Results 121 to 130 of about 799,916 (290)

Al–Cu Composite Casting of Laser‐Deoxidized Copper: Bonding, Interfacial Chemistry, and Thermal Conductivity

Advanced Engineering Materials, EarlyView.
This study investigates laser‐based oxide removal of Cu inserts in oxygen‐free conditions and examines long‐term oxidation kinetics and surface chemistry under different atmospheres via X‐ray photoelectron spectroscopy. Al–Cu compound casting with differently oxidized surfaces is performed, and intermetallic phase formation, morphology, and thermal ...
Timon Steinhoff, Finn‐Lennard Janthur, Sascha Zimmermann, Sarah Seffer, Jörg Hermsdorf, René Gustus, Ludger Overmeyer, Wolfgang Maus‐Friedrichs, Hans Jürgen Maier, Christian Klose   +9 more
wiley   +1 more source

Recovery of missing samples in Orthogonal Frequency Division Multiplexing signals with optimisation using data carriers

IET Radar, Sonar & Navigation
A method is proposed for reconstructing an Orthogonal Frequency Division Multiplexing (OFDM) signal that contains data gaps, with the aim to improve demodulation.
Anders Haglund, Per‐Olov Frölind, Lars M. H. Ulander   +2 more
doaj   +1 more source

Model-Based Calibration of Filter Imperfections in the Random Demodulator for Compressive Sensing

, 2013
The random demodulator is a recent compressive sensing architecture providing efficient sub-Nyquist sampling of sparse band-limited signals. The compressive sensing paradigm requires an accurate model of the analog front-end to enable correct signal ...
Arildsen, Thomas, Larsen, Torben, Pankiewicz, Pawel Jerzy   +2 more
core  

Low‐Angle Grain Boundaries and Re‐Segregation in Single‐Crystalline Ni‐Base Superalloys

Advanced Engineering Materials, EarlyView.
This work demonstrates that Re‐segregation at low‐angle grain boundaries (LAGBs) in Ni‐base superalloys is influenced by misorientation angle. Advanced microscopy and atom probe tomography reveal that higher misorientation angles increases Re‐segregation.
Alireza B. Parsa, Aparna Saksena, Alesander Kostka, Pascal Thome, Felicitas Scholz, Alexandre Mussi, Christoph Somsen, Baptiste Gault, Jan Frenzel, Gunther Eggeler   +9 more
wiley   +1 more source

Optimal Signal Reconstruction Using the Empirical Mode Decomposition

EURASIP Journal on Advances in Signal Processing, 2008
The empirical mode decomposition (EMD) was recently proposed as a new time-frequency analysis tool for nonstationary and nonlinear signals. Although the EMD is able to find the intrinsic modes of a signal and is completely self-adaptive, it does not have
Kenneth E. Barner, Binwei Weng
doaj   +1 more source

Depth-resolved backscattering signal reconstruction based OCT attenuation compensation. [PDF]

Proc SPIE Int Soc Opt Eng, 2022
Wang Y, Wei S, Kang JU.
europepmc   +1 more source

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Advanced Engineering Materials, EarlyView.
A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba, Sang‐Hyeok Lee, Christina Gasper, Martina Freund, Sandra Korte‐Kerzel, Ulrich Kerzel   +5 more
wiley   +1 more source

Dual mode imaging in mid infrared with thermal signal reconstruction for innovative diagnostics of the "Monocromo" by Leonardo da Vinci. [PDF]

Sci Rep, 2021
Daffara C, Parisotto S, Mariotti PI, Ambrosini D.   +3 more
europepmc   +1 more source

Unraveling the Effect of Tramp Elements on Phase Transformations in Steels by Combining CALPHAD Modeling and Experiments

Advanced Engineering Materials, EarlyView.
This study investigates how tramp elements from increased scrap usage influence phase transformations in low‐alloyed steel. Combining dilatometry and microscopy reveal that tramp elements delay transformations, reduce critical cooling rates and increase hardenability.
Lukas Hatzenbichler, Daniel Marian Ogris, Phillip Haslberger, Matthew Galler, Oleksandr Glushko, Ronald Schnitzer   +5 more
wiley   +1 more source

Development and Explainability of Models for Machine-Learning-Based Reconstruction of Signals in Particle Detectors

Particles
Machine learning methods are being introduced at all stages of data reconstruction and analysis in various high-energy physics experiments. We present the development and application of convolutional neural networks with modified autoencoder architecture
Kalina Dimitrova, Venelin Kozhuharov, Peicho Petkov   +2 more
doaj   +1 more source