LeggedWalking on Inclined Surfaces

The main contribution of this MS Thesis is centered around taking steps towards successful multi-modal demonstrations using Northeastern's legged-aerial robot, Husky Carbon. This work discusses the challenges involved in achieving multi-modal locomotion such as trotting-hovering and thruster-assisted incline walking and reports progress made towards overcoming these challenges. Animals like birds use a combination of legged and aerial mobility, as seen in Chukars' wing-assisted incline running (WAIR), to achieve multi-modal locomotion. Chukars use forces generated by their flapping wings to manipulate ground contact forces and traverse steep slopes and overhangs. Husky's design takes inspiration from birds such as Chukars. This MS thesis presentation outlines the mechanical and electrical details of Husky's legged and aerial units. The thesis presents simulated incline walking using a high-fidelity model of the Husky Carbon over steep slopes of up to 45 degrees.Comment: Masters thesi

Compound heat wave and PM2.5 pollution episodes in U.S. cities

This study analyzes heat waves (HWs), air pollution (AP) episodes, and compound HW and AP events (CE) in the urban environment and provides a comparison between events in urban areas (UAs) and rural areas (RAs). A 1-km gridded daily minimum temperature dataset and a 1-km gridded daily PM2.5 concentration dataset were used along with geospatial data to characterize events by their frequency, intensity in heat, intensity in pollution, and duration. The greatest differences between UAs and RAs in frequency, heat intensity, pollution intensity, and duration for all events were seen in the West and Southwest regions. For both UAs and RAs, it was found that HWs were the most frequent, intense, and longest lasting in the West and Southwest regions, AP episodes were the most frequent and longest lasting in the Northeast, Ohio Valley, and Southeast regions, and AP episodes were the most intense in the Northern Rockies and Plains and Upper Midwest regions. It was concluded that HWs (AP episodes) had a greater impact on CEs than AP episodes (HWs) in regions with more prominent HWs (AP episodes).Comment: National Weather Center Research Experience for Undergraduates Program (2023

JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution

Recent years have witnessed a rapid growth of deep-network based services and applications. A practical and critical problem thus has emerged: how to effectively deploy the deep neural network models such that they can be executed efficiently. Conventional cloud-based approaches usually run the deep models in data center servers, causing large latency because a significant amount of data has to be transferred from the edge of network to the data center. In this paper, we propose JALAD, a joint accuracy- and latency-aware execution framework, which decouples a deep neural network so that a part of it will run at edge devices and the other part inside the conventional cloud, while only a minimum amount of data has to be transferred between them. Though the idea seems straightforward, we are facing challenges including i) how to find the best partition of a deep structure; ii) how to deploy the component at an edge device that only has limited computation power; and iii) how to minimize the overall execution latency. Our answers to these questions are a set of strategies in JALAD, including 1) A normalization based in-layer data compression strategy by jointly considering compression rate and model accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall execution latency; and 3) An edge-cloud structure adaptation strategy that dynamically changes the decoupling for different network conditions. Experiments demonstrate that our solution can significantly reduce the execution latency: it speeds up the overall inference execution with a guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE

Solving a resource allocation problem in RFB-based 5G wireless networks

International audienceIn this work, we consider the 5G network architecture outcome of the Horizon 2020 project Superfluidity, where the main building blocks are virtual entities, namely Reusable Functional Blocks (RFBs). This 5G Superfluid network composed of RFBs and physical 5G nodes allows a high level of flexibility, agility, portability and high performance. The emergency problem we face is how to optimally minimize the total installation costs of such a Superfluid network while guaranteeing a minimum required user coverage and minimum downlink traffic demand. We propose an approach to break down the main resource allocation problem in a set of simplified problems that allow the computation of the solution in a more efficient way. Numerical results illustrate our findings