Catálogo de publicaciones - libros

We consider the experimental development of collaborating robots able to work within fields of crops. Specifically we investigate how AI principles can be applied to this agricultural domain and how the user should be involved in such a system. To support collaboration between agents a representation of responsibilities and dependencies is necessary. This is done by introducing groups and roles, from MAS theory, that the agent must adhere to, formalised by the Agent-Group-Role (AGR) model. To enable a human user to influence the system we adopt the principles declared by the VOWELS paradigm. We then show that the AGR model and the VOWELS paradigm, enable us to solve two practical agricultural problems, and lastly we argue that the obtained results can be transferred to other domains, such as pervasive computing.

In this paper, a study for indoor channel modeling is presented for the millimeter frequency band, by using various configurations of multiple element antenna systems. A multi-ray model is proposed and verified through simulation process for capacity prediction of a high data rate wireless system. The proposed model utilizes the geometric characteristics of the environment, the angle of arrival and angle of departure of each one of the propagation paths, the antenna elements and their spacing. The results showed that the system capacity increases significantly if two or four elements are used at both terminal antennas instead of the basic SISO configuration. In order to accomplish major improvement in the data rates, a MIMO system at 60 GHz should operate within a range of 10 to 20 m in an indoor environment with the view of obtaining sufficient Signal to Noise Ratios.

Steady state contingency analysis aims at the assessment of the risk certain contingencies may pose to an electrical network. This is a particularly important task of network operators, especially as network stability issues become of prime importance in the current era of electricity deregulation. The article focuses on the analysis of experimental data that are produced through operating point simulation, contingency application, machine- learning cross validation (based on pre-contingency network index selection algorithms) to point out the “nature” of given contingencies. Experimental statistical results of contingency prediction and selected network state indicators are translated to electric network data in an effort to further interpret the “nature” of each contingency and produce effective predicting algorithms that support operators.

Identifying people and tracking their locations is a key prerequisite to achieving context-awareness in smart spaces. Moreover, in realistic context-aware applications, these tasks have to be carried out in a non-obtrusive fashion. In this paper we present a set of robust person identification and tracking algorithms, based on audio and visual processing. A main characteristic of these algorithms is that they operate on far-field and unconstraint audio-visual streams, which ensures that they are non-intrusive. We also illustrate that the combination of their outputs can lead to composite multimodal tracking components, which are suitable for supporting a broad range of context-aware services. In combining audio-visual processing results, we exploit a context-modeling approach based on a graph of situations. Accordingly, we discuss the implementation of realistic prototype applications that make use of the full range of audio, visual and multimodal algorithms.

The complexity of group dynamics occurring in small group interactions often hinders the performance of teams. The availability of rich multimodal information about what is going on in meetings makes it possible to explore ways of providing support to dysfunctional teams from facilitation to training sessions, addressing both the individuals and the group as a whole. A necessary step in this direction is that of capturing and understanding group dynamics. In this paper, we discuss a particular scenario, in which meeting participants receive a multimedia feedback on their relational behavior, as a first step towards increasing self-awareness. We describe the background and the motivation for a coding scheme partially inspired by the Bales’ Interaction Process Analysis aimed at identifying suitable observable behavioral sequences and an experimental investigation on the acceptability of such a service.

At the Universitat Politècnica de Catalunya (UPC), a Smart Room has been equipped with 85 microphones and 8 cameras. This paper describes the setup of the sensors, gives an overview of the underlying hardware and software infrastructure and indicates possibilities for high- and low-level multi-modal interaction. An example of usage of the information collected from the distributed sensor network is explained in detail: the system supports a group of students that have to solve a lab assignment related problem.

The automatic detection, tracking, and identification of multiple people in intelligent environments is an important building block on which smart interaction systems can be designed. Those could be, e.g. gesture recognizers, head pose estimators or far field speech recognizers and dialog systems.

In this paper, we present a system which is capable of tracking multiple people in a smartroom environment while infering their identities in a completely automatic and unobtrusive way. It relies on a set of fixed and active cameras to track the users and get closeups of their faces for identification, and on several microphone arrays to determine active speakers and steer the attention of the system. Information coming asynchronously from several sources, such as position updates from audio or visual trackers and identification events from identification modules, is fused at higher level to gradually refine the room’s situation model. The system has been trained on a small set of users and showed good performance at acquiring and keeping their identities in a smart room environment.

This paper presents a driver simulator, which takes into account information about the user’s state of mind (level of attention, fatigue state, stress state). The user’s state of mind analysis is based on video data and biological signals. Facial movements such as eyes blinking, yawning, head rotations... are detected on video data: they are used in order to evaluate the fatigue and attention level of the driver. The user’s electrocardiogram and galvanic skin response are recorded and analyzed in order to evaluate the stress level of the driver. A driver simulator software is modified so that the system is able to appropriately react to these critical situations of fatigue and stress: some audio and visual messages are sent to the driver, wheel vibrations are generated and the driver is supposed to react to the alert messages. A multi threaded system is proposed to support multi messages sent by different modalities. Strategies for data fusion and fission are also provided.

In this paper a Fuzzy Expert System for the prediction of Hypovigilance-related accidents is presented. The system uses physiological modalities in order to detect signs of extreme hypovigilance. An advantage of such a system is its extensibility regarding the physiological modalities and features that it can use as inputs. In that way, even though currently only eyelid-related features are exploited, in the future and for prototypes designed for professionals other physiological modalities, such as EEG can be easily integrated in the existing system in order to make it more robust and reliable.

In the present paper a mixed reality cane simulation environment is presented that allows blind people to navigate in virtual worlds. The application is based on the combination of a real cane with a haptic force feedback system. The users can therefore handle the cane in a natural way and perceive realistic force feedback from virtual objects. Experimental results demonstrate the advantages of the MR approach when compared to the VR one.