PPT-Deep Space Network: The Next 50 Years

Author : trish-goza | Published Date : 2017-03-19

Dr Les Deutsch Dr Steve Townes Jet Propulsion Laboratory California Institute of Technology Phil Liebrecht Pete Vrotsos Dr Don Cornwell National Aeronautics and

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Deep Space Network: The Next 50 Years: Transcript

Dr Les Deutsch Dr Steve Townes Jet Propulsion Laboratory California Institute of Technology Phil Liebrecht Pete Vrotsos Dr Don Cornwell National Aeronautics and Space Administration . Rogstad Alexander Mileant Timothy T Pham ZELENCE A JOHN WILEY SONS INC PUBLICATION brPage 2br This Page Intentionally Left Blank brPage 3br Antenna Arraying Techniques in the Deep Space Network brPage 4br This Page Intentionally Left Blank brPage 5 Naiyan. Wang. Outline. Non-NN Approaches. Deep Convex Net. Extreme Learning Machine. PCAnet. Deep Fisher Net (Already . presented before). Discussion. Deep convex net. Each module is a two- layer convex network.. Packet Inspection of Next Generation Network Devices. . Prof. Anat Bremler-Barr. IDC . Herzliya. www.deepness-lab.org. This work was supported by European Research Council (ERC) Starting Grant no. 259085 , . Frank Hill, NSO, USA. Markus Roth, KIS, Germany. Michael J Thompson, HAO, USA. ESWW10, Antwerp. Nov. 19, 2013. What and Why a Network. No substitute for “complete” temporal and spatial observations of the Sun. Currently, the Deep Space Network (DSN) uses three primary earth stations with 70-meterantennas to support interplanetary missions. These three earth stations are positioned at 120-degree intervals a Andrew . Thomas. FISO . Telecon. 12 October, 2011 . The Emerging Environment and The Gap. Crewed Missions. ISS Operations. 2012 . 2013 . 2014 . 2015 2016 . 2017 . 2018 2019 2020 . Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . Han et al. Deep Compression : Compressing Deep Neural Networks with Pruning, Training Quantization and Huffman Coding, . Han et al. Deep Compression . Deep Learning on Embedded System ?. Some Statistics….. . Requires the best from all of us. William H. . Gerstenmaier. Human Exploration and Operations . Directorate. National . Aeronautics and Space Administration. Our Goal. The nation. ’. s goal for . Continuous. Scoring in Practical Applications. Tuesday 6/28/2016. By Greg Makowski. Greg@Ligadata.com. www.Linkedin.com/in/GregMakowski. Community @. . http. ://. Kamanja.org. . . Try out. Future . Peyman . Kazemian (Stanford University) . George Varghese. . (UCSD, Yahoo Labs). Nick McKeown (Stanford University). November 7. th. , 2012. IRTF. 1. Motivation. It is hard to understand and reason about end-to-end behavior of networks:. Asmitha Rathis. Why Bioinformatics?. Protein structure . Genetic Variants . Anomaly classification . Protein classification. Segmentation/Splicing . Why is Deep Learning beneficial?. scalable with large datasets and are effective in identifying complex patterns from feature-rich datasets . Topics: 1. st. lecture wrap-up, difficulty training deep networks,. image classification problem, using convolutions,. tricks to train deep networks . . Resources: http://www.cs.utah.edu/~rajeev/cs7960/notes/ . Cinematographer . Conrad Hall (who also shot American Beauty and Road to Perdition). Utilizes flat space in most of his films. Movement in the first scene takes place on the axis of the tripod: pans, tilts, zooms .

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