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1

Enhancement of Surgical Training Practice with the Spring Tensor Heuristic Model

Chiu C., Chaczko Z.

International Journal of Electronics and Telecommunications

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2013

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Vol. 59, No. 3

237-244

EN

The enhancement of surgical simulation tools is an important research study, to assist in the assessment and feedback of medical training practice. In this research, the Spring Tensor Model (STEM) has been used for laparoscopic end-effector navigation through obstacles and high-risk areas. The modelling of the surgical trainer as part of the laparoscopic simulator seeks to emulate the physical environment as a virtualized representation in the integrated infrastructure. Combining sensor network framework paradigms to a surgical knowledge-based construct demonstrates how STEM can enhance medical practice. The architectural hybridisation of the training framework has enabled the adaptation of STEM modelling techniques for a simulated laparoscopic training methodology. The primary benefit of the architecture is that this integration strategy has resulted in a seamless transition of the heuristic framework to be applied to surgical training.

2

Sensor-Actor Network Solution for Scalable Ad-hoc Sensor Networks

Chaczko Z., Chiu C., Aslanzadeh S., Dune T.

International Journal of Electronics and Telecommunications

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2012

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Vol, 58, No. 1

55-62

EN

Architects of ad-hoc wireless Sensor-Actor Networks (SANETS) face various problems and challenges. The main limitations relate to aspects such as the number of sensor nodes involved, low bandwidth, management of resources and issues related to energy management. In order for these networks to be functionally proficient, the underlying software system must be able to effectively handle unreliable and dynamic distributed communication, power constraints of wireless devices, failure of hardware devices in hostile environments and the remote allocation of distributed processing tasks throughout the wireless network. The solution must be solved in a highly scalable manner. This paper provides the requirements analysis and presents the design of a software system middleware that provides a scalable solution for ad-hoc sensor network infrastructure made of both stationary and mobile sensors and actuators.

3

N-Body Potential Interaction as a Cost Function in the Elastic Model for SANET Cloud Computing

Chaczko Z., Resconi G., Chiu C., Aslanzadeh S.

International Journal of Electronics and Telecommunications

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2012

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Vol, 58, No. 1

63-70

EN

Given a connection graph of entities that send and receive a flow of data controlled by effort and given the parameters, the metric tensor is computed that is in the elastic relational flow to effort. The metric tensor can be represented by the Hessian of the interaction potential. Now the interaction potential or cost function can be among two entities: 3 entities or 'N' entities and can be separated into two main parts. The first part is the repulsion potential the entities move further from the others to obtain minimum cost, the second part is the attraction potential for which the entities move near to others to obtain the minimum cost. For Pauli's model [1], the attraction potential is a functional set of parameters given from the environment (all the elements that have an influence in the module can be the attraction of one entity to another). Now the cost function can be created in a space of macro-variables or macro-states that is less of all possible variables. Any macro-variable collect a set of micro-variables or microstates. Now from the hessian of the macro-variables, the Hessian is computed of the micro-variables in the singular points as stable or unstable only by matrix calculus without any analytical computation - possible when the macro-states are distant among entities. Trivially, the same method can be obtained by a general definition of the macro-variable or macro-states and micro-states or variables. As cloud computing for Sensor-Actor Networks (SANETS) is based on the bonding concept for complex interrelated systems; the bond valence or couple corresponds to the minimum of the interaction potential V and in the SANET cloud as the minimum cost.

4

Deployment of an Agent-based SANET Architecture for Healthcare Services

Chiu C., Chaczko Z.

International Journal of Electronics and Telecommunications

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2011

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Vol. 57, No. 3

309-315

EN

This paper describes the adaptation of a computational technique utilizing Extended Kohonen Maps (EKMs) and Rao-Blackwell-Kolmogorov (R-B) Filtering mechanisms for the administration of Sensor-Actuator networks (SANETs). Inspired by the BDI (Belief-Desire-Intention) Agent model from Rao and Georgeff, EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize, while the Rao-Blackwell filtering mechanism reduces the external noise and interference in the problem set introduced through the self-organization process. Initial results demonstrate that a combinatorial approach to optimization with EKMs and Rao-Blackwell filtering provides an improvement in event trajectory approximation in comparison to standalone cooperative EKM processes to allow responsive event detection and optimization in patient healthcare.

5

Sensor Actor Network Modeling utilizing the Holonic Architectural Framework

Chiu C., Chaczko Z., Moses P.

International Journal of Electronics and Telecommunications

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2010

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Vol. 56, No. 1

49-54

EN

This paper discusses the results of utilizing advanced EKM modeling techniques to manage Sensor-Actor networks (SANETs) based upon the Holonic Architectural Framework. EKMs allow a quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize from a given input space to administer SANET routing and clustering functions with a control parameter space. Results demonstrate that in comparison to linear approximation techniques, indirect mapping with EKMs provide fluid control and feedback mechanisms by operating in a continuous sensory control space – thus enabling interactive detection and optimization of events in real-time environments.