Efficient Feature Learning Model of Motor Imagery EEG Signals with L1-Norm and Weighted Fusion.

Brain-computer interface (BCI) for motor imagery is an advanced technology used in the field of medical rehabilitation. However, due to the poor accuracy of electroencephalogram feature classification, BCI systems often misrecognize user commands. Although many state-of-the-art feature selection methods aim to enhance classification accuracy, they usually overlook the interrelationships between individual features, indirectly impacting the accuracy of feature classification. To overcome this issue, we propose an adaptive feature learning model that employs a Riemannian geometric approach to generate a feature matrix from electroencephalogram signals, serving as the model's input. By integrating the enhanced adaptive L1 penalty and weighted fusion penalty into the sparse learning model, we select the most informative features from the matrix. Specifically, we measure the importance of features using mutual information and introduce an adaptive weight construction strategy to penalize regression coefficients corresponding to each variable adaptively. Moreover, the weighted fusion penalty balances weight differences among correlated variables, reducing the model's overreliance on specific variables and enhancing accuracy. The performance of the proposed method was validated on BCI Competition IV datasets IIa and IIb using the support vector machine. Experimental results demonstrate the effectiveness and superiority of the proposed model compared to the existing models.

GNSS Spoofing Detection via the Intersection Angle between Two Directions of Arrival in a Single Rotating Antenna.

Spoofing against the Global Navigation Satellite System (GNSS) is an attack with strong concealment, posing a significant threat to the security of the GNSS. Many strategies have been developed to prevent such attacks, but current detection methods based on signal direction for multi-agent spoofing require multiple antennas/receivers, leading to increased cost and complexity in implementation. Additionally, methods utilizing a moving single antenna cannot effectively detect multi-agent spoofing. Therefore, we introduce a novel spoofing-detection technique based on the intersection angle between two directions of arrival (IA-DOA) using a single rotating antenna. The essence of this approach lies in estimating the IA-DOA between a pair of signals by utilizing the carrier-to-noise ratio (CNR) and carrier phase single difference (CPSD) of the received signal. The estimation of IA-DOA should be consistent with the prediction when there is no spoofing. With spoofing, it is difficult to accurately simulate the directionality of navigation signals, which can disrupt the consistency between the estimation and prediction of IA-DOA. Therefore, estimations and predictions of IA-DOA can be used to establish detection variables through generalized likelihood ratio testing (GLRT) to detect multi-agent spoofing. We conducted a simulation to analyze the impact of the antenna's parameters on the detection performance and evaluated it through on-site experiments. The results indicate that the method proposed in this article can efficiently achieve real-time detection of multi-agent spoofing.

Exogenous abscisic acid (ABA) promotes cadmium (Cd) accumulation in Sedum alfredii Hance by regulating the expression of Cd stress response genes.

Sedum alfredii Hance is a zinc (Zn) and cadmium (Cd) hyperaccumulator plant. However, the regulatory role of plant hormones in the Zn or Cd uptake and accumulation of S. alfredii remains unclear. In this work, the growth, Cd accumulation, abscisic acid (ABA) synthesis and catabolism, malonaldehyde (MDA) content, and transcriptional level of some Cd stress response genes under ABA and Cd co-treatment were investigated to reveal the impact of ABA on Cd resistance and Cd accumulation of S. alfredii. The results show that 0.2 mg/L ABA and 100 µmol/L Cd co-treatment enhanced Cd accumulation and growth in S. alfredii, whereas lower or higher ABA concentrations weaken or even reverse this effect, which was positively correlated with endogenous ABA content. The increase in endogenous ABA content might be the results of the increasing ABA synthetase activities and decreasing ABA lytic enzyme, which was induced by the application of 0.2 mg/L ABA under 100 µmol/L Cd treatment. Principal component analysis (PCA) indicated that ABA impacted the expression pattern of Cd stress response genes, which coincided with the Cd accumulation pattern in the shoots of S. alfredii. Cross-over analysis of partial least squares-discriminant analysis (PLS-DA) and correlation analysis indicated that HsfA4c, HMA4 expression in roots, and HMA2, HMA3, CAD, NAS expression in shoots were correlated with endogenous ABA, which suggests that endogenous ABA improves Cd resistance of seedlings, switches the root-to-shoot transporter from HMA2 to HMA4, and transports more Cd into apoplasts to promote Cd accumulation in the shoots of S. alfredii. Taken together, ABA plays an essential role not only in Cd resistance but also in Cd transport from root to shoot in S. alfredii under Cd stress.