Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications. Currently, deep learning has been widely used in the field of EEG emotion recognition and has achieved remarkable results. However, due to the cost of data collection, most EEG datasets have only a small amount of EEG data, and the sample categories are unbalanced in these datasets. These problems will make it difficult for the deep learning model to predict the emotional state. In this paper, we propose a new sample generation method using generative adversarial networks to solve the problem of EEG sample shortage and sample category imbalance. In experiments, we explore the performance of emotion recognition with the frequency band correlation and frequency band separation computational models before and after data augmentation on standard EEG-based emotion datasets. Our experimental results show that the method of generative adversarial networks for data augmentation can effectively improve the performance of emotion recognition based on the deep learning model. And we find that the frequency band correlation deep learning model is more conducive to emotion recognition.

Rapid and accurate extraction of shoreline is of great significance for the use and management of sea area. Remote sensing has a strong ability to obtain data and has obvious advantages in shoreline survey. Compared with visible-light remote sensing, synthetic aperture radar (SAR) has the characteristics of all-weather and all-day working. It has been well-applied in shoreline extraction. However, due to the influence of natural conditions there is a problem of weak boundary in extracting shoreline from SAR images. In addition, the complex micro topography near the shoreline makes it difficult for traditional visual interpretation and image edge detection methods based on edge information to obtain a continuous and complete shoreline in SAR images. In order to solve these problems, this paper proposes a method to detect the land–sea boundary based on a geometric active contour model. In this method, a new symbolic pressure function is used to improve the geometric active-contour model, and the global regional smooth information is used as the convergence condition of curve evolution. Then, the influence of different initial contours on the number and time of iterations is studied. The experimental results show that this method has the advantages of fewer iteration times, good stability and high accuracy.

Neonatal epilepsy is an early postnatal brain disorder, and automatic seizure detection is crucial for timely diagnosis and treatment to reduce potential brain damage. This work proposes a novel Lightweight Multi-Attention Network, LMA-EEGNet, for diagnosing neonatal epileptic seizures from multi-channel EEG signals employing dilated depthwise separable convolution (DDS Conv) for feature extraction and using pointwise convolution followed by global average pooling for classification. The proposed approach substantially reduces the model size, number of parameters, and computational complexity, which are crucial for real-time detection and clinical diagnosis of neonatal epileptic seizures. LMA-EEGNet integrates temporal and spectral features through distinct temporal and spectral branches. The temporal branch uses DDS Conv to extract temporal features, enhanced by a channel attention mechanism. The spectral branch utilizes similar convolutions alongside a spatial attention mechanism to highlight key frequency components. Outputs from both branches are merged and processed through a pointwise convolution layer and a global average pooling layer for efficient neonatal seizure detection. Experimental results show that our model, with only 2471 parameters and a size of 23 KB, achieves an accuracy of 95.71% and an AUC of 0.9862, demonstrating its potential for practical deployment. This study provides an effective deep learning solution for the early detection of neonatal epileptic seizures, improving diagnostic accuracy and timeliness.

Digital receivers are pursued as part of future civilian and military communication applications. Design and implementation of a broadband receiver is presented that operates for 4G wireless communications. Performance of the realized hardware is evaluated in terms of system parameters.

Quartz crystal microbalance (QCM) is still a new high-precision surface detection technique. However, the adsorption quality detected by the QCM currently contains a solvent-coupling quality and cannot separate the actual biomolecular mass. Local surface plasmon resonance (LSPR) can detect the mass of biomolecules, but requires a certain contrast between the solvent of the surrounding medium and the refractive index of the adsorbed layer. The sensor chip, combining two compatible technologies, can realize the simultaneous detection of biomolecules and improve the refractive index sensitivity. The structure of our chip is to prepare the ring-shaped gold electrode on the upper surface of the quartz crystal, the circular gold electrode on the bottom surface, and the spherical gold nanoparticles arrays in the center region of the ring electrode to form a QCM/LSPR dual-technology chip. Through simulation, we finally get the size of the best energy trap by the two electrodes on the upper surface and the lower surface: the ring-top electrode with a thickness of 100 nm, an inner diameter of 4 mm, and an outer diameter of 8 mm; and the bottom electrode with a thickness of 100 nm and a radius of 6 mm. By comparing the refractive index sensitivity, we chose a spherical gold nanoparticle with a radius of 30 nm and a refractive sensitivity of 61.34 nm/RIU to design the LSPR sensor chip.