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T1. Chaotic System Control for Brain Stimulation and FPGA Hardware Implementation T2. Be Mobile: Exploring Mobile Video Production and Mobile Social Media for Education & Industry T3. Agent Technology and the Internet of Things
Detaled description
T1. Chaotic System Control for Brain Stimulation and FPGA Hardware Implementation The analysis and control of chaotic systems is important for the study of deep brain stimulation. This first part of the tutorial presents a detailed analysis for the main characteristics of chaotic systems based on Henon map, including critical points, Jacobian matrix, eigenvalues, Lyapunov exponents and bifurcation. Chaos synchronization is widely used for communication and cryptography, and can be employed for brain stimulation applications to treat Parkinson's Disease (PD) and epilepsy. The Henon map chaotic system is implemented on FPGA using VHDL. The selection of the number of bits in the fixed-point data representation is discussed. The implementation results including resource utilization and speed are compared to a equivalent model-based design. The VHDL based implementation demonstrates better performance compared to the model-based counterpart using the direct Henon map equations. The design is optimized by reforming the Henon equations to reduce hardware resource utilization and power consumption. The implementation results show that performance is improved for both 32-bit and 16-bit fixed-point implementations. The second part of the tutorial presents an Artificial Neural Network (ANN) design for a chaotic generator, and the training performances for a three layer ANN architecture with different number of hidden neurons. Chaotic systems can be synchronized and used for secure communication. Chaotic systems such as Lorenz attractor, Rossler attractor and Chen's system are generally implemented directly based on their definitions represented by a unique group of ordinary differential equations (ODEs). A feed forward ANN can be trained using the output values of a chaotic system. The training process is carried out on a computer and the weights are generated for all neurons in the ANN architecture. These weights are then used for a trained ANN architecture model to generate the expected output for the target chaotic system. The complexity of the ANN architecture defines the implementation cost and speed. Therefore it is beneficial to use less number of hidden neurons to achieve the target training performance. Lorenz attractor has its significance in studying chaotic systems and is used as the design subject in this paper. The 3-layer ANN has one input, one hidden and one output layer. The ANN architecture with 1 to 16 hidden neurons is designed and trained respectively using MATLAB Neural Network Toolbox with three training algorithms: Levenberg-Marquardt, Scaled Conjugate Gradient algorithm and Bayesian Regulation. The optimized ANN architecture can be used to improve the efficiency of the fixed-point implementation on an Field Programmable Gates Array (FPGA) device.
T2. Be Mobile: Exploring Mobile Video Production and Mobile Social Media for Education & Industry This tutorial aims to give participants the skills to create innovative mash-ups of two of the unique affordances of today’s smartphones, tablets and phablets: 1. Mobile Media Production (creating) 2. Mobile Social Media (sharing). General Outline This tutorial will explore scenarios for innovative and collaborative team projects using these tools. The three key objectives of the workshop are that: Concept Mobile social media leverages the ubiquity of mobile device ownership and enables the formation of professional networks and serendipitous learning. Mobile learning provides powerful tools for enabling the nurturing of learning and developing communities across varied contexts that previously would have been impossible. Mobile social media is inherently collaborative, but requires a significant rethink of communication design, utilizing participatory user-content generation tools such as Vine, Cinamaker for collaborative video. This tutorial aims to challenge the current concept of mobile social media from a purely social domain to an academic and professional domain of use. Mobile social media can utilize a variety of collaborative presentation and interaction tools, such as Prezi, and wireless screen-mirroring via an AppleTV connected to a large screen display. Professional and academic rigor can be achieved by requiring stakeholders and/or students to annotate their content using accepted referencing styles, yet turning this into a collaborative curation activity via creating shared Mendeley or Zotero libraries for example. Specific activities will depend upon each individual's’ context, and should be negotiable, however the collaborative element of such projects needs to be clearly defined, as stakeholders and/or students experience of being active members within an authentic professional and/or academic global community of practice is one of the goals of such projects. Requirements Participants will need to bring: References Cochrane, T., Antonczak, L., & Wagner, D. (2013). Post Web 2.0 pedagogy: from student-generated content to international co-production enabled by mobile social media. International Journal of Mobile and Blended Learning, 5, in pre-print. Cochrane, T., & Antonczak, L. (2013, 18 September). Mobile Social Media as a Catalyst For Creative Pedagogy. Paper presented at the EC-TEL 2013 Eigth European conference on technology enhanced learning: Scaling up learning for sustained impact, Paphos, Cyprus. Cochrane, T., Antonczak, L., & Wagner, D. (2012, 15-18 October). Heutagogial approaches to mlearning: from student-generated content to international co-production. Paper presented at the Mlearn 2012: the 11th World Conference on Mobile and Contextual Learning, Helsinki Congress Paasitorni, Helsinki, Finland. Keegan, H. (2010, 15 June). Mobile Films: Learning through discontinuity. Blog posted to http://heloukee.wordpress.com/2010/05/29/usgfhks/ Keegan, H., Bell, F., Fraser, J., & Clay, J. (2010). Guerilla narratives of personal media creation, public media sharing:: a 21st century show and tell. Paper presented at the Association for Learning Technology: ALTC2010. Retrieved from http://altc2010.alt.ac.uk/talks/15004
T3. Agent Technology and the Internet of Things
In the tutorial, we will use the definition of an agent as given by Wooldridge and Jennings: “An agent is an encapsulated computer system that is situated in some environment and that is capable of flexible, autonomous action in that environment in order to meet its design objectives.” The second part of this tutorial will give a short overview of IoT. Three type of classification of IoT devices will be presented.
Finally the usage of agent technology in IoT will be presented. The concept of a life cycle agent will be the main focus of this part of the tutorial. The different roles that can be played by this type of agent during the lifecycle of a device are presented and discussed. The different parts of the lifecycle are:
In some cases, more elaborated details of the roles are given, based on examples that are already implemented by our research team. At the end of the tutorial, there will be time for discussion and questions. |
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