Results 201 to 210 of about 23,141,464 (287)

Riemannian Geometry for the Classification of Brain States with Intracortical Brain Recordings

open access: yesAdvanced Intelligent Systems, EarlyView.
Geometric machine learning is applied to decode brain states from invasive intracortical neural recordings, extending Riemannian methods to the invasive regime where data is scarcer and less stationary. A Minimum Distance to Mean classifier on covariance manifolds uses geodesic distances to outperform convolutional neural networks while reducing ...
Arnau Marin‐Llobet   +9 more
wiley   +1 more source

Speech Recognition with Cochlea‐Inspired In‐Sensor Computing

open access: yesAdvanced Intelligent Systems, EarlyView.
Traditional speech recognition methods rely on software‐based feature extraction that introduces latency and high energy costs, making them unsuitable for low‐power devices. A proof‐of‐concept demonstration is provided of a bioinspired tonotopic sensor for speech recognition that mimics the human cochlea, using a spiral‐shaped elastic metamaterial. The
Paolo H. Beoletto   +4 more
wiley   +1 more source

Auditory System Abnormalities in Early Graying of Hair: A Cross-Sectional Study. [PDF]

open access: yesIndian Dermatol Online J
Panwar S   +6 more
europepmc   +1 more source

Review of Memristors for In‐Memory Computing and Spiking Neural Networks

open access: yesAdvanced Intelligent Systems, EarlyView.
Memristors uniquely enable energy‐efficient, brain‐inspired computing by acting as both memory and synaptic elements. This review highlights their physical mechanisms, integration in crossbar arrays, and role in spiking neural networks. Key challenges, including variability, relaxation, and stochastic switching, are discussed, alongside emerging ...
Mostafa Shooshtari   +2 more
wiley   +1 more source

Robust Dysarthric Speech Recognition with GAN Enhancement and LLM Correction

open access: yesAdvanced Intelligent Systems, EarlyView.
This study tackles dysarthric speech recognition by combining generative adversarial network (GAN)‐generated synthetic data with large language model (LLM)‐based error correction. The approach integrates three key elements: an improved CycleGAN to generate synthetic dysarthric speech for data augmentation, a multimodal automatic speech recognition core
Yibo He   +3 more
wiley   +1 more source

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