Visible to the public Biblio

Filters: Author is Su, H.  [Clear All Filters]
Yang, S., Liu, S., Huang, J., Su, H., Wang, H..  2020.  Control Conflict Suppressing and Stability Improving for an MMC Distributed Control System. IEEE Transactions on Power Electronics. 35:13735–13747.
Compared with traditional centralized control strategies, the distributed control systems significantly improve the flexibility and expandability of an modular multilevel converter (MMC). However, the stability issue in the MMC distributed control system with the presence of control loop coupling interactions is rarely discussed in existing research works. This article is to improve the stability of an MMC distributed control system by inhibiting the control conflict due to the coupling interactions among control loops with incomplete control information. By modeling the MMC distributed control system, the control loop coupling interactions are analyzed and the essential cause of control conflict is revealed. Accordingly, a control parameter design principle is proposed to effectively suppress the disturbances from the targeted control conflict and improve the MMC system stability. The rationality of the theoretical analysis and the effectiveness of the control parameter design principle are confirmed by simulation and experimental results.
Su, H., Halak, B., Zwolinski, M..  2019.  Two-Stage Architectures for Resilient Lightweight PUFs. 2019 IEEE 4th International Verification and Security Workshop (IVSW). :19–24.
The following topics are dealt with: Internet of Things; invasive software; security of data; program testing; reverse engineering; product codes; binary codes; decoding; maximum likelihood decoding; field programmable gate arrays.
Su, H., Zwolinski, M., Halak, B..  2018.  A Machine Learning Attacks Resistant Two Stage Physical Unclonable Functions Design. 2018 IEEE 3rd International Verification and Security Workshop (IVSW). :52-55.

Physical Unclonable Functions (PUFs) have been designed for many security applications such as identification, authentication of devices and key generation, especially for lightweight electronics. Traditional approaches to enhancing security, such as hash functions, may be expensive and resource dependent. However, modelling attacks using machine learning (ML) show the vulnerability of most PUFs. In this paper, a combination of a 32-bit current mirror and 16-bit arbiter PUFs in 65nm CMOS technology is proposed to improve resilience against modelling attacks. Both PUFs are vulnerable to machine learning attacks and we reduce the output prediction rate from 99.2% and 98.8% individually, to 60%.