Abstract: It is standard practice in virtual reality applications to synthesize binaural audio based on a discrete set of directionally-dependent head-related impulse responses (HRIRs). This set of HRIRs is often time-aligned in a pre-processing step, to allow for high-fidelity interpolation between HRIRs corresponding with neighbouring directions. The fidelity of this operation depends on the similarity of neighbouring aligned HRIRs. The pairwise quality of similarity makes it a difficult criterion to optimize globally and consequently one often resorts to alignment methods based on a specific feature that can be extracted for each HRIR separately, e.g., the first-onset of the peak or the group delay. However, such proxies for similarity are very sensitive to noise and therefore require a high signal-to-noise ratio, which makes them less suitable for processing HRIRs acquired outside an anechoic room. In this paper, we advance a novel alignment method, which maximizes the similarity – defined as the correlation between the full-length HRIRs – between neighbouring aligned HRIRs for all directions at once. We show that this correlation-based alignment procedure outperforms the first-onset alignment with regards to the fidelity of the spherical harmonics representation of both the spectral and interaural time difference (ITD) information, when tested on the KEMAR HRIR and six human HRIRs. Finally, we show that the correlation-based alignment is more robust to noise.

Abstract: CO2 dissociation and its subsequent conversion into added-value chemicals is a promising strategy for recycling CO2 gas into reusable products. One of the possible methods is direct plasma-induced dissociation. In this work we study the efficiency of CO2 dissociation in pulsed atmospheric-pressure gas discharge between two conducting electrodes by a 0-D numerical plasma model. The purpose of the study is to provide results on the optimal conditions of CO2 conversion with respect to the energy efficiency and dissociation by varying the maximum power density value and the pulse length. The power density is directly related to the discharge current and the reduced electric field in the discharge. We consider pulse lengths in the range from hundreds of nanosecond up to milliseconds. The results obtained show that the dissociation degree and energy efficiency are sensitive to the pulse length (duration) and the power density, so that a considerable improvement of the discharge performance can be achieved by fine-tuning these parameters. The study is intended to provide guidance in designing an experimental set-up and a power supply with the characteristics necessary to achieve optimal conversion.

Abstract: Road traffic is the most widespread environmental noise source in Europe, proven to affect human health and well-being adversely. Noise barriers can be a very effective way to objectively reduce the noise levels to which the population is exposed, leading to positive effects on noise perception and quality of life. In this paper, surveys were used to assess subjective noise level indicators (annoyance and quality of life) from residents of the vicinity of a highway where obsolete noise barriers were to be replaced. %HA before the barrier replacement was measured from the surveys (26.8%) and estimated based on the acoustic simulation and two existing exposure/response relationships (14.6 and 18.8% before and 13.6 and 8.3% after). The difference in the measured %HA to those calculated from the ERRs shows that those models might not estimate %HA fairly for small samples or particular situations where high Lden is reported. Noise annoyance correlated differently with the quality of life indicators: a weak link was observed with health problems, while a strong correlation was found with the comfort level to perform activities outdoors. Objective noise measurements gave LA,eq,(15 min.) reductions of 4.1dB(A) due to the new barrier, while in acoustics models, calculated as Lday, expected this reduction to be 5.2 dB(A). After replacing the noise barriers, a second survey could still not be distributed due to the unknown effect of the COVID-19 measures that are still active

Abstract: We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET[1] based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition.

Abstract: We present a computational model to model the magnetic structure of two-dimensional (2D) dilute-magnetic-semiconductors (DMS) both the monolayers and multilayers using first-principles density functional theory (DFT), as well as their magnetic phase transition as a function of temperature using Monte-Carlo simulations. Using our method, we model the magnetic structure of bulk, bilayer, and monolayer MoS2 substitutionally doped with Fe atoms. We find that the out-of-plane interaction in bilayer MoS2 is weakly ferromagnetic, whereas in bulk MoS2 it is strongly anti-ferromagnetic. Finally, we show that the magnetic order is more robust in bilayer Fe-doped MoS2 compared to the monolayer and results in a room-temperature FM at an atomic substitution of 14-16%.