Results 61 to 70 of about 136,861 (303)

Integrated Field‐Free SOT Domain‐Wall Synapses and MTJ Stochastic Neurons for Hardware Boltzmann Machines

Advanced Functional Materials, EarlyView.
Field‐free spin‐orbit torque domain‐wall synapses integrated with stochastic MTJ neurons enable compact hardware Boltzmann machines. Leveraging intrinsic stochasticity and multi‐level conductance, the system achieves efficient probabilistic learning with high accuracy, demonstrating a scalable spintronic platform for energy‐efficient edge AI.
Aijaz H. Lone, Meng Tang, Camelia Florica, Bin He, Jingkai Xu, Selma Amara, Georgian Melinte, Xixiang Zhang, Gianluca Setti   +8 more
wiley   +1 more source

An Unsupervised Learning Approach to Condition Assessment on a Wound-Rotor Induction Generator

Energies, 2021
Accurate online diagnosis of incipient faults and condition assessment on generators is especially challenging to automate through supervised learning techniques, because of data imbalance.
Elsie Swana, Wesley Doorsamy
doaj   +1 more source

Engineered AuNPs/fMWCNT Nanocomposite Electrodes for High‐Sensitivity Methylglyoxal Sensing in Saliva and Sweat for Non‐Invasive Diabetes Monitoring

Advanced Healthcare Materials, EarlyView.
An AuNPs/fMWCNT nanocomposite‐modified screen‐printed carbon electrode was engineered via sequential electrodeposition and integrated into a 3D‐printed microfluidic platform for ultrasensitive methylglyoxal detection. The non‐invasive sensing platform enables rapid analysis in saliva and sweat, highlighting strong potential for wearable point‐of‐care ...
Ahadul Amin Soshi, Md Younus Ali, Dadbeh Pazuki, Matiar M.R. Howlader   +3 more
wiley   +1 more source

TopoART: A Topology Learning Hierarchical ART Network

, 2010
Tscherepanow M. TopoART: A Topology Learning Hierarchical ART Network. In: Diamantaras K, Duch W, Iliadis LS, eds. Artificial Neural Networks (ICANN 2010). Lecture Notes in Computer Science, 6354.
Iliadis, Lazaros S., Marko Tscherepanow, Tscherepanow, Marko, Diamantaras, Konstantinos, Duch, Wlodek   +4 more
core   +1 more source

CBARS: cluster based classification for activity recognition systems [PDF]

, 2012
Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition.
Gaber, Mohamed Medhat, Krishnaswamy, S., Shonali Krishnaswamy, Krishnaswamy, Shonali, Abdallah, Z., Srinivasan, B., Mohamed Medhat Gaber, Gaber, M., Srinivasan, B, Abdallah, Zahraa S., Zahraa Said Abdallah, Bala Srinivasan   +11 more
core   +1 more source

Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning

Energies, 2021
It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems.
Sungil Kim, Byungjoon Yoon, Jung-Tek Lim, Myungsun Kim   +3 more
doaj   +1 more source

Unsupervised Learning Bioreactor Regimes [PDF]

Computers & Chemical Engineering
Efficient operation of bioreactors is crucial for the success of biomanufacturing processes. Traditional Computational Fluid Dynamics (CFD) simulations provide detailed insights but often involve lengthy computation times and complexity, hindering their practicality for real-time applications.
Víctor Puig I Laborda, Lars Puiman, Teddy Groves, Cees Haringa, Lars Keld Nielsen   +4 more
openaire   +3 more sources

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Advanced Materials, EarlyView.
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll, Ivan Pinto‐Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, Yann‐Michel Niquet, Martin Børstad Eriksen, Sara Martí‐Sánchez, Georgios Katsaros, Giordano Scappucci, Peter Krogstrup, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Pablo Ordejón, Stephan Roche, Vincenzo Grillo, Jordi Arbiol   +19 more
wiley   +1 more source

Unsupervised two-class & multi-class support vector machines for abnormal traffic characterization. [PDF]

, 2009
Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this
Kim, Hyun-chul, Hutchison, D., Pezaros, D., Pezaros, Dimitrios P., Marnerides, A., Kim, H., Marnerides, Angelos, Hutchison, David   +7 more
core  

A Deep Embedded Clustering Algorithm for the Binning of Metagenomic Sequences

IEEE Access, 2022
The study of metagenomic sequences brings a deep understanding of microbial communities. One of the crucial steps in metagenomic projects is to classify sequences into different organisms, named the binning problem.
Huynh Quang Bao, Le Van Vinh, Tran Van Hoai   +2 more
doaj   +1 more source