Direct wavefront reconstruction with the cone wavefront sensor using the inverse radon transform.

The cone wavefront sensor consists of a cone (or axicon) placed at the focal plane of the imaging system, from which an annular intensity image is formed. Typically, the wavefront phase is estimated using inversion of an interaction matrix relating the intensity image to different aberration modes. In this paper, we show that the intensity image for the cone wavefront sensor is related to the radon transform of the wavefront phase.

Classification of alcoholic EEG signals using wavelet scattering transform-based features.

Following the research question and the relevant dataset, feature extraction is the most important component of machine learning and data science pipelines. The wavelet scattering transform (WST) is a recently developed knowledge-based feature extraction technique and is structurally like a convolutional neural network (CNN). It preserves information in high-frequency, is insensitive to signal deformations, and generates low variance features of real-valued signals generally required in classification tasks.

Detection of microsleep states from the EEG: a comparison of feature reduction methods.

Microsleeps are brief lapses in consciousness with complete suspension of performance. They are the cause of fatal accidents in many transport sectors requiring sustained attention, especially driving. A microsleep-warning device, using wireless EEG electrodes, could be used to rouse a user from an imminent microsleep.

Reservoir computing approaches to microsleep detection.

The detection of microsleeps in a wide range of professionals working in high-risk occupations is very important to workplace safety. A microsleep classifier is presented that employs a reservoir computing (RC) methodology. Specifically, echo state networks (ESN) are used to enhance previous benchmark performances on microsleep detection.

Mitigation of truncation effects in elongated Shack-Hartmann laser guide star wavefront sensor images.

Laser guide star Shack-Hartmann wavefront sensor images on extremely large telescopes (ELT) will be significantly elongated due to the off-axis projection of the laser relative to the subapertures. The finite number of pixels of the wavefront sensor detector means the most elongated images will be truncated, introducing errors in the centroid measurements. In this paper, we propose appending to the truncated wavefront sensor image the most likely missing tails from a high-resolution nontruncated reference image, which can be calculated from all of the low-resolution images.

Atomic Scale Dynamics Drive Brain-like Avalanches in Percolating Nanostructured Networks.

Self-assembled networks of nanoparticles and nanowires have recently emerged as promising systems for brain-like computation. Here, we focus on percolating networks of nanoparticles which exhibit brain-like dynamics. We use a combination of experiments and simulations to show that the brain-like network dynamics emerge from atomic-scale switching dynamics inside tunnel gaps that are distributed throughout the network.

Deep Learning with Convolutional Neural Network for detecting microsleep states from EEG: A comparison between the oversampling technique and cost-based learning.

Any occupation which involves critical decision making in real-time requires attention and concentration. When repetitive and expanded working periods are encountered, it can result in microsleeps. Microsleeps are complete lapses in which a subject involuntarily stops responding to the task that they are currently performing due to temporary interruptions in visual-motor and cognitive coordination.

Detection and Prediction of Microsleeps from EEG using Spatio-Temporal Patterns.

A microsleep is a brief lapse in performance due to an involuntary sleep-related loss of consciousness. These episodes are of particular importance in occupations requiring extended unimpaired visuomotor performance, such as driving. Detection and even prediction of microsleeps has the potential to prevent catastrophic events and fatal accidents.

Ensemble learning based on overlapping clusters of subjects to predict microsleep states from EEG.

Microsleeps are brief and involuntary instances of complete loss of sleep-related consciousness. We present a novel approach of creating overlapping clusters of subjects and training of an ensemble classifier to enhance the prediction of microsleep states from EEG. Overlapping clusters are created using Kullback-Leibler divergence between responsive state features of each pair of training subjects.

Predicting Microsleep States Using EEG Inter-Channel Relationships.

A microsleep is a brief and an involuntary sleep-related loss of consciousness of up to 15 s. We investigated the performances of seven pairwise inter-channel relationships-covariance, Pearson's correlation coefficient, wavelet cross-spectral power, wavelet coherence, joint entropy, mutual information, and phase synchronization index-in continuous prediction of microsleep states from EEG. These relationships were used as the feature sets of a linear discriminant analysis (LDA) and a linear support vector machine classifiers.

Prediction of microsleeps using pairwise joint entropy and mutual information between EEG channels.

Microsleeps are involuntary and brief instances of complete loss of responsiveness, typically of 0.5-15 s duration. They adversely affect performance in extended attention-driven jobs and can be fatal.

Bayesian multi-subject factor analysis to predict microsleeps from EEG power spectral features.

Prediction of an imminent microsleep has the potential to save lives and prevent catastrophic accidents. A microsleep is a brief episode of unintentional unconsciousness and, hence, loss of responsiveness. In this study, prediction of imminent microsleeps using EEG data from 8 subjects was examined.

A software framework for real-time multi-modal detection of microsleeps.

A software framework is described which was designed to process EEG, video of one eye, and head movement in real time, towards achieving early detection of microsleeps for prevention of fatal accidents, particularly in transport sectors. The framework is based around a pipeline structure with user-replaceable signal processing modules. This structure can encapsulate a wide variety of feature extraction and classification techniques and can be applied to detecting a variety of aspects of cognitive state.

Prediction of microsleeps from EEG: Preliminary results.

Brief episodes of momentarily falling asleep - microsleeps - can have fatal consequences, especially in the transportation sector. In this study, the EEG data of eight subjects, while performing a 1-D tracking task, were used to predict imminent microsleeps. A novel algorithm was developed to improve the accuracy of microsleep identification from two independent measures: tracking performance and face-video.

Optimized echo state networks with leaky integrator neurons for EEG-based microsleep detection.

The performance of a microsleep detection system was calculated in terms of its ability to detect the behavioural microsleep state (1-s epochs) from spectral features derived from 16-channel EEG sampled at 256 Hz. Best performance from a single classifier model was achieved using leaky integrator neurons on an echo state network (ESN) classifier with a mean phi correlation (φ) of 0.38 and accuracy of 67.

Optical test-benches for multiple source wavefront propagation and spatiotemporal point-spread function emulation.

Precise measurement of aberrations within an optical system is essential to mitigate combined effects of user-generated aberrations for the study of anisoplanatic imaging using optical test benches. The optical system point spread function (PSF) is first defined, and methods to minimize the effects of the optical system are discussed. User-derived aberrations, in the form of low-order Zernike ensembles, are introduced using a liquid crystal spatial light modulator (LC-SLM), and dynamic phase maps are used to study the spatiotemporal PSF.

EEG-based event detection using optimized echo state networks with leaky integrator neurons.

This study investigates the classification ability of linear and nonlinear classifiers on biological signals using the electroencephalogram (EEG) and examines the impact of architectural changes within the classifier in order to enhance the classification. Consequently, artificial events were used to validate a prototype EEG-based microsleep detection system based around an echo state network (ESN) and a linear discriminant analysis (LDA) classifier. The artificial events comprised infrequent 2-s long bursts of 15 Hz sinusoids superimposed on prerecorded 16-channel EEG data which provided a means of determining and optimizing the accuracy of overall classifier on `gold standard' events.