AIMC Topic: Signal-To-Noise Ratio

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Massively parallel WRNN reconstructors for spectrum recovery in astronomical photometrical surveys.

Neural networks : the official journal of the International Neural Network Society
The investigation of solar-like oscillations for probing star interiors has enjoyed a tremendous growth in the last decade. Once observations are over, the most notable difficulties in properly identifying the true oscillation frequencies of stars ar...

Reduction of false arrhythmia alarms using signal selection and machine learning.

Physiological measurement
In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy en...

An image-guided automated robot for MRI breast biopsy.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The IGAR (Image-guided Automated Robot) is a robotic platform capable of performing highly accurate clinical interventions under image guidance. The IGAR is unique in that it demonstrates MRI compatibility and maintains safe operation, ad...

Low-dose CT reconstruction via L1 dictionary learning regularization using iteratively reweighted least-squares.

Biomedical engineering online
BACKGROUND: In order to reduce the radiation dose of CT (computed tomography), compressed sensing theory has been a hot topic since it provides the possibility of a high quality recovery from the sparse sampling data. Recently, the algorithm based on...

Lung sound classification using cepstral-based statistical features.

Computers in biology and medicine
Lung sounds convey useful information related to pulmonary pathology. In this paper, short-term spectral characteristics of lung sounds are studied to characterize the lung sounds for the identification of associated diseases. Motivated by the succes...

Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals.

Physical review. E
This paper addresses the challenge of extracting meaningful information from measured bioelectric signals generated by complex, large scale physiological systems such as the brain or the heart. We focus on a combination of the well-known Laplacian ei...

Noise further expresses exponential decay for globally exponentially stable time-varying delayed neural networks.

Neural networks : the official journal of the International Neural Network Society
This paper shows that the globally exponentially stable neural network with time-varying delay and bounded noises may converge faster than those without noise. And the influence of noise on global exponential stability of DNNs was analyzed quantitati...

Sparse Inverse Covariance Estimation with L0 Penalty for Network Construction with Omics Data.

Journal of computational biology : a journal of computational molecular cell biology
Constructing coexpression and association networks with omics data is crucial for studying gene-gene interactions and underlying biological mechanisms. In recent years, learning the structure of a Gaussian graphical model from high-dimensional data u...

Classifying Regularized Sensor Covariance Matrices: An Alternative to CSP.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Common spatial patterns (CSP) is a commonly used technique for classifying imagined movement type brain-computer interface (BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback o...