Results 111 to 120 of about 8,142,358 (299)
, 2013 Power spectra of the filters from a bilinear demosaicing filter implementation (black means high power spectral density).
Jan Aelterman (286985) +4 more
core +1 more source
Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif +17 more
wiley +1 more source
, 2013 Mean spectral density and cross power spectral density of two ECoG channels, averaged across trials. The errorbars represent the standard error computed across trials.
Vasily A. Vakorin (287766) +4 more
core +1 more source
Natural Frequencies of Levodopa‐Induced Dyskinesia in Parkinson's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objectives Abnormal involuntary movements, known as dyskinesias, are common complications of levodopa treatment in patients with Parkinson's disease and can significantly impair quality of life. The underlying pathophysiology remains unclear, and current therapeutic options are limited.
Ioannis U. Isaias +3 more
wiley +1 more source
Power spectral density comparison of FBMC and OFDM signals.
Power spectral density comparison of FBMC and OFDM signals.
Emad S. Hassan (17775798)
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Treatment of disorders of consciousness (DoC) remains a major clinical challenge, and noninvasive, targeted modulation of deep brain structures has emerged as a promising therapeutic strategy. We aimed to evaluate the feasibility/safety and preliminary effects of thalamic temporal interference stimulation (TIS) targeting centromedian‐
Gengyao Hu +7 more
wiley +1 more source
Spectrum sensing algorithm based on average value of middle part of power spectral density
Dianxin kexue, 2016 The difference between maximum and minimum value of the received signal power spectrum is distinct when the primary user signal is present or absent.Using this characteristic,the spectrum sensing algorithm based on the average of the middle part of power
Zhijin ZHAO, Xi LV, Shilian ZHENG
doaj +2 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective The prognosis of glioblastoma (GBM) remains highly unfavorable, largely due to high tumor heterogeneity and an immunosuppressive microenvironment. However, the functional role of PANoptosis in this context is poorly understood. Methods Patients were stratified via K‐means clustering. A risk score model was constructed using prognosis‐
Langfei Tian +6 more
wiley +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Hereditary Spastic Paraplegia (HSP) comprises a group of rare genetic diseases characterized by length‐dependent axonal degeneration of the corticospinal tracts and dorsal columns, whose main clinical feature is spastic gait. Pathogenic variants in the SPG4 gene cause Spastic Paraplegia Type 4 (SPG4‐HSP), the most common form of HSP.
Gaia Fattorini +12 more
wiley +1 more source
Power spectral density of the SPI-3, -6, -12, and -24 values.
, 2019 Power spectral density of the SPI-3, -6, -12, and -24 values.
Dean Whitman (6325619) +3 more
core +1 more source