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2 PhD Studentships in audio-visual intelligent sensing at Queen Mary University of London
PhD Studentships in audio-visual intelligent sensing
Applications are invited for 2 (two) PhD Studentships to undertake research in the areas of computer vision and audio processing for people monitoring in multi-camera multi-microphone environments, and will be part of an interdisciplinary project on mobile audio-visual monitoring for smart interactive and reactive environments. The Studentships (to be started in or after January 2017) are part of an interdisciplinary project between the Centre for Intelligent Sensing (http://cis.eecs.qmul.ac.uk) at Queen Mary University of London (QMUL) and the Centre for Information Technology (http://ict.fbk.eu) at the Fondazione Bruno Kessler (FBK), Trento, Italy.
The Project will focus on methods for people tracking, activity recognition, acoustic scene analysis, behaviour analysis, distant-speech recognition and understanding applied to individuals as well as groups. Such information will enable learning 'patterns of usage' of the environment, and patterns can in turn be used to adapt and optimise the sensing accordingly.
Each PhD student will spend approximatively 50% of their time in London and 50% of their PhD time in Trento and will have access to state-of-the-art audio-visual laboratories, including robotic sensors, a multi-camera multi-microphone installation at a large open hallway and a smart home facility equipped with cameras and microphones.
Candidates should have a first-class honours degree or equivalent, or a good MSc Degree, in Computer Science, Physics, Mathematics or Electronic Engineering. Candidates must be confident in applied mathematics, and should have good programming experience, in particular of C/C++ language and of MATLAB environment. Previous knowledge of Signal Processing is a requirement. Previous knowledge of Computer Vision or Deep Learning/Machine Learning or Robotic Sensing or Audio Signal Processing and/or Speech Recognition is desired, but not required.
The studentships will be based at Centre for Intelligent Sensing in the School of Electronic Engineering and Computer Science at Queen Mary, University of London and will be supervised by Professor Andrea Cavallaro (http://www.eecs.qmul.ac.uk/~andrea/) and Dr Oswald Lanz (https://tev.fbk.eu/people/profile/lanz) or, depending on the type of the PhD project chosen by the candidate, Dr Maurizio Omologo (http://shine.fbk.eu/people/omologo). To apply please follow the on-line process at http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html by selecting Electronic Engineering or Computer Science in the A-Z list of research opportunities and following the instructions on the right hand side of the web page.
Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest (no more than 500 words or one side of A4 paper) should state whether you are interested in a computer vision PhD project, or an audio processing PhD project, or an audio-visual processing PhD project. Moreover, your Statement of Research Interest should answer two questions: Why are you interested in the proposed area? What is your experience in the proposed area? In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at:www.eecs.qmul.ac.uk/phd/apply.php .
Informal enquiries can be made by email to Professor Andrea Cavallaro (a.cavallaro@qmul.ac.uk).
The closing date for the applications is 15 November 2016.
Interviews are expected to take place during the week commencing 28 November 2016.
Edinburgh PhD places in vision, robotics, datascience, parallel processing
Edinburgh University has 3 Centres for Doctoral Training (CDT)
that would be of interest to computer vision and robotics students.
(See: http://www.ed.ac.uk/schools-departments/informatics/postgraduate/cdts/informatics-cdts)
EPSRC CDT in Robotics and Autonomous Systems –
including computer vision:
http://www.edinburgh-robotics.org/programme-information
http://www.edinburgh-robotics.org/
10 new PhD places
EPSRC CDT in Data Science –
includes analysis of datasets (including `big data') that might arise
from computer vision applications:
http://datascience.inf.ed.ac.uk/about-course/
http://datascience.inf.ed.ac.uk/
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:
http://web.inf.ed.ac.uk/infweb/student-services/cdt/ppar/programme-summary
http://pervasiveparallelism.inf.ed.ac.uk/
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.
Office en el ámbito educativo para alumnos, profesores y escuelas
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.
Origen: Office en el ámbito educativo para alumnos, profesores y escuelas
The 2016 Top Programming Languages
C is No. 1, but big data is still the big winner
Talking with your hands: How Microsoft researchers are moving beyond keyboard and mouse
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.
MsC Track in Signal Processing and Machine Learning for Big Data
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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© 2016 Department of Signals Systems and Radiocommunications, UPM. All Rights Reserved.
Our mailing address is: info-mstc@ssr.upm.es
Paper accepted at 2016 IEEE International Conference on Consumer Electronics (Berlin)
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.
New Master in Signal Theory and Communications (MSTC)
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Welcome to MSTC
ETSIT – Telecommunication Engineering School – at Universidad Politécnica de Madrid (UPM) currently offers an Official Master Program in Signal Theory and Communications (MSTC).
Students can choose between three tracks to meet their academic needs and achieve their personal and professional objectives. The first two tracks offer advanced training in Radiofrequency and Radiocomunications and Multimedia where the Signals, Systems and Radiocommunications Department at ETSIT-UPM has a unique expertise. The third track offers a cutting-edge program in Signal Processing and Machine Learning for Big Data designed to address the increasing demand for professionals and researchers with the right skills to extend Big Data and Analytics paradigms to applications that scale to massive signals and multimedia data.
Graduates, master students and professionals are encouraged to visit our web pagehttp://www.mstc.ssr.upm.es/ or contact directly at info-mstc@ssr.upm.es.
RF Technologies and Systems
Learn about the last advances in radiofrequency technologies and their application to communication systems. You will be able to design such communication subsystems as transmitters, receivers, synthesyzers and antennas.
Radiocommunications and multimedia
This track provides an integrated profile in radiocommunications and multimedia emphasizing most hot technological areas where our department is leading industrial transfer and basic research.
Signal Processing and Machine Learning for Big Data
The track program has been designed to provide the scientific foundations and major technological challenges for extracting knowledge from the increasing number of real-world signals, such as speech, images, movies, music, biological and sensor readings, financial series, etc., as well as data provided by current and future communication systems.
| More information | Pre-register now |
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© 2016 Department of Signals Systems and Radiocommunications, UPM. All Rights Reserved.
Our mailing address is: info-mstc@ssr.upm.es
Code: face recognition in depth imagery
The code of the publication "Visual Face Recognition using Bag of Dense Derivative Depth Patterns" has been realeased. This code performs face recognition using depth information. The key is the the computation of a new "bag_d3p" feature descriptor, which is delivered to a SVM for the classification task. More information in [Link].
