Light Detection and Ranging (LiDAR) is an active remote sensing technique that uses pulsed lasers to sense the surrounding environment. It works on the principle of Time of Travel (ToT) for acquiring highly accurate and high spatial resolution 3D information about the surrounding environment. LiDAR data possesses immense potential for (near) real-time applications (e.g., forestry, disaster, border security, etc.) Real-time applications demand quick analysis of the data and cannot wait for the entire data pertaining to the area of interest to be captured before producing useful insights. Thus, 3D LiDAR points should be processed as and when they are captured in the form of a continuous stream. Due to the lack of prior knowledge about the (near) real-time data and the underlying distribution, in this work, unsupervised stream mining approaches have been adapted for the analysis of streaming geospatial 3D LiDAR data. By applying different unsupervised data mining (clustering) algorithms on a huge set of 3D LiDAR data points, we have evaluated the quality of clustering based on different evaluation metrics.

We designed a mid-air input space for restful interactions on the couch. We observed people gesturing in various postures on a couch and found that posture affects the choice of arm motions when no constraints are imposed by a system. Study participants that sat with the arm rested were more likely to use the forearm and wrist, as opposed to the whole arm. We investigate how a spherical input space, where forearm angles are mapped to screen coordinates, can facilitate restful mid-air input in multiple postures. We present two controlled studies. In the first, we examine how a spherical space compares with a planar space in an elbow-anchored setup, with a shoulder-level input space as baseline. In the second, we examine the performance of a spherical input space in four common couch postures that set unique constraints to the arm. We observe that a spherical model that captures forearm movement facilitates comfortable input across different seated postures.

Remote sensing lidar point cloud dataset embeds inherent 3D topological, topographical and complex geometrical information which possess immense potential in applications involving machine-understandable 3D perception. The lidar point clouds are unstructured, unlike images, and hence are challenging to process. In our work, we are exploring the possibility of deep learning-based question-answering on the lidar 3D point clouds. We are proposing a deep CNN-RNN parallel architecture to learn lidar point cloud features and word embedding from the questions and fuse them to form a feature mapping for generating answers. We have restricted our experiments for the urban domain and present preliminary results of binary question-answering (yes/no) using the urban lidar point clouds based on the perplexity, edit distance, evaluation loss, and sequence accuracy as the performance metrics. Our proposed hypothesis of lidar question-answering is the first attempt, to the best of our knowledge, and we envisage that our novel work could be a foundation in using lidar point clouds for enhanced 3D perception in an urban environment. We envisage that our proposed lidar question-answering could be extended for machine comprehension-based applications such as rendering lidar scene descriptions and content-based 3D scene retrieval.

Smartwatches enable rapid access to information anytime and anywhere. However, current smartwatch content navigation techniques, for panning and zooming, were directly adopted from those used on smartphones. These techniques are cumbersome when performed on small smartwatch screens and have not been evaluated for their support in mobility and encumbrance contexts (when the user's hands are busy). We studied the effect of mobility and encumbrance on common content navigation techniques and found a significant decrease in performance as the pace of mobility increases or when the user was encumbered with busy hands. Based on these initial findings, we proposed a design space which would improve efficiency when navigation techniques, such as panning and zooming, are employed in mobility contexts. Our results reveal that our design space can effectively be used to create novel interaction techniques that improve smartwatch content navigation in mobility and encumbrance contexts.