12 results for Abdulla, Waleed

  • Speech Enhancement by Multi-Channel Crosstalk Resistant Adaptive Noise Cancellation

    Zeng, Qingning; Abdulla, Waleed (2006)

    Conference paper
    The University of Auckland Library

    An open access copy of this article is available and complies with the copyright holder/publisher conditions. A novel Multi-channel Crosstalk Resistant Adaptive Noise Cancellation (MCRANC) algorithm is presented in this paper to enhance noise carrying speech signals. The algorithm would permit locating the microphones in close proximity as it cancels out the crosstalk effect. Results have indicated that this method outperforms the commonly used techniques in the sense of SNR improvement and speech intelligibility. A SNR improvement of 17.8dB using MCRANC keeping highly intelligible speech was achieved in our experiments versus 9.1dB using Multi-channel ANC (MANC) with far less speech quality.

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  • Speech recognition system based on new dynamic time warping technique.

    Abdulla, Waleed; Chow, David; Sin, Garry (2011)

    Dataset
    The University of Auckland Library

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  • Adaptive motor current signature analysis for condition monitoring of induction machines

    Tabatabaei Ardekani, I; Abdulla, Waleed; Faiz, J (2010)

    Conference item
    The University of Auckland Library

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  • Multi-agent Software Control System with Hybrid Intelligence for Ubiquitous Intelligent Environments

    Wang, IK; Abdulla, Waleed; Salcic, Zoran (2007)

    Conference item
    The University of Auckland Library

    In this paper, a novel ubiquitous intelligent environment platform and its multi-agent control system are presented. The platform named Distributed Embedded Intelligence Room (DEIR) has been constructed with embedded sensors, actuators and computing devices. All the devices are interconnected using five different physical networks. This platform aims to facilitate realistic data collection and online system performance evaluation. The multi-agent control system incorporating two machine learning algorithms, fuzzy inference and decision tree, has been designed to conform to DEIR architecture. Devices to be controlled are classified based on their possible output states and modelled separately by fuzzy inference agents and decision tree agents in the system. The multi-agent control system with hybrid intelligence shows 11% improvements on overall control accuracy and 84% improvements on learning time compared to its predecessor control system. The vast improvement on computational time shows suitability of the approach towards real-time, embedded applications.

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  • Automatic segmentation of retinal vasculature

    Chalakkal, Renoh; Abdulla, Waleed (2017)

    Conference item
    The University of Auckland Library

    Segmentation of retinal vessels from retinal fundus images is the key step in the automatic retinal image analysis. In this paper we propose a new unsupervised automatic method to segment the retinal vessels from retinal fundus images. Contrast enhancement and illumination correction are carried out through a series of image processing steps followed by adaptive histogram equalization and anisotropic diffusion filtering. This image is then converted to a gray scale using weighted scaling. The vessel edges are enhanced by boosting the detail curvelet coefficients. Optic disk pixels are removed before applying fuzzy C-mean classification to avoid the misclassification. Morphological operations and connected component analysis are applied to obtain the segmented retinal vessels. The performance of the proposed method is evaluated using DRIVE database to be able to compare with other state-of-art supervised and unsupervised methods. The overall segmentation accuracy of the proposed method is 95.18% which outperforms the other algorithms.

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  • Multi-agent System with Hybrid Intelligence Using Neural Network and Fuzzy Inference Techniques

    Wang, Kevin I-Kai; Abdulla, Waleed; Salcic, Zoran (2007)

    Conference item
    The University of Auckland Library

    In this paper, a novel multi-agent control system incorporating hybrid intelligence and its physical testbed are presented. The physical testbed is equipped with a large number of embedded devices interconnected by three types of physical networks. It mimics a ubiquitous intelligent environment and allows real-time data collection and online system evaluation. Human control behaviours for different physical devices are analysed and classified into three categories. Physical devices are grouped based on their relevance and each group is assigned to a particular behaviour category. Each device group is independently modelled by either fuzzy inference or neural network agents according to the behaviour category. Comparative analysis shows that the proposed multi-agent control system with hybrid intelligence achieves significant improvement in control accuracy compared to other offline control systems.

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  • Neonatal EEG Signal Characteristics Using Time Frequency Analysis

    Abdulla, Waleed; WONG, L (2011-03-15)

    Journal article
    The University of Auckland Library

    Time-frequency analysis is a way to represent the energy contents of a signal in the joint time-frequency domain. It provides a good visual way to separate the frequency contents of a multi-component signal, and display the changes of these components with respect to time. This paper outlines investigative work on neonatal EEG signals using timefrequency analysis. The Cohen???s class distributions are discussed, and kernel optimisation for the Cohen???s class distributions is outlined. Segments of EEG with different background continuity states are analysed using a Cohen???s class distribution, and their characteristics are discussed. Through this paper, interesting information that offers insight towards the EEG signal can be visualized from the time frequency analysis.

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  • Theoretical convergence analysis of FxLMS algorithm

    Tabatabaei Ardekani, Iman; Abdulla, Waleed (2010-12)

    Journal article
    The University of Auckland Library

    In system identification when a secondary path follows the adaptive filter, the FxLMS algorithm is usually applied for updating the adaptive filter. Although several FxLMS convergence analyses have been conducted in detail, only a few have intended to derive a convergence condition. In fact, available FxLMS convergence conditions are only accurate for simplified cases with pure delay secondary paths or multi-sinusoidal input signals. This paper studies the FxLMS convergence behavior for moving average secondary paths and stochastic input signals. A novel model for predicting the FxLMS convergence behavior is developed. Based on this model, a necessary and sufficient condition for the convergence of the FxLMS is derived. Also, the condition leading to the fastest convergence is proposed. Compared to previously derived convergence conditions, the proposed condition applies to more general secondary paths. Results obtained from this study are found to correspond very well to those obtained from simulation experiments.

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  • DIALED TELEPHONE NUMBER ANNOUNCER

    Abdulla, Waleed; ABUALKHAIL, MN (1989-05-01)

    Journal article
    The University of Auckland Library

    This paper describes the implementation of a device which may be used as an add on to any telephone set. The device announces the dialled numbers immediately after any number is dialled. This verifies the subscriber's call and tells the operator which number was dialled. The device accepts a pulse input and decodes it into the address of the phonemes (building blocks of speech) of the dialled digits. Then the phoneme addresses drive an SC-01 synthesizer to announce the dialled digit.

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  • Hardware???Software Codesign of Automatic Speech Recognition System for Embedded Real-Time Applications

    Cheng, O; Abdulla, Waleed; Salcic, Z (2011-03)

    Journal article
    The University of Auckland Library

    We present a hardware-software coprocessing speech recognizer for real-time embedded applications. The system consists of a standard microprocessor and a hardware accelerator for Gaussian mixture model (GMM) emission probability calculation implemented on a field-programmable gate array. The GMM accelerator is optimized for timing performance by exploiting data parallelism. In order to avoid large memory requirement, the accelerator adopts a double buffering scheme for accessing the acoustic parameters with no assumption made on the access pattern of these parameters. Experiments on widely used benchmark data show that the real-time factor of the proposed system is 0.62, which is about three times faster than the pure software-based baseline system, while the word accuracy rate is preserved at 93.33%. As a part of the recognizer, a new adaptive beam-pruning algorithm is also proposed and implemented, which further reduces the average real-time factor to 0.54 with the word accuracy rate of 93.16%. The proposed speech recognizer is suitable for integration in various types of voice (speech)-controlled applications.

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  • Ambient intelligence platform using multi-agent system and mobile ubiquitous hardware

    Wang, Kevin I-Kai; Abdulla, Waleed; Salcic, Zoran (2009)

    Journal article
    The University of Auckland Library

    In this paper, a novel ambient intelligence (AmI) platform is proposed to facilitate fast integration of different control algorithms, device networks and user interfaces. This platform defines the overall hardware/software architecture and communication standards. It consists of four layers, namely the ubiquitous environment, middleware, multi-agent system and application layer. The multi-agent system is implemented using Java Agent DEvelopment (JADE) framework and allows users to incorporate multiple control algorithms as agents for managing different tasks. The Universal Plug and Play (UPnP) device discovery protocol is used as a middleware, which isolates the multi-agent system and physical ubiquitous environment while providing a standard communication channel between the two. An XML content language has been designed to provide standard communication between various user interfaces and the multi-agent system. A mobile ubiquitous setup box is designed to allow fast construction of ubiquitous environments in any physical space. The real time performance analysis shows the potential of the proposed AmI platform to be used in real-life AmI applications. A case study has also been carried out to demonstrate the possibility of integrating multiple control algorithms in the multi-agent system and achieving a significant improvement on the overall offline learning performance.

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  • On the stability of adaptation process in active noise control systems

    Ardekani, IT; Abdulla, Waleed (2011)

    Journal article
    The University of Auckland Library

    The stability analysis of the adaptation process, performed by the filtered-x least mean square algorithm on weights of active noise controllers, has not been fully investigated. The main contribution of this paper is conducting a theoretical stability analysis for this process without utilizing commonly used simplifying assumptions regarding the secondary electro-acoustic channel. The core of this analysis is based on the root locus theory. The general rules for constructing the root locus plot of the adaptation process are derived by obtaining root locus parameters, including start points, end points, asymptote lines, and breakaway points. The conducted analysis leads to the derivation of a general upper-bound for the adaptation step-size beyond which the mean weight vector of the active noise controller becomes unstable. Also, this analysis yields the optimum step-size for which the adaptive active noise controller has its fastest dynamic performance. The proposed upper-bound and optimum values apply to general secondary electro-acoustic channels, unlike the commonly used ones which apply to only pure delay channels. The results are found to agree very well with those obtained from numerical analyses and computer simulation experiments.

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