Ye Chow Kuang

Real-Time Malaysian Sign Language Translation using Colour Segmentation and Neural Network

In this paper we present an automatic visual-based sign language translation system. Our proposed automatic sign-language translator provides a real-time English translation of the Malaysia SL. To date, there have been studies on sign language recognition based on visual approach (video camera). However, the emphasis on these works is limited to a small lexicon of sign language or solely focuses on fingerspelling, which takes different approaches respectively. In practical sense, fingerspelling is used if a word cannot be expressed via sign language. Our sign language translator can recognise both fingerspelling and sign gestures that involve static and motion signs. Trained neural networks are used to identify the signs to translate into English.

Gaussian Process Dynamical Models for hand gesture interpretation in Sign Language

Classifying human hand gestures in the context of a Sign Language has been historically dominated by Artificial Neural Networks and Hidden Markov Model with varying degrees of success. The main objective of this paper is to introduce Gaussian Process Dynamical Model as an alternative machine learning method for hand gesture interpretation in Sign Language. In support of this proposition, the paper presents the experimental results for Gaussian Process Dynamical Model against a database of 66 hand gestures from the Malaysian Sign Language. Furthermore, the Gaussian Process Dynamical Model is tested against established Hidden Markov Model for a comparative evaluation. A discussion on why Gaussian Process Dynamical Model is superior over existing methods in Sign Language interpretation task is then presented.

Getting More From the Semiconductor Test: Data Mining With Defect-Cluster Extraction

High-volume production data shows that dies, which failed probe test on a semiconductor wafer, have a tendency to form certain unique patterns, i.e., defect clusters. Identifying such clusters is one of the crucial steps toward improvement of the fabrication process and design for manufacturing. This paper proposes a new technique for defect-cluster identification that combines data mining with a defect-cluster extraction using a Segmentation, Detection, and Cluster-Extraction algorithm. It offers high defect-extraction accuracy, without any significant increase in test time and cost.

Analytical Standard Uncertainty Evaluation Using Mellin Transform

Uncertainty evaluation plays an important role in ensuring that a designed system can indeed achieve its desired performance. There are three standard methods to evaluate the propagation of uncertainty: 1) analytic linear approximation; 2) Monte Carlo (MC) simulation; and 3) analytical methods using mathematical representation of the probability density function (pdf). The analytic linear approximation method is inaccurate for highly nonlinear systems, which limits its application. The MC simulation approach is the most widely used technique, as it is accurate, versatile, and applicable to highly nonlinear systems. However, it does not define the uncertainty of the output in terms of those of its inputs. Therefore, designers who use this method need to resimulate their systems repeatedly for different combinations of input parameters. The most accurate solution can be attained using the analytical method based on pdf. However, it is unfortunately too complex to employ. This paper introduces the use of an analytical standard uncertainty evaluation (ASUE) toolbox that automatically performs the analytical method for multivariate polynomial systems. The backbone of the toolbox is a proposed ASUE framework. This framework enables the analytical process to be automated by replacing the complex mathematical steps in the analytical method with a Mellin transform lookup table and a set of algebraic operations. The ASUE toolbox was specifically designed for engineers and designers and is, therefore, simple to use. It provides the exact solution obtainable using the MC simulation, but with an additional output uncertainty expression as a function of its input parameters. This paper goes on to show how this expression can be used to prevent overdesign and/or suboptimal design solutions. The ASUE framework and toolbox substantially extend current analytical techniques to a much wider range of applications.

Defect cluster recognition system for fabricated semiconductor wafers

The International Technology Roadmap for Semiconductors (ITRS) identifies production test data as an essential element in improving design and technology in the manufacturing process feedback loop. One of the observations made from the high-volume production test data is that dies that fail due to a systematic failure have a tendency to form certain unique patterns that manifest as defect clusters at the wafer level. Identifying and categorising such clusters is a crucial step towards manufacturing yield improvement and implementation of real-time statistical process control. Addressing the semiconductor industrys needs, this research proposes an automatic defect cluster recognition system for semiconductor wafers that achieves up to 95% accuracy (depending on the product type).

Benchmark Test Distributions for Expanded Uncertainty Evaluation Algorithms

Expanded uncertainty estimation is normally required for mission-critical applications, e.g., those involving health and safety. It helps to get a distribution range of the required confidence level for the uncertainty evaluation of a system. There are a number of available techniques to estimate the expanded uncertainty. However, there is currently no commonly accepted benchmark test distribution set adopted to compare the performances of different techniques when they are used to estimate the expanded uncertainty. Without such a common benchmarking platform, the relative reliability of a particular technique in comparison to other techniques can be untrustworthy. To address the shortcoming, this paper proposes a set of analytically derived benchmark test distributions. It goes on to show the benefits of using them by comparing the performance of existing distribution fitting techniques when applied to the moment-based expanded uncertainty evaluation. The most commonly used moment-based distribution fitting techniques, such as Pearson, Tukeys gh, Cornish-Fisher expansion, and extended generalized lambda distributions, are employed as test cases in this paper. The test distribution set proposed in this paper provides a common benchmarking platform for metrologists intending to assess the performance of different expanded uncertainty estimation techniques. Results from the performance comparison would help practitioners to make a better choice of a distribution fitting technique that would best suit their respective systems.

Multivariate alternating decision trees

Decision trees are comprehensible, but at the cost of a relatively lower prediction accuracy compared to other powerful black-box classifiers such as SVMs. Boosting has been a popular strategy to create an ensemble of decision trees to improve their classification performance, but at the expense of comprehensibility advantage. To this end, alternating decision tree (ADTree) has been proposed to allow boosting within a single decision tree to retain comprehension. However, existing ADTrees are univariate, which limits their applicability. This research proposes a novel algorithm - multivariate ADTree. It presents and discusses its different variations (Fishers ADTree, Sparse ADTree, and Regularized Logistic ADTree) along with their empirical validation on a set of publicly available datasets. It is shown that multivariate ADTree has high prediction accuracy comparable to that of decision tree ensembles, while retaining good comprehension which is close to comprehension of individual univariate decision trees. Novel concept of multivariate alternating decision tree (ADTree) with boosting.Offering high prediction accuracy similar to decision tree ensembles.Retaining good comprehension similar to individual univariate decision trees.Bridging powerful regularization techniques to decision tree research.Introduction and validation of multivariate ADTree algorithms on public datasets.

Statistical measures of two dimensional point set uniformity

Three different classes of statistical measures of uniformity, namely, discrepancy, point-to-point measures and volumetric measures, are described and compared in this paper. Correlation studies are carried out to compare their performance in discerning uniformity of random and quasi-random point sets with respect to human perception of uniformity. Some of the measures reported in the literature are found to be able to characterize and rank very limited class of point sets correctly. A new approach to better characterize uniformity based on the physical analogy of potential energy is proposed. An approximate closed-form expression measuring the average uniformity of point set generated by spatial Poisson process is also derived theoretically. A novel application in signal processing is presented and extensive simulations are carried out to corroborate the validity of the proposed technique.

Fast and Accurate Automatic Defect CLuster Extraction for Semiconductor Wafers

Reduction in integrated circuit (IC) half technology, which will no longer be sustainable by traditional fault isolation and failure analysis techniques. There is an urgent need for diagnostic software tools with (which manifest as clusters) observed from manufacturing defects can be traced back to a specific process, equipment or technology, a novel data mining algorithm defects from test data logs. This algorithm and provides accurate detection of 99%.

Evaluating the Performance of Different Classification Algorithms for Fabricated Semiconductor Wafers

Defect detection and classification is crucial in ensuring product quality and reliability. Classification provides information on problems related to the detected defects which can then be used to perform yield prediction, fault diagnosis, correcting manufacturing issues and process control. Accurate classification requires good selection of features to help distinguish between different cluster types. This research investigates the use of two features for classification: Polar Fourier Transform (PFT) and image Rotational Moment Invariant (RMI). It provides a comprehensive critical evaluation of several classification schemes in terms of performance and accuracy based on these features. It concludes by discussing the suitability of each classifier for classifying different types of defect clusters on fabricated semiconductor wafers.