Abstract
Underwater acoustic localization is a challenging task. Most techniques rely on a network of acoustic sensors and beacons to estimate relative position, therefore localization uncertainty becomes highly dependent on the selected sensor configuration. Although several works in literature exploit optimal sensor placement to improve localization over large regions, the conditions contemplated in these are not applicable for the optimization of the acoustic sensors on constrained 3D shapes, such as the body of small underwater vehicles or structures. Additionally, most commercial systems used for localization with ultra-short baseline (USBL) configurations have compact acoustic sensors that cannot be spatially positioned independently. This work tackles the optimization of acoustic sensor placement in a limited 3D shape, in order to improve the localization accuracy for USBL applications. The implemented multi-objective memetic algorithm combines the Cramer-Rao Lower Bound (CRLB) configuration evaluation with incidence angle considerations for the sensor placement. © 2022 IEEE.

Abstract
Autonomous underwater vehicles (AUV) are increasing in popularity and importance for the realization of underwater explorations. Nowadays, these types of vehicles are implemented in underwater environments to accomplish tasks for military, scientific and industrial purposes. These vehicles can use imaging sonars that are effective in detecting the AUV's distance to an obstacle. The main goals of this paper were to extract meaningful information gathered by sonar, use it to map the surrounding environment, and locate the vehicle on the estimated map. To accomplish these goals, the system is composed of a constant false alarm rate (CFAR) algorithm to filter the sonar information, a feature extractor that filters the first obstacle for each sonar beam in a 360° revolution, an Octomap to build the estimated map and a Particle Filter (PF) to locate the vehicle in the environment. This system was developed using a set of measurements in a rectangular tank where the AUV was in static positions and in motion. © 2022 IEEE.

Abstract
Autonomous Underwater Vehicles (AUVs) are seen as a safe and cost-effective platforms for performing a myriad of underwater missions. These vehicles are equipped with multiple sensors which, combined with their long endurance, can produce large amounts of data, especially when used for video capturing. These data need to be transferred to the surface to be processed and analyzed. When considering deep sea operations, where surfacing before the end of the mission may be unpractical, the communication is limited to low bitrate acoustic communications, which make unfeasible the timely transmission of large amounts of data unfeasible. The usage of AUVs as data mules is an alternative communications solution. Data mules can be used to establish a broadband data link by combining short-range, high bitrate communications (e.g., RF and wireless optical) with a Delay Tolerant Network approach. This paper presents an enhanced version of UDMSim, a novel simulation platform for data muling communications. UDMSim is built upon a new realistic AUV Motion and Localization (AML) simulator and Network Simulator 3 (ns-3). It can simulate the position of the data mules, including localization errors, realistic position control adjustments, the received signal, the realistic throughput adjustments, and connection losses due to the fast SNR change observed underwater. The enhanced version includes a more realistic AML simulator and the antenna radiation patterns to help evaluating the design and relative placement of underwater antennas. The results obtained using UDMSim show a good match with the experimental results achieved using an underwater testbed. UDMSim is made available to the community to support easy and faster evaluation of underwater data muling oriented communications solutions and to enable offline replication of real world experiments.

Abstract
Optical networks-on-chip (ONoCs) are a promising solution for high-performance multicore integration with better latency and bandwidth than traditional electrical NoCs. Wavelength-routed ONoCs (WRONoCs) offer yet additional performance guarantees. However, WRONoC design presents new EDA challenges which have not yet been fully addressed. So far, most topology analysis is abstract, i.e., overlooks layout concerns, while for layout the tools available perform place and route (P&R) but no topology optimization. Thus, a need arises for a novel optimization method combining both aspects of WRONoC design. In this article, such a method, PSION+, is laid out. This new procedure uses a linear programming model to optimize a WRONoC physical layout template to optimality. This template-based optimization scheme is a new idea in this area that seeks to minimize problem complexity while keeping design flexibility. A simple layout template format is introduced and explored. Finally, multiple model reduction techniques to reduce solver run-time are also presented and tested. When compared to the state-of-the-art design procedure, results show a decrease in maximum optical insertion loss of 41%.

Abstract