Optimal LiDAR Data Resolution Analysis for Object Classification.

When classifying objects in 3D LiDAR data, it is important to use efficient collection methods and processing algorithms. This paper considers the resolution needed to classify 3D objects accurately and discusses how this resolution is accomplished for the RedTail RTL-450 LiDAR System. We employ VoxNet, a convolutional neural network, to classify the 3D data and test the accuracy using different data resolution levels. The results show that for our data set, if the neural network is trained using higher resolution data, then the accuracy of the classification is above 97%, even for the very sparse testing set (10% of original test data set point density). When the training is done on lower resolution data sets, the classification accuracy remains good but drops off at around 3% of the original test data set point density. These results have implications for determining flight altitude and speed for an unmanned aerial vehicle (UAV) to achieve high accuracy classification. The findings point to the value of high-resolution point clouds for both the training of the convolutional neural network and in data collected from a LiDAR sensor.

Haptics-Based Apps for Middle School Students with Visual Impairments.

Twenty haptics-based computer applications (apps) have been created to utilize a low-cost, force feedback haptic device, the Novint Falcon, to provide students with tactile and kinesthetic sensations while learning about math and science. These low-cost apps, developed specifically for students with visual impairments (yet practical for all students), add to the accessible resources available for math and science. This article outlines the motivation, development, and testing of these PC-based applications that incorporate computer haptics, auditory cues, and high-contrast visuals. Included is a brief overview of two of the apps, one with science content and one with math content, in order to provide the reader with some insight into the student experience. The results of testing six of the apps in classroom settings show that the device and software are feasible for teachers to implement and significant learning gains can be achieved for students who use them. Student attitudes toward the apps were positive, implying that not only are the apps useful in the classroom, but engaging as well.

Analyses of crime patterns in NIBRS data based on a novel graph theory clustering method: Virginia as a case study.

This paper suggests a novel clustering method for analyzing the National Incident-Based Reporting System (NIBRS) data, which include the determination of correlation of different crime types, the development of a likelihood index for crimes to occur in a jurisdiction, and the clustering of jurisdictions based on crime type. The method was tested by using the 2005 assault data from 121 jurisdictions in Virginia as a test case. The analyses of these data show that some different crime types are correlated and some different crime parameters are correlated with different crime types. The analyses also show that certain jurisdictions within Virginia share certain crime patterns. This information assists with constructing a pattern for a specific crime type and can be used to determine whether a jurisdiction may be more likely to see this type of crime occur in their area.