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This suggests that the hyperacuity VEP is a cortical correlate of a very specific type of hyperacuity, that produced by vernier offsets (colinearity failure).
The vernier VEP paradigm, when applied in the manner described, can be interpreted as a measure of position sensitivity.
Open accessJournal ArticleDOI
01 May 2003-Vision Research
11 Citations
The data are consistent with the idea of task dependent broadening of orientation tuned mechanisms responsible for detecting small Vernier offsets.
So, this new tool can help physicians to diagnose patients of CVD easily.
The observed characteristics make CVD diamond a potential dosemeter.

Related Questions

How is CVD diagnosed?4 answersCVD is diagnosed using various methods such as blood tests, electrocardiography (ECG) signals, and medical imaging techniques. One of the most effective imaging techniques used for CVD diagnosis is cardiac magnetic resonance imaging (CMR). However, diagnosing CVD from CMR data can be challenging due to the large amount of data and low contrast. To address these challenges, deep learning (DL) techniques have been employed to diagnose CVD using CMR images. ML algorithms are also used for accurate and quick diagnosis of CVD by extracting patterns and hidden relationships in medical datasets. In addition, nanomaterials-based molecular imaging has gained attention for non-invasive visualization and quantification of CVD. Another approach involves the integration of information from retinal images and dual-energy X-ray absorptiometry (DXA) data, which has shown promising results in diagnosing CVD. Furthermore, deep learning approaches, such as long-short term memory (LSTM) based neural networks, are used to classify electrocardiogram (ECG) signals for CVD diagnosis.
Which machine learning models are commonly used for CVD?5 answersMachine learning models commonly used for Cardiovascular Disease (CVD) prediction include K-Nearest Neighbors Classifier, Naive Bayes Classifier, Decision Tree Classifier, Random Forest Classifier, Support Vector Machine Classifier, and Linear Discriminant Analysis. These models are used to analyze health risk factors such as Blood Pressure and Body Mass Index, which are important in predicting CVD. Additionally, machine learning methods have been applied in electrophysiology, interventional cardiology, heart failure, and preventive cardiovascular care. Artificial Intelligence and Data Mining techniques are also used to predict CVD and identify behavioral patterns in large volumes of data. In the field of chemical vapor deposition (CVD) virtual metrology, nonlinear feature selection and regression algorithms combined with nearest data imputing algorithms have been found to provide high prediction accuracy. Machine learning methods are also used in automated methods of segmentation of arterial walls in ultrasound imaging for improved diagnosis of CVD.
Is CVV and CVD same?6 answers
What does CVD stand for diamonds?8 answers
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What does CVD stand for?5 answers

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