Shape descriptors and statistical classification on areal topography data for tile inspection in tessellated surfaces

Verification of conformance to design specifications in production, and identification of defects related to wear or other damage during maintenance, are key metrological aspects that must be addressed for micro-scale tessellated surfaces. A new algorithmic approach is presented that operates on topography datasets as obtained by areal topography instruments. The approach combines segmentation algorithms with a novel implementation of the angular radial transform, originally adopted by the MPEG-7 standard, to implement shape descriptors and associated similarity metrics. Applications to the inspection and verification of laser-manufactured micro-embossing topographies are illustrated. The topographies are first segmented to extract the individual tiles; the tiles are then encoded through shape descriptors. Principal component analysis and cluster analysis are used to investigate the behaviour of the angular radial transform coefficients. Finally, an algorithmic classifier based on supervised learning (k-nearest neighbours) is implemented and shown to be effective at identifying defects and at discriminating between defect types

An Investment Framework To Help Equity Financiers Select Tech SMEs In Malaysia

The research is geared towards understanding the development cycle of technology based companies especially in the small and medium sized sector and refer to a model to help private investors in Malaysia evaluate business proposals to make funding decisions. There is always confusión when technology entrepreneurs want to evaluate the right investors to approach for fund, becuase not all specialize in funding start-ups and risky ventures. Indept investigation of past research indicates that not much have been written on venture capital financing of small and medium sized companies in Malaysia, and this work aims to shed more light and to add to literature on venture capital. Past research have failed to recommend appropriate and a simplified framework for growth firms.However, there is need to understand the relationship between investment evaluation criteria and the development cycle.The research focusses on secondary sources available from literatures on venture capital activities. Findings from this research will be of interest to investors, investees, academics and government

Assessment of surface topography modifications through feature-based registration of areal topography data

Surface topography modifications due to wear or other factors are usually investigated by visual and microscopic inspection, and – when quantitative assessment is required – through the computation of surface texture parameters. However, the current state-of-the-art areal topography measuring instruments produce detailed, areal reconstructions of surface topography which, in principle, may allow accurate comparison of the individual topographic formations before and after the modification event. The main obstacle to such an approach is registration, i.e. being able to accurately relocate the two topography datasets (measured before and after modification) in the same coordinate system. The challenge is related to the measurements being performed in independent coordinate systems, and on a surface which, having undergone modifications, may not feature easily-identifiable landmarks suitable for alignment. In this work, an algorithmic registration solution is proposed, based on the automated identification and alignment of matching topographic features. A shape descriptor (adapted from the scale invariant feature transform) is used to identify landmarks. Pairs of matching landmarks are identified by similarity of shape descriptor values. Registration is implemented by resolving the absolute orientation problem to align matched landmarks. The registration method is validated and discussed through application to simulated and real topographies selected as test cases

A Conceptualised Approach towards Building a Growth Model for Venture Capitalists Financing of TBFs

The purpose of this study is to develop a conceptual framework towards building a model for venture capitalists to evaluate investments in technology based firms, considering them from their growth stage. Fast growth entrepreneurs need this model to determine the right investors to approach, since not all specialize in funding early stage high risk companies. Previous studies revealed that major problems TBF owners face is identifying financial institutions to approach for funding, and what criteria that financial institutions used to evaluate the technologies? And also, several capital are lying idle with retired and successful individuals who also encounter difficulty of identifying the right companies to fund, because they do not possess the requisite skills to evaluate businesses based on their proposals. Past research have failed to represent the growth stages of the TBFs in a model such as this, which makes it easier for interest parties to adopt when evaluating thousands of business proposals, though they all mentioned the various growth stages except the Death stage mentioned in this study. Literature review is from secondary sources such as journals, textbooks, e-journals, websites, newspaper articles, online materials, personal discussions with colleagues. The findings of this study are important because it contributes more insights to the academic research of financing technology based firms, and call for further research on presenting the growth stages using graphical approach. Furthermore, the result of this research will be useful to link theory to practice

Technology Based Firm's Financing: An Operational Model For Malaysia

Venture capital (VC) as a method of funding technology based firms (TBFs) is a concept which emerged from the United States of America since over 30 years and has spread tremendously across the world. This concept has gained considerable awareness in Malaysia since the early 1990’s when goverment established the first venture capital company to promote and accelerate the development of the venture capital concept and also encourage the commercialization of technology based products through the management of the technology transfer funds. Due to the difficulty technology based firms owners go through in the process of growing their innovations particularly during the initial phase of growth of their businesses their is need to encourage financial managers to take up equity stakes and help nurture technology based firms to maturity. As a result of the perception in some quarters that their is a dearth of investments in the Malaysian VC industry which they opined has contributed to the slow pace of growth recorded in the industry despite huge government financial support. This study adopts a grounded theory approach (within-method triangulation) to collect data from 34 respondents in the industry. The model proposed in this study will benefit stakeholders to evaluate technology based firms

Combined inkjet printing and infrared sintering of silver nanoparticles using a swathe-by-swathe and layer-by-layer approach for 3-dimensional structures

Despite the advancement of additive manufacturing (AM)/3-dimensional (3D) printing, single-step fabrication of multifunctional parts using AM is limited. With the view of enabling multifunctional AM (MFAM), in this study, sintering of metal nanoparticles was performed to obtain conductivity for continuous line inkjet printing of electronics. This was achieved using a bespoke three dimensional (3D) inkjet-printing machine, JETx®, capable of printing a range of materials and utilizing different post processing procedures to print multi-layered 3D structures in a single manufacturing step. Multiple layers of silver were printed from an ink containing silver nanoparticles (AgNPs) and infra-red sintered using a swathe-by-swathe (SS) and layer-by-layer sintering (LS) regime. The differences in the heat profile for the SS and LS was observed to influence the coalescence of the AgNPs. Void percentage of both SS and LS samples was higher towards the top layer than the bottom layer due to relatively less IR exposure in the top than the bottom. The results depicted a homogeneous microstructure for LS of AgNPs and showed less deformation compared to the SS. Electrical resistivity of the LS tracks (13.6 ± 1μΩ cm) was lower than the SS tracks (22.5 ± 1 μΩ cm). This study recommends the use of LS method to sinter the AgNPs to obtain a conductive track in 25% less time than SS method for MFAM

Comparison of normalization methods for differential gene expression analysis in RNA-Seq experiments: A matter of relative size of studied transcriptomes

In recent years, RNA-Seq technologies became a powerful tool for transcriptome studies. However, computational methods dedicated to the analysis of high-throughput sequencing data are yet to be standardized. In particular, it is known that the choice of a normalization procedure leads to a great variability in results of differential gene expression analysis. The present study compares the most widespread normalization procedures and proposes a novel one aiming at removing an inherent bias of studied transcriptomes related to their relative size. Comparisons of the normalization procedures are performed on real and simulated data sets. Real RNA-Seq data sets analyses, performed with all the different normalization methods, show that only 50% of significantly differentially expressed genes are common. This result highlights the influence of the normalization step on the differential expression analysis. Real and simulated data sets analyses give similar results showing 3 different groups of procedures having the same behavior. The group including the novel method named “Median Ratio Normalization” (MR N) gives the lower number of false discoveries. Within this group the MR N method is less sensitive to the modification of parameters related to the relative size of transcriptomes such as the number of down- and upregulated genes and the gene expression levels. The newly proposed MR N method efficiently deals with intrinsic bias resulting from relative size of studied transcriptomes. Validation with real and simulated data sets confirmed that MR N is more consistent and robust than existing methods

Decision making process in the commercialization of University patent in Malaysia

Commercialization of university patent has become a major issue around the world, where many universities are moving into technology and entrepreneurial based universities. Even though the university possessed a number of patents, still a big portion of them are yet to be commercialized. This may be as a result of the complexity of the process involved and also the commitment of the parties involved in the decision making process. The purpose of this research is to find out how the commercialization process is done and understand why just a few of the university patents are commercialized. The study uses qualitative method incorporating a case study approach. Interviews were conducted with the relevant respondents from faculty of mechanical engineering, faculty of chemical engineering and natural resources, faculty of science and faculty of electrical engineering who have registered their inventions with Research Management Centre (RMC) of University Teknologi Malaysia (UTM). The study finds that the commercialisation process is influenced by: first, the motivation and opportunity recognition of the inventor and industry; second, the royalties and funding opportunity; and lastly, and most importantly, the role played by the RMC and Innovation and Commercialization Centre (ICC) in the whole process. The study concludes with suggestions on how the decision making process in commercialising university patents could effectively be carried out. Further study should adopt multiple cases from two or more universities and could also consider patents that have not been exploited

Surface characterisation with light scattering and machine learning

Light scattering technology has been intensively investigated for surface measurement [1, 2]. However, most of developments have focused on the estimation of roughness indicators via area integrating methods, while, due to the high nonlinearity of the scattering process, few have addressed the challenge of reconstructing the actual topography, which implies solving a more complex inverse problem. In this study, rather than attempting to obtain a full reconstruction of surface topography from light scattering data, a novel approach is proposed to use light scattering information combined with machine learning to discriminate amongst different topographies. This is useful not only to compare surfaces, but also to automatically detect any type of undesired variation in manufacturing, e.g. the appearance of defects, or any other type of drift. The preliminary solution presented here operates on 2D geometry (topography profiles) and 2D light scattering far fields, investigating performance and behaviour purely via simulation. First, virtual models of different classes of surface topographies are artificially generated and labelled. Then, the far field scattering signals are obtained by simulation under different conditions of incident light through a boundary element method (BEM) [3, 4]. The scattering signals are used as the training datasets for a machine learning system, based on neural networks (NNs) [5], to implement an automated multiclass classifier. With the trained classifier, new observed surfaces can be classified with high accuracy using the associated far field scattering result. Preliminary experiments have been conducted to characterise three types of grating surfaces (blaze, sinusoidal and square gratings). The NN was designed as a three-layer densely connected network. In the experiment, 3300 datasets (3000 for training, 300 for testing) were used, consisting of gratings with different spacings. For the case studies, the accuracy of classification (number of correct predictions over number of total predictions) was higher than 99%. The results demonstrate that the proposed method is effective for discrimination of surfaces classes. For future work, the proposed method will be verified with scattering measurements of real surfaces. The method will also be implemented for defect detection in different kinds of surfaces and a 3D version of BEM model will be developed and utilised for characterisation of 3D surfaces