Publications (38)49.22 Total impact


 [Show abstract] [Hide abstract]
ABSTRACT: The new measures computed here are the spectral detrended fluctuation analysis (sDFA) and spectral multitaper 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 timeseries 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 KolmogorovSinai entropy (KSEntropy) for the proposed chosen models; and pvalues when the models were compared statistically by KruskalWallis 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 KSEntropy.  [Show abstract] [Hide abstract]
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 continuousvalued 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.  [Show abstract] [Hide abstract]
ABSTRACT: In this chapter, a twochannel 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 joint norm relaxed sequential quadratic programming and 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. © 2013 SpringerVerlag Berlin Heidelberg. All rights are reserved. 

Conference Paper: Extracting underlying trend and predicting power usage via joint SSA and sparse binary programming
[Show abstract] [Hide abstract]
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 precursor 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.  [Show abstract] [Hide abstract]
ABSTRACT: This paper develops a combined optimal pulse width modulation (PWM) and pulse frequency modulation (PFM) strategy for controlling switched mode DCDC converters. The peak ripple magnitudes of both the outputvoltages 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 multiobjective 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 multiobjective 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 outputvoltages 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. 
Conference Paper: Trend extraction based on HilbertHuang transform
[Show abstract] [Hide abstract]
ABSTRACT: Trend extraction is an important tool for the analysis of data sequences. This paper presents a new methodology for trend extraction based on HilbertHuang 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. 
Article: Global Optimal Design of IIR Filters via Constraint Transcription and Filled Function Methods
[Show abstract] [Hide abstract]
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.  [Show abstract] [Hide abstract]
ABSTRACT: In this paper, a nonlinear phase finite impulse response (FIR) filter is designed without imposing a desired phase response. The maximum passband group delay of the filter is minimized subject to a positivity constraint on the passband group delay response of the filter as well as a specification on the maximum absolute difference between the desired magnitude square response and the designed magnitude square response over both the passband and the stopband. This filter design problem is a quadratic NP hard functional inequality constrained optimization problem. To tackle this problem, first, the one norm functional inequality constraint of the optimization problem is approximated by a smooth function so that the quadratic NP hard functional inequality constrained optimization problem is converted to a nonconvex functional inequality constrained optimization problem. Then, a modified filled function method is applied for finding the global minimum of the nonconvex optimization problem. By using a local minimum of the corresponding unconstrained optimization problem as the initial condition of our proposed global optimization algorithm, computer numerical simulation results show that our proposed approach could efficiently and effectively design a minimax passband group delay nonlinear phase peak constrained FIR filter without imposing a desired phase response. 
Article: TwoStage Optimization Method for Efficient Power Converter Design Including Light Load Operation
[Show abstract] [Hide abstract]
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. Computeraided design optimization is developed in this research study, to optimize offline power converter efficiency from light load to full load. A twostage 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 twoFET forward converters are built to verify the optimization results. 
Conference Paper: Optimal overcomplete kernel design for sparse representations via discrete fractional Fourier transforms
[Show abstract] [Hide abstract]
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.  [Show abstract] [Hide abstract]
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 2norms 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.  [Show abstract] [Hide abstract]
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.  [Show abstract] [Hide abstract]
ABSTRACT: This Letter displays, via the numerical simulation of a real digital filter, that a finitestate machine may behave in a nearchaotic way even when its corresponding infinitestate machine does not exhibit chaotic behavior. 
Article: TwoChannel Linear Phase FIR QMF Bank Minimax Design via Global Nonconvex Optimization Programming
[Show abstract] [Hide abstract]
ABSTRACT: In this correspondence, a twochannel linear phase finiteimpulseresponse (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. 
Conference Paper: Single step optimal block matched motion estimation with motion vectors having arbitrary pixel precisions
[Show abstract] [Hide abstract]
ABSTRACT: This paper proposes a nonlinear 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. 
Conference Paper: Decentralized spectral resource allocation for OFDMA downlink of coexisting macro/femto networks using filled function method
[Show abstract] [Hide abstract]
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. 
Publication Stats
146  Citations  
49.22  Total Impact Points  
Top Journals
Institutions

20102014

University of Lincoln
 School of Engineering
Lincoln, England, United Kingdom


2012

GuangDong University of Technology
Shengcheng, Guangdong, China


20062011

King's College London
 Department of Electronic Engineering
Londinium, England, United Kingdom


20052011

The Hong Kong Polytechnic University
 • Department of Electronic and Information Engineering
 • Department of Applied Mathematics
Hong Kong, Hong Kong
