Real-time safety-critical control is essential for high-level autonomous driving. Existing methods usually formulate safety-critical control as a constrained optimal control problem (COCP), and suffer from high computational complexity of the underlying iterative optimization processes. To address the issue of complexity, this paper presents an explicit safety-critical control method called the Control Safety Function (CSF) approach, which can replace online optimization with an analytical control law, dramatically enhancing real-time control capabilities. The CSF is formulated as the weighted sum of Control Lyapunov Function (CLF) and Control Barrier Functions (CBFs), with the value of CSF increasing to infinity as the state approaches the boundary of a safe set. The explicit control law is then derived from the gradient of CSF and system dynamics. Different from existing explicit controllers that can only apply to systems of relative degree one, the CSF method provides an approach to enforce safety constraints to systems with high relative degree, making CSF especially suitable for autonomous driving. The CSF approach is evaluated in a vehicle path-tracking scenario with multiple obstacles, accompanied by a comparative analysis against the Model Predictive Control (MPC) method. Simulation results indicate that CSF achieves control accuracy comparable to MPC, with significant reduction in computation time - approximately 3.23 ms per step, which is about 94.0% faster. These results suggest that CSF is a promising approach for real-time safety-critical control of high-level autonomous driving.

Obstructive sleep apnea (OSA) is a prevalent sleep disorder, affecting approximately one billion individuals globally. In this study, we aim to address the limitations of Polysomnography (PSG), the gold standard for OSA diagnosis, by developing SlAction, a non-intrusive system that utilizes infrared videos for OSA detection in daily sleep settings. Considering the privacy-sensitive nature of sleep videos, SlAction is designed to analyze data directly on the camera-capturing device, eliminating the need to transmit video data to a server. With the collaboration of clinical experts, we extensively analyze the largest dataset worldwide that we collected, establishing correlations between OSA events and human motions during sleep. Our novel approach achieved an OSA prediction performance with an F1 score of 0.88. Notably, even when running on a low-spec CPU, our SlAction operates approximately 75 times faster than previous work evaluated on high-performance GPU servers.

Advanced Persistent Threat (APT) attacks are complex, employing diverse attack elements and increasingly intelligent techniques. This paper introduces a tool for security risk assessment specifically designed for these attacks. This tool assists security teams in systematically analyzing APT attacks to derive adaptive security requirements for mission-critical target systems. Additionally, the tool facilitates the assessment of security risks, providing a comprehensive understanding of their impact on target systems. By leveraging this tool, security teams can enhance defense strategies, mitigating potential threats and ensuring the security of target systems.

In this paper, we present ALH-E (ALternative Healthcare for Expressing ache), an assistive robot with a deformable and flexible interface for pain communication. It consists of two components: a squeezable device for inputting the patient’s pain intensity and a flexible output device that expresses the pain by twisting-bending movements in response to the input signals. The interconnectivity between the devices allows for communication of pain intensity between patients and caregivers, anywhere and anytime. A field study in the hospital (dental clinic and orthopedic) was conducted to verify the usability and effectiveness of ALH-E in pain communication. Our field study results demonstrated the unique advantages of ALH-E over conventional methods, providing significant assistance to patients and caregivers.

Describing pain intensity constitutes an essential part of pain communication. A medical practitioner cannot depend on pain scales because of the criteria differences between the patient and caregiver. However, online pain communication is dependent on a patient’s description and an assessment on a pain scale. This paper proposes a robot-assisted pain communication system with a tangible user interface that enables non-numerical pain communication. The SQTT interface is proposed using design processes consisting of a novel squeezable input device and a twisting robot. The twisting expression of the robot represents the pain intensity, which is gauged from the squeezing power on the input device. Integrating input and output requires defining how the twisting motion of the robot is rendered from the squeezing input. An experiment was conducted to evaluate the four methods of rendering: raw, smoothing (moving average), dynamic smoothing, and updating peak. The result of a non-parametric one-way analysis of variance indicates a significant difference between the rendering methods. As a result, an appropriate rendering method is proposed based on the ranges: smoothing for mild pain, vibrating for moderate pain, and exaggerating for severe pain. In conclusion, the robot-assisted pain communication system can be implemented with intuitive interaction primitives for online context. This paper contributes a lesson in designing a robot-assisted online pain communication, which uses a new method of measuring deformation.