Domain adaptation (DA) for electroencephalography (EEG) plays an important role in cross-subject emotion recognition. However, traditional DA methods are often limited by target domain complexities, leading to inaccurate knowledge transfer. Recent advances in subdomain adaptation, which focuses on dividing data into subdomains using pseudo-labels, have shown promise, but still rely on the quality of the generated pseudo-labels. To address this issue, we propose a novel approach, a Domain Adversarial Neural Network with Reliable Pseudo-Label Iteration (DANN-RPLI), for cross-subject emotion recognition. This method assumes that high-quality samples are close to the center and stable under perturbations. Thus, we introduced a reliable pseudo-label generation strategy with an iterative process and increased the confidence in the selected labels using perturbations. A domain adversarial network was further used to confuse subdomains, enabling a more effective cross-domain emotion representation. Our method achieved state-of-the-art results on the SEED, SEED-IV, and DEAP datasets. The superior stability of the algorithm was proven through parameter comparison experiments. Furthermore, this study reduces the impact of unreliable pseudo-labels on EEG measurements and provides a new solution for emotion recognition in practical EEG-BCI scenarios. • We propose a domain adversarial neural network with reliable pseudo-label iteration. • A pseudo-label generation strategy was designed to achieve more accurate knowledge transfer. • This method improves the performance in cross-subject EEG emotion recognition.

We have designed PanguVision: an Android software system that can be deployed on personal handheld devices (cell phones, PCs, etc.). The prototype described enables the detection of hidden devices and recognition of their types. It utilizes augmented reality to visualize the location of these hidden devices, facilitating swift identification. The demo showcases a prototype mounted on a user's handheld device, enabling users to detect and recognize hidden devices in unfamiliar environments. Test results show that the prototype achieves 98% accuracy in recognizing IoT device types, and the average error is controlled at 0.8m when locating the device.

Multimedia information systems have emerged as an essential component of many application domains ranging from library information systems to entertainment technology. However, most implementations of these systems cannot support the continuous display of multimedia objects and suffer from frequent disruptions and delays termed hiccups. This is due to the low I/O bandwidth of the current disk technology, the high bandwidth requirement of multimedia objects, and the large size of these objects that almost always requires them to be disk resident. One approach to resolve this limitation is to decluster a multimedia object across multiple disk drives in order to employ the aggregate bandwidth of several disks to support the continuous retrieval (and display) of objects. This paper describes staggered striping as a novel technique to provide effective support for multiple users accessing the different objects in the database. Detailed simulations confirm the superiority of staggered strip...