8,186 research outputs found
STR: a student developed star tracker for the ESA-LED ESMO moon mission
In the frame of their engineering degree, ISAE’s students are developing a Star Tracker, with the aim of being the core attitude estimation equipment of the European Moon Student Orbiter. This development goes on since several years and is currently in phase B. We intend to start building an integrated breadboard for the end of the academic year.
The STR is composed of several sub-systems: the optical and detection sub-system, the electronics, the mechanics and the software. The optical detection part is based on an in-house developed new generation of APS detectors. The optical train is made of several lenses enclosed in a titanium tube. The electronics includes a FPGA for the pre-processing of the image and a microcontroller in order to manage the high level functions of the instrument. The mechanical part includes the electronics box, as well as the sensor baffle. The design is optimized to minimize the thermo-elastic noise of the assembly.
Embedded on ESMO platform, this Star Tracker will be able to compute the satellite‘s attitude, taking into account the specific requirements linked to a Moon mission (illumination, radiation requirements and baffle adaptation to lunar orbit).
In order to validate the design, software end-to-end simulation will include a complete simulation of the STR in its lunar dynamic environment. Therefore, we are developing a simple orbital model for the mission (including potential dazzling by celestial bodies)
From Big Data to Big Displays: High-Performance Visualization at Blue Brain
Blue Brain has pushed high-performance visualization (HPV) to complement its
HPC strategy since its inception in 2007. In 2011, this strategy has been
accelerated to develop innovative visualization solutions through increased
funding and strategic partnerships with other research institutions.
We present the key elements of this HPV ecosystem, which integrates C++
visualization applications with novel collaborative display systems. We
motivate how our strategy of transforming visualization engines into services
enables a variety of use cases, not only for the integration with high-fidelity
displays, but also to build service oriented architectures, to link into web
applications and to provide remote services to Python applications.Comment: ISC 2017 Visualization at Scale worksho
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
In this paper we develop a Quality Assessment approach for face recognition
based on deep learning. The method consists of a Convolutional Neural Network,
FaceQnet, that is used to predict the suitability of a specific input image for
face recognition purposes. The training of FaceQnet is done using the VGGFace2
database. We employ the BioLab-ICAO framework for labeling the VGGFace2 images
with quality information related to their ICAO compliance level. The
groundtruth quality labels are obtained using FaceNet to generate comparison
scores. We employ the groundtruth data to fine-tune a ResNet-based CNN, making
it capable of returning a numerical quality measure for each input image.
Finally, we verify if the FaceQnet scores are suitable to predict the expected
performance when employing a specific image for face recognition with a COTS
face recognition system. Several conclusions can be drawn from this work, most
notably: 1) we managed to employ an existing ICAO compliance framework and a
pretrained CNN to automatically label data with quality information, 2) we
trained FaceQnet for quality estimation by fine-tuning a pre-trained face
recognition network (ResNet-50), and 3) we have shown that the predictions from
FaceQnet are highly correlated with the face recognition accuracy of a
state-of-the-art commercial system not used during development. FaceQnet is
publicly available in GitHub.Comment: Preprint version of a paper accepted at ICB 201
Multi-Aperture CMOS Sun Sensor for Microsatellite Attitude Determination
This paper describes the high precision digital sun sensor under development at the University of Naples. The sensor determines the sun line orientation in the sensor frame from the measurement of the sun position on the focal plane. It exploits CMOS technology and an original optical head design with multiple apertures. This allows simultaneous multiple acquisitions of the sun as spots on the focal plane. The sensor can be operated either with a fixed or a variable number of sun spots, depending on the required field of view and sun-line measurement precision. Multiple acquisitions are averaged by using techniques which minimize the computational load to extract the sun line orientation with high precision. Accuracy and computational efficiency are also improved thanks to an original design of the calibration function relying on neural networks. Extensive test campaigns are carried out using a laboratory test facility reproducing sun spectrum, apparent size and distance, and variable illumination directions. Test results validate the sensor concept, confirming the precision improvement achievable with multiple apertures, and sensor operation with a variable number of sun spots. Specifically, the sensor provides accuracy and precision in the order of 1 arcmin and 1 arcsec, respectively
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