B.W.-K. Ling

University of Lincoln, Lincoln, England, United Kingdom

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Publications (32)43.47 Total impact

  • David Matthew Garner, Bingo Wing Kuen Ling
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    ABSTRACT: The new measures computed here are the spectral detrended fluctuation analysis (sDFA) and spectral multi-taper method (sMTM). sDFA applies the standard detrended fluctuation analysis (DFA) algorithm to power spectra. sMTM exploits the minute increases in the broadband response, typical of chaotic spectra approaching optimal values. The authors chose the Brusselator, Lorenz, and Duffing as the proposed models to measure and locate chaos and severe irregularity. Their series of chaotic parametric responses in short time-series is advantageous. Where cycles have only a limited number of slow oscillations such as for systems biology and medicine. It is difficult to create, locate, or monitor chaos. From 50 linearly increasing starting points applied to the chaos target function (CTF); the mean percentage increases in Kolmogorov-Sinai entropy (KS-Entropy) for the proposed chosen models; and p-values when the models were compared statistically by Kruskal-Wallis and ANOVA1 test with distributions assumed normal are Duffing (CTF: 31%: p < 0.03); Lorenz (CTF: 2%: p < 0.03), and Brusselator (CTF: 8%: p < 0.01). Principal component analysis (PCA) is applied to assess the significance of the objective functions for tuning the chaotic response. From PCA the conclusion is that CTF is the most beneficial objective function overall delivering the highest increases in mean KS-Entropy.
    Journal of Systems Science and Complexity 06/2014; 27(3-3):494-506. DOI:10.1007/s11424-014-2197-7 · 0.37 Impact Factor
  • Zhijing Yang, B.W.-K. Ling, Chris Bingham
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    ABSTRACT: This paper presents a novel methodology for extracting the underlying trends of signals via a joint empirical mode decomposition (EMD) and sparse binary programming approach. The EMD is applied to the signals and the corresponding intrinsic mode functions (IMFs) are obtained. The underlying trends of the signals are obtained by the sums of the IMFs where these IMFs are either selected or discarded. The total number of the selected IMFs is minimized subject to a specification on the maximum absolute differences between the denoised signals (signals obtained by discarding the first IMFs) and the underlying trends. Since the total number of the selected IMFs is minimized, the obtained solutions are sparse and only few IMFs are selected. The selected IMFs correspond to the components of the underlying trend of the signals. On the other hand, the L∞ norm specification guarantees that the maximum absolute differences between the underlying trends and the denoised signals are bounded by an acceptable level. This forces the underlying trends to follow the global changes of the signals. As the IMFs are either selected or discarded, the coefficients are either zero or one. This problem is actually a sparse binary programming problem with an L0 norm objective function subject to an L∞ norm constraint. Nevertheless, the problem is nonconvex, nonsmooth, and NP hard. It requires an exhaustive search for solving the problem. However, the required computational effort is too heavy to be implemented practically. To address these difficulties, we approximate the L0 norm objective function by the L1 norm objective function, and the solution of the sparse binary programming problem is obtained by applying the zero and one quantization to the solution of the corresponding continuous-valued L1 norm optimization problem. Since the isometry condition is satisfied and the number of the IMFs is small for most - f practical signals, this approximation is valid and verified via our experiments conducted on practical data. As the L1 norm optimization problem can be reformulated as a linear programming problem and many efficient algorithms such as simplex or interior point methods can be applied for solving the linear programming problem, our proposed method can be implemented in real time. Also, unlike previously reported techniques that require precursor models or parameter specifications, our proposed adaptive method does not make any assumption on the characteristics of the original signals. Hence, it can be applied to extract the underlying trends of more general signals. The results show that our proposed method outperforms existing EMD, classical lowpass filtering and the wavelet methods in terms of the efficacy.
    IEEE Transactions on Instrumentation and Measurement 10/2013; 62(10):2673-2682. DOI:10.1109/TIM.2013.2265451 · 1.71 Impact Factor
  • Zhijing Yang, B.W.-K. Ling, C. Bingham
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    ABSTRACT: This paper proposes a novel methodology for extracting the underlying trend and predicting the power usage through a joint singular spectrum analysis (SSA) and sparse binary programming approach. The underlying trend is approximated by the sum of a part of SSA components, in which the total number of the SSA components in the sum is minimized subject to a specification on the maximum absolute difference between the original signal and the approximated underlying trend. As the selection of the SSA components is binary, this selection problem is to minimize the L0 norm of the selection vector subject to the L∞ norm constraint on the difference between the original signal and the approximated underlying trend as well as the binary valued constraint on the elements of the selection vector. This problem is actually a sparse binary programming problem. To solve this problem, first the corresponding continuous valued sparse optimization problem is solved. That is, to solve the same problem without the consideration of the binary valued constraint. This problem can be approximated by a linear programming problem when the isometry condition is satisfied, and the solution of the linear programming problem can be obtained via existing simplex methods or interior point methods. By applying the binary quantization to the obtained solution of the linear programming problem, the approximated solution of the original sparse binary programming problem is obtained. Unlike previously reported techniques that require a pre-cursor model or parameter specifications, the proposed method is completely adaptive. Experiment results show that our proposed method is very effective and efficient for extracting the underlying trend and predicting the power usage.
    Circuits and Systems (ISCAS), 2013 IEEE International Symposium on; 01/2013
  • B.W.K.Ling, C.Y.Ho, T.C.W.Kok, W.C.Siu, K.L.Teo
    Digital Signal Processing 01/2013; · 1.50 Impact Factor
  • B. W. -K. Ling, C. Bingham, H.H.-C. Lu, K.-L. Teo
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    ABSTRACT: This paper develops a combined optimal pulse width modulation (PWM) and pulse frequency modulation (PFM) strategy for controlling switched mode DC-DC converters. The peak ripple magnitudes of both the output-voltages and -currents during all operating modes over a wide range of loads are minimised subject to specifications on the minimum efficiency bounds of the converters. This problem is posed as a multi-objective functional inequality constrained optimal control problem. By expressing the initial state of each operating mode at the steady state as a function of the switched time instants, as well as applying the time scaling transform method and the constraint transcription method, the multi-objective functional inequality constrained optimal control problem is converted to a conventional optimal control problem. Finally, a control parameterisation technique is applied to solve the problem. Computer numerical simulations show that the combined control strategy could achieve low peak ripple magnitudes of both the output-voltages and -currents for all operating modes over a wide range of loads and guarantees the satisfaction of the specifications on the minimum efficiency bounds of the converter over a wide range of loads.
    IET Control Theory and Applications 09/2012; 6(13):1973-1983. DOI:10.1049/iet-cta.2011.0396 · 1.84 Impact Factor
  • B. W. K. Ling, C. Z. Wu, K. L. Teo, V. Rehbock
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    ABSTRACT: In this paper, we consider a globally optimal design of IIR filters. We formulate the design problem as a nonconvex optimization problem with a continuous inequality constraint and a nonconvex constraint. To solve this problem, the constraint transcription method is applied to tackle the continuous inequality constraint. In order to avoid the obtained solution being on the boundary of the feasible set, more than one initial points are used. Moreover, since the objective and the constraints are nonconvex functions, there may be many local minima. To address this problem, the filled function method is applied to escape from the local minima. Some numerical computer simulation results are presented to illustrate the effectiveness and efficiency of the proposed method.
    Circuits Systems and Signal Processing 06/2012; 32(3). DOI:10.1007/s00034-012-9511-1 · 1.26 Impact Factor
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    Ruiyang Yu, B.M.H. Pong, B.W.-K. Ling, James Lam
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    ABSTRACT: Power converter efficiency is always a hot topic for switch mode power supplies. Nowadays, high efficiency is required over a wide load range, e.g., 20%, 50%, and 100% load. Computer-aided design optimization is developed in this research study, to optimize off-line power converter efficiency from light load to full load. A two-stage optimization method to optimize power converter efficiency from light load to full load is proposed. The optimization procedure first breaks the converter design variables into many switching frequency loops. In each fixed switching frequency loop, the optimal designs for 20%, 50%, and 100% load are derived separately in the first stage, and an objective function using the optimization results in the first stage is formed in the second stage to consider optimizing efficiencies at 20%, 50%, and 100% load. Component efficiency models are also established to serve as the objective functions of optimizations. Prototypes 400 V to 12 V/25 A 300 W two-FET forward converters are built to verify the optimization results.
    IEEE Transactions on Power Electronics 04/2012; 27(3-27):1327 - 1337. DOI:10.1109/TPEL.2011.2114676 · 5.73 Impact Factor
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    ABSTRACT: This paper proposes to use a set of discrete fractional Fourier transform (DFrFT) matrices with different rotational angles to construct an overcomplete kernel for sparse representations of signals. The design of the rotational angles is formulated as an optimization problem. To solve the problem, it is shown that this design problem is equivalent to an optimal sampling problem. Furthermore, the optimal sampling frequencies are the roots of a set of harmonic functions. As the frequency responses of the filters are required to be computed only at frequencies in a discrete set, the globally optimal rotational angles can be found very efficiently and effectively.
    Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2012 8th International Symposium on; 01/2012
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    ABSTRACT: This paper proposes a novel methodology for the optimal and simultaneous designs of both Hermitian transforms and masks for reducing the intraclass separations of feature vectors for anomaly detection of diabetic retinopathy images. Each class of training images associates with a Hermitian transform, a mask and a known represented feature vector. The optimal and simultaneous designs of both the Hermitian transforms and the masks are formulated as least squares optimization problems subject to the Hermitian constraints. Since the optimal mask of each class of training images is dependent on the corresponding optimal Hermitian transform, only the Hermitian transforms are required to be designed. Nevertheless, the Hermitian transform design problems are optimization problems with highly nonlinear objective functions subject to the complex valued quadratic Hermitian constraints. This kind of optimization problems is very difficult to solve. To address the difficulty, this paper proposes a singular value decomposition approach for deriving a condition on the solutions of the optimization problems as well as an iterative approach for solving the optimization problems. Since the matrices characterizing the discrete Fourier transform, discrete cosine transform and discrete fractional Fourier transform are Hermitian, the Hermitian transforms designed by our proposed approach are more general than existing transforms. After both the Hermitian transforms and the masks for all classes of training images are designed, they are applied to test images. The test images will assign to the classes where the Euclidean 2-norms of the differences between the processed feature vectors of the test images and the corresponding represented feature vectors are minimum. Computer numerical simulation results show that the proposed methodology for the optimal and simultaneous designs of both the Hermitian transforms and the masks is very efficient and effective. The proposed technique is also - ery efficient and effective for reducing the intraclass separations of feature vectors for anomaly detection of diabetic retinopathy images.
    Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2012 8th International Symposium on; 01/2012
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    ABSTRACT: Trend extraction is an important tool for the analysis of data sequences. This paper presents a new methodology for trend extraction based on Hilbert-Huang transform. Signals are initially decomposed through use of EMD into a finite number of intrinsic mode functions (IMFs). The Hilbert marginal spectrum of each IMF is then calculated and a new criterion, termed the cross energy ratio of the Hilbert marginal spectrum of consecutive IMFs, is defined. Finally, through use of the new criterion, the underlying trend is obtained by adaptively selecting appropriate IMFs obtained by EMD. Results from experimental trials are included to demonstrate the benefits of the proposed method for extracting trends in data streams.
    Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2012 8th International Symposium on; 01/2012
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    J. H. C.nga, H. H. C.iu, B. W. K.ling, H. K.lam
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    ABSTRACT: This paper studies the bifurcation and chaos phenomena in average queue length in a developed Transmission Control Protocol (TCP) model with Random Early Detection (RED) mechanism. Bifurcation and chaos phenomena are nonlinear behavior in network systems that lead to degradation of the network performance. The TCP/RED model used is a model validated previously. In our study, only the average queue size is considered, and the results are based on analytical model rather than actual measurements. The instabilities in the model are studied numerically using the conventional nonlinear bifurcation analysis. Extending from this bifurcation analysis, a modified RED algorithm is derived to prevent the observed bifurcation and chaos regardless of the selected parameters. Our modification is for the simple scenario of a single RED router carrying only TCP traffic. The algorithm neither compromises the throughput nor the average queuing delay of the system.
    International Journal of Bifurcation and Chaos 11/2011; 18(08). DOI:10.1142/S0218127408021816 · 1.02 Impact Factor
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    B. W. K.ling, F. C. K.luk, P. K. S.tam
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    ABSTRACT: This Letter displays, via the numerical simulation of a real digital filter, that a finite-state machine may behave in a near-chaotic way even when its corresponding infinite-state machine does not exhibit chaotic behavior.
    International Journal of Bifurcation and Chaos 11/2011; 13(02). DOI:10.1142/S0218127403006625 · 1.02 Impact Factor
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    ABSTRACT: In this correspondence, a two-channel linear phase finite-impulse-response (FIR) quadrature mirror filter (QMF) bank minimax design problem is formulated as a nonconvex optimization problem so that a weighted sum of the maximum amplitude distortion of the filter bank, the maximum passband ripple magnitude and the maximum stopband ripple magnitude of the prototype filter is minimized subject to specifications on these performances. A modified filled function method is proposed for finding the global minimum of the nonconvex optimization problem. Computer numerical simulations show that our proposed design method is efficient and effective.
    IEEE Transactions on Signal Processing 09/2010; DOI:10.1109/TSP.2010.2049107 · 3.20 Impact Factor
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    ABSTRACT: This paper proposes a non-linear block matched motion model with motion vectors having arbitrary pixel precisions. The optimal motion vector which minimizes the mean square error is solved analytically in a single step. Our proposed algorithm can be regarded as a generalization of conventional half pixel search algorithms and quarter pixel search algorithms because our proposed algorithm could achieve motion vectors with arbitrary pixel precisions. Also, the computational effort of our proposed algorithm is lower than that of conventional quarter pixel search algorithms because our proposed algorithm could achieve motion vectors in a single step.
    Communication Systems Networks and Digital Signal Processing (CSNDSP), 2010 7th International Symposium on; 08/2010
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    ABSTRACT: For an orthogonal frequency division multiple access (OFDMA) downlink of a spectrally coexisting macro and femto network, a resource allocation scheme would aim to maximize the area spectral efficiency (ASE) subject to constraints on the radio resources per transmission interval accessible by each femtocell. An optimal resource allocation scheme for completely decentralized deployments leads however to a nonconvex optimization problem. In this paper, a filled function method is employed to find the global maximum of the optimization problem. Simulation results show that our proposed method is efficient and effective.
    Communication Systems Networks and Digital Signal Processing (CSNDSP), 2010 7th International Symposium on; 08/2010
  • IEEE Transactions on Signal Processing 08/2010; · 3.20 Impact Factor
  • Bingo Wing Kuen Ling, Hak Keung Lam, Herbert Ho Ching Iu
    Circuits Systems and Signal Processing 12/2008; 27(6):775-780. DOI:10.1007/s00034-008-9085-0 · 1.26 Impact Factor
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    ABSTRACT: In this paper, a general optimum full-band, high-order discrete-time differentiator design problem is formulated as a peak-constrained least squares optimization problem. That is, the objective of the optimization problem is to minimize the total weighted square error of the magnitude response subject to the peak constraint of the weighted error function. This problem formulation provides great flexibility for the tradeoff between the ripple energy and the ripple magnitude of the discrete-time differentiator. The optimization problem is actually a semi-infinite programming problem. Our recently developed dual parameterization algorithm is applied to solve the problem. The main advantages of employing the dual parameterization algorithm to solve the problem are as follows: (1) the guarantee of the convergence of the algorithm and (2) the obtained solution being the global optimal solution that satisfies the corresponding continuous constraints. Moreover, the computational cost of the algorithm is lower than that of algorithms that are implementing the semidefinite programming approach.
    IEEE Transactions on Instrumentation and Measurement 11/2008; DOI:10.1109/TIM.2008.922090 · 1.71 Impact Factor
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    ABSTRACT: In this paper, the dynamics of weights of perceptrons are investigated based on the perceptron training algorithm. In particular, the condition that the system map is not injective is derived. Based on the derived condition, an invariant set that results to a bijective invariant map is characterized. Also, it is shown that some weights outside the invariant set will be moved to the invariant set. Hence, the invariant set is attracting. Computer numerical simulation results on various perceptrons with exhibiting various behaviors, such as fixed point behaviors, limit cycle behaviors and chaotic behaviors, are illustrated.
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on; 07/2008
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    ABSTRACT: Since different multiwavelets, pre- and post-filters have different impulse responses and frequency responses, different multiwavelets, pre- and post-filters should be selected and applied at different noise levels for signal denoising if signals are corrupted by additive white Gaussian noises. In this paper, some fuzzy rules are formulated for integrating different multiwavelets, pre- and post-filters together so that expert knowledge on employing different multiwavelets, pre- and post-filters at different noise levels on denoising performances is exploited. When an ECG signal is received, the noise level is first estimated. Then, based on the estimated noise level and our proposed fuzzy rules, different multiwavelets, pre- and post-filters are integrated together. A hard thresholding is applied on the multiwavelet coefficients. According to extensive numerical computer simulations, our proposed fuzzy rule based multiwavelet denoising algorithm outperforms traditional multiwavelet denoising algorithms by 30%.
    Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on; 07/2008

Publication Stats

107 Citations
43.47 Total Impact Points

Institutions

  • 2012–2014
    • University of Lincoln
      • School of Engineering
      Lincoln, England, United Kingdom
    • GuangDong University of Technology
      Shengcheng, Guangdong, China
  • 2011
    • The Hong Kong Polytechnic University
      • Department of Electronic and Information Engineering
      Hong Kong, Hong Kong
  • 2006–2011
    • King's College London
      • Department of Electronic Engineering
      Londinium, England, United Kingdom
  • 2006–2010
    • Queen Mary, University of London
      • School of Mathematical Sciences
      London, ENG, United Kingdom
  • 2008
    • University of London
      Londinium, England, United Kingdom