Results 71 to 80 of about 622,624 (287)
Doppler Shift Estimation Method for Frequency Diverse Array Radar Based on Graph Signal Processing
Remote SensingIn this paper, a novel Doppler shift estimation method for frequency diverse array (FDA) radar based on graph signal processing (GSP) theory is proposed and investigated. First, a well-designed graph signal model for a monostatic linear FDA is formulated.
Ningbo Xie +5 more
doaj +1 more source
Graph Variogram: A novel tool to measure spatial stationarity
, 2018 Irregularly sampling a spatially stationary random field does not yield a graph stationary signal in general. Based on this observation, we build a definition of graph stationarity based on intrinsic stationarity, a less restrictive definition of ...
Girault, Benjamin +2 more
core +1 more source
Molecular Oncology, EarlyView.We developed and validated a DNA methylation–based biomarker panel to distinguish pleural mesothelioma from other pleural conditions. Using the IMPRESS technology, we translated this panel into a clinically applicable assay. The resulting two classifier models demonstrated excellent performance, achieving high AUC values and strong diagnostic accuracy.
Janah Vandenhoeck +12 more
wiley +1 more source
Autoregressive Moving Average Graph Filtering
, 2016 One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem.
Isufi, Elvin +3 more
core +1 more source
Molecular Oncology, EarlyView.We developed a cost‐effective methylation‐specific droplet digital PCR multiplex assay containing tissue‐conserved and tumor‐specific methylation markers. The assay can detect circulating tumor DNA with high accuracy in patients with localized and metastatic colorectal cancer.
Luisa Matos do Canto +8 more
wiley +1 more source
Schatten Graph Neural Networks
IEEE Access, 2022 Graph Neural Networks (GNNs) have been intensively studied in recent years because of their promising performance over graph-structural data and have provided assistance in many fields.
Youfa Liu +3 more
doaj +1 more source
A Distributed Tracking Algorithm for Reconstruction of Graph Signals
, 2015 The rapid development of signal processing on graphs provides a new perspective for processing large-scale data associated with irregular domains. In many practical applications, it is necessary to handle massive data sets through complex networks, in ...
Gu, Yuantao, Wang, Mengdi, Wang, Xiaohan
core +2 more sources
Molecular Oncology, EarlyView.Combining PTEN protein assessment and transcriptomic profiling of prostate tumors, we uncovered a network enriched in senescence and extracellular matrix (ECM) programs associated with PTEN loss and conserved in a mouse model. We show that PTEN‐deficient cells trigger paracrine remodeling of the surrounding stroma and this information could help ...
Ivana Rondon‐Lorefice +16 more
wiley +1 more source
Learning of robust spectral graph dictionaries for distributed processing
EURASIP Journal on Advances in Signal Processing, 2018 We consider the problem of distributed representation of signals in sensor networks, where sensors exchange quantized information with their neighbors. The signals of interest are assumed to have a sparse representation with spectral graph dictionaries ...
Dorina Thanou, Pascal Frossard
doaj +1 more source
Near-Optimal Graph Signal Sampling by Pareto Optimization
Sensors, 2021 In this paper, we focus on the bandlimited graph signal sampling problem. To sample graph signals, we need to find small-sized subset of nodes with the minimal optimal reconstruction error.
Dongqi Luo +4 more
doaj +1 more source