A system development methodology for embedded applications

In recent years, Singapore’s manufacturing sector has contributed more than a quarter of the total Gross Domestic Product (GDP) and has established global leadership positions in several manufacturing areas such as electronics, Information Technology (IT) and industrial automation. The Singapore Economic Review Committee (ERC) recommendation states that “software and embedded systems that drive products are one of the most important technologies for the manufacturing sector. “ With the increasing adoption of automated and intelligent products, embedded systems have emerged as a crucial technology for Singapore. However, the development of embedded applications is not a trivial undertaking as it can usually involve multi-discipline parties and different application platforms. Most embedded application developments use either vendor specific or desktop based methodologies. Vendor specific methodologies constrain the company to rely on the specific vendor's solutions, whereas desktop-based methodologies are not well suited to embedded application development. Therefore, this research aims to develop a standard-based system development methodology for embedded applications. The research programme comprises 5 stages. The first stage reviews the existing system development methodologies for embedded applications. The next stage formulates the proposed conceptual methodology followed by the development of the proof-of-concept tool to demonstrate the merits of the proposed approach. The methodology is then tested and evaluated respectively by using industrial experiments and feedback from a workshop. The final stage refines the methodology based on the feedback and presents the final system development methodology. The research has provided a sound foundation which future research in methodology for embedded applications to develop further.Eng

A system development methodology for embedded applications

In recent years, Singapore’s manufacturing sector has contributed more than a quarter of the total Gross Domestic Product (GDP) and has established global leadership positions in several manufacturing areas such as electronics, Information Technology (IT) and industrial automation. The Singapore Economic Review Committee (ERC) recommendation states that “software and embedded systems that drive products are one of the most important technologies for the manufacturing sector. “ With the increasing adoption of automated and intelligent products, embedded systems have emerged as a crucial technology for Singapore. However, the development of embedded applications is not a trivial undertaking as it can usually involve multi-discipline parties and different application platforms. Most embedded application developments use either vendor specific or desktop based methodologies. Vendor specific methodologies constrain the company to rely on the specific vendor's solutions, whereas desktop-based methodologies are not well suited to embedded application development. Therefore, this research aims to develop a standard-based system development methodology for embedded applications. The research programme comprises 5 stages. The first stage reviews the existing system development methodologies for embedded applications. The next stage formulates the proposed conceptual methodology followed by the development of the proof-of-concept tool to demonstrate the merits of the proposed approach. The methodology is then tested and evaluated respectively by using industrial experiments and feedback from a workshop. The final stage refines the methodology based on the feedback and presents the final system development methodology. The research has provided a sound foundation which future research in methodology for embedded applications to develop further.Eng

Transcriptional Gene Regulatory Network Reconstruction through Cross Platform Gene Network Fusion

Microarray gene expression data is used to model differential activity in Gene Regulatory Networks (GRN) to elucidate complex cellular processes, though network modeling is susceptible to errors due to both noisy nature of gene expression data and platform bias. This intuitively provided the motivation for the development of an innovative technique, which effectively integrates GRN using cross-platform data to minimize the two aforementioned effects. This paper presents a GRN integration (GeNi) framework that fuses cross-platform GRN to remove platform and experimental bias using the Dempster Shafer Theory of Evidence. The proposed model estimates gene co-regulation strength by using mutual information and removes spurious co-regulations through data processing inequality. The method automatically adapts to the data distribution using Belief theory, which does not require a preset threshold to accept co-regulated links. GeNi is applied to identify common cancer-related regulatory links in ten different datasets generated by different microarray platforms including cDNA and Affymetrix arrays. Experimental results demonstrate that GeNi can be effectively applied for GRN reconstruction and cross-platform gene network fusion for any gene expression data

Coordinating multiple agents via reinforcement learning

In this paper, we focus on the coordination issues in a multiagent setting. Two coordination algorithms based on reinforcement learning are presented and theoretically analyzed. Our Fuzzy Subjective Task Structure (FSTS) model is described and extended so that the information essential to the agent coordination is effectively and explicitly modeled and incorporated into a general reinforcement learning structure. When compared with other learning based coordination approaches, we argue that due to the explicit modeling and exploitation of the interdependencies among agents, our approach is more efficient and effective, thus widely applicable.