Mang I Vai

Bio: Mang I Vai is an academic researcher from University of Macau. The author has contributed to research in topics: Wavelet transform & Computer science. The author has an hindex of 21, co-authored 181 publications receiving 2193 citations. Previous affiliations of Mang I Vai include University of Hong Kong.

Wavelet analysis and applications

Abstract: Wavelet Theory.- Local Smoothness Conditions on a Function Which Guarantee Convergence of Double Walsh-Fourier Series of This Function.- Linear Transformations of ?N and Problems of Convergence of Fourier Series of Functions Which Equal Zero on Some Set.- Sidon Type Inequalities for Wavelets.- Almansi Decomposition for Dunkl-Helmholtz Operators.- An Uncertainty Principle for Operators.- Uncertainty Principle for Clifford Geometric Algebras Cl n,0, n = 3 (mod 4) Based on Clifford Fourier Transform.- Orthogonal Wavelet Vectors in a Hilbert Space.- Operator Frames for .- On the Stability of Multi-wavelet Frames.- Biorthogonal Wavelets Associated with Two-Dimensional Interpolatory Function.- Parameterization of Orthogonal Filter Bank with Linear Phase.- On Multivariate Wavelets with Trigonometric Vanishing Moments.- Directional Wavelet Analysis with Fourier-Type Bases for Image Processing.- Unitary Systems and Wavelet Sets.- Clifford Analysis and the Continuous Spherical Wavelet Transform.- Clifford-Jacobi Polynomials and the Associated Continuous Wavelet Transform in Euclidean Space.- Wavelet Leaders in Multifractal Analysis.- Application of Fast Wavelet Transformation in Parametric System Identification.- Image Denoising by a Novel Digital Curvelet Reconstruction Algorithm.- Condition Number for Under-Determined Toeplitz Systems.- Powell-Sabin Spline Prewavelets on the Hexagonal Lattice.- Time-Frequency Aspects of Nonlinear Fourier Atoms.- Mono-components for Signal Decomposition.- Signal-Adaptive Aeroelastic Flight Data Analysis with HHT.- An Adaptive Data Analysis Method for Nonlinear and Nonstationary Time Series: The Empirical Mode Decomposition and Hilbert Spectral Analysis.- Wavelet Applications.- Transfer Colors from CVHD to MRI Based on Wavelets Transform.- Medical Image Fusion by Multi-resolution Analysis of Wavelets Transform.- Salient Building Detection from a Single Nature Image via Wavelet Decomposition.- SAR Images Despeckling via Bayesian Fuzzy Shrinkage Based on Stationary Wavelet Transform.- Super-Resolution Reconstruction Using Haar Wavelet Estimation.- The Design of Hilbert Transform Pairs in Dual-Tree Complex Wavelet Transform.- Supervised Learning Using Characteristic Generalized Gaussian Density and Its Application to Chinese Materia Medica Identification.- A Novel Algorithm of Singular Points Detection for Fingerprint Images.- Wavelet Receiver: A New Receiver Scheme for Doubly-Selective Channels.- Face Retrieval with Relevance Feedback Using Lifting Wavelets Features.- High-Resolution Image Reconstruction Using Wavelet Lifting Scheme.- Mulitiresolution Spatial Data Compression Using Lifting Scheme.- Ridgelet Transform as a Feature Extraction Method in Remote Sensing Image Recognition.- Analysis of Frequency Spectrum for Geometric Modeling in Digital Geometry.- Detection of Spindles in Sleep EEGs Using a Novel Algorithm Based on the Hilbert-Huang Transform.- A Wavelet-Domain Hidden Markov Tree Model with Localized Parameters for Image Denoising.

Implementation of SSVEP based BCI with Emotiv EPOC

Abstract: In recent years, steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has received much attentions. However, most SSVEP based BCI devices are not portable and have high price, which are not suitable to be used for clinical and commercial purpose. Thanks to the low cost and portable Emotiv EPOC, it brings BCI into daily life. In this paper, SSVEP based BCI through Emotiv EPOC is implemented. BCI 2000 is employed to connect Emotiv EPOC and Matlab to implement the online system. The online experiments have the accuracy of 95.83±3.59 %, information transfer rate (ITR) with 22.85±1.85 bits/min and detection duration of 5.25±2.14 sec.

A 0.83- $\mu {\rm W}$ QRS Detection Processor Using Quadratic Spline Wavelet Transform for Wireless ECG Acquisition in 0.35- $\mu{\rm m}$ CMOS

Abstract: Healthcare electronics count on the effectiveness of the on-patient signal preprocessing unit to moderate the wireless data transfer for better power efficiency. In order to reduce the system power in long-time ECG acquisition, this work describes an on-patient QRS detection processor for arrhythmia monitoring. It extracts the concerned ECG part, i.e., the RR-interval between the QRS complex for evaluating the heart rate variability. The processor is structured by a scale-3 quadratic spline wavelet transform followed by a maxima modulus recognition stage. The former is implemented via a symmetric FIR filter, whereas the latter includes a number of feature extraction steps: zero-crossing detection, peak (zero-derivative) detection, threshold adjustment and two finite state machines for executing the decision rules. Fabricated in 0.35-μm CMOS the 300-Hz processor draws only 0.83 μW, which is favorably comparable with the prior arts. In the system tests, the input data is placed via an on-chip 10-bit SAR analog-to-digital converter, while the output data is emitted via an off-the-shelf wireless transmitter (TI CC2500) that is configurable by the processor for different data transmission modes: 1) QRS detection result, 2) raw ECG data or 3) both. Validated with all recordings from the MIT-BIH arrhythmia database, 99.31% sensitivity and 99.70% predictivity are achieved. Mode 1 with solely the result of QRS detection exhibits 6× reduction of system power over modes 2 and 3.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Multimodal fast optical interrogation of neural circuitry

Abstract: Our understanding of the cellular implementation of systems-level neural processes like action, thought and emotion has been limited by the availability of tools to interrogate specific classes of neural cells within intact, living brain tissue. Here we identify and develop an archaeal light-driven chloride pump (NpHR) from Natronomonas pharaonis for temporally precise optical inhibition of neural activity. NpHR allows either knockout of single action potentials, or sustained blockade of spiking. NpHR is compatible with ChR2, the previous optical excitation technology we have described, in that the two opposing probes operate at similar light powers but with well-separated action spectra. NpHR, like ChR2, functions in mammals without exogenous cofactors, and the two probes can be integrated with calcium imaging in mammalian brain tissue for bidirectional optical modulation and readout of neural activity. Likewise, NpHR and ChR2 can be targeted together to Caenorhabditis elegans muscle and cholinergic motor neurons to control locomotion bidirectionally. NpHR and ChR2 form a complete system for multimodal, high-speed, genetically targeted, all-optical interrogation of living neural circuits.

Statistical Pattern Recognition

Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Introduction To Robust Estimation And Hypothesis Testing

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