Special issue on design and programming of signal processors for multimedia communication:Special issue on design and programming of signal processors for multimedia communication

The technical challenges in the design and programming of signal processors for multimedia communication are discussed. The development of terminal equipment to meet such demand presents a significant technical challenge, considering that it is highly desirable that the equipment be cost effective, power efficient, versatile, and extensible for future upgrades. The main challenges in the design and programming of signal processors for multimedia communication are, general-purpose signal processor design, application-specific signal processor design, operating systems and programming support and application programming. The size of FFT is programmable so that it can be used for various OFDM-based communication systems, such as digital audio broadcasting (DAB), digital video broadcasting-terrestrial (DVB-T) and digital video broadcasting-handheld (DVB-H). The clustered architecture design and distributed ping-pong register files in the PAC DSP raise new challenges of code generation.

Extending BPM Environments of Your Choice with Performance Related Decision Support

What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools process optimizations or a combination Of Such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.

Semi-formal Models to Support Program Development: Autonomic Management within Component Based Parallel and Distributed Programming

Functional and non-functional concerns require different programming effort, different techniques and different methodologies when attempting to program efficient parallel/distributed applications. In this work we present a "programmer oriented" methodology based on formal tools that permits reasoning about parallel/distributed program development and refinement. The proposed methodology is semi-formal in that it does not require the exploitation of highly formal tools and techniques, while providing a palatable and effective support to programmers developing parallel/distributed applications, in particular when handling non-functional concerns.

Enhancing the teaching of professional practice and key skills in engineering through the use of computer animation

This paper presents some observations on how computer animation was used in the early years of a degree program in Electrical and Electronic Engineering to enhance the teaching of key skills and professional practice. This paper presents the results from two case studies. First, in a first year course which seeks to teach students how to manage and report on group projects in a professional way. Secondly, in a technical course on virtual reality, where the students are asked to use computer animation in a way that subliminally coerces them to come to terms with the fine detail of the mathematical principles that underlie 3D graphics, geometry, etc. as well as the most significant principles of computer architecture and software engineering. In addition, the findings reveal that by including a significant element of self and peer review processes into the assessment procedure students became more engaged with the course and achieved a deeper level of comprehension of the material in the course.

A hierarchical multiclass support vector machine incorporated with holistic triple learning units

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.

Introduction to Domain-Driven Data Mining Special Section

In the last decade, data mining has emerged as one of the most dynamic and lively areas in information technology. Although many algorithms and techniques for data mining have been proposed, they either focus on domain independent techniques or on very specific domain problems. A general requirement in bridging the gap between academia and business is to cater to general domain-related issues surrounding real-life applications, such as constraints, organizational factors, domain expert knowledge, domain adaption, and operational knowledge. Unfortunately, these either have not been addressed, or have not been sufficiently addressed, in current data mining research and development.Domain-Driven Data Mining (D3M) aims to develop general principles, methodologies, and techniques for modeling and merging comprehensive domain-related factors and synthesized ubiquitous intelligence surrounding problem domains with the data mining process, and discovering knowledge to support business decision-making. This paper aims to report original, cutting-edge, and state-of-the-art progress in D3M. It covers theoretical and applied contributions aiming to: 1) propose next-generation data mining frameworks and processes for actionable knowledge discovery, 2) investigate effective (automated, human and machine-centered and/or human-machined-co-operated) principles and approaches for acquiring, representing, modelling, and engaging ubiquitous intelligence in real-world data mining, and 3) develop workable and operational systems balancing technical significance and applications concerns, and converting and delivering actionable knowledge into operational applications rules to seamlessly engage application processes and systems.

The use of process simulation methods in support of organisational learning in availability contracting

Traditional business models in the aerospace industry are based on a conventional supplier to customer relationship based on the design, manufacture and subsequent delivery of the physical product. Service provision, from the manufacturer's perspective, is typically limited to the supply of procedural documentation and the provision of spare parts to the end user as the product passes through the latter stages of its intended lifecycle. Challenging economic and political conditions have resulted in end users re-structuring their core business activities, particularly in the defence sector. This has resulted in the need for original equipment manufacturers (OEMs) to integrate and manage support service activities in partnership with the customer to deliver platform availability. This improves the probability of commercial sustainability for the OEM through shared operational risks while reducing the cost of platform ownership for the customer. The need for OEMs to evolve their design, manufacture and supply strategies by focusing on customer requirements has revealed a need for reconstruction of traditional internal behaviours and design methodologies. Application of organisational learning is now a well recognised principle for innovative companies to achieve long term growth and sustained technical development, and hence, greater market command. It focuses on the process by which the organisation's knowledge and value base changes, leading to improved problem solving ability and capacity for action. From the perspective of availability contracting, knowledge and the processes by which it is generated, used and retained, become primary assets within the learning organisation. This paper will introduce the application of digital methods to asset management by demonstrating how the process of learning can benefit from a digital approach, how product and process design can be integrated within a virtual framework and finally how the approach can be applied in a service context.

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.