Vol. 33 · No. 6

A multi-camera monitoring system for online recognition of moving objects is considered. The system consists of several autonomous vision subsystems. Each of them is able to monitor an area of interest with the aim to reveal and recognize characteristic patterns and to track the motion of the selected configuration. Each subsystem recognizes the existence of the predefined objects in order to report expected motion while automatically tracking the selected object. Simultaneous tracking by two or more cameras is used to measure the instant distance of the tracked object. A modular conception enables simple extension by several static and mobile cameras mechanically oriented in space by the pan and tilt heads. The open architecture of the system allows additional subsystems integration and the day and night image processing algorithms extension.

Advances in CFD (Computational Fluid Dynamics) and significant increase of computational power of current computers have led to widespread use of CFD in aerodynamics, fluid dynamics, combustion engineering and other academic disciplines. One of such disciplines is computer modelling and simulation of fire in human structures. Fire is a very complicated and complex phenomenon. Fire research deals with such processes as combustion, radiation, heat transfer, turbulence, fluid dynamics, and other physical and chemical processes. Several advanced fire and smoke simulation systems have been developed to solve various aspects of fire safety in various conditions and environments. In this paper, the use of parallel version of the CFD simulator FDS (Fire Dynamics Simulator) for the simulation of fire spread and smoke development in a short road tunnel is described. In order to study the impact of the computational domain decomposition on the accuracy and reliability of simulation results, several simulations of a chosen fire scenario ran on the HP blade cluster utilizing different numbers of processors. The obtained parameters of fire and smoke were used to investigate the influence of the fire on people evacuation in the tunnel with active ventilation for a given traffic situation.

Simulation of evacuation plans is a relatively complex problem. It is necessary to simulate a number of separate processes which interact in the result. Namely, they are pedestrian-pedestrian interactions, pedestrian-static object (e.g. wall) interactions and pedestrian-environment (fire, smoke, etc.) interactions. In this case, the evacuation simulation is controled on the microscopic level. Microscopic level considers each individual separately and pedestrian is planning his/her path to the exit with regard to the above-mentioned interactions. In this article we focus on path planning during evacuation and describe algorithms applied in this area. At the end we propose a method of the space evaluation with linear time complexity and planned path compared with commercial software tools.

Multiagent Systems are a promising paradigm for software development. It is feasible to model such systems with many components where each one can solve a specific problem. This division of responsibilities allows multiagent systems to work in dynamically changing environments. An example of an environment that is very changeable is related with emergencies management. Emergency management systems depend on the cooperation of all their components due to their specialization. In order to obtain this cooperation, the components need to interact with each other and adapt their interactions depending on their purpose and the system components they are interacting with. Also, new components may arrive on the scene, which must be informed about the interaction policies that original components are using. Although Multiagent Systems are suited to managing scenarios of this kind, their effectiveness depends on their capacity to dynamically modify and adapt the protocols that control the interactions among agents in the system. In this paper, an infrastructure to support dynamically changing interaction protocols is presented.

Presence of appropriate acoustic cues of affective features in the synthesized speech can be a prerequisite for the proper evaluation of the semantic content by the message recipient. In the recent work the authors have focused on the research of expressive speech synthesis capable of generating naturally sounding synthetic speech at various levels of arousal. Automatic information and warning systems can be used to inform, warn, instruct and navigate people in dangerous, critical situations, and increase the effectiveness of crisis management and rescue operations. One of the activities in the frame of the EU SF project CRISIS was called ``Extremely expressive (hyper-expressive) speech synthesis for urgent warning messages generation''. It was aimed at research and development of speech synthesizers with high naturalness and intelligibility capable of generating messages with various expressive loads. The synthesizers will be applicable to generate public alert and warning messages in case of fires, floods, state security threats, etc. Early warning in relation to the situations mentioned above can be made thanks to fire and flood spread forecasting; modeling thereof is covered by other activities of the CRISIS project. The most important part needed for the synthesizer building is the expressive speech database. An original method is proposed to create such a database. The current version of the expressive speech database is introduced and first experiments with expressive synthesizers developed with this database are presented and discussed.

The cloud offers clear benefits for computations as well as for storage for diverse application areas. Security concerns are by far the greatest barriers to the wider uptake of cloud computing, particularly for privacy-sensitive applications. The aim of this article is to propose an approach for establishing trust between users and providers of cloud infrastructures (IaaS model) based on certified trusted agents. Such approach would remove barriers that prevent security sensitive applications being moved to the cloud. The core technology encompasses a secure agent platform for providing the execution environment for agents and the secure attested software base which ensures the integrity of the host platform. In this article we describe the motivation, concept, design and initial implementation of these technologies.

The paper deals with the design and implementation of an intelligent modular information system (IMIS) for modeling and predictive decision making supervisory control of some important critical processes in a nuclear power plant (nuclear reactor) using selected soft computing methods. The developed IMIS enables monitoring critical states, safety impact analysis and prediction of dangerous situations. It also recommends the operator possibilities how to proceed to ensure safety of operations and humans and environment. The proposed complex IMIS has been tested on real data from a nuclear power plant process primarily used as supervisory information for decision making support and management of critical processes. The core of the proposed IMIS is a general nonlinear neural network mathematical model. For prediction of selected process variables an artificial neural network of multilayer perceptron type (MLP) has been used. The effective Levenberg-Marquardt method was used to train the MLP network. Testing and verification of the neural prediction model were carried out on real operating data measurements obtained from the NPP Jaslovske Bohunice.

This paper describes performance tuning experiences with a parallel CFD code to enhance its performance and flexibility on large scale parallel computers. The code solves the incompressible Navier-Stokes equations based on the novel Slightly Compressible Model on three-dimensional structure grids. High level loop transformations and argument based code specialization are utilized to optimize its uniprocessor performance. Static arrays are converted into dynamically allocated arrays to improve the flexibility. The grid generator is coupled with the flow solver so that they can exchange grid data in the memory. A detailed performance evaluation is performed. The results show that our uniprocessor optimizations improve the performance of the flow solver for 1.38 times to 3.93 times on Tianhe-1A supercomputer. In memory grid data exchange optimization speeds up the application startup time by nearly two magnitudes. The optimized code exhibits an excellent parallel scalability running realistic test cases. On 4 096 CPU cores, it achieves a strong scaling parallel efficiency of 77.39

Ubiquitous spam messages cause a serious waste of time and resources. This paper addresses the practical spam filtering problem, and proposes a universal approach to fight with various spam messages. The proposed active multi-field learning approach is based on: 1) It is cost-sensitive to obtain a label for a real-world spam filter, which suggests an active learning idea; and 2) Different messages often have a similar multi-field text structure, which suggests a multi-field learning idea. The multi-field learning framework combines multiple results predicted from field classifiers by a novel compound weight, and each field classifier calculates the arithmetical average of multiple conditional probabilities predicted from feature strings according to a data structure of string-frequency index. Comparing the current variance of field classifying results with the historical variance, the active learner evaluates the classifying confidence and regards the more uncertain message as the more informative sample for which to request a label. The experimental results show that the proposed approach can achieve the state-of-the-art performance at greatly reduced label requirements both in email spam filtering and short text spam filtering. Our active multi-field learning performance, the standard (1-ROCA)

A neuronal tree is a rooted tree with n leaves whose each internal node has at least two children; this class not only is defined based on the structure of dendrites in neurons, but also refers to phylogenetic trees or evolutionary trees. More precisely, neuronal trees are rooted-multistate phylogenetic trees whose size is defined as the number of leaves. In this paper, a new encoding over an alphabet of size 3 (minimal cardinality) is introduced for representing the neuronal trees with a given number of leaves. This encoding is used for generating neuronal trees with n leaves in A-order with constant average time and O(n) time complexity in the worst case. Also, new ranking and unranking algorithms are presented in time complexity of O(n) and O(n łog n), respectively.