Myo Taeg Lim

Physiological signals contain considerable information regarding emotions. This paper investigated the ability of photoplethysmogram (PPG) signals to recognize emotion, adopting a two-dimensional emotion model based on valence and arousal to represent human feelings. The main purpose was to recognize short term emotion using a single PPG signal pulse. We used a one-dimensional convolutional neural network (1D CNN) to extract PPG signal features to classify the valence and arousal. We split the P

Emotion recognition research has been conducted using various physiological signals. In this paper, we propose an efficient photoplethysmogram-based method that fuses the deep features extracted by two deep convolutional neural networks and the statistical features selected by Pearson’s correlation technique. A photoplethysmogram (PPG) signal can be easily obtained through many devices, and the procedure for recording this signal is simpler than that for other physiological signals. The normal-t

A new obstacle avoidance method for autonomous vehicles called obstacle-dependent Gaussian potential field (ODG-PF) was designed and implemented. It detects obstacles and calculates the likelihood of collision with them. In this paper, we present a novel attractive field and repulsive field calculation method and direction decision approach. Simulations and the experiments were carried out and compared with other potential field-based obstacle avoidance methods. The results show that ODG-PF perf

In this paper we present a method that allows complete time scale separation and parallelism of the H/sub /spl infin// optimal filtering problem for linear systems with slow and fast modes (singularly perturbed linear systems). The algebraic Riccati equation of singularly perturbed H/sub /spl infin// filtering problem Is decoupled into two completely independent reduced-order pure-slow and pure-fast H/sub /spl infin// algebraic Riccati equations. The corresponding H/sub /spl infin// filter is de

Path planning research plays a vital role in terms of safety and comfort in autonomous driving systems. This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications and proposes autonomous driving path planning through an optimal controller integrating obstacle-dependent Gaussian (ODG) and model prediction control (MPC). The ODG algorithm integrates the information from the sensors and calculates the risk factors in the driving environment. The M

This paper proposes a real-time emotion recognition system that utilizes photoplethysmography (PPG) and electromyography (EMG) physiological signals. The proposed approach employs a complex-valued neural network to extract common features from the physiological signals, enabling successful emotion recognition without interference. The system comprises three stages: single-pulse extraction, a physiological coherence feature module, and a physiological common feature module. The experimental resul

In this paper we introduce a transformation for the exact closed-loop decomposition of the optimal control and Kalman filtering tasks of linear weakly coupled stochastic systems composed of N subsystems. In addition to having obtained N completely independent reduced-order subsystem Kalman filters working in parallel, we have obtained the exact solution of the algebraic regulator and filter Riccati equations in terms of the solutions of the corresponding reduced-order subsystem algebraic Riccati

This article proposes a new preceding vehicle detection framework for challenging lighting environments using a novel feature fusion technique based on an adaptive neuro-fuzzy inference system. A combination of two feature descriptors, the histogram of oriented gradients and local binary patterns, is adopted to improve the vehicle detection accuracy of the proposed framework, and the performance of the combination in image transformations is evaluated. Furthermore, we tested the detection perfor