Does Oblique Effect Affect SSVEP-Based Visual Acuity Assessment?

This study aimed to explore whether there was an effect on steady-state visual evoked potential (SSVEP) visual acuity assessment from the oblique effect or the stimulus orientation. SSVEPs were induced by seven visual stimuli, e.g., the reversal sinusoidal gratings with horizontal, two oblique, and vertical orientations, reversal checkerboards with vertical and oblique orientations, and oscillating expansion-contraction concentric-rings, at six spatial frequency steps. Ten subjects participated in the experiment. Subsequently, a threshold estimation criterion was used to determine the objective SSVEP visual acuity corresponding to each visual stimulus. Taking the SSVEP amplitude and signal-to-noise-ratio (SNR) of the fundamental reversal frequency as signal characteristics, both the SSVEP amplitude and SNR induced by the reversal sinusoidal gratings at 3.0 cpd among four stimulus orientations had no significant difference, and the same finding was also shown in the checkerboards between vertical and oblique orientation. In addition, the SSVEP visual acuity obtained by the threshold estimation criterion for all seven visual stimuli showed no significant difference. This study demonstrated that the SSVEPs induced by all these seven visual stimuli had a similarly good performance in evaluating visual acuity, and the oblique effect or the stimulus orientation had little effect on SSVEP response as well as the SSVEP visual acuity.

Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning

Acquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long–short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.

Late Pleistocene and Holocene aeolian activity in the Deliblato Sands, Serbia

The Deliblato Sands is among the largest uniform dune fields of Europe, with a very pronounced topography reflecting extensive past aeolian events. Although lacking numerical age data, previous researchers have hypothesized various periods of dune formation. Our research goals were to map the main morphological units of the Deliblato Sands, and to provide the first optically stimulated luminescence (OSL) ages for the major dune types. Mapping was carried out using digital elevation models, satellite images, and GPS profiles. Dune development was investigated using OSL. Several tests were performed concerning thermal treatment, signal characteristics, dose recovery, and dose distributions to assess the suitability of sediments for luminescence dating. Based on our results, two dune generations could be identified that differed in morphology and age. Older dune forms are primarily low sand-supply, hairpin-like parabolic dunes that developed from the last glacial maximum until the end of the early Holocene, then became stabilized. Younger, superimposed parabolic dunes record an intensive aeolian signal from the eighteenth and nineteenth centuries. The history of the Deliblato Sands fits with those from other European sand dune areas, and provides further details to understand paleoenvironmental changes in the region.

Training a deep learning model for single-cell segmentation without manual annotation

Advances in the artificial neural network have made machine learning techniques increasingly more important in image analysis tasks. Recently, convolutional neural networks (CNN) have been applied to the problem of cell segmentation from microscopy images. However, previous methods used a supervised training paradigm in order to create an accurate segmentation model. This strategy requires a large amount of manually labeled cellular images, in which accurate segmentations at pixel level were produced by human operators. Generating training data is expensive and a major hindrance in the wider adoption of machine learning based methods for cell segmentation. Here we present an alternative strategy that trains CNNs without any human-labeled data. We show that our method is able to produce accurate segmentation models, and is applicable to both fluorescence and bright-field images, and requires little to no prior knowledge of the signal characteristics.

A Wireless Animal Robot Stimulation System Based on Neuronal Electrical Signal Characteristics

As a stimulus signal, coded electrical signals can control the motion behavior of animals, which has been widely used in the field of animal robots. In current research, most of the stimulus signals used by researchers are traditional waveforms, such as square waves. To enrich the stimulus waveform, a wireless animal robot stimulation system based on neuronal electrical signal characteristics is presented in this paper. The stimulator uses the CC1101 wireless module to control animal behavior through brain stimulation. The LabVIEW-based graphical user interface(GUI) can manipulate brain stimulation remotely while the stimulator powered by battery. Additionally, The spikes of animals have been simulated by this system through Direct Digital Synthesizer(DDS) algorithm. The GUI enable users to customize the combination of these analog spike signals. The recombined signals are sent to the stimulator through CC1101 as stimulus signals. In vivo experiments conducted on five pigeons verified the efficacy of the stimulation mechanism. The analog spike signal with an amplitude of 3-5V successfully caused the pigeon’s turning behavior. The feasibility of the analog spike signals as stimulus signals was successfully verifified. Increased the diversity of stimulus waveforms in the field of animal robots.

Internal Leakage Predicition of Hydraulic Spool valves Based on Acoustic Emission Technology

In order to realize the nondestructive testing (NDT) of the internal leakage fault of hydraulic spool valves, the internal leakage rate must be predicted by AE (acoustic emission) technology. An AE experimental platform of internal leakage of hydraulic spool valves is built to study the characteristics of AE signals of internal leakage and the relationship between AE signals and leakage rates. The research results show the AE signals present a wideband characteristic. The main frequencies are concentrated in 30~50 kHz and the peak frequency is around 40 kHz. When the leakage rate is large, there are significant signal characteristics appearing in the high frequency band of 75~100 kHz. The exponent of the root mean square(RMS) of AE signals is positively correlated with the exponent of the leakage rate only if the leakage rate is greater than 2~3 mL/min. This find could be used to predict the internal leakage rate of hydraulic spool valves.

Characterization of acoustic emission signals under 3-point bending test

This paper summarizes a master’s thesis project which explored whether the characteristics of Acoustic Emission Testing (AET) signals can be used to detect yielding in steel samples undergoing a three-point bending test. A subset of existing data from a three-point bending test was exported and used as input. Data was processed by utilizing and developing tools to visualize and analyse the signal characteristics, primarily through a parameter-based approach. Signals were visualized, and parameters were optimized to identify and classify signal types. According to the obtained results, some limitations on classification were experienced due to the length of the hit data recorded. Though the work reported in this article lead to a reliable method for detecting yielding, the developed algorithms were not successful in identifying characteristics that could be used to detect yielding.

Estimation of Probability Distributions of Geoacoustic Signal Characteristics

The paper presents an estimation of probability distributions of geoacoustic signal characteristics. The studied signals have a pulsed nature. The ones have been recording at the geodynamic test site of the IKIR FEB RAS (Kamchatka Peninsula) for more than 20 years. To estimate the distribution of characteristics, such time intervals were determined in which histograms of the distribution did not change. The following characteristics were chosen for the estimation: maximum amplitude, the position of pulse envelope maximum, duration, filling frequency, and pulse-to-pulse interval. The obtained estimates made it possible to develop an empirical model of the geoacoustic emission signal. The model can help to test new and existing algorithms for the processing and analysis of geoacoustic signals. The paper also shows that the formalization of the selected characteristics makes it possible to search for anomalies, including those associated with seismic events, by the characteristic variations.