Related papers: Evaluating Learning of Motion Graphs with a LiDAR-…
This paper reports the use of Tracker as a pedagogical tool in supporting effective learning and teaching of toss up and free fall motion for beginning grade 9 students. This is a case study with (N=123) students of express-pure physics…
The ability to detect and segment moving objects in a scene is essential for building consistent maps, making future state predictions, avoiding collisions, and planning. In this paper, we address the problem of moving object segmentation…
Science students must deal with the errors inherent to all physical measurements and be conscious of the need to expressvthem as a best estimate and a range of uncertainty. Errors are routinely classified as statistical or systematic.…
Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and…
The integration of new technologies in the classrooms opens new possibilities for the teaching and learning process. Technologies such as student response system (e.g. Clicker) are getting popularity among teachers due to its effects on…
Understanding spatial transformations and their mathematical representations are essential in computer-aided design, robotics, etc. This research has developed and tested an Augmented Reality (AR) application (BRICKxAR/T) to enhance…
We demonstrate how students' use of modeling can be examined and assessed using student notebooks collected from an upper-division electronics lab course. The use of models is a ubiquitous practice in undergraduate physics education, but…
LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing, which directly benefited the recent rapid deployment of autonomous driving (AD). Meanwhile, such a safety-critical application…
Nowadays, modeling exercises on software development objects are conducted in higher education institutions for information technology. Not only are there many defects such as missing elements in the models created by learners during the…
Science students must deal with the errors inherent to all physical measurements and be conscious of the necessity to express their as a best estimate and a range of uncertainty. Errors are routinely classified as statistical or systematic.…
Tracking of objects in 3D is a fundamental task in computer vision that finds use in a wide range of applications such as autonomous driving, robotics or augmented reality. Most recent approaches for 3D multi object tracking (MOT) from…
Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local. Federated Learning (FL) can serve as an ideal candidate for training spatial temporal models that…
We present a home-lab experimental activity, successfully proposed to our students during covid19 pandemic, based on \textit{state-of-the-art} technologies to teach error analysis and uncertainties to science and engineering students. In…
Tracking physical activity reliably is becoming central to many research efforts. In the last years specialized hardware has been proposed to measure movement. However, asking study participants to carry additional devices has drawbacks. We…
This paper presents a novel technique to correct for bias in a classical estimator using a learning approach. We apply a learned bias correction to a lidar-only motion estimation pipeline. Our technique trains a Gaussian process (GP)…
Predicting the behavior of road users accurately is crucial to enable the safe operation of autonomous vehicles in urban or densely populated areas. Therefore, there has been a growing interest in time series motion prediction research,…
It is common for people to engage in information acquisition tasks while on the move. To understand how users' visual behaviors influence microlearning, a form of mobile information acquisition, we conducted a shadowing study with 8…
Highly dynamic environments, with moving objects such as cars or humans, can pose a performance challenge for LiDAR SLAM systems that assume largely static scenes. To overcome this challenge and support the deployment of robots in real…
The emerging technologies related to mobile data especially CDR data has great potential for mobility and transportation applications. However, it presents some challenges due to its spatio-temporal characteristics and sparseness.…
LiDAR object detection algorithms based on neural networks for autonomous driving require large amounts of data for training, validation, and testing. As real-world data collection and labeling are time-consuming and expensive,…