Due to the difficulty and expense of underwater field trials, a high fidelity underwater simulator is a necessity for testing and developing algorithms. To fill this need, we present HoloOcean, an open source underwater simulator, built upon Unreal Engine 4 (UE4). HoloOcean comes equipped with multi-agent support, various sensor implementations of common underwater sensors, and simulated communications support. We also implement a novel sonar sensor model that leverages an octree representation of the environment for efficient and realistic sonar imagery generation. Due to being built upon UE4, new environments are straightforward to add, enabling easy extensions to be built. Finally, HoloOcean is controlled via a simple python interface, allowing simple installation via pip, and requiring few lines of code to execute simulations.

HoloOcean: An Underwater Robotics Simulator

This paper investigates the joint impact of nodes’ mobility and imperfect channel estimates on the secrecy performance of an underlay cognitive radio vehicular network over Nakagami- m fading channels. Specifically, the secondary network consists of a single-antenna source vehicle, an $N_{ {D}}$ -antenna destination vehicle, and an $N_{ {E}}$ -antenna passive eavesdropper vehicle, whereas the primary network comprises of a single-antenna primary receiver vehicle. The time selective fading links arise due to nodes’ mobility are modeled via first-order auto-regressive process, and the channel state information is estimated using linear minimum mean square error estimation method. Moreover, the transmit power of secondary source is constrained by both the interference threshold of the primary receiver and the maximum transmit power of secondary network. Under such a realistic scenario, we derive the analytical closed-form secrecy outage probability (SOP) and ergodic secrecy capacity expressions. Furthermore, we present asymptotic SOP analysis to obtain key insights into the system's secrecy diversity order. We also report several practical cases of interest to reveal valuable information about the system's secrecy behavior. Moreover, we illustrate the impacts of system/channel parameters, nodes’ mobility, imperfect channel estimates, interference temperature limit, and the maximum available source power. Finally, the simulation studies corroborate our derived analytical findings.

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Joint Impact of Nodes Mobility and Imperfect Channel Estimates on the Secrecy Performance of Cognitive Radio Vehicular Networks Over Nakagami- m Fading Channels

Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, it is uncommon for cities to have real-time reactive systems that detect the different problems they have to fix on the pavement. This work proposes a solution to detect anomalies in streets through state analysis using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network. The system detects and classifies the main road problems or abnormal conditions in streets and avenues using Machine Learning Algorithms (MLA), comparing roughness against a flat reference. An instrumented vehicle obtained the reference through accelerometry sensors and then sent the data through a mid-range communication system. With these data, the system compared an Artificial Neural Network (supervised MLA) and a K-Nearest Neighbor (Supervised MLA) to select the best option to handle the acquired data. This system makes it desirable to visualize the streets’ quality and map the areas with the most significant anomalies.

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Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning

This article highlights the potential contribution of physical-layer security in making vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) networks more secure against eavesdropping. We showcase a set of experiments using a mobile testbed for both types of networks and demonst rate that finite block-length physical-layer security coding increases the security while maintaining reliable communications. The fundamental tradeoffs in achieving both reliability and security through physical-layer efforts are also discussed in terms of relative eavesdropper location in an urban environment.

Physical-Layer Security for Vehicle-to-Everything Networks: Increasing Security While Maintaining Reliable Communications

This article reports on recent progress in robot perception and control methods developed by taking the symmetry of the problem into account. Inspired by existing mathematical tools for studying the symmetry structures of geometric spaces, geometric sensor registration, state estimator, and control methods provide indispensable insights into the problem formulations and generalization of robotics algorithms to challenging unknown environments. When combined with computational methods for learning hard-to-measure quantities, symmetry-preserving methods unleash tremendous performance. The article supports this claim by showcasing experimental results of robot perception, state estimation, and control in real-world scenarios.

/pdf/progress-in-symmetry-preserving-robot-perception-and-control-1v7vqm9x.pdf

Progress in symmetry preserving robot perception and control through geometry and learning

Recent advances in the utilization of Lie Groups for robotic localization have led to dramatic increases in the accuracy of estimation and uncertainty characterization. One of the novel methods, the Invariant Extended Kalman Filter (InEKF) extends the Extended Kalman Filter (EKF) by leveraging the fact that some error dynamics defined on matrix Lie Groups satisfy a log-linear differential equation. Utilization of these observations result in linearization with minimal approximation error, no dependence on current state estimates, and excellent convergence and accuracy properties. In this letter we show that the primary sensors used for underwater localization, inertial measurement units (IMUs) and doppler velocity logs (DVLs) meet the requirements of the InEKF. Furthermore, we show that singleton measurements, such as depth, can also be used in the InEKF update with minor modifications, thus expanding the set of measurements usable in an InEKF. We compare convergence, accuracy and timing results of the InEKF to a quaternion-based EKF using a Monte Carlo simulation and show notable improvements in long-term localization and much faster convergence with negligible difference in computation time.

Invariant Extended Kalman Filtering for Underwater Navigation

This paper applies channel sounding measurements to enable physical-layer security coding. The channel measurements were acquired in an indoor environment and used to assess the secrecy capacity as a function of physical location. A variety of Reed-Muller wiretap codes were applied to the channel measurements to determine the most effective code for the environment. The results suggest that deploying physical-layer security coding is a three-point design process, where channel sounding data guides 1) the physical placement of the antennas, 2) the power settings of the transmitter, and 3) the selection of wiretap coding.

Physical-Layer Security: Does it Work in a Real Environment?

Physical-Layer Security: Does it Work in a Real Environment?.

This paper analyzes statistical properties of the wireless vehicle-to-vehicle (V2V) communication channel in mountainous terrain near Provo, Utah with a channel sounding experiment. We employ a software-defined radio (SDR) wideband channel sounder that transmits a 64-carrier unmodulated waveform that conforms to the IEEE 802.11p standard. The channel statistics analyzed are the complementary cumulative distribution function (CCDF) of the received power and its correlation with the line-of-sight (LOS) signal, the power delay profile, and the average delay spread. The experimental results indicate the presence of non-LOS signals that appear capable of supporting short intervals of communication, although the LOS signal is still the dominant factor for V2V communication in mountainous terrain. The power delay profile also indicates the presence of non-LOS signals, and furthermore follows an exponential decay model in general.

Kalin Norman

Papers

Invariant Extended Kalman Filtering for Underwater Navigation

Physical-Layer Security for Vehicle-to-Everything Networks: Increasing Security While Maintaining Reliable Communications

Physical-Layer Security: Does it Work in a Real Environment?

Physical-Layer Security: Does it Work in a Real Environment?.

V2V Propagation in Mountainous Terrain: Part I—Experimental Configuration and Measurement Results