Results 141 to 150 of about 349,795 (327)
Electromagnetic Side-Channel Attack Resilience against PRESENT Lightweight Block Cipher
Nilupulee A. Gunathilake +3 more
openalex +2 more sources
Microplastics from Wearable Bioelectronic Devices: Sources, Risks, and Sustainable Solutions
Bioelectronic devices (e.g., e‐skins) heavily rely on polymers that at the end of their life cycle will generate microplastics. For research, a holistic approach to viewing the full impact of such devices cannot be overlooked. The potential for devices as sources for microplastics is raised, with mitigation strategies surrounding polysaccharide and ...
Conor S. Boland
wiley +1 more source
A FeN4─O/Clu@NC‐0.1Ac catalyst containing atomically‐dispersed FeN4─O sites (medium‐spin Fe2+) and Fe clusters delivered a half‐wave potential of 0.89 V for ORR and an overpotential of 330 mV at 10 mA cm−2 for OER in 0.1 m KOH. When the catalyst was used in a rechargeable Zn–air battery, a power density of 284.5 mW cm−2 was achieved with excellent ...
Yongfang Zhou +8 more
wiley +1 more source
The MEET Approach: Securing Cryptographic Embedded Software Against Side Channel Attacks
Giovanni Agosta +3 more
openalex +2 more sources
Advanced deep learning-assisted side-channel attack framework and transfer learning [PDF]
Ziyue Zhang
openalex +1 more source
Naofumi Homma, Takafumi Aoki
openaire +2 more sources
Droplet Triboelectrification on Liquid‐Like Polymer Brushes
This work investigates the triboelectrification of water droplets on polymer brush‐coated surfaces exhibiting minimal contact line pinning. Such surfaces enable the systematic study of electrode patterning and controlled changes in droplet contact area.
Mohammad Soltani +5 more
wiley +1 more source
Leakage Conversion For Training Machine Learning Side Channel Attack Models Faster
Rohan Kumar Manna
openalex +1 more source
Side channel attacks and countermeasures – Analysis of secure implementations
Πασχάλης Κυρανούδης +1 more
openalex +2 more sources
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source

