2D Computer Animation. 

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... Server, allowing a different set of devices to interact with the model. Thus, to query the model the client only has to set the needed parameters in the URL. Each client will interact with the same simulation, which allows testing different scenarios. The Dispar (Discrete Particle distribution model) transport model is used in the geo- referenced model module of this application. Dispar is a mathematical formulation to solve advection-diffusion problems in aquatic systems. The Dispar transport model is a 2D model able to simulate several pollutant scenarios (Ferreira and Costa, 2002). In our application, Dispar is applied to the Tejo estuary (Lisbon). Thus, it is possible to perform a previous calibration of the system and map latitude and longitude values gathered from the GPS to model coordinates. In addition, client orientation values are also mapped against the orientation of the module to supply correct information to the clients. To interact with the server and be correctly identified in the model, the server stores an identifier (ID) linked with the information about the screen size of the client device. Thus, at bootstrap the client sends a request to the server with information about its position and orientation, view mode and screen size. In the reply, server sends a unique ID to the client. The first client interacting with the geo-referenced model also has to send a request to the server to perform a new simulation, which is carried by the interface and hidden from the user. Afterwards, each client can request the model to add or remove agents acting in the simulation process (see Figure 2). Agents involved in the simulation can be pollutant sources, namely factories and swine farms, or waste water treatment plants. Agents are templates embedded with predefined values, used to change parameters in the simulation and allowing for an easier interaction with the model. These templates allow common users to interact with the model and be able to visualize the impact in water quality when related agents are set near a water stream or artificial lake. Environmental systems are spatial and temporal. Thus, all information retrieved from the geo-referenced model is generated dynamically, calculated in real time and controlled by the user. The user’s position and orientation is updated in each request to improve simulation view in the client application. The result will also be tailored according to the view mode in the client, whether in either computer animation view or AR view. When the client is in computer animation view the geo-referenced model returns a map view of the model evolution in the user vicinity with an icon representing the user’s position and current orientation. Hence, the user can easily identify the environment in his or her vicinity. To allow users to control the view detail, a zoom facility was implemented enabling them to see a more detailed view of the nearby region or to see a more general view of a wider region. When a position is selected in this view, agents can be linked to their related position in the geo-referenced model. Sewage pipes connect pollutant agents and a point in the water body to illustrate pollution release. If a pollutant agent is released within the range of a waste water treatment plant the wastes are conducted to it and pollutant discharge is reduced. For visualization purposes, an AR view can also be selected. In this view the users cannot interact with the simulation. However, they are able to visualize the model evolution superimposed in the image of the real environment. In this view the degree of realism is increased, also improving environmental decision support. A river surface is flat, so the image superimposed in the real environment is the pollution dispersion retrieved from the geo-referenced model tailored to the position and perspective of the users. Since each user is tracked within each request it is possible to tailor the image to each of them. Another advantage of this system is that some accuracy errors in image superimposition in water surface can be tolerated by the users, because there are no accurate visual references. Being a mobile device, the client has technological limitations. Thus, operations performed in the client-side are reduced, i.e. the operations are mainly to collect user position and orientation, to superimpose additional information and to deal with interactions from the users, whether in AR view or computer animation view, respectively. In computer animation view, the client first requests a unique ID from the server sending it all parameters referred in the server architecture. After identifying the view mode used by the client, a map view of the server is sent to the client. Afterwards, in every time step a new image of model evolution is requested to the server and updated in the client. By taping on the screen, the user can see a menu presenting the agents that can be added to the simulation in progress (see Figure 3). When an agent is selected, a request is sent to the server to add the related agent to the simulation. In that request, screen coordinates where the agent should be added, together with user position and orientation are sent to the server. With this information, a translation to model coordinates is performed in the server and the correspondent agent is added to the simulation. Removing an agent from the simulation is performed in the same way. After adding or removing an agent from the simulation, the changes will be reflected in all users. In AR view, a unique ID is also requested as in the computer animation view. Afterwards, an image of the model evolution is superimposed over the real image of the environment captured by a camera installed in the client device. The retrieved image is previously adjusted to client position and orientation in the geo-referenced model saving resources in the mobile client. The users will then be able to visualize the model evolution in their vicinity, superimposed over the real image. While in the field of observation, the users are tracked via: GPS data, to grab the absolute position of the user; orientation tracker, to obtain the current orientation of the user’s head; and environment mapping, knowledge of the physical form and position of the entities on the environment that is being augmented. For this project, two versions of the client are in development. First, a client is being developed for a PDA. The setup is formed with the following devices: HP iPAQ 5450 with Pocket PC 2002, Pretec CompactGPS, Lifeview FlyJacket i3800, Lifeview FlyJacket iCAM, Intersense Intertrax 2, Compaq iPAQ Serial Adapter Cable (3800/3900/5400 Series). In Figure 4, a picture of the PDA setup is shown without Pretec CompactGPS. Finally, an additional client is in development for laptop computers. This setup is as follows: Laptop computer, Pretec CompactGPS, Phillips ToUcam Pro, Intersense Intertrax 2. In addition, to provide the necessary mobility to the roaming users, a wireless network is used to connect the client and server module. The described system doesn’t take into account gaps in the connection. A reliable connection is needed to perform updates in the client view. The presented architecture was studied and tailored for environmental management, namely visualization of pollution dispersion in water streams and artificial lakes. With this architecture multiple users can interact with the geo-referenced model, enabling common users to interact with a pollution transport model. However, it can be applied in other domains adjusting the geo-referenced model for each situation. The modularity of the present architecture facilitates the development of new modules for additional devices or different geo-referenced models. Different clients with different screen sizes can also be easily used with the geo-referenced model. Moreover, interaction with the model is improved with the usage of templates, also enabling common users to interact with a pollutant transport model and assess environmental impact of pollutant agents or waste water treatment plants. The AR composition module supplies two different views to observe the model simulation. The first view, a computer animation, is suitable to interact with the model. The second view, supplies an additional degree of realism with pollution dispersion in the user’s vicinity superimposed over the real image. In the future, improvements in current modules should be made, namely the AR modules, either for PDA or laptop computers. The ANTS project is founded by Fundação para a Ciência e para a Tecnologia (FCT, Portugal) (project no MGS/34376/99-00). The contribution of the first author is founded by FCT, Portugal under research contract no SFRH/BD/6196/2001. We would like to thank YDreams (www.ydreams.com) for support in the work described in this paper. Azuma, R., Lee, J.W., Jing, B., Park, J., You, S. and Neumann, U., 1999: Tracking in unprepared environments for augmented reality systems. Computers and Graphics 23(6), 787-793. Dias, A.E., Silva, J.P. and Câmara, A.S., 1995: Bits: browsing in time and space in comp. CHI’95 Human Factors in Computing Systems (Denver, CO, 7-11 May 1995), 248-249. Ferreira, J.S. and Costa, M.A, 2002: Determinist advection-diffusion model based on markov processes. Journal of Hydraulic Engineering , ASCE, 128(4), 399-411. Ghadirian, P. and Bishop, I.D.. 2002: Composition of augmented reality and GIS to visualize environmental changes. AURISA, Adelaide, Australasian Urban and Regional Information Systems Association , November 25-30. Hedley, N.F., Billinghurst, M., Postner, L. and May, R., 2002: Explorations in the use of augmented reality for geographic visualization. ACM Presence: Teleoperators and Virtual Environments 11(2), 119-133 Pasman, W., Schaaf, A. van der, Lagendijk, R.L. and Jansen, F.W., 1998: Information display for mobile augmented reality: Merging the real and virtual world. ...