86 research outputs found
Hidden Markov Models
Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research
Real-time Immersive human-computer interaction based on tracking and recognition of dynamic hand gestures
With fast developing and ever growing use of computer based technologies, human-computer interaction (HCI) plays an increasingly pivotal role. In virtual reality (VR), HCI technologies provide not only a better understanding of three-dimensional shapes and spaces, but also sensory immersion and physical interaction. With the hand based HCI being a key HCI modality for object manipulation and gesture based communication, challenges are presented to provide users a natural, intuitive, effortless, precise, and real-time method for HCI based on dynamic hand gestures, due to the complexity of hand postures formed by multiple joints with high degrees-of-freedom, the speed of hand movements with highly variable trajectories and rapid direction changes, and the precision required for interaction between hands and objects in the virtual world.
Presented in this thesis is the design and development of a novel real-time HCI system based on a unique combination of a pair of data gloves based on fibre-optic curvature sensors to acquire finger joint angles, a hybrid tracking system based on inertia and ultrasound to capture hand position and orientation, and a stereoscopic display system to provide an immersive visual feedback. The potential and effectiveness of the proposed system is demonstrated through a number of applications, namely, hand gesture based virtual object manipulation and visualisation, hand gesture based direct sign writing, and hand gesture based finger spelling.
For virtual object manipulation and visualisation, the system is shown to allow a user to select, translate, rotate, scale, release and visualise virtual objects (presented using graphics and volume data) in three-dimensional space using natural hand gestures in real-time. For direct sign writing, the system is shown to be able to display immediately the corresponding SignWriting symbols signed by a user using three different signing sequences and a range of complex hand gestures, which consist of various combinations of hand postures (with each finger open, half-bent, closed, adduction and abduction), eight hand orientations in horizontal/vertical plans, three palm facing directions, and various hand movements (which can have eight directions in horizontal/vertical plans, and can be repetitive, straight/curve, clockwise/anti-clockwise). The development includes a special visual interface to give not only a stereoscopic view of hand gestures and movements, but also a structured visual feedback for each stage of the signing sequence. An excellent basis is therefore formed to develop a full HCI based on all human
gestures by integrating the proposed system with facial expression and body posture recognition methods. Furthermore, for finger spelling, the system is shown to be able to recognise five vowels signed by two hands using the British Sign Language in real-time
State discovery for autonomous learning
Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (p. 163-171).This thesis is devoted to the study of algorithms for early perceptual learning for an autonomous agent in the presence of feedback. In the framework of associative perceptual learning with indirect supervision, three learning techniques are examined in detail: * short-term on-line memory-based model learning; * long-term on-line distribution-based statistical estimation; * mixed on- and off-line continuous learning of gesture models. The three methods proceed within essentially the same framework, consisting of a perceptual sub-system and a sub-system that implements the associative mapping from perceptual categories to actions. The thesis contributes in several areas - it formulates the framework for solving incremental associative learning tasks; introduces the idea of incremental classification with utility, margin and boundary compression rules; develops a technique of sequence classification with Support Vector Machines; introduces an idea of weak transduction and offers an EM-based algorithm for solving it; proposes a mixed on- and off-line algorithm for learning continuous gesture with reward-based decomposition of the state space. The proposed framework facilitates the development of agents and human-computer interfaces that can be trained by a naive user. The work presented in this dissertation focuses on making these incremental learning algorithms practical.by Yuri A. Ivanov.Ph.D
Harbour seals (Phoca vitulina) in a tidal stream environment : movement ecology and the effects of a renewable energy installation
Despite ever increasing information on the importance of oceanographic processes for marine predators, movement ecology of higher trophic level species in tidal stream environments remains relatively under-studied. This represents a significant knowledge gap for certain species which spend large portions of their lives in these energetic habitats. In this thesis I show that a top predator, the harbour seal (Phoca vitulina), inhabiting one of the most tidally energetic regions in Europe, the Pentland Firth, shows a complex range of behaviours as a consequence of the strong current flows they are subjected to. Both horizontal movement and diving behaviour elucidate a degree of foraging plasticity, hitherto undocumented in a single population of harbour seals. I also demonstrate that, by using multiple perspectives of movement, researchers can better tease apart ecologically important areas for animals inhabiting these habitats. Given the importance of tidally energetic systems for harbour seals, I then go on to study the impact of tidal energy installations on their movements and physical fitness. Using telemetry data, I determine an overt avoidance response of the local population to an operational turbine array and demonstrate the effect this can have on our understanding of collision risk. To further augment our predictions of the population level effect of these devices, I then go on to demonstrate that not all collisions between seals and tidal turbine blades are likely to result in fatality. In combination, these results suggest that currently held views on the lethal effects of tidal turbines are overly-conservative, and the likely behavioural and physical responses to these devices may result in a more ecologically favourable outcome than previously assumed."This PhD was funded by co-funded by Scottish Natural Heritage (SNH), Marine Scotland (project
reference MMSS/002/15) and the Marine Alliance for Science and Technology (MASTS; grant reference
HR09011)." -- Acknowledgement
Proceedings of the SAB'06 Workshop on Adaptive Approaches for Optimizing Player Satisfaction in Computer and Physical Games
These proceedings contain the papers presented at the Workshop on Adaptive approaches
for Optimizing Player Satisfaction in Computer and Physical Games held at the Ninth
international conference on the Simulation of Adaptive Behavior (SAB’06): From
Animals to Animats 9 in Rome, Italy on 1 October 2006.
We were motivated by the current state-of-the-art in intelligent game design using
adaptive approaches. Artificial Intelligence (AI) techniques are mainly focused on
generating human-like and intelligent character behaviors. Meanwhile there is generally
little further analysis of whether these behaviors contribute to the satisfaction of the
player. The implicit hypothesis motivating this research is that intelligent opponent
behaviors enable the player to gain more satisfaction from the game. This hypothesis may
well be true; however, since no notion of entertainment or enjoyment is explicitly
defined, there is therefore little evidence that a specific character behavior generates
enjoyable games.
Our objective for holding this workshop was to encourage the study, development,
integration, and evaluation of adaptive methodologies based on richer forms of humanmachine
interaction for augmenting gameplay experiences for the player. We wanted to
encourage a dialogue among researchers in AI, human-computer interaction and
psychology disciplines who investigate dissimilar methodologies for improving gameplay
experiences. We expected that this workshop would yield an understanding of state-ofthe-
art approaches for capturing and augmenting player satisfaction in interactive systems
such as computer games.
Our invited speaker was Hakon Steinø, Technical Producer of IO-Interactive, who
discussed applied AI research at IO-Interactive, portrayed the future trends of AI in
computer game industry and debated the use of academic-oriented methodologies for
augmenting player satisfaction. The sessions of presentations and discussions where
classified into three themes: Adaptive Learning, Examples of Adaptive Games and Player
Modeling.
The Workshop Committee did a great job in providing suggestions and informative
reviews for the submissions; thank you! This workshop was in part supported by the
Danish National Research Council (project no: 274-05-0511). Finally, thanks to all the
participants; we hope you found this to be useful!peer-reviewe
Computational intelligence techniques for missing data imputation
Despite considerable advances in missing data imputation techniques over the last three decades, the
problem of missing data remains largely unsolved. Many techniques have emerged in the literature
as candidate solutions, including the Expectation Maximisation (EM), and the combination of autoassociative
neural networks and genetic algorithms (NN-GA). The merits of both these techniques
have been discussed at length in the literature, but have never been compared to each other. This
thesis contributes to knowledge by firstly, conducting a comparative study of these two techniques..
The significance of the difference in performance of the methods is presented. Secondly, predictive
analysis methods suitable for the missing data problem are presented. The predictive analysis in
this problem is aimed at determining if data in question are predictable and hence, to help in
choosing the estimation techniques accordingly. Thirdly, a novel treatment of missing data for online
condition monitoring problems is presented. An ensemble of three autoencoders together with
hybrid Genetic Algorithms (GA) and fast simulated annealing was used to approximate missing
data. Several significant insights were deduced from the simulation results. It was deduced that for
the problem of missing data using computational intelligence approaches, the choice of optimisation
methods plays a significant role in prediction. Although, it was observed that hybrid GA and Fast
Simulated Annealing (FSA) can converge to the same search space and to almost the same values
they differ significantly in duration. This unique contribution has demonstrated that a particular
interest has to be paid to the choice of optimisation techniques and their decision boundaries.
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Another unique contribution of this work was not only to demonstrate that a dynamic programming
is applicable in the problem of missing data, but to also show that it is efficient in addressing the
problem of missing data. An NN-GA model was built to impute missing data, using the principle
of dynamic programing. This approach makes it possible to modularise the problem of missing
data, for maximum efficiency. With the advancements in parallel computing, various modules of
the problem could be solved by different processors, working together in parallel. Furthermore, a
method for imputing missing data in non-stationary time series data that learns incrementally even
when there is a concept drift is proposed. This method works by measuring the heteroskedasticity
to detect concept drift and explores an online learning technique. New direction for research, where
missing data can be estimated for nonstationary applications are opened by the introduction of this
novel method. Thus, this thesis has uniquely opened the doors of research to this area. Many
other methods need to be developed so that they can be compared to the unique existing approach
proposed in this thesis.
Another novel technique for dealing with missing data for on-line condition monitoring problem was
also presented and studied. The problem of classifying in the presence of missing data was addressed,
where no attempts are made to recover the missing values. The problem domain was then extended
to regression. The proposed technique performs better than the NN-GA approach, both in accuracy
and time efficiency during testing. The advantage of the proposed technique is that it eliminates
the need for finding the best estimate of the data, and hence, saves time. Lastly, instead of using
complicated techniques to estimate missing values, an imputation approach based on rough sets is
explored. Empirical results obtained using both real and synthetic data are given and they provide a
valuable and promising insight to the problem of missing data. The work, has significantly confirmed
that rough sets can be reliable for missing data estimation in larger and real databases
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