Contact no.: 9501351188
Nidhi Kalra is a Assistant Professor in the Computer Science and Engineering Department at Thapar Institute of Engineering & Technology. She has done her PhD in Computer Science and Engineering Department from Thapar Institute of Engineering & Technology, Master of Engineering from PEC Univ of Technology, Chandigarh, India and B.Tech from AIET, PTU, (Pb.) India. Her main research interests are Theoretical Computer Science, Smartphone Sensors and Application of formal grammar in Biology. She is the member of IEEE and ACM. Contact her at kalranidhi8@gmail.com, nidhi.kalra@thapar.edu
[1] Member of IEEE
[2] Member of ACM
SCI Publications
Non-SCI Publications
Ph.D. Thesis
Thesis Title - On Variant of Regulated Grammar and Regulated Automaton [Funded by Ministry of Electronics & Information Technology, Government of India]
Supervisor - Dr. Ajay Kumar Loura
Brief Synopsis of Research:
The main aim of this work is design and development of a variant of regulated grammar or regulated automata and to apply their concept in RNA and DNA bio-molecular structures. It will be an extension of regulated grammar and regulated automata to express uncertainty, imprecision, and vagueness in natural language fragments that will make regulated grammar or regulated automata more robust. In this work, we have proposed novel concept of fuzzy state grammar and fuzzy deep pushdown automaton. We have also designed deterministic version of deep pushdown automata, transducer and their parallel versions. Further we have also applied the concept of regulated grammar and regulated automata in RNA and DNA bio-molecular structures. It will help to parse the above biological structures with lesser time complexity.
ME Thesis
Thesis Title-Analyzing Driver Behavior Using Smartphone Sensors [Work done under the sponsored project CARTS (Communication Assisted Road Transportation System) funded by Media Lab Asia]
Supervisor - Dr. Divya Bansal
Brief Synopsis of Research:
The main aim of this work is to design and develop a technique for analyzing driver behavior using Smartphone sensors. In this work machine learning techniques have been used to analyse driving patterns using the data collected from accelerometer sensors present in a smartphone. Patterns were generated to characterize driving behaviors. By observing the pattern of different driving events, from the collected data, decision tree model was built, which is further used to classify unknown data into different classes.
