A recent research article by Dr. Gurmukh Singh, a senior lecturer in the Department of Computer and Information Sciences, has been accepted for publication in an international refereed journal, Phyisca A: Statistical Mechanics and its Applications (Ms. Ref. No.: PHYSA-17896R2), Elsevier Publication, recognized by the European Physical Society, France.
The title of the scholarly paper is “Multifractal analysis of multiparticle emission data in the framework of visibility graph and sandbox algorithm.” Current research work has been accomplished in a collaboration effort with Dr. A. Mukhopadhyay, North Bengal University, Darjeeling, India. Research on fractals is performed in several research fields. However, in computer science, fractals are called pattern recognition.
Basically, present investigation involves big data acquisition in the experiments conducted at two internationally renowned Labs: Brookhaven National Laboratory (BNL), Upton, N.Y., and European Organization for Nuclear Research (CERN), Geneva, Switzerland. Big data analysis and its simulation work are performed using Monte Carlo techniques, the Ultra-relativistic Molecular Dynamics (UrQMD). Fractal analysis of complex networks has underwent a paradigm shift in recent years, where the box-counting algorithm of fractals has been applied to several emerging research areas such as financial modeling, biology, social and communication networks (very popular social networks such as Facebook. Twitter, Instagram, LinkedIn, ResearchGate etc.). Studies of complex networks play an important role to explore complexities present in the dynamics of a natural and/or social network process.
It has been proven in many research areas that the complex network analysis might be an efficient tool to extract information embedded in time-series and their sequential measurements. Technically speaking, usual time-series analysis provides users with the information regarding the dynamic behavior of a system, while a network analysis of time-series provides a structural framework of complex systems. In recent times, quite a few algorithms are proposed that could reconstruct complex networks from time-series, such as complex networks from pseudo-periodic time-series, visibility graphs, state space networks, recurrence networks, nearest-neighbor networks, and complex networks based on phase-space reconstruction etc. Among the aforementioned techniques, the Visibility Graphs (VGs) and Horizontal Visibility Graph (HVGs) are frequently used in many applications belonging to diverse fields and the last cited technique is used to achieve the objectives of the present, accepted scholarly article.
In Summer 2017, Singh had an honor of evaluating Doctor of Philosophy (Ph.D.) dissertation of a candidate from the Faculty of Sciences, Pune University, Pune, India. Singh was selected, from a panel of three foreign examiners, to act as an external foreign examiner of Ph.D. thesis “Physics of percussion instruments used in Indian music in particular Tabla and Sambal.” This Ph.D. work involved three major interdisciplinary research areas: computer science, acoustics (one of the branches of physics) and chemistry. Tabla and Sambal are drum-type percussion instruments, and these musical instruments are often beaten during social and religious gatherings in the State of Mahanrashtra, India. Due to worldwide interest on percussion instruments such as Tabla and Sambal, the Ph.D. candidate chose pretty interesting topic under investigation on the Indian Classical Music.
