The Impact of Various Cockpit Display Interfaces on Novice Pilots' Mental Workload and Situational Awareness: A Comparative Study.

Future airspace is expected to become more congested with additional in-service cargo and commercial flights. Pilots will face additional burdens in such an environment, given the increasing number of factors that they must simultaneously consider while completing their work activities. Therefore, care and attention must be paid to the mental workload (MWL) experienced by operating pilots.

Guidewire Endpoint Detection Based on Pixel Adjacent Relation in Robot-assisted Cardiovascular Interventions.

Visualization of endovascular tools like guidewire and catheter is essential for procedural success of endovascular interventions. This requires tracking the tool pixels and motion during catheterization; however, detecting the endpoints of the endovascular tools is challenging due to their small size, thin appearance, and flexibility. As this still limit the performances of existing methods used for endovascular tool segmentation, predicting correct object location could provide ways forward.

Deep Learning Models for Stress Analysis in University Students: A Sudoku-Based Study.

Due to the phenomenon of "involution" in China, the current generation of college and university students are experiencing escalating levels of stress, both academically and within their families. Extensive research has shown a strong correlation between heightened stress levels and overall well-being decline. Therefore, monitoring students' stress levels is crucial for improving their well-being in educational institutions and at home.

ScatterHough: Automatic Lane Detection from Noisy LiDAR Data.

Lane detection plays an essential role in autonomous driving. Using LiDAR data instead of RGB images makes lane detection a simple straight line, and curve fitting problem works for realtime applications even under poor weather or lighting conditions. Handling scatter distributed noisy data is a crucial step to reduce lane detection error from LiDAR data.

TAFFIES: Tailored Automated Feedback Framework for Developing Integrated and Extensible Feedback Systems.

Delivering high-quality, timely and formative feedback for students' code-based coursework submissions is a problem faced by Computer Science (CS) educators. Automated Feedback Systems (AFSs) can provide immediate feedback on students' work, without requiring students to be physically present in the classroom-an increasingly important consideration for education in the context of COVID-19 lockdowns. There are concerns, however, surrounding the quality of the feedback provided by existing AFSs, with many systems simply presenting a score, a binary classification (pass/fail), or a basic error identification ("The program could not run").

Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1.

A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular fingerprints.

Smart Wearables with Sensor Fusion for Fall Detection in Firefighting.

During the past decade, falling has been one of the top three causes of death amongst firefighters in China. Even though there are many studies on fall-detection systems (FDSs), the majority use a single motion sensor. Furthermore, few existing studies have considered the impact sensor placement and positioning have on fall-detection performance; most are targeted toward fall detection of the elderly.

Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device.

In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-electrode placement on the scalp; two of these electrodes were placed in the frontal lobe, and the other six electrodes were placed in the temporal lobe. We performed experiments on eight subjects while they watched emotive videos.

Sensor Fusion of Motion-Based Sign Language Interpretation with Deep Learning.

Sign language was designed to allow hearing-impaired people to interact with others. Nonetheless, knowledge of sign language is uncommon in society, which leads to a communication barrier with the hearing-impaired community. Many studies of sign language recognition utilizing computer vision (CV) have been conducted worldwide to reduce such barriers.

American Sign Language Recognition Using Leap Motion Controller with Machine Learning Approach.

Sign language is intentionally designed to allow deaf and dumb communities to convey messages and to connect with society. Unfortunately, learning and practicing sign language is not common among society; hence, this study developed a sign language recognition prototype using the Leap Motion Controller (LMC). Many existing studies have proposed methods for incomplete sign language recognition, whereas this study aimed for full American Sign Language (ASL) recognition, which consists of 26 letters and 10 digits.

Smartwatch-based driver alertness monitoring with wearable motion and physiological sensor.

Studies have shown that a high precision driver alertness monitoring system is an essential and a monetary countermeasure to reduce the road accidents. This paper presents a novel approach to measure the driver alertness, evaluated by a smartwatch device based on fusion of direct and indirect method. The driver chronic physiological state is monitor by adopting a photoplethysmography sensor on the driver finger that is connected to a wrist-type wearable device.

Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals.

Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains.

A smartphone-based driver safety monitoring system using data fusion.

This paper proposes a method for monitoring driver safety levels using a data fusion approach based on several discrete data types: eye features, bio-signal variation, in-vehicle temperature, and vehicle speed. The driver safety monitoring system was developed in practice in the form of an application for an Android-based smartphone device, where measuring safety-related data requires no extra monetary expenditure or equipment. Moreover, the system provides high resolution and flexibility.