Towards epistemic autonomy in adaptive biomimetic middleware for cooperative sensornets

University of Technology, Sydney. Faculty of Engineering and Information Technology.The importance of studying biomimetic models of software infrastructure for sensornet systems lies in the fact that they are not entirely formal models and thus have to cover a range of issues of epistemic autonomy as well as linguistic and mental adaptation. This adaptation considers the context of software ability to reflect upon the verifiability and validity of its actions and measurements. This research elucidates and explores epistemological consequences of embodying biological autonomic patterns in software architectural models. Autonomy in software systems is a complex issue that raises many fundamental inquiries. The proposal is to initially concentrate on transformations of biological paradigms into epistemological queries and then adapt suitable biomimetic mechanisms into the development of software structure and ethology. Such methodology has proven to be very successful in the design of many engineering systems. The approach leads to a better understanding of the ontology of biomimetic patterns in software as well as a confirmation of requirements validity and design verifiability of autonomous software systems. In a dynamic, cooperative but often hostile environment, a software system infrastructure requires autonomic abilities to execute its normal operations, detect faults and perform necessary recovery actions without the need for external intervention. We approach this problem from the point of view of cognitive and mimetic systems research. The simplest way to make an autonomous and adaptive sensornet system is to include a hierarchy of layers in its middleware, not only to monitor activities of its components but to learn and adapt new behavioural patterns of these components in a changing environment. There are situations, however, where the components will not be able to adapt, learn new behaviour and evolve by themselves. For instance, these may not have yet encountered the new situation while others already have. A solution to this problem is to distribute the new behaviour to neighbouring elements via direct and indirect stigmergy mechanisms so that collaborating components can mutually improve their individual and team performance. The main objective is to disallow distribution of multiple versions of the software components and rather allow each software component to acquire and share with others, new “skills”. The components have to compare/verify these new behavioural patterns against their own set of beliefs, desires and intentions. In this thesis we intend to present simulations to test the learning capability of biomimetic algorithms, build a proof-of-concept middleware solution and demonstrate that such systems can not only adapt and evolve but they are robust and highly interoperable (co-operative). The thesis also assesses the suitability of various biomimetic design patterns and algorithms for building autonomic software infrastructure systems for cooperative networked agents

Detection of Microplastics Using Machine Learning

© 2019 IEEE. Monitoring the presence of micro-plastics in human and animal habitats is fast becoming an important research theme due to a need to preserve healthy ecosystems. Microplastics pollute the environment and can represent a serious threat for biological organisms including the human body, as they can be inadvertently consumed through the food chain. To perceive and understand the level of microplastics pollution threats in the environment there is a need to design and develop reliable methodologies and tools that can detect and classify the different types of the microplastics. This paper presents results of our work related to exploration of methods and techniques useful for detecting suspicious objects in their respective ecosystem captured in hyperspectral images and then classifying these objects with the use of Neural Networks technique

Enhancement of surgical training practice with the spring tensor heuristic model

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 virtualised 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

Learning data engineering: Creating IoT apps using the node-RED and the RPI technologies

© 2017 IEEE. This paper demonstrates the suitability and the practicality of using the advanced open source tools such as the Raspberry Pi and the Node-RED for teaching and learning in the Internet of Things (IOT) subject within a newly created major of Data Engineering in the Faculty of Engineering and IT at University of Technology, Sydney. Understanding and practicing of the Internet of Things largely depend on the high availability of tools, their low cost, and ease of use that can accelerate learning processes. This paper demonstrates relatively uncomplicated practical lab exercises involving the Raspberry Pi hardware, firmware and the Node-RED programming environment that students can execute to stimulate their learning, understanding of the Internet of Things technology and acquire fundamental data engineering skills

Automated tablet quality assurance and identification for hospital pharmacies

The tablet quality checking and identification in hospital pharmacies is done manually and does not use any automated solution. Manual sorting and handling makes this activity laborious and error-prone. This paper describes a low cost solution that is characterised by a small size of the infrastructure involved. Discussed are design and implementation details of Tablet Inspection System based on Machine Vision. The described process uses a dedicated sequence of operation to perform dispensing, scanning and sorting using mini factory setup. Machine Vision System uses a novel Genetic Evolution algorithm. The algorithm provides robust and scalable output. Due to its versatile nature and easy shape recognition ability the approach can be easily adapted to a large variety of medical tablets. The proposed solution attempts to follow the concept of single objective with multiple optima in GA that is designed to scan multiple number of tablets in one cycle of operation

Generalized Spring Tensor Algorithms: with Workflow Scheduling Applications in Cloud Computing

In Cloud Computing, designing an efficient workflow scheduling algorithm is considered as a main goal. Load balancing is one of the most sophisticated methodologies, which can optimize workflow scheduling by distributing the load evenly among available resources. A well-designed load balancing algorithm has significant impact on performance and output in Cloud Computing. Therefore, designing robust load balancing techniques to manage the networks' load has always been a priority. Researchers have proposed and examined different load balancing methods; there is, however, a large knowledge gap in adopting an efficient load balancing algorithm in the Cloud system. This paper describes how a generalized spring tensor, an evolutionary algorithm with mathematical apparatus, can be utilized for a more efficient and effective load management in Cloud Computing. Considering the fluctuation and magnitude of the load, a novel application of workflow scheduling is investigated in the context of various mathematical patterns. The preliminary results of the research show that defining the dependency ratio between workflow tasks in Cloud Computing, results in better resource management, maximized performance and minimized response time while dealing with customer's requests

Adoption of emerging technologies established on Comprehensive capability maturity model framework: A new practical model

Copyright © 2016 International Business Information Management Association Organizations have adopted information communications technologies (ICT) at various time lines driven by business needs or due to technologies evolution. This has given raise to disparate systems based on various technologies and spaghetti architecture. This paper discusses why it's critical for organization to adopt the emerging technologies. The reasons behind the current state of the architecture. Suggests how organizations can make use of, The open group architecture framework (TOGAF) to develop enterprise architecture. Then they paper emphasis on the importance of Capability Maturity Assessment. The current practice of Capability Maturity Assessment by TOGAF, its drawbacks. Then based on the practical experiences, proposes Comprehensive Capability Maturity Model Assessment (CCMM) that covers across the phases of Architecture development method that provides the assessment of maturity to be more realistic

That flipping classroom - Getting engineering students to be consciously competent on their own

© 2015 IEEE. This paper is inspired by the Keynote Speech which I gave at ITHET 2014 in York in September 2014. The material was very well received, and it seemed appropriate to offer it for publication in the proceedings of ITHET 2015

Teaching multidisciplinary engineering using concepts and technology of WSN

This paper discusses teaching and learning strategies of Wireless Sensor Networks technology in a new postgraduate subject run at the Faculty of Engineering and IT, University of Technology, Sydney. The aim is to present the role of using practice based and multidisciplinary methodologies in the context of new ICT technologies. This includes shared experiences, observations and common problems experienced in teaching new concepts and paradigms, standards, protocols and algorithms, embedded systems and sensor technologies. The theory of WSN is applied as a driver of system development for the group projects that students undertake in the subject. © 2012 IEEE

Deployment of an agent-based SANET architecture for healthcare services

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