EEG-based driving intuition and collision anticipation using joint temporal-frequency multi-layer dynamic brain network.

Intuition plays a crucial role in human driving decision-making, and this rapid and unconscious cognitive process is essential for improving traffic safety. We used the first proposed multi-layer network analysis method, "Joint Temporal-Frequency Multi-layer Dynamic Brain Network" (JTF-MDBN), to study the EEG data from the initial and advanced phases of driving intuition training in the theta, alpha, and beta bands. Additionally, we conducted a comparative study between these two phases using multi-layer metrics as well as local and global metrics of single layers.

Joint interframe separation and gamma correction for asynchronous optical camera communication systems based on high-order statistics.

Optical camera communication (OCC), which is enabled by large-scale light-emitting diodes (LEDs) arrays and image-sensor (IS) based cameras, has garnered significant attention from both researchers and industries. Existing OCC synchronization techniques typically rely on either super-Nyquist sampling or on computationally expensive asynchronous recovery algorithms to relax the required camera frame rate. In this paper, we propose a kurtosis-based asynchronous interference cancellation (K-AIC) algorithm, enabling the estimation for both the asynchronous interframe overlapping ratios and nonlinear Gamma distortion levels for each grayscale frame captured by camera.