Hide menu

Examensarbeten och uppsatser / Final Theses

Framläggningar på IDA / Presentations at IDA


Se även framläggningar annonserade hos LinTek och ITN i Norrköping / See also presentations announced at LinTek and ITN in Norrköping (in Swedish)

If nothing is stated about the presentation language then the presentation is in Swedish.


WExUpp - kommande framläggningar
2014-10-24 - AIICS
Oövervakad maskininlärning och igenkänning av aktiviteter i ett ström-baserat ramverk
Mattias Tiger
Avancerad (30hp)
kl 14:30, Gödel (In English)
[Abstract]
Learning to recognize and predict common activities, performed by objects and observed by sensors, is an important and challenging problem related both to Artificial Intelligence and robotics. In this thesis, the general problem of dynamic adaptive situation awareness is considered and we argue for the need for an on-line bottom-up approach. A candidate for a bottom layer to a solution is proposed, which we consider to be capable of future extensions that can bring us closer towards this goal. We present a novel approach to adaptive activity learning, where a mapping between raw data and primitive activity concepts are learned and continuously improved on-line and unsupervised. The approach takes streams of observations of objects as input and learns a probabilistic representation of both the observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using Bayesian graphs. The learned model supports both estimating the most likely activity and predicting the most likely future (and past) activities. Methods and ideas from a wide range of previous work are combined to provide a uniform and efficient way to handle a variety of common problems related to learning, classifying and predicting activities. The framework is evaluated both by learning activities in a simulated traffic monitoring application and by learning the flight patterns of an internally developed autonomous quadcopter system. The conclusion is that our framework is capable of learning the observed activities in real-time with good accuracy. We see this work as a step towards learning activities unsupervised for robotic systems to adapt to new circumstances autonomously and to learn new activities on the fly that can be detected and predicted immediately.
2014-10-27 - SaS
Re-targeting SkePU for the Movidius Myriad Platform
Rosandra Cuello
Avancerad (30hp)
kl 13:00, Donald Knuth (In English)
[Abstract]
The Movidius Myriad1 Platform is a multicore embedded platform primed to offer high performance and power efficiency for computer vision applications in mobile devices. The challenges of programming multicore environments are well known and skeleton programming offers a high-level programming alternative for parallel computing, intended to hide the complexities of the system from the programmer. The SkePU Skeleton Programming Framework includes backend implementations for CPU and GPU systems and it has the capacity to support more platforms by extending its backend implementations.
With this master thesis project we aim to extend the SkePU Skeleton Programming Framework to provide support for execution in the Movidius Myriad1 embedded platform. Our SkePU backend for Myriad1 consists on a set of macros and functions to compose the different elements of a Myriad1 application, data communication structures to exchange data between the host systems and Myriad1, and a helper script and auxiliary files to generate a Myriad1 application.
Evaluation and testing demonstrate that our backend is usable, however further optimizations are needed to obtain good performance that would make it practical to use in real life applications, particularly when it comes to data communication. As part of this project, we have outlined some improvements that could be applied to obtain better performance overall in the future, addressing the issues found with the methods of data communication.


Page responsible: Johan Åberg
Last updated: 2011-03-22