This paper outlines how we modified the torch2trt library which allowed us to build a recursive framework that can quantize previously unsupported PyTorch models. The framework partitions the PyTorch model into supported and unsupported modules, and then rebuilds the PyTorch model by replacing the supported PyTorch modules with faster TensorRT modules. The framework allows us to optimize and deploy more advanced Deep Neural Network algorithms that are not natively supported by torch2trt.

The research in this paper proposes a novel method for online camera-to-lidar calibration, which aligns the parameters of both lidar and camera sensors during run-time without requiring manual annotations or specialized equipment. The proposed method leverages the robot’s motion and observability constraints to optimize the parameters, resulting in improved accuracy and robustness compared to traditional methods. The approach consists of two main components: a motion-based module that estimates the parameters using the robot’s motion, and an observation-based module that refines the estimated parameters using observability constraints. The method is implemented on popular edge computing platforms and evaluated through experiments in various environments. The results demonstrate the superiority of the proposed method over baseline approaches, showcasing its potential for practical applications. The design of our algorithm and results of its testing on edge computing platforms will be discussed in this paper.

Network simulations are widely used for evaluating network functionality and protocols. Large scale network simulations in particular, produce huge amounts of data, making automatic data visualization tools extremely useful. Visual network analysis tools are capable of bringing the salient network performance metrics to the forefront revealing hidden relationships between different entities. This paper describes a custom visualization framework for rapid visual analysis of performance metrics in tactical network simulations. By utilizing network data gathered via the ns-3 simulator, a series of analytical tests using this framework have been conducted. Results from these tests indicate the usefulness of such a visualization framework when studying large scale network topologies.

Tactical wireless networks operate in complex environments with various resource constraints as well as constant link-state changes. These network topologies are predominantly based on some flavor of adhoc network design in order to accomplish rapid deployment goals. Utilizing the ns-3 simulator and a small tactical network topology with 50 nodes we evaluate the performance of both voice and video transmissions. A combination of nix-vector routing and link-state tables are used to create dynamic routing within the simulation. Then the performance of a SIP based real-time audio and video application in an adhoc network is analyzed. Additionally, we have implemented QoS in ns-3 to support traffic prioritization as needed by voice and video applications. Finally we compare the performance of the real-time voice and video applications using different traffic model.

In this paper, we describe and experimentally evaluate the performance of our Network Link Outlier Factor (NLOF) for detecting soft-failures in communication networks. The NLOF is computed using the throughput values derived from NetFlow records. The flow throughput values are clustered in two stages, outlier values are determined within each cluster, and the flow outliers are used to compute the outlier factor or score for each network link. When sampling NetFlow records across the full span of a network, NLOF enables the detection of soft-failures across the span of the network; large NLOF scores correlate well with links experiencing failure.

We present a visualization architecture to support 2D and 3D visual analytics applications. The architecture is designed to be data-flow-oriented and reconfigurable such that several diverse visualization components can operate as one integrated system. Our prototype application allows users to visually analyze the results of a complex 3D network simulation data both on large high-resolution display and HTC Vive Head Mounted Display. The network simulation outputs variables describing various characteristics of network connectivity between the moving nodes on the ground and in the air interacting in a dynamically changing 3D environment. Our system uses 2D charting tools to visualize the statistical relationships between simulation variables. We developed a Unity application to animate the network simulation in a virtual environment showing the timevarying results in a 3D environment. The Unity application runs on a complete-immersive Head Mounted Display device. The 2D visualization framework running on our Large High-Resolution Display system supports multiple coordinated views across all the different 2D visualization components including a 2D map. Preliminary results show our data-centric design provides a usercentric visualization tool that can greatly enhance the analytical process and speed up the derivation of insights from data.

Tactical network environments are complex, resource constrained, highly mobile in nature and performance of the communication links between the nodes is significantly affected by the path loss caused by the buildings. In this paper, we have used the ns-3 discrete event simulator to simulate an urban tactical network environment using the Rosslyn, VA city model. The simulated model was visualized using unity/worldwind. We have calculated path loss data using VPL and injected the path loss data into link selection to visually study the effects of path loss on the link formation in urban environments.