Nikola Tomašević

Artificial neural network based approach to EEG signal simulation.

In this paper a new approach to the electroencephalogram (EEG) signal simulation based on the artificial neural networks (ANN) is proposed. The aim was to simulate the spontaneous human EEG background activity based solely on the experimentally acquired EEG data. Therefore, an EEG measurement campaign was conducted on a healthy awake adult in order to obtain an adequate ANN training data set. As demonstration of the performance of the ANN based approach, comparisons were made against autoregressive moving average (ARMA) filtering based method. Comprehensive quantitative and qualitative statistical analysis showed clearly that the EEG process obtained by the proposed method was in satisfactory agreement with the one obtained by measurements.

A Decision Support System for Building Evacuation Based on the EMILI SITE Environment

This article introduces a decision support system for determining a safe and short (in both time and distance sense) evacuation path and for increasing situation awareness in the case of a hazard in buildings of large critical infrastructures (CIs). CIs are infrastructures that are complex, difficult to monitor and with an integrated SCADA system, such as airports or metro stations. The FP-7 project Emergency Management In Large Infrastructures (EMILI) aims at a new generation of data management and control systems in CIs [1] by building a further data management layer on already existing systems. Part of the EMILI project is the implementation of a simulation and training environment (SITE). The introduced decision support system is meant to be integrated into SITE, using results from event processing and an optimization algorithm to determine evacuation paths and increase the awareness of the safety situation. The decision support system was developed for an airport use case.

Application of Complex Event Processing Paradigm in Situation Awareness and Management

This paper describes a novel approach to situation awareness and management in critical infrastructures using an emerging Complex Event Processing paradigm in combination with ECA (Event Condition Action) rules. Every modern infrastructural facility, especially critical ones, would want to catch or predict exceptions and threats at the earliest possible moment. The crucial prerequisite for this is a holistic and accurate situation assessment. To be able to assess the situation, evaluate the risk and provide decision support to the emergency managers, we need to define event chains that identify what is normal, compliant and expected, and what is exceptional and/or dangerous. These problems introduce a novel genre of applications - event-driven applications that make automated decisions based on a complex event or pattern of events, their detection, correlation and aggregation. In cases where an automated reaction is not possible, a recommendation is given to a human operator who remains in the control loop.

Self-Adaptive Prototype for Seat Adaption

Self-adaptive prototype for seat adaptation aims at enhancing the physical comfort of a driver by taking into account not only the state of the environment (state of the road, car settings), but also the driver’s emotional, cognitive and physical state. To implement this prototype we used a REFLECTive middleware, which provides a programming framework for the development of pervasive-adaptive applications. The REFLECTive middleware supports self-adaptive behavior and is generally composed of three tiers: Tangible tier contains services that read sensors data and send commands to actuators, REFLECTive tier is responsible for analyzing the data collected from sensors and for defining the actions that will be performed by actuators, Application tier facilitates high-level decision making. The seat adaptation prototype uses the information about Center of Pressure (COP) speed and number of bumps to determine the driver’s physical state, and then it combines this information with the driver’s cognitive and emotional state to figure out if the driver feels uncomfortable, and to change the state of seat cushions in an attempt to make driver feel more comfortable. The components of the seat adaptation prototype in the REFLECTive and Application tier are implemented using reaction rules.

NIMFA - Natural language Implicit Meaning Formalization and Abstraction

There are many general purpose Knowledge Representation, Natural Language Processing and Information Extraction techniques that were successfully applied in many applications. However, their more broad use is still limited by the relatively high costs of their application. It seems that these limitations are partly caused by some essential characteristics and some weaknesses of these techniques. In this paper we propose a radically new knowledge representation and interpretation technique, NIMFA, specialized for knowledge expressed in natural languages. To test the basic ideas underlying NIMFA we have implemented a prototype Information Center that provides answers to natural language queries using Web services.

The Importance of Cross-layer Considerations in a Standardized WSN Protocol Stack Aiming for IoT: The Internet of Things (Ubiquity symposium)

The Internet of Things (IoT) envisages expanding the current Internet with a huge number of intelligent communicating devices. Wireless sensor networks (WSNs) integrating IoT will rely on a set of the open standards striving to offer scalability and reliability in a variety of operating scenarios and conditions. Standardized protocols will tackle some of the major WSN challenges like energy efficiency, intrinsic impairments of low-power wireless medium, and self-organization. After more then a decade of tremendous standardization efforts, we can finally witness an integral IP-based WSN standardized protocol stack for IoT. Nevertheless, the current state of standards has redundancy issues and can benefit from further improvements. We would like to highlight some of the cross-layer aspects that need to be considered to bring further improvements to the standardized WSN protocol stack for the IoT.

Short-term fading simulator based on artificial neural networks

In this paper, a novel technique for simulation of the short-term fading is presented and analyzed. A proposed solution is based on a trained artificial neural network (in further text ANN) and an oscillator for regulating the simulation process. In order to obtain adequate input data for training, cross-validation and testing, extensive measurements of electric field level were carried out in indoor environment. Statistical analysis of gained results has shown good performances of the proposed technique, and thereby the possibility for applying it in the real life situations.

Correlated EEG Signals Simulation Based on Artificial Neural Networks

In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

Simulating Adaptive Control in Multimedia Applications

The paper describes an approach for design and development of an adaptive control framework for media-rich applications. As novel techniques require complex computation and wide spectrum of devices, a modular simulator is being developed that can be used for inexpensive and effective analyses of different design strategies. The kernel of the simulator is the same as the kernel of the real system, making the final implementation easier. Various application scenarios can be exercised and their feasibility and applicability can be tested before the actual deployment of real application is done.