Thermal Fringe Formation During a Hologram Recording Using a Dry Photopolymer

In this study we investigated the undesired but possible fringe formation during the recording of large size holographic optical elements (HOE) using a dry photopolymer. We identified the deformation of the recording element during hologram exposure as the main source for this fringe formation. This deformation is caused mainly by the one-sided heating of the recording element, namely, the dry photopolymer–recording plate stack. It turned out that the main source for this heating was the heat of polymerization in the dry photopolymer released during the exposure interval. These insights were translated into a physical model with which quantitative predictions about thermal fringe formation can be made depending on the actual HOE recording geometry, recording conditions and characteristics of the dry photopolymer. Using this model, different types of large size HOEs, used as components to generate a steerable confined view box for a 23” diagonal size display demonstrator, could be recorded successfully without thermal fringe formation. Key strategies to avoid thermal fringe formation deduced from this model include balancing the ratio of lateral recording plate dimension R to its thickness h, recording the power density P or equivalently the exposure time texp at a fixed recording dosage E, and most importantly recording the the linear coefficient of thermal expansion (CTE) of the recording plate material. Suitable glass plates with extremely low CTE were identified and used for recording of the above-mentioned HOEs.

Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running

We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four different speeds while performing two brain-computer interface (BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 9-channel inertial measurement units placed at the forehead, left ankle, and right ankle. The recording conditions were as follows: standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0 m/s, respectively. For each speed, two different BCI paradigms, event-related potential and steady-state visual evoked potential, were recorded. To evaluate the signal quality, scalp- and ear-EEG data were qualitatively and quantitatively validated during each speed. We believe that the dataset will facilitate BCIs in diverse mobile environments to analyze brain activities and evaluate the performance quantitatively for expanding the use of practical BCIs.

Age-Related Differences in Resting-State EEG and Allocentric Spatial Working Memory Performance

During normal aging resting-state brain activity changes and working memory performance declines as compared to young adulthood. Interestingly, previous studies reported that different electroencephalographic (EEG) measures of resting-state brain activity may correlate with working memory performance at different ages. Here, we recorded resting-state EEG activity and tested allocentric spatial working memory in healthy young (20–30 years) and older (65–75 years) adults. We adapted standard EEG methods to record brain activity in mobile participants in a non-shielded environment, in both eyes closed and eyes open conditions. Our study revealed some age-group differences in resting-state brain activity that were consistent with previous results obtained in different recording conditions. We confirmed that age-group differences in resting-state EEG activity depend on the recording conditions and the specific parameters considered. Nevertheless, lower theta-band and alpha-band frequencies and absolute powers, and higher beta-band and gamma-band relative powers were overall observed in healthy older adults, as compared to healthy young adults. In addition, using principal component and regression analyses, we found that the first extracted EEG component, which represented mainly theta, alpha and beta powers, correlated with spatial working memory performance in older adults, but not in young adults. These findings are consistent with the theory that the neurobiological bases of working memory performance may differ between young and older adults. However, individual measures of resting-state EEG activity could not be used as reliable biomarkers to predict individual allocentric spatial working memory performance in young or older adults.

Amplitude of Somatosensory Evoked Potentials (SEPs) in Short-Latency SEPs Condition is 80% of that in Giant SEPs

The recording conditions of somatosensory evoked potentials (giant SEPs) are different from those of short-latency SEPs (SSEPs). We investigated the waveform characteristics obtained for each condition. Forty-eight upper limbs of 24 adult normal subjects (12 males, age 35.5 ± 9.7 years (mean ± SD)) were investigated. The main differences in recording conditions were reference electrodes (giant SEPs: the earlobe electrode ipsilateral to the stimulated limb, SSEPs: Fz), stimulus rate (1 Hz, 5 Hz), and bandpass filter (1 Hz–1 kHz, 20 Hz–3 kHz). SEPs were elicited by unilateral percutaneous electrical stimulation of the median nerve at the wrist. The amplitudes of N20o–N20 and N20–P25 were significantly larger in the giant SEP condition than in the SSEP condition (p<0.001). The mean + 3SD of N20–P25 amplitude was 10.0 µV in the giant SEP condition and 7.8 µV in the SSEP condition. The N20–P25 amplitude was significantly correlated between the giant SEP and SSEP conditions (R=0.64, p<0.001). Thus, the amplitude of SEPs in the SSEPs condition is equivalent to 80% of that in the giant SEPs condition. The information is useful for detecting cortical hyperexcitability in various neurological disorders including myoclonic epilepsy.

Robust Feature Learning on Long-Duration Sounds for Acoustic Scene Classification

Acoustic scene classification (ASC) aims to identify the type of scene (environment) in which a given audio signal is recorded. The log-mel feature and convolutional neural network (CNN) have recently become the most popular time-frequency (TF) feature representation and classifier in ASC. An audio signal recorded in a scene may include various sounds overlapping in time and frequency. The previous study suggests that separately considering the long-duration sounds and short-duration sounds in CNN may improve ASC accuracy. This study addresses the problem of the generalization ability of acoustic scene classifiers. In practice, acoustic scene signals' characteristics may be affected by various factors, such as the choice of recording devices and the change of recording locations. When an established ASC system predicts scene classes on audios recorded in unseen scenarios, its accuracy may drop significantly. The long-duration sounds not only contain domain-independent acoustic scene information, but also contain channel information determined by the recording conditions, which is prone to over-fitting. For a more robust ASC system, We propose a robust feature learning (RFL) framework to train the CNN. The RFL framework down-weights CNN learning specifically on long-duration sounds. The proposed method is to train an auxiliary classifier with only long-duration sound information as input. The auxiliary classifier is trained with an auxiliary loss function that assigns less learning weight to poorly classified examples than the standard cross-entropy loss. The experimental results show that the proposed RFL framework can obtain a more robust acoustic scene classifier towards unseen devices and cities.

Evaluation of film stimuli for the assessment of social-emotional processing

Background: Social-emotional difficulties have been proposed to play a key role in the progression of psychiatric disorders, including anorexia nervosa. The aim of the current study was to build on existing work and develop more ecologically valid measures of interpretation biases and evoked emotional responses. Methodology: 124 healthy female participants aged 18 – 25 took part in the study. We evaluated two sets of 18 brief film clips to be used in two tasks assessing interpretation of ambiguous scenarios and reactions to emotionally provoking scenes. Written interpretations were coded, the valence of the interpretations was analysed, and an ambiguity check was conducted. Evoked facial affect and mood ratings in response to each film clip during the second, evoked emotions task were analysed using exploratory factor analyses. Results: Most of the film clips were interpreted in the expected neutral, positive, and negative manner while still retaining some ambiguity. However, participants were more attuned to the negative cues in the ambiguous film clips and three film clips were identified as unambiguous. The exploratory factor analyses showed that the positive and negative film clips used in the evoked emotions task formed their own factors as expected. However, there was substantial cross-loading of the neutral film clips when participants’ facial expression data was analysed.Discussion: A subset of the film clips evaluated here could be used to assess interpretation biases and evoked emotional responses among people with anorexia nervosa. Ambiguous negatively valenced film clips should have more subtle negative cues to ensure there is enough room for interpretation. Additionally, care should be taken to ensure optimal recording conditions if evoked facial expressions are to be assessed.

Event-based Action Recognition Using Motion Information and Spiking Neural Networks

Event-based cameras have attracted increasing attention due to their advantages of biologically inspired paradigm and low power consumption. Since event-based cameras record the visual input as asynchronous discrete events, they are inherently suitable to cooperate with the spiking neural network (SNN). Existing works of SNNs for processing events mainly focus on the task of object recognition. However, events from the event-based camera are triggered by dynamic changes, which makes it an ideal choice to capture actions in the visual scene. Inspired by the dorsal stream in visual cortex, we propose a hierarchical SNN architecture for event-based action recognition using motion information. Motion features are extracted and utilized from events to local and finally to global perception for action recognition. To the best of the authors’ knowledge, it is the first attempt of SNN to apply motion information to event-based action recognition. We evaluate our proposed SNN on three event-based action recognition datasets, including our newly published DailyAction-DVS dataset comprising 12 actions collected under diverse recording conditions. Extensive experimental results show the effectiveness of motion information and our proposed SNN architecture for event-based action recognition.

Using the PIV Techniques in the Expertise of Gas Explosions

Given the possibilities offered by PIV systems, it was considered appropriate to test them in the study of the formation of explosive atmospheres. In this sense, within the INCD INSEMEX Petroșani laboratories, a test stand was created subject to the conditions of applying PIV techniques. Thus, this stand was built of transparent materials, having the possibility to introduce, in the inner volume, the suspended particles and the combustible gas, being at the same time able to be sectioned by the light plane generated by a laser source and to provide the recording conditions using a CMOS camera. The physical experiments concerned both the situation of a normal atmosphere inside the test stand and the situations of the appearance of a fuel gas leak and an efficient source of initiation of the formed explosive atmosphere.

Magnetic Holography and Its Application to Data Storage

The principle of magnetic holograms and its application to holographic memory are reviewed. A magnetic hologram was recorded through a thermomagnetic recording as a difference in magnetization direction and reconstructed with the magneto-optical effect. To achieve a bright reconstruction image, it is important to record deep magnetic fringes on the materials with large Faraday rotation coefficients. This technique was applied to the holographic memory using transparent magnetic garnets as a recording material. The first reconstruction image was dark and noisy, but improvements in the recording conditions resulted in error-free recording and reconstruction of the magnetic hologram. To form deep magnetic fringes, insertion of heat dissipation (HD) layers into recording layer was proposed. It was found that this HD multilayer medium showed diffraction efficiency higher than that of a single layer medium, and error-free recording and reconstruction were also achieved, using magnetic assisted recording. These results suggest that HD multilayer media have potential applications in recording media of magnetic holographic data storage. In future, a high recording density technique, such as multiple recording, should be developed.

Removal of Electrocardiogram Artifacts From Local Field Potentials Recorded by Sensing-Enabled Neurostimulator

Sensing-enabled neurostimulators are an advanced technology for chronic observation of brain activities, and show great potential for closed-loop neuromodulation and as implantable brain-computer interfaces. However, local field potentials (LFPs) recorded by sensing-enabled neurostimulators can be contaminated by electrocardiogram (ECG) signals due to complex recording conditions and limited common-mode-rejection-ratio (CMRR). In this study, we propose a solution for removing such ECG artifacts from local field potentials (LFPs) recorded by a sensing-enabled neurostimulator. A synchronized monopolar channel was added as an ECG reference, and two pre-existing methods, i.e., template subtraction and adaptive filtering, were then applied. ECG artifacts were successfully removed and the performance of the method was insensitive to residual stimulation artifacts. This approach to removal of ECG artifacts broadens the range of applications of sensing-enabled neurostimulators.