Results 81 to 90 of about 5,709,929 (152)

Short-Term Traffic Flow Prediction Based on Improved Dilated Temporal-Spatio Graph Convolutional Network [PDF]

Jisuanji gongcheng
Traffic flow prediction for road networks plays a key role in intelligent transportation. Traffic flow not only exhibits high spatial correlation but also exhibits long-term correlation and periodicity in time characteristics.
LUO Xianglong, XU Zhongcheng, SU Yongdong, HE Xibin, LIU Ruochen
doaj   +1 more source

An interpretable model based on graph learning for diagnosis of Parkinson’s disease with voice-related EEG

npj Digital Medicine
Parkinson’s disease (PD) exhibits significant clinical heterogeneity, presenting challenges in the identification of reliable electroencephalogram (EEG) biomarkers. Machine learning techniques have been integrated with resting-state EEG for PD diagnosis,
Shuzhi Zhao, Guangyan Dai, Jingting Li, Xiaoxia Zhu, Xiyan Huang, Yongxue Li, Mingdan Tan, Lan Wang, Peng Fang, Xi Chen, Nan Yan, Hanjun Liu   +11 more
doaj   +1 more source

Joint Topology Learning and Latent Input Identification Using Spatio-Temporally Linear Structured SEM

Mathematics
Topology identification and signal inference are cornerstone tasks in graph signal processing (GSP). Structural Equation Modeling (SEM) is particularly effective for network inference as it explicitly captures causal dependencies.
Jie Zhou, Rui Yang, Xintong Shi, Shuyang Feng   +3 more
doaj   +1 more source

Structural and functional interplay in anxiety related classification: a graph signal processing approach [PDF]

, 2021
International audienceAnxiety disorders are one of the most common mental health conditions with a high rate of everyday life disability. Connectivity is steadily gaining relevance to increase our knowledge of psychiatric diseases.
Maurel, Pierre, Coloigner, Julie, Orrù, Giovanna   +2 more
core  

Network System Performance Insights through Graph Signal Smoothness

IEEE Open Journal of Systems Engineering
In the field of network analysis and graph theory, the integration of graph signals into the study of complex systems has opened new horizons for understanding and extracting meaningful information.
Claudio M. Rocco, Kash Barker
doaj   +1 more source

Understanding Concepts in Graph Signal Processing for Neurophysiological Signal Analysis [PDF]

Multivariate signals measured simultaneously over time by sensor networks are becoming increasingly common. The emerging field of graph signal processing (GSP) promises to analyse spectral characteristics of these multivariate signals, while also taking ...
Wu, Min, Goerttler, Stephan; id_orcid, He, Fei; id_orcid, Goerttler, Stephan, He, Fei   +4 more
core   +1 more source

Learning Graphs From Data: A Signal Representation Perspective [PDF]

, 2019
The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis, and visualization of structured data.
Rabbat, Michael, Dong, Xiaowen, Thanou, Dorina, Frossard, Pascal   +3 more
core   +2 more sources

Time-varying graph learning from smooth and stationary graph signals with hidden nodes

EURASIP Journal on Advances in Signal Processing
Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available.
Rong Ye, Xue-Qin Jiang, Hui Feng, Jian Wang, Runhe Qiu, Xinxin Hou   +5 more
doaj   +1 more source

Topological Signal Processing Over Generalized Cell Complexes [PDF]

Topological Signal Processing (TSP) over simplicial complexes is a framework that has been recently proposed, as a generalization of graph signal processing (GSP), to extend GSP to analyzing signals defined over sets of any order (i.e., not only vertices
Sergio Barbarossa, Stefania Sardellitti
core   +1 more source

Generalizing Graph Laplacian Learning: a Graph Signal Processing Perspective [PDF]

Graph Signal Processing (GSP) extends classical signal processing (SP) to non-Euclidean domains. For a complex system, GSP studies the matrix representation of its graph abstraction.
Shi, Changhao
core