Nanoscience and technology is an exciting and rapidly emerging branch of science and technology which work at the atomic, molecular and macromolecular levels. Nanotechnology deals with manipulation and use various tools and functional materials at nanoscale. The concept of nanotechnology was first time proposed by American Nobel laureate Richard Feynman in 1959. However, the basic idea of definition and popularization was explored much more in 1980s. Nanotechnology has opened up new panoramas for applications in molecular biology, biotechnology in revolutionizing almost all the disciplines of veterinary and animal sciences by providing new, small scale tools and materials that are beneficial for living organisms. The different types nanomaterials are being used for disease diagnosis, treatment, drug delivery, animal nutrition, animal breeding. The various types of nanoparticles are used such as metallic nanoparticles, quantum dots, carbon nanotubes, magnetic nanoparticles, fullerenes, liposomes and dendrimers Therefore, scientists, doctors, engineers and biologists should work at the cellular and molecular levels for significant benefits and there should be public awareness about the potential risks.

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An adaptive filter is defined as a digital filter that has the capability of self adjusting its transfer function under the control of some optimizing algorithms. Most common optimizing algorithms are Least Mean Square (LMS) and Recursive Least Square (RLS). Although RLS algorithm perform superior to LMS algorithm, it has very high computational complexity so not useful in most of the practical scenario. So most feasible choice of the adaptive filtering algorithm is the LMS algorithm including its various variants. The LMS algorithm uses transversal FIR filter as underlying digital filter. This paper is based on implementation and optimization of LMS algorithm for the application of unknown system identification.