<p>The numerous possible applications for Bluetooth Low Energy (BLE) Beacons constantly motivate researchers to come up with new methods to optimize the utilizations of beacons and improve the positioning accuracy while maintaining the cost to benefit balance. In this study, a novel method was proposed to optimize the localization of an indoor BLE positioning system with low beacon density in a real-world environment. The proposed method combines three major machine learning concepts (Semi-Supervised Learning, Informed Machine Learning, and Soft Computing) in its analysis pipeline and presents a different perspective to approaching Genetic Fuzzy Systems (GFSs). The presented method was competitive when benchmarked against a fully connected neural network and a kNN model. </p>

The numerous possible applications for Bluetooth Low Energy (BLE) Beacons constantly motivate researchers to come up with new methods to optimize the utilizations of beacons and improve the positioning accuracy while maintaining the cost to benefit balance. In this study, a novel method was proposed to optimize the localization of an indoor BLE positioning system with low beacon density in a real-world environment. The proposed method combines three major machine learning concepts (Semi-Supervised Learning, Informed Machine Learning, and Soft Computing) in its analysis pipeline and presents a different perspective to approaching Genetic Fuzzy Systems (GFSs). The presented method was competitive when benchmarked against a fully connected neural network and a kNN model.

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<p>One of the critical components of pavement structures are shoulders which provide lateral support to the entire pavement structure. The vertical elevation between pavement structure surface and adjacent shoulder surface results in roadway edge drop-off. Shoulder drop-off can affect the vehicle's stability and driver's ability to handle the vehicle resulting in fatalities or damage, which is also one of transportation agencies' most cited accident-related highway conditions. The maintenance staff of state transportation departments visually inspect the shoulders at near highway speeds, which might be challenging and involves human judgment, affecting the assessment's accuracy and reliability. In this work, we provide a lost-cost solution to automate the process of shoulder drop-off assessment. Our approach analyzes the point cloud data acquired from a 3D solid-state LIDAR sensor mounted on a car traveling at highway speeds. We have explored statistical, machine learning, and end-to-end deep learning methods to predict the vertical elevation of shoulder drop-off with accuracy greater than 95$\%$. We have validated the accuracy of proposed learning-based methods with actual data collected using inexpensive LIVOX LiDAR and an Astra camera along various highway sections. </p>

No abstract