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Ultrafast Coherence-Based Power Doppler Estimation Using Nonlinear Compounding With Complementary Subset Transmit.
Ultrasound Med Biol
January 2025
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
Objective: Conventional coherent plane wave compounding (CPWC) and sum-of-square power Doppler (PD) estimation lead to low contrast and high noise level in ultrafast PD imaging when the number of plane-wave angle and the ensemble length is limited. The coherence-based PD estimation using temporal-multiply-and-sum (TMAS) of high-lag autocorrelation can effectively suppress the uncorrelated noises but at the cost of signal power due to the blood flow decorrelation.
Methods: In this study, the TMAS PD estimation is incorporated with complementary subset transmit in nonlinear compounding (DMAS-CST) to leverage the signal coherence in both angular and temporal dimensions for improvement of PD image quality.
Neural Dynamics of the Processing of Speech Features: Evidence for a Progression of Features from Acoustic to Sentential Processing.
J Neurosci
January 2025
Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742
When we listen to speech, our brain's neurophysiological responses "track" its acoustic features, but it is less well understood how these auditory responses are enhanced by linguistic content. Here, we recorded magnetoencephalography (MEG) responses while subjects of both sexes listened to four types of continuous-speech-like passages: speech-envelope modulated noise, English-like non-words, scrambled words, and a narrative passage. Temporal response function (TRF) analysis provides strong neural evidence for the emergent features of speech processing in cortex, from acoustics to higher-level linguistics, as incremental steps in neural speech processing.
View Article and Find Full Text PDFReversing Cochlear Nucleus Maladaptive Plasticity via Customized Extracochlear Stimulation: A New Approach for Tinnitus Treatment.
Adv Sci (Weinh)
January 2025
ENT Institute and Department of Otolaryngology, Eye & ENT Hospital of Fudan University, Shanghai, 200031, China.
Tinnitus, a widespread condition affecting numerous individuals worldwide, remains a significant challenge due to limited effective therapeutic interventions. Intriguingly, patients using cochlear implants (CIs) have reported significant relief from tinnitus symptoms, although the underlying mechanisms remain unclear and intracochlear implantation risks cochlear damage and hearing loss. This study demonstrates that targeted intracochlear electrical stimulation (ES) in guinea pigs with noise-induced hearing loss reversed tinnitus-related maladaptive plasticity in the cochlear nucleus (CN), characterized by reduced auditory innervation, increased somatosensory innervation, and diminished inhibitory neural networks.
View Article and Find Full Text PDFA kurtosis-ESPRIT algorithm for RealTime stability assessment in droop controlled microgrids.
Sci Rep
January 2025
Advanced Power and Energy Center (APEC), Electrical Engineering Department, Khalifa University, Abu Dhabi, UAE.
Although detailed analytical models for droop-controlled microgrids are available, they are computationally complex and do not consider real-time variations in microgrid parameters and operating conditions. This paper proposes Kurtosis-Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) to identify the dominant modes in droop-controlled inverter-based microgrids (IBMGs) using local real-time measurements. In the proposed approach, a short-duration small disturbance is applied to the selected DG's active power droop gain, and then, the system's dominant modes are estimated from its local measurements.
View Article and Find Full Text PDFApplication research of convolutional neural network and its optimization in lightning electric field waveform recognition.
Sci Rep
January 2025
Institute of Atmospheric Physics, LAGEO, Chinese Academy of Sciences, Beijing, China.
Quickly identifying and classifying lightning waveforms is the foundation of lightning forecasting and early warning. In this paper, based on the electric field observation of the Beijing lightning location website of the Institute of Atmospheric Physics, Chinese Academy of Sciences, a recognition and classification method of pulse signal waveform based on Convolutional Neural Network(CNN) algorithm is designed and implemented. The CNN network model and its parameters were optimized from three aspects: dataset, model parameters, and network structure, achieving a recognition rate of over 90%.
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