Results 91 to 100 of about 5,709,929 (152)

Extreme Learning Machine for Graph Signal Processing

, 2018
In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal.
Peter Handel, Chatterjee, Saikat,, Händel, Peter,, Saikat Chatterjee, Arun Venkitararnan, Venkitaraman, Arun,   +5 more
core   +1 more source

GSP = DSP + Boundary Conditions -- The Graph Signal Processing Companion Model

The paper presents the graph signal processing (GSP) companion model that naturally replicates the basic tenets of classical signal processing (DSP) for GSP.
Moura, Jose M. F., Shi, John
core  

Enhancing PET/CT Radiomics Robustness Through Graph Signal Processing. [PDF]

Diagnostics (Basel)
Latino T, Stefano A, Pasini G, Marinozzi F, Russo G, Bini F.   +5 more
europepmc   +1 more source

Emotional states detection using electrodermal activity and graph signal processing

This study introduces a novel Graph Signal Processing (GSP) method to analyze Electrodermal Activity (EDA) signals for emotional state detection. EDA, influenced by the sympathetic nervous system, is a sensitive indicator of emotional states but is ...
Veeranki, Y.R., Marmolejo Ramos, F., Posada Quintero, H.F., Mercado Diaz, L.R.   +3 more
core   +1 more source

Enhancing EEG based cognitive state classification using graph Fourier transform. [PDF]

AIMS Neurosci
Sharma S, Kotwal A, Bijral RK, Sharma V, Manhas J.   +4 more
europepmc   +1 more source

Verifying the Smoothness of Graph Signals: A Graph Signal Processing Approach

Graph signal processing (GSP) deals with the representation, analysis, and processing of structured data, i.e. graph signals that are defined on the vertex set of a generic graph.
Routtenberg, Tirza, Dabush, Lital
core  

Adaptive graph signal processing for robust multimodal fusion with dynamic semantic alignment. [PDF]

Sci Rep
Karthikeya KV, Rajasekaran AS, Das AK, K VB, Pal S.   +4 more
europepmc   +1 more source

Graph Signal Processing for Global Stock Market Realized Volatility Forecasting

This paper introduces an innovative realized volatility (RV) forecasting framework that extends the conventional Heterogeneous autoregressive (HAR) model via integrating Graph Signal Processing (GSP).
Chao Wang, Junbin Gao, Zhengyang Chi
core  

Graph-based analysis of frequency response measurements for assessment of winding faults in power autotransformers. [PDF]

Sci Rep
Azad VT, Afsharirad H, Ghazijahani HA, Safari A.   +3 more
europepmc   +1 more source

Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation. [PDF]

Hum Brain Mapp
Liu Y, Li J, Wisnowski JL, Leahy RM.
europepmc   +1 more source