Edinburgh University has 3 Centres for Doctoral Training (CDT)
that would be of interest to computer vision and robotics students.
EPSRC CDT in Robotics and Autonomous Systems –
including computer vision:
10 new PhD places
EPSRC CDT in Data Science –
includes analysis of datasets (including `big data') that might arise
from computer vision applications:
10 new PhD places
First deadline: 9 December 2016
EPSRC CDT in Pervasive Parallelism –
investigating approaches to parallelism that could be used
with big datasets, video, etc:
10 new PhD places
Funding is primarily for UK & EC students, but there is a small amount of
funding for a few outstanding overseas students.
Si eres alumno o profesor, no dejes escapar esta oportunidad. Obtén las versiones online de Office y 1 TB de almacenamiento online de forma totalmente gratuita.
C is No. 1, but big data is still the big winner
Kfir Karmon imagines a world in which a person putting together a presentation can add a quote or move an image with a flick of the wrist instead of a click of a mouse.
Jamie Shotton envisions a future in which we can easily interact in virtual reality much like we do in actual reality, using our hands for small, sophisticated movements like picking up a tool, pushing a button or squeezing a soft object in front of us.
And Hrvoje Benko sees a way in which those types of advances could be combined with simple physical objects, such as a few buttons on a piece of wood, to recreate complex, immersive simulators – replacing expensive hardware that people use today for those purposes.
Microsoft researchers are looking at a number of ways in which technology can start to recognize detailed hand motion — and engineers can put those breakthroughs to use in a wide variety of fields.
The ultimate goal: Allowing us to interact with technology in more natural ways than ever before.
Master In Signal Theory and Communications
The MSTC program offered by the Signals, Systems and Radiocommunications department is aimed to equip highly motivated students with up-to-date skills in some of the hottest topics demanded worldwide by industries, research centers and academia.
Track: Signal Processing and Machine Learning for Big Data
The MSTC track on Signal Processing and Machine Learning for Big Data extends Big Data and Analytics instruction to new scientific carriers training professionals and researchers in principles and technologies for extracting knowledge from the increasing number of real-world signals: speech, images, movies, music, biological and sensor readings, robotic sensors, financial series, etc. Students will learn through practical application projects and real case studies. The program provides fundamental courses on statistical analysis, time series analysis and optimization, machine learning courses (predictive and descriptive learning, reinforcement learning and biologically-inspired models), and courses on advanced signal processing techniques for large-scale data and massive processing.
In MSTC students can choose between three tracks to meet their academic needs and achieve their personal and professional objectives. For more information please follow the Full Track Program link below.
|Full Track Program||Pre-register now|
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A San Leandro, Calif.-based startup called Scanse has developed a 2D LIDAR system that promises to be simultaneosly much cheaper and much better than what’s out there. For $250, you get a spinning LIDAR sensor with a range of 40 meters, even outdoors.
Continue reading here: [Link]
El fabricante chino de electrónica crea un aparato capaz de volar 27 minutos mientras graba en calidad 4K o hace fotos de 16 megapíxeles.
Lea la noticia entera en [http://tecnologia.elpais.com/tecnologia/2016/05/25/actualidad/1464188379_614912.html]
Título del artículo: "An extended Volumegrams of Local Binary sub-Patterns Descriptor for Hand-Gesture Recognition"
Autores: Ana I Maqueda, Carlos R. del-Blanco, Fernando Jaureguizar, Narciso García
Paper title: "Improved 2D-to-3D video conversion by fusing optical flow analysis and scene depth learning"
Authors: J.L. Herrera, C.R. delBlanco, N. García
Paper title: "Fast Image Decoding for Block Compressed Sensing based encoding by using a Modified Smooth L0-norm"
Authors: J. Xiao, C.R. delBlanco, C. Cuevas, N. García.