Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...
Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the ...
Non-terrestrial networks have their own challenges that cellular networks didn't have. Will AI help solve them dynamically?
Sophelio Introduces the Data Fusion Labeler (dFL) for Multimodal Time-Series Data - The only labeling and harmonization ...
Representation learning lies at the core of modern artificial intelligence, enabling neural networks to uncover meaningful, ...
A new publication from Opto-Electronic Technology; DOI 10.29026/oet.2025.250010, discusses how a photonic vibration ...
A signal-processing–based framework converts DNA sequences into numerical signals to identify protein-coding regions. By integrating spectral analysis and SVM classification, the approach improves ...
Researchers at RIT have improved the electronics used in communication and radar systems to better process signals using electromagnetic radio waves. This breakthrough could advance computing ...
Coarse-Grained Reconfigurable Arrays (CGRAs) have been in the academic world for decades ([1], [2]). They are considered ideal to accelerate compute-intensive Digital Signal Processing (DSP) and ...
Traditional signal processing techniques have achieved much, but they face notable limitations when confronted with complex, high-volume data. Classical methods often rely on manual feature extraction ...
Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...
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