Data processing of physiological sensor data and alarm determination utilising activity recognition

Current physiological sensors are passive and transmit sensed data to Monitoring centre (MC) through wireless body area network (WBAN) without processing data intelligently. We propose a solution to discern data requestors for prioritising and inferring data to reduce transactions and conserve battery power, which is important requirements of mobile health (mHealth). However, there is a problem for alarm determination without knowing the activity of the user. For example, 170 beats per minute of heart rate can be normal during exercising, however an alarm should be raised if this figure has been sensed during sleep. To solve this problem, we suggest utilising the existing activity recognition (AR) applications. Most of health related wearable devices include accelerometers along with physiological sensors. This paper presents a novel approach and solution to utilise physiological data with AR so that they can provide not only improved and efficient services such as alarm determination but also provide richer health information which may provide content for new markets as well as additional application services such as converged mobile health with aged care services. This has been verified by experimented tests using vital signs such as heart pulse rate, respiration rate and body temperature with a demonstrated outcome of AR accelerometer sensors integrated with an Android app.

Enhancement of sensor data transmission by inference and efficient data processing

When wearable and personal health device and sensors capture data such as heart rate and body temperature for fitness tracking and health services, they simply transfer data without filtering or optimising. This can cause over-loading to the sensors as well as rapid battery consumption when they interact with Internet of Things (IoT) networks, which are expected to increase and de-mand more health data from device wearers. To solve the problem, this paper proposes to infer sensed data to reduce the data volume, which will affect the bandwidth and battery power reduction that are essential requirements to sensor devices. This is achieved by applying beacon data points after the inferencing of data processing utilising variance rates, which compare the sensed data with ad-jacent data before and after. This novel approach verifies by experiments that data volume can be saved by up to 99.5% with a 98.62% accuracy. Whilst most existing works focus on sensor network improvements such as routing, operation and reading data algorithms, we efficiently reduce data volume to reduce band-width and battery power consumption while maintaining accuracy by implement-ing intelligence and optimisation in sensor devices.

Curriculum history of business computing in Victorian tertiary institutions from 1960-1985

Fifty years ago there were no stored-program electronic computers in the world. Even thirty years ago a computer was something that few organisations could afford, and few people could use. Suddenly, in the 1960s and 70s, everything changed and computers began to become accessible. Today* the need for education in Business Computing is generally acknowledged, with each of Victoria's seven universities offering courses of this type. What happened to promote the extremely rapid adoption of such courses is the subject of this thesis. I will argue that although Computer Science began in Australia's universities of the 1950s, courses in Business Computing commenced in the 1960s due to the requirement of the Commonwealth Government for computing professionals to fulfil its growing administrative needs. The Commonwealth developed Programmer-in-Training courses were later devolved to the new Colleges of Advanced Education. The movement of several key figures from the Commonwealth Public Service to take up positions in Victorian CAEs was significant, and the courses they subsequently developed became the model for many future courses in Business Computing. The reluctance of the universities to become involved in what they saw as little more than vocational training, opened the way for the CAEs to develop this curriculum area.

Data pre-processing for more effective gene clustering

The high-throughput experimental data from the new gene microarray technology has spurred numerous efforts to find effective ways of processing microarray data for revealing real biological relationships among genes. This work proposes an innovative data pre-processing approach to identify noise data in the data sets and eliminate or reduce the impact of the noise data on gene clustering, With the proposed algorithm, the pre-processed data sets make the clustering results stable across clustering algorithms with different similarity metrics, the important information of genes and features is kept, and the clustering quality is improved. The primary evaluation on real microarray data sets has shown the effectiveness of the proposed algorithm.

An approach to filtering RFID data streams

RFID is gaining significant thrust as the preferred choice of automatic identification and data collection system. However, there are various data processing and management problems such as missed readings and duplicate readings which hinder wide scale adoption of RFID systems. To this end we propose an approach that filters the captured data including both noise removal and duplicate elimination. Experimental results demonstrate that the proposed approach improves missed data restoration process when compared with the existing method.

A general communication cost optimization framework for big data stream processing in geo-distributed data centers

With the explosion of big data, processing large numbers of continuous data streams, i.e., big data stream processing (BDSP), has become a crucial requirement for many scientific and industrial applications in recent years. By offering a pool of computation, communication and storage resources, public clouds, like Amazon's EC2, are undoubtedly the most efficient platforms to meet the ever-growing needs of BDSP. Public cloud service providers usually operate a number of geo-distributed datacenters across the globe. Different datacenter pairs are with different inter-datacenter network costs charged by Internet Service Providers (ISPs). While, inter-datacenter traffic in BDSP constitutes a large portion of a cloud provider's traffic demand over the Internet and incurs substantial communication cost, which may even become the dominant operational expenditure factor. As the datacenter resources are provided in a virtualized way, the virtual machines (VMs) for stream processing tasks can be freely deployed onto any datacenters, provided that the Service Level Agreement (SLA, e.g., quality-of-information) is obeyed. This raises the opportunity, but also a challenge, to explore the inter-datacenter network cost diversities to optimize both VM placement and load balancing towards network cost minimization with guaranteed SLA. In this paper, we first propose a general modeling framework that describes all representative inter-task relationship semantics in BDSP. Based on our novel framework, we then formulate the communication cost minimization problem for BDSP into a mixed-integer linear programming (MILP) problem and prove it to be NP-hard. We then propose a computation-efficient solution based on MILP. The high efficiency of our proposal is validated by extensive simulation based studies.

Agents and stream data mining: a new perspective

Many organizations struggle with the massive amount of data they collect. Today, data does more than serve as the ingredients for churning out statistical reports. They help support efficient operations in many organizations, and to some extent, data provide the competitive intelligence organizations need to survive in today's economy. Data mining can't always deliver timely and relevant results because data are constantly changing. However, stream-data processing might be more effective, judging by the Matrix project.

A framework for material management in the building demolition industry

Conventional mechanical building demolition produces numerous solid wastes, most of which are sent to landfill directly and severely degrade the living environment. Just-in-time building demolition has been developed recently with a management strategy to facilitate waste reuse. Procurement management plays a significant role in just-in-time building demolition. In particular, the demolition tendering selection needs to consider contractors' environmental performance in addition to project costs. Moreover, the flow of building materials in a demolition project may be regarded as a supply chain involving the building owner, demolition contractor and material demanders. This paper develops a framework for salvaged materials management in the emerging demolition industry. The research is to promote the recycling and reuse of building demolition materials in order to achieve better environmental and financial performance for building demolition projects.

A matrix-based model for web page community construction and more

The rapid development of network technologies has made the web a huge information source with its own characteristics. In most cases, traditional database-based technologies are no longer suitable for web information processing and management. For effectively processing and managing web information, it is necessary to reveal intrinsic relationships/structures among concerned web information objects such as web pages. In this work, a set of web pages that have their intrinsic relationships is called a web page community. This paper proposes a matrix-based model to describe relationships among concerned web pages. Based on this model, intrinsic relationships among pages could be revealed, and in turn a web page community could be constructed. The issues that are related to the application of the model are deeply investigated and studied. The concepts of community and intrinsic relationships, as well as the proposed matrix-based model, are then extended to other application areas such as biological data processing. Some application cases of the model in a broad range of areas are presented, demonstrating the potentials of this matrix-based model.

Teaching practices for effective cooperative learning in an online learning environment (OLE)

As a teaching practice the application of cooperative learning in tertiary education can present unique challenges for both the practitioner and her students. Mastering this teaching approach requires careful planning, design and implementation for effective deployment in a face-to-face setting. In this setting the success of the cooperative learning approach has been demonstrated. The complexity is significantly increased by additional variables such as the selection and application of technological teaching tools and the change in nature of existing variables including awareness of students' social and communication skills when applying this practice in an Online Learning Environment (OLE). In addition student acceptance of this e-learning approach to learning also needs to be carefully considered. The practitioner must be aware of these factors and have suitable methods in place to support collaboration and interaction between student groups to ensure the ultimate goal with regard to students' learning is achieved. This paper considers how cooperative learning can be combined effectively with these variables and factors of an OLE, and begins with the presentation of a conceptual framework to represent this relationship as a constructive teaching practice. It is anticipated that the conceptual framework would be applied by other practitioners to facilitate cooperative teaching within their OLE. To demonstrate the validity of the framework a case scenario is provided using an Information Technology (IT) undergraduate unit named 'IT Practice'. This is a wholly online unit where extensive participation by the students within small groups is a core requirement. The paper demonstrates the themes of designing curriculum and assessment, as well as flexible teaching strategies for learner diversity but primarily concentrates on managing an effective OLE; that is managing small groups in an online teaching environment.

Processing and mechanical properties of hollow sphere aluminum foams

Hollow sphere metallic foams are a new class of cellular material that possesses the attractive advantages of uniform cell size distribution and regular cell shape. These result in more predictable physical and mechanical properties than those of cellular materials with a random cell size distribution and irregular cell shapes. In the present study, single aluminum hollow spheres with three kinds of sphere wall thickness as 0.1 mm, 0.3 mm and 0.5 mm were processed by a new pressing method. Hollow sphere aluminum foam samples were prepared by bonding together single hollow spheres with simple cubic packing (SC) and body-centered cubic packing (BCC). Compressive tests were carried out to evaluate the deformation behaviors and mechanical properties of the hollow sphere aluminum foams. Effects of the sphere wall thickness and packing style on the mechanical properties were investigated.

Development of DAL and DAPL languages for building distributed applications

A common characteristic among parallel/distributed programming languages is that the one language is used to specify not only the overall organisation of the distributed application, but also the functionality of the application. That is, the connectivity and functionality of processes are specified within a single program. Connectivity and functionality are independent aspects of a distributed application. This thesis shows that these two aspects can be specified separately, therefore allowing application designers to freely concentrate on either aspect in a modular fashion. Two new programming languages have been developed for specifying each aspect. These languages are for loosely coupled distributed applications based on message passing, and have been designed to simplify distributed programming by completely removing all low level interprocess communication. A suite of languages and tools has been designed and developed. It includes the two new languages, parsers, a compilation system to generate intermediate C code that is compiled to binary object modules, a run-time system to create, manage and terminate several distributed applications, and a shell to communicate with the run-tune system. DAL (Distributed Application Language) and DAPL (Distributed Application Process Language) are the new programming languages for the specification and development of process oriented, asynchronous message passing, distributed applications. These two languages have been designed and developed as part of this doctorate in order to specify such distributed applications that execute on a cluster of computers. Both languages are used to specify orthogonal components of an application, on the one hand the organisation of processes that constitute an application, and on the other the interface and functionality of each process. Consequently, these components can be created in a modular fashion, individually and concurrently. The DAL language is used to specify not only the connectivity of all processes within an application, but also a cluster of computers for which the application executes. Furthermore, sub-clusters can be specified for individual processes of an application to constrain a process to a particular group of computers. The second language, DAPL, is used to specify the interface, functionality and data structures of application processes. In addition to these languages, a DAL parser, a DAPL parser, and a compilation system have been designed and developed (in this project). This compilation system takes DAL and DAPL programs to generate object modules based on machine code, one module for each application process. These object modules are used by the Distributed Application System (DAS) to instantiate and manage distributed applications. The DAS system is another new component of this project. The purpose of the DAS system is to create, manage, and terminate many distributed applications of similar and different configurations. The creation procedure incorporates the automatic allocation of processes to remote machines. Application management includes several operations such as deletion, addition, replacement, and movement of processes, and also detection and reaction to faults such as a processor crash. A DAS operator communicates with the DAS system via a textual shell called DASH (Distributed Application SHell). This suite of languages and tools allowed distributed applications of varying connectivity and functionality to be specified quickly and simply at a high level of abstraction. DAL and DAPL programs of several processes may require a few dozen lines to specify as compared to several hundred lines of equivalent C code that is generated by the compilation system. Furthermore, the DAL and DAPL compilation system is successful at generating binary object modules, and the DAS system succeeds in instantiating and managing several distributed applications on a cluster.

Learning from large data : bias, variance, sampling, and learning curves

One of the fundamental machine learning tasks is that of predictive classification. Given that organisations collect an ever increasing amount of data, predictive classification methods must be able to effectively and efficiently handle large amounts of data. However, it is understood that present requirements push existing algorithms to, and sometimes beyond, their limits since many classification prediction algorithms were designed when currently common data set sizes were beyond imagination. This has led to a significant amount of research into ways of making classification learning algorithms more effective and efficient. Although substantial progress has been made, a number of key questions have not been answered. This dissertation investigates two of these key questions. The first is whether different types of algorithms to those currently employed are required when using large data sets. This is answered by analysis of the way in which the bias plus variance decomposition of predictive classification error changes as training set size is increased. Experiments find that larger training sets require different types of algorithms to those currently used. Some insight into the characteristics of suitable algorithms is provided, and this may provide some direction for the development of future classification prediction algorithms which are specifically designed for use with large data sets. The second question investigated is that of the role of sampling in machine learning with large data sets. Sampling has long been used as a means of avoiding the need to scale up algorithms to suit the size of the data set by scaling down the size of the data sets to suit the algorithm. However, the costs of performing sampling have not been widely explored. Two popular sampling methods are compared with learning from all available data in terms of predictive accuracy, model complexity, and execution time. The comparison shows that sub-sampling generally products models with accuracy close to, and sometimes greater than, that obtainable from learning with all available data. This result suggests that it may be possible to develop algorithms that take advantage of the sub-sampling methodology to reduce the time required to infer a model while sacrificing little if any accuracy. Methods of improving effective and efficient learning via sampling are also investigated, and now sampling methodologies proposed. These methodologies include using a varying-proportion of instances to determine the next inference step and using a statistical calculation at each inference step to determine sufficient sample size. Experiments show that using a statistical calculation of sample size can not only substantially reduce execution time but can do so with only a small loss, and occasional gain, in accuracy. One of the common uses of sampling is in the construction of learning curves. Learning curves are often used to attempt to determine the optimal training size which will maximally reduce execution time while nut being detrimental to accuracy. An analysis of the performance of methods for detection of convergence of learning curves is performed, with the focus of the analysis on methods that calculate the gradient, of the tangent to the curve. Given that such methods can be susceptible to local accuracy plateaus, an investigation into the frequency of local plateaus is also performed. It is shown that local accuracy plateaus are a common occurrence, and that ensuring a small loss of accuracy often results in greater computational cost than learning from all available data. These results cast doubt over the applicability of gradient of tangent methods for detecting convergence, and of the viability of learning curves for reducing execution time in general.