Add to ORKGAbstract: We address suppression of artifacts in NIRS time-series imaging. We report a fast algorithm, combining sparse optimization and filtering, that jointly estimates two explicitly modeled artifact types: transient disruptions and step discontinuities. OCIS codes: 000.4430, 120.2440 1
Abstract. A novel reconstruction technique, called Wiener Filtered Recon-struction Technique (WIRT),...
In this work we analyze the problem of the ghosting artifacts coming out from non-uniformity correct...
Near-infrared spectroscopy (NIRS) is susceptible to signal artifacts caused by relative motion betwe...
We address suppression of artifacts in NIRS time-series imaging. We report a fast algorithm, combini...
Weaddress suppression of artifacts in NIRS time-series imaging. We report a fast algorithm, combinin...
Abstract—This paper addresses the suppression of transient artifacts in signals, e.g., biomedical ti...
Background: Muscle artifacts and electrode noise are an obstacle to interpretation of EEG and other ...
As near-infrared spectroscopy (NIRS) broadens its application area to different age and disease grou...
Near-infrared spectroscopy (NIRS) enables the non-invasive measurement of changes in hemodynamics an...
Abstract—This paper seeks to combine linear time-invariant (LTI) filtering and sparsity-based denois...
Motion artifact contamination in near-infrared spectroscopy (NIRS) data has become an important chal...
Two complementary solution strategies to the least-squares migration problem with sparseness- & cont...
This chapter studies the problem of time-series classification and presents an overview of recent de...
This paper addresses image and signal processing problems where the result most consistent with prio...
We introduce a new adaptive method for analyzing nonlinear and nonstationary data. This method is in...
Abstract. A novel reconstruction technique, called Wiener Filtered Recon-struction Technique (WIRT),...
In this work we analyze the problem of the ghosting artifacts coming out from non-uniformity correct...
Near-infrared spectroscopy (NIRS) is susceptible to signal artifacts caused by relative motion betwe...
We address suppression of artifacts in NIRS time-series imaging. We report a fast algorithm, combini...
Weaddress suppression of artifacts in NIRS time-series imaging. We report a fast algorithm, combinin...
Abstract—This paper addresses the suppression of transient artifacts in signals, e.g., biomedical ti...
Background: Muscle artifacts and electrode noise are an obstacle to interpretation of EEG and other ...
As near-infrared spectroscopy (NIRS) broadens its application area to different age and disease grou...
Near-infrared spectroscopy (NIRS) enables the non-invasive measurement of changes in hemodynamics an...
Abstract—This paper seeks to combine linear time-invariant (LTI) filtering and sparsity-based denois...
Motion artifact contamination in near-infrared spectroscopy (NIRS) data has become an important chal...
Two complementary solution strategies to the least-squares migration problem with sparseness- & cont...
This chapter studies the problem of time-series classification and presents an overview of recent de...
This paper addresses image and signal processing problems where the result most consistent with prio...
We introduce a new adaptive method for analyzing nonlinear and nonstationary data. This method is in...
Abstract. A novel reconstruction technique, called Wiener Filtered Recon-struction Technique (WIRT),...
In this work we analyze the problem of the ghosting artifacts coming out from non-uniformity correct...
Near-infrared spectroscopy (NIRS) is susceptible to signal artifacts caused by relative motion betwe...