E
Elias Kougianos
Researcher at University of North Texas
Publications - 73
Citations - 2490
Elias Kougianos is an academic researcher from University of North Texas. The author has contributed to research in topics: Computer science & Blockchain. The author has an hindex of 23, co-authored 46 publications receiving 1706 citations.
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Journal ArticleDOI
VLSI architecture and chip for combined invisible robust and fragile watermarking
TL;DR: The development of a very-large-scale integration architecture for a high-performance watermarking chip is presented which can perform both invisible robust and invisible fragile image water marking in the spatial domain.
Journal ArticleDOI
Design of Parasitic and Process-Variation Aware Nano-CMOS RF Circuits: A VCO Case Study
TL;DR: A novel flow for parasitic and process-variation aware design of radio-frequency integrated circuits (RFICs) and the first work focussed on a current starved VCO in which the combined effect of parasitics and process variations has been considered is presented.
Proceedings ArticleDOI
Proof-of-Authentication for Scalable Blockchain in Resource-Constrained Distributed Systems
TL;DR: A novel consensus algorithm is presented to replace Proof-of-Work and introduce authentication in such environments to make the blockchain application-specific and to evaluate its sustainability and applicability for the IoT and edge computing.
Journal ArticleDOI
Design of a High-Performance System for Secure Image Communication in the Internet of Things
TL;DR: This paper presents a modular and extensible quadrotor architecture and its specific prototyping for automatic tracking applications and is the first ever proposed hardware architecture for SBPG compression integrated with an SDC.
Journal ArticleDOI
Big-Sensing-Data Curation for the Cloud is Coming: A Promise of Scalable Cloud-Data-Center Mitigation for Next-Generation IoT and Wireless Sensor Networks
TL;DR: This article focuses on big-sensing-data curation and preparation issues with cloud computing under the theme of the IoT, and focuses on three especially critical issues that need to be addressed: scalable big-Sensing- data cleaning, scalablebig-s sensing-data compression, and cloud-based data curation response for IoT device optimization.