Results 81 to 90 of about 482 (253)

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

An Estimation of Signal Emitter Parameters from the Amplitude Measurements by an ESM Receiver

Advances in Electrical and Computer Engineering
In passive radar data processing, some emitter parameters can be extracted from the amplitude data measured by an Electronic Support Measures (ESM) receiver over a specific time.
PHAM, V. T., HUBACEK, P.
doaj   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Recent Advances in Programmable Metasurfaces and Meta‐Devices

Advanced Electronic Materials, EarlyView.
Programmable metasurfaces enable various novel functionalities by dynamically tuning electromagnetic wavefronts. This article provides a comprehensive review of recent advances in microwave and terahertz programmable metasurfaces, covering electrical, thermal, optical, and mechanical control mechanisms.
Linda Shao, Chong He, Nan Wang, Liming Si, Weiren Zhu   +4 more
wiley   +1 more source

Multiple receiver specific emitter identification

IET Radar, Sonar & Navigation
Specific emitter identification (SEI) is a technique for identifying emitters based on the principle that the hardware chain is not ideal, causing the emitted signal to contain emitter‐specific information.
Liting Sun, Zheng Liu, Zhitao Huang
doaj   +1 more source

Tuning Interfacial Water Dynamics via Gd‐Doped Cu2O for High‐Rate Selective CO2‐to‐Ethanol Conversion

Advanced Energy Materials, EarlyView.
A Gd‐doped Cu2O electrocatalyst disrupts rigid interfacial hydrogen‐bonding networks, creating a free‐water‐rich microenvironment. This accelerates proton‐coupled electron transfer, enabling selective high‐rate CO2‐to‐ethanol conversion with 48.5% Faradaic efficiency at industrially relevant current densities.
Hyunwoo Kim, Hyo Sang Jeon, Eugene Huh, Man Ho Han, Hyeon‐Seok Bang, Junho Lee, Jae‐Young Choi, Dong Ki Lee, Kyeongsu Kim, Byoung Koun Min, Woong Hee Lee, Hyung‐Suk Oh   +11 more
wiley   +1 more source

Radar Emitter Recognition Based on Spiking Neural Networks

Remote Sensing
Efficient and effective radar emitter recognition is critical for electronic support measurement (ESM) systems. However, in complex electromagnetic environments, intercepted pulse trains generally contain substantial data noise, including spurious and ...
Zhenghao Luo, Xingdong Wang, Shuo Yuan, Zhangmeng Liu   +3 more
doaj   +1 more source

A Novel Data-Driven Specific Emitter Identification Feature Based on Machine Cognition

, 2020
Machine learning becomes increasingly promising in specific emitter identification (SEI), particularly in feature extraction and target recognition. Traditional features, such as radio frequency (RF), pulse amplitude (PA), power spectral density (PSD ...
Mingzhe Zhu, Zhenpeng Feng, Xianda Zhou
core   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Investigating the effects of destructive factors on pulse repetition interval modulation type recognition using deep convolutional neural networks based on transfer learning

IET Radar, Sonar & Navigation
Automation and self‐sufficiency in the complex environment of modern electronic warfare (EW) are critical and necessary issues in electronic intelligence and support systems to detect real‐time and accurate threat radars.
Mahshid Khodabandeh, Azar Mahmoodzadeh, Hamed Agahi   +2 more
doaj   +1 more source