Risk free to risk taking developing the renaissance manager

Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1985.MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY.Bibliography: leaves 115-116.by James G. Cosgrove anf Ubiratan N. Guzzi.M.S

The Planck-LFI flight model composite waveguides

The Low Frequency Instrument on board the PLANCK satellite is designed to give the most accurate map ever of the CMB anisotropy of the whole sky over a broad frequency band spanning 27 to 77 GHz. It is made of an array of 22 pseudo-correlation radiometers, composed of 11 actively cooled (20 K) Front End Modules (FEMs), and 11 Back End Modules (BEMs) at 300K. The connection between the two parts is made with rectangular Wave Guides. Considerations of different nature (thermal, electromagnetic and mechanical), imposed stringent requirements on the WGs characteristics and drove their design. From the thermal point of view, the WG should guarantee good insulation between the FEM and the BEM sections to avoid overloading the cryocooler. On the other hand it is essential that the signals do not undergo excessive attenuation through the WG. Finally, given the different positions of the FEM modules behind the focal surface and the mechanical constraints given by the surrounding structures, different mechanical designs were necessary. A composite configuration of Stainless Steel and Copper was selected to satisfy all the requirements. Given the complex shape and the considerable length (about 1.5-2 m), manufacturing and testing the WGs was a challenge. This work deals with the development of the LFI WGs, including the choice of the final configuration and of the fabrication process. It also describes the testing procedure adopted to fully characterize these components from the electromagnetic point of view and the space qualification process they underwent. Results obtained during the test campaign are reported and compared with the stringent requirements. The performance of the LFI WGs is in line with requirements, and the WGs were successfully space qualified.Comment: this paper is part of the Prelaunch status LFI papers published on JINST: http://www.iop.org/EJ/journal/-page=extra.proc5/jins

High Performances Corrugated Feed Horns for Space Applications at Millimetre Wavelengths

We report on the design, fabrication and testing of a set of high performance corrugated feed horns at 30 GHz, 70 GHz and 100 GHz, built as advanced prototypes for the Low Frequency Instrument (LFI) of the ESA Planck mission. The electromagnetic designs include linear (100 GHz) and dual shaped (30 and 70 GHz) profiles. Fabrication has been achieved by direct machining at 30 GHz, and by electro-formation at higher frequencies. The measured performances on side lobes and return loss meet the stringent Planck requirements over the large (20%) instrument bandwidth. Moreover, the advantage in terms of main lobe shape and side lobes levels of the dual profiled designs has been demonstrated.Comment: 16 pages, 7 figures, accepted for publication in Experimental Astronom

Planck LFI flight model feed horns

this paper is part of the Prelaunch status LFI papers published on JINST: http://www.iop.org/EJ/journal/-page=extra.proc5/jinst The Low Frequency Instrument is optically interfaced with the ESA Planck telescope through 11 corrugated feed horns each connected to the Radiometer Chain Assembly (RCA). This paper describes the design, the manufacturing and the testing of the flight model feed horns. They have been designed to optimize the LFI optical interfaces taking into account the tight mechanical requirements imposed by the Planck focal plane layout. All the eleven units have been successfully tested and integrated with the Ortho Mode transducers.Comment: This is an author-created, un-copyedited version of an article accepted for publication in JINST. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher authenticated version is available online at 10.1088/1748-0221/4/12/T1200

Fully Onboard AI-Powered Human-Drone Pose Estimation on Ultralow-Power Autonomous Flying Nano-UAVs

Many emerging applications of nano-sized unmanned aerial vehicles (UAVs), with a few cm(2) form-factor, revolve around safely interacting with humans in complex scenarios, for example, monitoring their activities or looking after people needing care. Such sophisticated autonomous functionality must be achieved while dealing with severe constraints in payload, battery, and power budget (similar to 100 mW). In this work, we attack a complex task going from perception to control: to estimate and maintain the nano-UAV's relative 3-D pose with respect to a person while they freely move in the environment-a task that, to the best of our knowledge, has never previously been targeted with fully onboard computation on a nano-sized UAV. Our approach is centered around a novel vision-based deep neural network (DNN), called Frontnet, designed for deployment on top of a parallel ultra-low power (PULP) processor aboard a nano-UAV. We present a vertically integrated approach starting from the DNN model design, training, and dataset augmentation down to 8-bit quantization and deployment in-field. PULP-Frontnet can operate in real-time (up to 135 frame/s), consuming less than 87 mW for processing at peak throughput and down to 0.43 mJ/frame in the most energy-efficient operating point. Field experiments demonstrate a closed-loop top-notch autonomous navigation capability, with a tiny 27-g Crazyflie 2.1 nano-UAV. Compared against an ideal sensing setup, onboard pose inference yields excellent drone behavior in terms of median absolute errors, such as positional (onboard: 41 cm, ideal: 26 cm) and angular (onboard: 3.7 degrees, ideal: 4.1 degrees). We publicly release videos and the source code of our work

Fully Onboard AI-powered Human-Drone Pose Estimation on Ultra-low Power Autonomous Flying Nano-UAVs

Many emerging applications of nano-sized unmanned aerial vehicles (UAVs), with a few form-factor, revolve around safely interacting with humans in complex scenarios, for example, monitoring their activities or looking after people needing care. Such sophisticated autonomous functionality must be achieved while dealing with severe constraints in payload, battery, and power budget ( 100). In this work, we attack a complex task going from perception to control: to estimate and maintain the nano-UAV’s relative 3D pose with respect to a person while they freely move in the environment – a task that, to the best of our knowledge, has never previously been targeted with fully onboard computation on a nano-sized UAV. Our approach is centered around a novel vision-based deep neural network (DNN), called PULP-Frontnet, designed for deployment on top of a parallel ultra-low-power (PULP) processor aboard a nano-UAV. We present a vertically integrated approach starting from the DNN model design, training, and dataset augmentation down to 8-bit quantization and deployment in-field. PULP-Frontnet can operate in real-time (up to 135frame/), consuming less than 87 for processing at peak throughput and down to 0.43/frame in the most energy-efficient operating point. Field experiments demonstrate a closed-loop top-notch autonomous navigation capability, with a tiny 27-grams Crazyflie 2.1 nano-UAV. Compared against an ideal sensing setup, onboard pose inference yields excellent drone behavior in terms of median absolute errors, such as positional (onboard: 41, ideal: 26) and angular (onboard: 3.7, ideal: 4.1). We publicly release videos and the source code of our work