Implementation of mechanical, electrical, and feedback control systems in unmanned aerial vehicles

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, June 2006.Includes bibliographical references (leaf 53).The thesis objective was to design an unmanned aerial vehicle that was capable of stable, autonomous flight. A fixed wing aircraft was chosen to simplify some of the flight characteristics and avoid some of the challenges found in rotary wing machines. Two aircraft were tested: a large and heavy gasoline powered aircraft and a smaller and much lighter electric powered sailplane. An autopilot was implemented into both platforms that would fly the aircraft and allow the measurement of flight vehicle characteristics. A link with the vehicle was created by installing a radio modem that allowed communication between the autopilot and a ground computer. This allowed updates to the controllers PID feedback loops to change flight characteristics and made the recording of flight parameters possible. This would be useful later in the analysis of data. To control the vehicle remotely, a ground computer was used that ran systems monitoring software. It also allowed the programming of flight plans to the autopilot. Combining these systems together proved successful and stable flight was achieved in both aircraft. By using the same autopilot in both vehicles, it was proven that the electronic system could be modular and transplanted between various vehicles.by Derrick Tan.S.B

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present