Search Results (5)

A terrestrial reference frame (TRF) is derived based on historical geodetic data and is normally updated every 5–6 years. The three most recent International Terrestrial Reference System (ITRS) realizations, ITRF2014, DTRF2014, and JTRF2014, were determined with different strategies, which has resulted in different signals in the reference frame parameters. In this paper, we used the continuous site position time series of International GNSS Service (IGS) from 1995 to 2020 as a benchmark to investigate the characteristics of the three frames. In the comparison, the ITRS realizations were divided into the determination and prediction sections, where the site coordinates of the TRFs were extrapolated in the prediction period. The results indicated that the orientation and scale parameters of the ITRF2014, and the IGS solutions showed excellent agreement during the determination period of ITRF2014, while, during the prediction period, the orientation parameter diverged from IGS with rates of 11.9, 5.5, and 8.4 μas/yr, and the scale degraded with a rate of −0.038 ppb/yr. The consistency of the origin parameters between the DTRF2014 and the IGS solutions during the two periods changed from 0.07, 0.11, and −0.15 mm/yr to −0.17, −0.18, and −0.12 mm/yr; the consistency of orientation parameters from −3.6, −1.9, and 2.9 μas/yr to 15.9, −2.3, and 13.2 μas/yr; and the consistency of scale from 0.007 to −0.005 ppb/yr. In the comparison between the JTRF2014 and IGS solutions, annual signals in the origin differences were 1.5, 3.0, and 2.4 mm in the X, Y, and Z components, respectively, and the temporal variation trends in different periods disagreed with their long-term trends. Obvious trend switches in the rotation parameters were also observable, and the complex temporal variation characteristics of the scale offsets may be related to the scale definition strategy applied in different TRFs. Full article

In this paper, we propose a breakthrough single-trial P300 detector that maximizes the information translate rate (ITR) of the brain–computer interface (BCI), keeping high recognition accuracy performance. The architecture, designed to improve the portability of the algorithm, demonstrated full implementability on a dedicated embedded platform. The proposed P300 detector is based on the combination of a novel pre-processing stage based on the EEG signals symbolization and an autoencoded convolutional neural network (CNN). The proposed system acquires data from only six EEG channels; thus, it treats them with a low-complexity preprocessing stage including baseline correction, windsorizing and symbolization. The symbolized EEG signals are then sent to an autoencoder model to emphasize those temporal features that can be meaningful for the following CNN stage. This latter consists of a seven-layer CNN, including a 1D convolutional layer and three dense ones. Two datasets have been analyzed to assess the algorithm performance: one from a P300 speller application in BCI competition III data and one from self-collected data during a fluid prototype car driving experiment. Experimental results on the P300 speller dataset showed that the proposed method achieves an average ITR (on two subjects) of 16.83 bits/min, outperforming by +5.75 bits/min the state-of-the-art for this parameter. Jointly with the speed increase, the recognition performance returned disruptive results in terms of the harmonic mean of precision and recall (F1-Score), which achieve 51.78 ± 6.24%. The same method used in the prototype car driving led to an ITR of ~33 bit/min with an F1-Score of 70.00% in a single-trial P300 detection context, allowing fluid usage of the BCI for driving purposes. The realized network has been validated on an STM32L4 microcontroller target, for complexity and implementation assessment. The implementation showed an overall resource occupation of 5.57% of the total available ROM, ~3% of the available RAM, requiring less than 3.5 ms to provide the classification outcome. Full article

The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling. Full article

Over the years, Global Navigation Satellite Systems (GNSS) have been established in the geosciences as a tool that determines the positions of discrete points (stations) on the Earth’s surface, on global to local spatial scales in a very simple and economical manner. Coordinates obtained by space geodetic measurements ought to be processed, adjusted, and propagated in a given reference frame. As points on the Earth’s surface do not have a fixed position, but rather, are moving with associated velocities, it is inevitable to include those velocities in the coordinate transformation procedure. Station velocities can be obtained from kinematic models of tectonic plate motions. The development and realization of an all-in-one standalone desktop application is presented in this paper. The application unifies coordinate transformation between different realizations (reference frames) of the International Terrestrial Reference System (ITRS) and European Terrestrial Reference System 1989 (ETRS89) following European Reference Frame Technical Note (EUREF TN) recommendations with temporal shifts of discrete points on the Earth’s surface caused by plate tectonics by integrating no-net rotation (NNR) kinematic models of the Eurasian tectonic plate. Full article

An innovative bilayer cathode interlayer (CIL) with a nanostructure consisting of in situ thermal reduced graphene oxide (ITR-GO) and poly[(9,9-bis(3′-(N,N-dimethylamion)propyl)-2,7-fluorene)-alt-2,7-(9,9-dioctyl) fluorene] (PFN) has been fabricated for inverted organic solar cells (OSCs). An approach to prepare a CIL of high electronic quality by using ITR-GO as a template to modulate the morphology of the interface between the active layer and electrode and to further reduce the work function of the electrode has also been realized. This bilayer ITR-GO/PFN CIL is processed by a spray-coating method with facile in situ thermal reduction. Meanwhile, the CIL shows a good charge transport efficiency and less charge recombination, which leads to a significant enhancement of the power conversion efficiency from 6.47% to 8.34% for Poly({4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b′]dithiophene-2,6-diyl}{3-fluoro-2-[(2-ethylhexyl)carbonyl]thieno[3,4-b]thiophenediyl} (PTB7):[6,6]-phenyl-C₇₁-butyric acid methyl ester (PC₇₁BM)-based OSCs. In addition, the long-term stability of the OSC is improved by using the ITR-GO/PFN CIL when compared with the pristine device. These results indicate that the bilayer ITR-GO/PFN CIL is a promising way to realize high-efficiency and stable OSCs by using water-soluble conjugated polymer electrolytes such as PFN. Full article