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Anju P. Johnson
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2020 – today
- 2023
- [j9]Abdul Wahab Qurashi
, Zohaib A. Farhat
New Avenues for Automated Railway Safety Information Processing in Enterprise Architecture: An NLP Approach. IEEE Access 11: 44413-44424 (2023) - [j8]Siyue Song
Predicting Drug Review Polarity Using the Combination Model of Multi-Sense Word Embedding and Fuzzy Latent Dirichlet Allocation (FLDA). IEEE Access 11: 118538-118546 (2023) - [c15]Mohammed Bello Aliyu, Maryam Hafeez, Anju P. Johnson
LoRa-PUF: A Two-Step Security Solution for LoRaWAN. VTC2023-Spring 2023: 1-6 - 2020
- [j7]Anju P. Johnson
Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems. Sensors 20(3): 844 (2020) - [j6]Hussain Al-Aqrabi
Hardware-Intrinsic Multi-Layer Security: A New Frontier for 5G Enabled IIoT. Sensors 20(7): 1963 (2020) - [c14]Abdul Wahab Qurashi, Violeta Holmes
Document Processing: Methods for Semantic Text Similarity Analysis. INISTA 2020: 1-6
2010 – 2019
- 2019
- [j5]Junxiu Liu
Exploring Self-Repair in a Coupled Spiking Astrocyte Neural Network. IEEE Trans. Neural Networks Learn. Syst. 30(3): 865-875 (2019) - [c13]Junxiu Liu, Liam McDaid, Jim Harkin
Autonomous Learning Paradigm for Spiking Neural Networks. ICANN (1) 2019: 737-744 - [c12]Hussain Al-Aqrabi
A Multi-layer Security Model for 5G-Enabled Industrial Internet of Things. iSCI 2019: 279-292 - [c11]Hussain Al-Aqrabi
Dynamic Multiparty Authentication using Cryptographic Hardware for the Internet of Things. SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2019: 21-28 - 2018
- [j4]Anju P. Johnson
Homeostatic Fault Tolerance in Spiking Neural Networks: A Dynamic Hardware Perspective. IEEE Trans. Circuits Syst. I Regul. Pap. 65-I(2): 687-699 (2018) - [c10]Shvan Karim, Jim Harkin, Liam McDaid, Bryan Gardiner
FPGA-based Fault-injection and Data Acquisition of Self-repairing Spiking Neural Network Hardware. ISCAS 2018: 1-5 - [c9]Anju P. Johnson
Time-multiplexed System-on-Chip using Fault-tolerant Astrocyte-Neuron Networks. SSCI 2018: 1076-1083 - [c8]Anju P. Johnson
Fault-Tolerant Learning in Spiking Astrocyte-Neural Networks on FPGAs. VLSID 2018: 49-54 - 2017
- [j3]Anju P. Johnson
Remote dynamic partial reconfiguration: A threat to Internet-of-Things and embedded security applications. Microprocess. Microsystems 52: 131-144 (2017) - [j2]Anju P. Johnson
An Improved DCM-Based Tunable True Random Number Generator for Xilinx FPGA. IEEE Trans. Circuits Syst. II Express Briefs 64-II(4): 452-456 (2017) - [c7]Anju P. Johnson
Homeostatic fault tolerance in spiking neural networks utilizing dynamic partial reconfiguration of FPGAs. FPT 2017: 195-198 - [c6]Junxiu Liu, Liam McDaid, Jim Harkin
Self-repairing Learning Rule for Spiking Astrocyte-Neuron Networks. ICONIP (6) 2017: 384-392 - [c5]Shvan Karim, Jim Harkin
Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks. ISVLSI 2017: 421-426 - 2016
- [c4]Anju P. Johnson
Remote Dynamic Clock Reconfiguration Based Attacks on Internet of Things Applications. DSD 2016: 431-438 - [c3]Anju P. Johnson
An FPGA-based hardware-efficient fault-tolerant astrocyte-neuron network. SSCI 2016: 1-8 - 2015
- [j1]Anju P. Johnson
A PUF-Enabled Secure Architecture for FPGA-Based IoT Applications. IEEE Trans. Multi Scale Comput. Syst. 1(2): 110-122 (2015) - [c2]Anju P. Johnson
A Novel Attack on a FPGA based True Random Number Generator. WESS 2015: 6 - 2014
- [c1]Anju P. Johnson
Fault attack on AES via hardware Trojan insertion by dynamic partial reconfiguration of FPGA over ethernet. WESS 2014: 1:1-1:8
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