit functions defined for the techniques. The research paper focuses on watermarking techniques. The techniques of watermarking have their advantages and outperforms better when combined with wavelets transformations (DWT) followed by interpolations. The wavelets and interpolations provide a good quality enhanced and zoomed watermarked images at the time of its encoding and decoding processes. The images are embedded with sample images considered as hidden information. After the extraction process image interpolation method is applied to the image to get a quality image. The process is suggested in order to view the changed pixels of images after encoding of two images. The combination of DWT watermarking and interpolation provides 52% better results when compared to existing techniques. 

The usability of Mobile devices and portable computers has been increased very rapidly. The main focus of modern research is to handle the big data in Mobile and pervasive computing. This paper gives an overview of Mobile Data Challenge which was a smart phone based research done by Nokia through Lausanne Data Collection Campaign. The Mobile Data Challenge was introduced when the amount of mobile data was seen to be increased very much in a short period of time. The rise of big data demands that this data can be accessed from everywhere and anytime. For handling such a huge amount of data e.g. SMS, images, videos etc, this data must be compressed for easy transmission over the network. It also helps in reducing the storage requirements. There are various techniques for the compression of data that are discussed in this paper. SMAZ and ShortBWT techniques are used to compress SMS. JPEG and Anamorphic Stretch Transform are used to compress the images. 

Big data technologies like Hadoop, NoSQL, Messaging Queues etc. helps in BigData analytics, drive business growth and to take right decisions in time. These Big Data environments are very dynamic and complex; they require performance validation, root cause analysis, and tuning to ensure success. In this paper we talk about how we can analyse and test the performance of these systems. We present the important factors in a big data that are primary candidates for performance testing like data ingestion capacity and throughput, data processing capacity, simulation of expected usage, map reduce jobs and so on and suggest measures to improve performance of bigdata.