Arunava Naha
Researcher at Department of Electrical Engineering; Signals and Systems
- Telephone:
- +46 18 471 70 58
- E-mail:
- arunava.naha@angstrom.uu.se
- Visiting address:
- Ångströmlaboratoriet, Lägerhyddsvägen 1
752 37 Uppsala - Postal address:
- Box 65
751 03 UPPSALA
- CV:
- Download CV
- ORCID:
- 0000-0002-7112-8269
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Biography
Arunava Naha is a Researcher at the Division of Signals and Systems, Department of Electrical Engineering, Uppsala University, Sweden. He received his MS and PhD from Electrical Engineering Department, Indian Institute of Technology (IIT) Kharagpur, India in 2013 and 2018, respectively. His PhD research was on the topic of incipient fault detection of induction motors applying subspace-based frequency estimation techniques. He has worked for two and a half years at Samsung R&D Institute India- Bangalore (SRIB) as Chief Engineer till 2019. At Samsung, his research was focused on Li-ion battery health monitoring. From 2019 to 2021, he was a Postdoctoral fellow at the Department of Electrical Engineering, Uppsala University, Sweden. His postdoctoral and current research involves the security of cyber-physical systems, sequential change detection in stochastic processes, reinforcement learning, signal processing, and control.
Dr. Naha is currently an Associate Editor of IEEE Trans. Instrumentation and Measurement.
Research
My current research is focused on the physical layer security of cyber-physical systems, which involves the study of sequential change detection in stochastic processes, reinforcement learning, signal processing, and control. My research interest also includes fault diagnosis and condition monitoring of different systems, such as electrical machines, Li-ion batteries, etc. applying signal processing and data-driven methods.
Publications
Recent publications
- Bayesian Quickest Change-Point Detection With an Energy Harvesting Sensor and Asymptotic Analysis (2024)
- Automated Analysis of Nano-Impact Single-Entity Electrochemistry Signals Using Unsupervised Machine Learning and Template Matching (2024)
- Sequential detection of Replay attacks (2023)
- Quickest physical watermarking-based detection of measurement replacement attacks in networked control systems (2023)
- Quickest detection of deception attacks on cyber-physical systems with a parsimonious watermarking policy (2023)
All publications
Articles
- Bayesian Quickest Change-Point Detection With an Energy Harvesting Sensor and Asymptotic Analysis (2024)
- Automated Analysis of Nano-Impact Single-Entity Electrochemistry Signals Using Unsupervised Machine Learning and Template Matching (2024)
- Sequential detection of Replay attacks (2023)
- Quickest physical watermarking-based detection of measurement replacement attacks in networked control systems (2023)
- Quickest detection of deception attacks on cyber-physical systems with a parsimonious watermarking policy (2023)
- Minimum Distance-Based Detection of Incipient Induction Motor Faults Using Rayleigh Quotient Spectrum of Conditioned Vibration Signal (2021)
Conferences
- Sequential Detection of Replay Attacks with a Parsimonious Watermarking Policy (2022)
- Structural analyses of a parsimonious watermarking policy for data deception attack detection in networked control systems (2022)
- Deception Attack Detection using Reduced Watermarking (2021)
- Deception Attack Detection Using Reduced Watermarking (2021)