IDEaS

This magazine tells about things happening in the College of Engineering and Science as well as highlighting current students, faculty and alumni of the college

Safe and Efficient E-wayfinding (SeeWay) Assistive Navigation for the Visually Impaired

69A3551747117Despite its challenges, independent travel for blind and visually impaired (BVI) individuals is an essential component of quality of life, enabling travel to work and recreational activities. Autonomous vehicle technologies have the potential of meeting these challenges. However, efficiently and safely guiding BVI travelers between indoor environments and vehicles outdoors remains a key obstacle. In the future transportation system, assistive navigation technologies, connecting BVI travelers and vehicles, will be of extraordinary importance for BVI individuals in the context of social justice and health care/public health. Conventional research is mainly based on robotic navigation approaches through localization, mapping, and path-planning frameworks. They require heavy manual annotation of semantic information in maps and its alignment with sensor mapping. Inspired by the fact that we human beings naturally rely on language instruction inquiry and visual scene understanding to navigate in an unfamiliar environment, this study proposes a novel vision-language model-based approach for BVI navigation. It does not need heavy-labeled indoor maps and provides a Safe and Efficient E-Wayfinding (SeeWay) assistive solution for BVI individuals. The system consists of a scene-graph map construction module, a navigation path generation module for global path inference by vision-language navigation (VLN), and a navigation with obstacle avoidance module for real-time local navigation. The SeeWay system was deployed on portable iPhone devices with cloud computing assistance for the VLN model inference. The field tests show the effectiveness of the VLN global path finding and local path re-planning. Experiments and quantitative results reveal that heuristic-style instruction outperforms direction/detailed-style instructions for VLN success rate (SR), and the SR decreases as the navigation length increases

A Machine Learning-Assisted Framework for Determination of Performance Degradation Causes and Selection of Channel Switching Strategy in Vehicular Networks

69A3551747117As all three major US mobile carriers have launched their own 5G networks and are working hard to expand their coverage nationwide, 5G has come into everyone\u2019s daily life. 5G networks use millimeter-wave (mm-Wave) for higher speeds, while 4G long-term evolution (LTE) networks favor lower-band spectrum for better coverage. Vehicle-to-vehicle (V2V) communication enables wireless communication between cars and exchanges their speed, location, and acceleration information. 5G mm-Wave and 4G LTE bands are used in V2V sidelink transmissions. These two wireless channels are affected by different weather conditions, such as rain, snow, dust, and sand. Compared with 4G networks, 5G networks are designed to accommodate the increasing number of devices with higher transfer speed, lower latency, and improved security. However, our study shows that severe weather degrades the 5G performance more significantly than 4G. In this paper, we use NS-3 as a simulator to study the effect of harsh weather of dust or sand on the propagating loss of 5G mm-Wave and 4G LTE signal. We investigate their performance degradation and use a time-series machine learning technique, long short-term memory (LSTM), to predict future signal strength for 5G and 4G. Our simulation results show that LSTM performs well in forecasting signal strength, and we plan to design a system that can dynamically choose the better wireless channel in the future

IDEaS: Fall 2011

This IDEaS Newsletter was released by Clemson University's College of Engineering and Science. IDEaS is dedicated to a single department, the Glenn Department of Civil Engineering, and provides educational information for staff and students within the department as well updates on the work they are doing

IDEaS: Fall 2009

This IDEaS Newsletter was released by Clemson University's College of Engineering and Science. IDEaS is dedicated to a single department, the Glenn Department of Civil Engineering, and provides educational information for staff and students within the department as well updates on the work they are doing

IDEaS: Fall 2010

This IDEaS Newsletter was released by Clemson University's College of Engineering and Science. IDEaS is dedicated to a single department, the Glenn Department of Civil Engineering, and provides educational information for staff and students within the department as well updates on the work they are doing

IDEaS: Spring 2015

This IDEaS Newsletter was released by Clemson University's College of Engineering and Science. IDEaS is dedicated to a single department, the Glenn Department of Civil Engineering, and provides educational information for staff and students within the department as well updates on the work they are doing

IDEaS: Fall 2012

This IDEaS Newsletter was released by Clemson University's College of Engineering and Science. IDEaS is dedicated to a single department, the Glenn Department of Civil Engineering, and provides educational information for staff and students within the department as well updates on the work they are doing

IDEaS: Spring 2009

This IDEaS Newsletter was released by Clemson University's College of Engineering and Science. IDEaS is dedicated to a single department, the Glenn Department of Civil Engineering, and provides educational information for staff and students within the department as well updates on the work they are doing