M. L. Dennis Wong

Enhanced multi-line code for minutiae-based fingerprint template protection

In this paper, we propose a cancellable fingerprint template technique based on our previous work on multi-line code (MLC) (Wong et al., 2012). The modification and improvement focuses on the change of MLC values and the generation of binary MLC. In addition, an enhanced similarity measure is also proposed to compensate the loss in accuracy for binary MLC, called the dynamically weighted integrated Dice (DWID) similarity. Comprehensive experiments on three FVC datasets are carried out to compare the performance among different settings of MLC. The lowest equal error rate (EER) obtained in the stolen-key scenario is 1.93% for FVC2002 DB1. Besides, analysis on the revocability, non-reversibility and template size of the enhanced MLC have been presented.

Semi-blind algorithms for automatic classification of digital modulation schemes

The problem of automatic classification of digital communication modulation schemes is considered in this work. Firstly, the maximum likelihood (ML) classifier for classifying phase-amplitude modulated schemes in coherent environment is presented. It is well known that the ML classifier requires the knowledge of the signal-to-noise ratio (SNR) and has a higher computational complexity. To relax the first requirement, we introduce a novel idea to estimate the SNR and this gives rise to a novel estimated ML (EsML) classifier. After which, in an attempt to reduce the computational complexity of the EML and EsML classifiers, we propose a simplified minimum distance (MD) classifier. The performance of these classifiers are compared against each others under the ideal channel condition as well as under a channel condition with an unknown carrier phase offset. In the second part of the paper, we adapt a closed form blind source separation (BSS) algorithm for rectifying the carrier phase offset prior to the actual classification procedures.

Kernel PCA enabled bit-string representation for minutiae-based cancellable fingerprint template

Minutiae set is one of the prevalent features used to represent a fingerprint. Many minutiae protection schemes have been proposed in recent literature, but only a few have demonstrated successful conversion from minutiae set to fixed-length bit-string. In this paper, we develop a fixed-length binary cancellable fingerprint template generation scheme based on a minutia descriptor known as the multi-line code (MLC). While retaining the core of MLC algorithm, we transform the unordered and variable-size MLC template into an ordered and fixed-length bit-string using kernel principal components analysis (KPCA) and state-of-the-art binarization techniques. The construction of a proper kernel suited for the scenario was validated using Mercers Theorem. Evaluation of the proposed scheme was performed over several FVC datasets and the best equal-error rate (EER) obtained for the final bit-string is 1.61%. In addition, extensive analysis was done to justify the non-invertibility and revocability property of the cancellable template. HighlightsA fixed-length binary representation of fingerprint using kernel PCA is proposed.Performance after applying KPCA drops slightly.Performance of the final bit-string is excellent, achieving average EER of 0.09%.The method is strong against inverse and linkage attacks but not masquerade attack.The computational complexity of the proposed scheme is high.

A genetic algorithm for optimizing gravity die casting's heat transfer coefficients

Research highlights?A simple but novel genetic algorithm for the purpose of achieving uniform solidification in a die casting thermal control problem. ? To mitigate computational complexity, the Medial Axis skeleton of the casting geometry was computed to reduce the dimension of the problem. ? A trivial geometry was used for as a case study. Empirical results showed minimal temperature difference was achieved through GA within a reasonable time frame. Numerical simulation of solidification has improved our understanding of casting processes significantly over the last two decades. One of the most desirable features in the design of casting of high strength components is directional solidification. Generally, expertise from skilled foundry men is required during the design of casting-mould assembly interrogation in order to achieve a satisfactory thermal control, thus directional solidification. This process is not only costly, both financially and temporally to foundries, it also heavily rely on foundry mens experiences. Our main aim in this project is to explore a novel and fully automated computer scheme that ties the geometric features of the casting with evolutionary algorithms to achieve thermal control. By extracting the medial axes of the casting geometry and correlate it with the interfacial heat transfer coefficient via evolutionary algorithm, we are able to perform non-exhaustive search of the optimized solution. Preliminary results from our computer experiments showed favourable results. In this paper, the focus is sharpened on the convergence and optimality of the developed GA.

Classification of Ball Bearing Faults using a Hybrid Intelligent Model

Professor Nandi is a Distinguished Visiting Professor at Tongji University, Shanghai, China. This work was partly supported by the National Science Foundation of China grant number 61520106006 and the National Science Foundation of Shanghai grant number 16JC1401300.

Optimal statistical design of variable sample size multivariate exponentially weighted moving average control chart based on median run-length

Abstract Conventionally, a standard control chart implements fixed sample size in process monitoring. In this study, we propose an optimal statistical design for the variable sample size (VSS) multivariate exponentially weighted moving average (MEWMA) chart based on the median run-length (MRL). The proposal is based on the fact that the percentiles of the run-length distribution, especially the MRL, are more reflective and reliable for performance evaluation with respect to a skewed run-length distribution. The MRL for the VSS MEWMA chart computed using the Markov chain approach is verified with Monte Carlo simulation. For benchmarking purposes, the performance of the VSS MEWMA chart is compared against the standard MEWMA chart and the synthetic T2 chart, in terms of the MRL. The numerical results show that the VSS MEWMA chart performs better than the standard MEWMA chart and the synthetic T2 chart, in detecting shifts in the process mean vector. Finally, an application is provided as an illustration for the implementation of the VSS MEWMA chart based on the MRL.

PPG signal reconstruction using a combination of discrete wavelet transform and empirical mode decomposition

Photoplethysmographic (PPG) signals, which are measured by pulse oximeter embedded in a form of wristband, are typically used for measuring heart rates. Such wearable sensors may be used for early detection of abnormal conditions for preventive actions in monitoring individual health. However, it is challenging to estimate heart rates using PPG signals with high accuracy due to the irregular motion artifacts, thus making the estimation of heart rate unreliable. In this paper, we proposed the use of Empirical Mode Decomposition (EMD) followed by Discrete Wavelet Transform (DWT) for noise reduction of the PPG signals. We calculated the heart beat rate per minute (BPM) from the reconstructed PPG signals and evaluated the performance of the proposed method in terms of Absolute Maximum Error (AME) and Mean Sum Error (MSE) with the provided ground-truth BPM computed from ECG signals. We have shown an improvement in the MSE values from 67% of the datasets used in this study. We also analyzed the relationship between the performances obtained based the level of movement intensity which are measured using the accelerometer.

Compressive sensing strategy for classification of bearing faults

Owing to the importance of rolling element bearings in rotating machines, condition monitoring of rolling element bearings has been studied extensively over the past decades. However, most of the existing techniques require large storage and time for signal processing. This paper presents a new strategy based on compressive sensing for bearing faults classification that uses fewer measurements. Under this strategy, to match the compressed sensing mechanism, the compressed vibration signals are first obtained by resampling the acquired bearing vibration signals in the time domain with a random Gaussian matrix using different compressed sensing sampling rates. Then three approaches have been chosen to process these compressed data for the purpose of bearing fault classification these includes using the data directly as the input of classifier, and extract features from the data using linear feature extraction methods, namely, unsupervised Principal Component Analysis (PCA) and supervised Linear Discriminant Analysis (LDA). Classification performance using Logistic Regression Classifier (LRC) achieved high classification accuracy with significantly reduced bandwidth consumption compared with the existing techniques.

Smart hybrid energy storage for stand-alone PV microgrid: Optimization of battery lifespan through dynamic power allocation

In islanded microgrid system, the battery tends to be the most vulnerable element in terms of durability. Poorly managed battery charge/discharge process is one of the main life-limiting factors. To improve the battery life, a novel energy storage system topology and a power allocation strategy are proposed in this paper. A standalone 6kW Photovoltaic (PV) microgrid system with hybrid energy storage that combines battery and supercapacitor (SC) is considered. The performance of the proposed system is evaluated via model simulation using Matlab/Simulink. The system is simulated under a typical 24-hours residential load profile and the results demonstrated that the proposed system can provide sufficient power to regulate the fluctuations in supply and load whilst maintaining optimal batteries charged / discharged rate. The batteries were also operated under a low depth-of-discharge which prolongs the battery lifetime.

A Security- and Privacy-Driven Hybrid Biometric Template Protection Technique

Biometric template protection techniques are used to secure biometric templates from various attacks and threats. As the trade-off between the performance and the security and privacy of said techniques has drawn increasing concerns from the researchers, a single approach can no longer satisfy the elevated requirement. In this paper, a state-of-the-art hybrid template protection method, called the cancelable secure sketch (CaSS) is proposed. The concept of CaSS is demonstrated via a cancelable fingerprint, namely the multi-line code (MLC) with the code-offset construction of secure sketch. The proposed method boasts high security and privacy by virtue of the revocability of MLC. Besides, inheriting the high performance of binary MLC, the CaSS method is able to achieve 0% FRR and FAR.