Results 41 to 50 of about 279 (112)

A programmable multi-bit fault injection for embedded system

Sustainable Engineering and Innovation
Fault injection technique is commonly used to intentionally introducing attack on embedded systems, specifically advanced FPGAs and microcontrollers. The FPGA-based embedded system uses SRAM for storage of configuration data.
Rahul Shandilya, R. K. Sharma
doaj   +1 more source

Detection of Unknown Radar Pulses Using Spiking Neural Networks

IET Radar, Sonar &Navigation, Volume 20, Issue 1, January/December 2026.
This article proposes to use spiking neural networks (SNNs) to detect multiple pulses in observation windows from a superheterodyne receiver. The method is described from encoding to postprocessing. New metrics are introduced to compare the proposed detection method to the energy detector (ED), to a detection based on difference Of boxes (DOB) filters ...
Alexia Tachet, Fabrice Comblet, Ali Khenchaf   +2 more
wiley   +1 more source

Hybrid and Depth Separable Convolutional Neural Network Approaches for an Efficient and Accurate Siren Recognition With Resource Limited Devices

Journal of Sensors, Volume 2026, Issue 1, 2026.
Emergency vehicles, such as ambulances, fire trucks, and police cars, play a crucial role in road traffic management. While these vehicles typically use sirens for identification, many drivers rely primarily on visual cues. This reliance presents challenges in scenarios where the emergency vehicles are obscured or outside the observer’s field of vision,
Rakshaa Munirathinam, Stanislav Vitek, Deepali   +2 more
wiley   +1 more source

Symbolic Reservoir Computing within Memristive Crossbar Arrays as a Cellular Automata

Advanced Intelligent Systems, Volume 7, Issue 12, December 2025.
In quest of a neuro‐symbolic system with both strong intelligent computing capability and better explainability, a memristor crossbar array‐based cellular automata (symbolic model) for reservoir computing (neural network) is proposed and experimentally demonstrated using an algorithm–hardware codesign approach.
Yunpeng Guo, Chenfei Miao, Xinxin Wang, Xue Liu, Cheng Ma, Lidan Wang, Shukai Duan, Wenrui Duan, Huanglong Li   +8 more
wiley   +1 more source

What is next for LLMs? Pushing the boundaries of next‐gen AI computing hardware with photonic chips

Nanophotonics, Volume 14, Issue 22, Page 3499-3525, 01 November 2025.
Abstract Large language models (LLMs) are rapidly pushing the limits of contemporary computing hardware. For example, training GPT‐3 has been estimated to consume around 1,300 MWh of electricity, and projections suggest future models may require city‐scale (gigawatt) power budgets.
Renjie Li, Qi Xin, Wenjie Wei, Sixuan Mao, Erik Ma, Zijian Chen, Jingxing Gao, Malu Zhang, Haizhou Li, Zhaoyu Zhang   +9 more
wiley   +1 more source

Voltage‐Summation‐Based Compute‐in‐Memory Technology with Capacitive Synaptic Devices

Advanced Intelligent Systems, Volume 7, Issue 10, October 2025.
Compute‐in‐memory (CIM) technologies leveraging capacitive coupling offer significant advantages in energy efficiency and IR‐drop elimination. This work introduces voltage‐summation‐based CIM technology, employing capacitive synaptic devices for matrix–vector multiplication.
Jung Nam Kim, Yong Woo Kim, Boram Kim, Doo‐Hyun Kim, Gil Sung Lee, Dong‐Hyuk Chae, Minkyu Je, Minsuk Koo, Yoon Kim   +8 more
wiley   +1 more source

Research on the Security of SRAM-Based FPGAs in the Era of Artificial Intelligence

Journal of Low Power Electronics and Applications
SRAM-based FPGAs, with their flexible programmability and parallel execution features, have been widely used, and the security of such devices has drawn significant attention.
Jing Zhou, Xiangyu Zhao, Shengbing Zhang, Lei Chen, Ke Xiao, Shuo Wang   +5 more
doaj   +1 more source

Hardware Security in the Connected World

WIREs Data Mining and Knowledge Discovery, Volume 15, Issue 3, September 2025.
Depicts the components covered in the article. ABSTRACT The rapid proliferation of the Internet of Things (IoT) has integrated billions of smart devices into our daily lives, generating and exchanging vast amounts of critical data. While this connectivity offers significant benefits, it also introduces numerous security vulnerabilities.
Durba Chatterjee, Shuvodip Maitra, Nimish Mishra, Shubhi Shukla, Debdeep Mukhopadhyay   +4 more
wiley   +1 more source

Securing the Future: Lightweight Blockchain Solutions for IIoT and IoT Networks

SECURITY AND PRIVACY, Volume 8, Issue 4, July/August 2025.
ABSTRACT This paper presents an innovative lightweight blockchain architecture tailored for Internet of Things (IoT) and Industrial Internet of Things (IIoT) applications. Building upon Byzantine Fault Tolerance (BFT) and Directed Acyclic Graph (DAG) technologies, our proposed BFT‐DAG framework introduces key enhancements to optimize its performance in
Fatemeh Stodt, Mohammed Alshawki, Christoph Reich, Peter Ligeti, Fabrice Theoleyre   +4 more
wiley   +1 more source

A Review of Performance and Reliability Issues in D Flip‐Flops for Future Artificial Intelligence and Internet of Things Applications

IET Circuits, Devices &Systems, Volume 2025, Issue 1, 2025.
In this paper, various D flip‐flops (FFs) (DFFs) are studied and analyzed based on the performance and reliability effects of different architectures, technology, area, power, delay, and several other key performance parameters of DFFs. Based on these parameters, a few selected DFFs such as C2SFF, conditional‐bridging FF (CBFF)‐S, self‐shut‐off pulsed ...
Syeda Hurmath Juveria, R. Shashank, J. Ajayan, Amit Krishna Dwivedi, D. Nirmal, Shashikant Patil   +5 more
wiley   +1 more source