Utility of signal processing theories and practices in data processing

6^th International Conference on Biostatistics and Bioinformatics

November 13-14, 2017 | Atlanta, USA

Gahangir Hossain

Texas A&M University, USA

Posters & Accepted Abstracts: J Biom Biostat

Abstract :

Data vs. signal is just like the yoghurt vs. curd. Data is considered as the basic form of information in the age of big data. Data can be in the form of numbers, letters, or a set of characters and often collected through measurements. Data processing represents data in a form of structure, such as table, data tree, a data graph, etc. Signal is the term often used by electrical engineers to represent electronic form of data. For, data to be transferred electronically, it must first be converted into electromagnetic signals. In signal processing, signals are considered either as analog (a continuous stream of data) or digital (discreate states, binary codes). Furthermore, the analog signals can have infinite number of values in range, whereas digital can have only a limited number of values. In this way, the data processing and the signal processing are related. The main goal of this study is to explore some useful theories and best practices in signal processing vs. data processing to utilize in big data challenges. The study will connect the dots among wide range of research from bio-signal processing to bio-statistics.