ANNUAL REPORT 2012

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waveform is under construction [7]. The main objective for this part of the project is to identify important parameters, their time constants and their interdependencies. The working hypothesis for high level reasoning and learning (the core of a cognitive engine) is to apply a new cognitive model based on emotional learning. The proposed cognitive model is based on the function and the anatomical structure of the human emotional system, figure 2. The function of the emotional system is to control basic instincts like: aggression, fear, laughter, sexual arousal etc. In the proposed model the structural sub parts, figure 2, mimic the limbic system regions [8] in the human brain including: thalamus, sensory cortex, amygdala, and orbitofrontal cortex. The connections between these sub parts are similar to the structure of those regions of the limbic system of a human brain that have a role in emotional learning. Figure 2. Structural model of emotional learning. Developing an artificial cognitive model for high level reasoning and learning is a challenge since it must provide four capabilities: decision, prediction, control and, optimization. The focus has so far been to evaluate the prediction capability of the proposed computational model of emotional learning. Especially, evaluating its capability of predicting stochastic time series [9]-[10], this unfortunately is a very common parameter context that a network management system has to operate within. For the evaluation of the prediction capability of the cognitive model as well artificial stochastic time series as natural stochastic time series have been used, Lorenz time series, Henon time series and sun spot time series, respectively. The graph in figure 3 displays the monthly smoothed sunspot number from 1976 to 1996 both the real value and the value predicted 6 month ahead by using a proposed implementation of emotional learning model [10]. Figure 3. The real and predicted values of smoothed monthly sunspots from January 1976 to April 1996 using BELFIS. References [1]S. R. Atkinsson and J. Moffat, The Agile Organization – From informal networks to complex effects and agility, Command and Control Research Program (CCRP) publishing series, 2005. [2] M. J. Ryan an d M. R. Frater, Tactical Communication for the Digitized Battlefield, Artech House, 2002. [3]ARAGORN homepage: http://www.ict-aragorn.eu/ [4] M. Parsapoor, U. Bilstrup, " Using the grouping genetic algorithm (GGA) for channel assignment in a cluster-based mobile ad hoc network ," in proceedings of the 8th Swedish National Computer Networking Workshop. SNCNW 2012, June, 2012. [5] M. Parsapoor and U. Bilstrup, "Imperialist Competition Algorithm for DSA in Cognitive Radio Networks," in proceedings of 8th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2012, Shanghai, China September, 2012. [6] M. Parsapoor and U. Bilstrup, “Merging ant colony optimization based clustering and an imperialist competitive algorithm for spectrum management of a cognitive mobile ad hoc network,” in proceedings of The Wireless Innovation Forum Conference on Communications Technologies and Software Defined Radio, SDR-WInnComm 2013, Wahington D.C., U.S. January 2013. [7] K. Kunert, M. Jonsson and U. Bilstrup, “Deterministic real-time medium access for cognitive industrial radio networks,” in Proceedings of the 9th IEEE International Workshop on Factory Communication Systems, WFCS 2012, Lemgo, Germany, 2012. [8] M.S. Gazzaniga, R. B. Ivry and G.S. Mangun, Cognitive Neuroscience – The Biologu of the Mind, 3 ed ., W. W. Norton & Company, 2009. [9] M. Parsapoor and U. Bilstrup, " Neuro-Fuzzy Models, BELRFS and LOLIMOT, for Prediction of Chaotic Time Series," in proceedings of the International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2012, Trabzon, Turkey, July, 2012. [10] M. Parsapoor and U. Bilstrup, “Brain Emotional Learning Based Fuzzy Inference System (BELFIS) for Solar Activity Forecasting,” in proceedings of 24 th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2012, Athens, Greece November, 2012. ______________________________________________ PARTNERS AND STATUS Funding: CERES+, KK-Foundation Partners: Saab Security and Defense Solutions Project period: January 2012 – December 2013. Project members: Urban Bilstrup, Hans Åkermark, Mahboobeh Parsapoor, Kristina Kunnert Project leader: Urban Bilstrup ______________________________________________ 30 CERES Annual Report 2012
WisCon - Wireless sensor concept node WiSCoN – WIRELESS SENSOR CONCEPT NODE P. Enoksson 1 , B. Fliesberg 2 , E. Johansson 3 , M. Jonsson 4 , K. Kunert 4 , and M. Öhman 3 1. Chalmers University of Technology, Gothenburg, Sweden 2. Volvo 3P, Gothenburg, Sweden 3. Volvo Technology Corporation, Gothenburg, Sweden 4. Centre for Research on Embedded Systems (CERES), Halmstad University, Halmstad, Sweden The objective of the project is to explore and show the concrete benefits and potential of self-sustaining wireless sensors and to understand their limitations. The scope of the project includes not only wireless communication, but also aspects related to energy supply and storage. The intention is to build a wireless-sensor concept node that can be used to realistically assess the feasibility of self-sustaining wireless sensors from an industrialization viewpoint. 1. Background and Motivation In today’s advanced and complex vehicles systems, sensors are key components, acting as sources of much of the data that is required input into a large number of complex invehicle control functions. Typically, the sensors’ power supply, as well as the data exchange, is realized with standard wiring. A reduction or elimination of sensor wiring will allow for a reduction of material cost, product weight (leading to better fuel economy), and issues with the wiring harness quality. It will also enable new concepts that are infeasible today due to limitations set by the wiring harness (routing and packaging issues, placement of moving parts, etc.). The purpose of this project is to build a knowledge base covering the technology behind self-sustaining wireless sensors in vehicles. The goal is to realistically assess the feasibility of wireless sensors from an industrialization viewpoint. We aim at developing a wireless-sensor concept node for studying and evaluating various concepts and technologies needed for wireless sensors for automotive applications, including energy supply, communication technologies, and power management. The overall goal is to explore and show the concrete benefits and potential of self-sustaining wireless sensors and also to understand its limitations. Some of the prospective benefits of wireless sensors in automotive applications include: Reduction of product cost by elimination of wiring harness and connectors Increased quality by removing sources of failures related to wiring and connectors Reduction of manufacturing and aftermarket costs by reducing the installation and replacement time for sensors Reduction of wiring harness variants (→ simpler variant handling → lower development and product cost) Possibility to place sensors where it is infeasible to have wired sensors (such as moving and sealed parts) 2. Approach In order to achieve the vision of self-sustaining wireless sensors, it is necessary to achieve a good level of maturity in a number of technologies, such as energy harvesting, local energy storage, wireless energy distribution, lowpower sensor technologies, and short-range wireless communication. Local energy storage Energy storage can be dimensioned to carry energy for short time intervals (as a container for locally generated energy), or for long time intervals (service period or lifetime of a vehicle). Possible short-time storage technologies include, e.g., rechargeable batteries (NiMh, LiIon, etc.) or small supercaps. For long-time storage nonrechargeable battery technologies are a possibility. Energy harvesting devices MEMS (Microelectromechanical systems) based energy harvesting devices can generate energy in the µW range from vibrations. In most cases this is too low for powering sensors, but combined with efficient energy storage, a complete system can be built. Low power sensors Ultra-low power sensors available on the market together with proper energy management make it possible to lower the energy consumption of a sensor node so it can be powered by an energy scavenger. Short range wireless digital communication Communication between the sensor nodes need to be energy efficient. Quality of service issues need to be addressed, and frequency and modulation must be chosen correctly in order to increase the probability of signals reaching the receivers in a space full of metallic structures. Partners and Status Academic partner: Chalmers University of Technology Industrial partner: Volvo Technology Corporation Project funding: Funded by VINNOVA through the FFI – Strategic Vehicle Research and Innovation program (Vehicle Development collaboration program) Project volume: 5.24 MSEK (HH share: 0.35 MSEK) Duration: January 1, 2011 – December 31, 2013. Project leader: Mikaela Öhman, Volvo Technology Corporation Participating researchers at CERES: Dr Kristina Kunert, Prof. Magnus Jonsson CERES Annual Report 2012 31
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ANNUAL REPORT 2012

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