scholarly journals Application of Linear Prediction for Phase and Magnitude Correction in Partially Acquired MRI

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Joseph Suresh Paul ◽  
Uma Krishna Swamy Pillai
Keyword(s):  
Linear Prediction ◽  
Prediction Method ◽  
Phase Correction ◽  
Space Representation ◽  
Subspace Projection ◽  
Magnetic Resonance Angiogram ◽  
Signal Space ◽  
Structural Details ◽  
Space Projection ◽  
Projection Filtering

Using the boxcar representation in the spatial domain and a signal-space representation of its frequency-weighted k-space, an iterative prediction method is developed to derive an improved low-resolution phase approximation for phase correction. Compared to the homodyne filter, the proposed predictor is found to be more efficient due to its capability of exhibiting an equivalent degree of performance using a lower number of fractional lines. The phase correction performance is illustrated using partially acquired susceptibility weighted images (SWI). An extension of the predictor into higher frequency regions of phase-encodes in conjunction with a signal-space projection in the frequency-weighted partial k-space is shown to provide restoration of fine structural details of sparse magnitude images. The application of subspace projection filtering is demonstrated using magnetic resonance angiogram (MRA).

scholarly journals Signal-space projection suppresses the tACS artifact in EEG recordings

2019 ◽  
Author(s):  
Johannes Vosskuhl ◽  
Tuomas P. Mutanen ◽  
Toralf Neuling ◽  
Risto J. Ilmoniemi ◽  
Christoph S. Herrmann
Keyword(s):  
Brain Activity ◽  
Current Source ◽  
Oscillatory Activity ◽  
Signal Space ◽  
Artifact Correction ◽  
Eeg Data ◽  
Before And After ◽  
Transcranial Alternating Current Stimulation ◽  
Space Projection ◽  
Eeg Recordings

1.AbstractBackgroundTo probe the functional role of brain oscillations, transcranial alternating current stimulation (tACS) has proven to be a useful neuroscientific tool. Because of the huge tACS-caused artifact in electroencephalography (EEG) signals, tACS–EEG studies have been mostly limited to compare brain activity between recordings before and after concurrent tACS. Critically, attempts to suppress the artifact in the data cannot assure that the entire artifact is removed while brain activity is preserved. The current study aims to evaluate the feasibility of specific artifact correction techniques to clean tACS-contaminated EEG data.New MethodIn the first experiment, we used a phantom head to have full control over the signal to be analyzed. Driving pre-recorded human brain-oscillation signals through a dipolar current source within the phantom, we simultaneously applied tACS and compared the performance of different artifact-correction techniques: sine subtraction, template subtraction, and signal-space projection (SSP). In the second experiment, we combined tACS and EEG on a human subject to validate the best-performing data-correction approach.ResultsThe tACS artifact was highly attenuated by SSP in the phantom and the human EEG; thus, we were able to recover the amplitude and phase of the oscillatory activity. In the human experiment, event-related desynchronization could be restored after correcting the artifact.Comparison with existing methodsThe best results were achieved with SSP, which outperformed sine subtraction and template subtraction.ConclusionsOur results demonstrate the feasibility of SSP by applying it to human tACS–EEG data.

On the detection of chipless RFID through signal space representation

2013 ◽  
Vol 68 (7-8) ◽  
pp. 437-445 ◽  
Author(s):  
Prasanna Kalansuriya ◽  
Nemai Chandra Karmakar ◽  
Emanuele Viterbo
Keyword(s):  
Space Representation ◽  
Signal Space ◽  
Chipless Rfid

A New Method for Discharge State Prediction of Micro-EDM Using Empirical Mode Decomposition

2009 ◽  
Vol 132 (1) ◽  
Author(s):  
Zhenyuan Jia ◽  
Lingxuan Zhang ◽  
Fuji Wang ◽  
Wei Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Linear Prediction ◽  
Electrical Discharge Machining ◽  
Model Identification ◽  
Prediction Method ◽  
Ar Model ◽  
Micro Edm ◽  
Multisensor Data Fusion ◽  
State Prediction ◽  
Mode Decomposition

The property of high frequency in micro-EDM (electrical discharge machining) causes the discharge states to vary much faster than in conventional EDM, and discharge states of micro-EDM have the characteristics of nonstationarity, nonlinearity, and internal coupling, all of this makes it very difficult to carry out stable control. Thus empirical mode decomposition is adopted to conduct the prediction of the discharge states obtained through multisensor data fusion and fuzzy logic in micro-EDM. Combined with the autoregressive (AR) model identification and linear prediction, the mathematical model for EDM discharge state prediction using empirical mode decomposition is established and the corresponding prediction method is presented. Experiments demonstrate that the new prediction method with short identification data is highly accurate and operates quickly. Even using short model identification data, the accuracy of empirical mode decomposition prediction can stably reach a correlation of 74%, which satisfies statistical expectations. Additionally, the new process can also effectively eliminate the lag of conventional prediction methods to improve the efficiency of micro-EDM, and it provides a good basis to enhance the stability of the control system.

Short-range forecast of atmospheric pollutants using non-linear prediction method

2008 ◽  
Vol 42 (31) ◽  
pp. 7284-7292 ◽  
Author(s):  
Valeriy N. Khokhlov ◽  
Alexander V. Glushkov ◽  
Nataliya S. Loboda ◽  
Yulia Y. Bunyakova
Keyword(s):  
Short Range ◽  
Linear Prediction ◽  
Prediction Method ◽  
Atmospheric Pollutants ◽  
Non Linear
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