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Video calling or voice calling through the use of the internet is a very common thing in today's world. Nevertheless, users have to pay charges directly or indirectly, tolerating all the delays even if you use the facility within the same network or sub-network. Over 30 million US households used Voice over IP (VoIP) until the end of 2010. Specialist found that 31.4% of the household used VoIP as the main and only home phone line by 2012. Users up till now have multiplied by a huge factor. There are several drawbacks of using video calling over the internet within a small network such as delay of packets (travel to the main server outside network and reenter the same network again), direct or indirect costs, users need to be always connected online and efficiency and speed of the user's system vary according to connection quality and bandwidth. In this project, we implemented a WebRTC enabled web application which provides free peer to peer video calling and conferencing tool for any private organization on top of their existing private network.

Neuroscientists seek efficient solutions for deciphering the sophisticated unknowns of the brain. Effective development of complicated brain-related tools is the focal point of research in neuroscience and neurotechnology. Thanks to today's technological advancements, the physical development of high-density and high-resolution neural interfaces has been made possible. This is where the critical bottleneck in receiving the expected functionality from such devices shifts to transferring, processing, and subsequently analyzing the massive neurophysiological extra-cellular data recorded. To respond to this inevitable concern, a spectrum of neuronal signal processing techniques have been proposed to extract task-related informative content of the signals conveying neuronal activities, and eliminate the irrelevant contents. Such techniques provide powerful tools for a wide range of neuroscience research, from low-level perception to high-level cognition. Data transformations are among the most efficient processing techniques that serve this purpose by properly changing the data representation. Mapping the data from its original domain (i.e., the time-space domain) to a new representational domain, data transformations change the viewing angle of observing the informative content of the data. This paper reviews the employment of data transformations in order to process neuronal signals and their three key applications, including spike detection, spike sorting, and data compression.

A power and area efficient hardware encoding system tailored for wireless implantable applications is presented. Constant medical monitoring allowed by implantable devices is the most relevant alternative to current bulky monitoring systems, which, in case of severe mental diseases, require heavy surgery and long term hospitalization periods. In this work, the circuit design and the signal processing algorithm dovetail in order to allow real-time neuronal signal monitoring. Two main features must be met on the circuit level to facilitate the acceptance of the implant from the human body: small area and low power consumption. The presented work proposes a new compression scheme based on the Learning-Based Compressive Subsampling approach, which allows an area reduction with respect to recent published works, while allowing high signal reconstruction quality within low power requirements. The proposed method implements on-the-fly compression coefficients generation, which does not require large static memories. This new fully digital architecture handles the data compression of each individual neuronal acquisition channel with an area of 200 x 190 mu m in 0.18 mu m, CMOS technology, and a power dissipation of only 1.15 mu W.