Polydimethylsiloxane (PDMS)-based microfluidic channel with integrated commercial pressure sensors

The precise characterisation of boiling in microchannels is essential for the optimisation of applications requiring two phase cooling. In this paper polydimethylsiloxane (PDMS) is employed to make microchannels for characterising microboiling. In particular the material properties of PDMS facilitate rapid prototyping and its optical transparency provides the capability to directly view any fluid flow. The production of microchannels is complicated by the need to integrate custom made sensors. This paper presents a PDMS microfluidic device with integrated commercial pressure sensors, which have been used to perform a detailed characterisation of microboiling phenomena. The proposed approach of integrating commercial pressure sensors into the channel also has potential applications in a range of other microsystems

Indexing Metric Spaces for Exact Similarity Search

With the continued digitalization of societal processes, we are seeing an explosion in available data. This is referred to as big data. In a research setting, three aspects of the data are often viewed as the main sources of challenges when attempting to enable value creation from big data: volume, velocity and variety. Many studies address volume or velocity, while much fewer studies concern the variety. Metric space is ideal for addressing variety because it can accommodate any type of data as long as its associated distance notion satisfies the triangle inequality. To accelerate search in metric space, a collection of indexing techniques for metric data have been proposed. However, existing surveys each offers only a narrow coverage, and no comprehensive empirical study of those techniques exists. We offer a survey of all the existing metric indexes that can support exact similarity search, by i) summarizing all the existing partitioning, pruning and validation techniques used for metric indexes, ii) providing the time and storage complexity analysis on the index construction, and iii) report on a comprehensive empirical comparison of their similarity query processing performance. Here, empirical comparisons are used to evaluate the index performance during search as it is hard to see the complexity analysis differences on the similarity query processing and the query performance depends on the pruning and validation abilities related to the data distribution. This article aims at revealing different strengths and weaknesses of different indexing techniques in order to offer guidance on selecting an appropriate indexing technique for a given setting, and directing the future research for metric indexes

A feature based frequency domain analysis algorithm for fault detection of induction motors

10.1109/ICIEA.2011.5975545Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 201127-3