A Differential Evolution Approach for Protein Folding Using a Lattice Model

Protein folding is a relevant computational problem in Bioinformatics, for which many heuristic algorithms have been proposed. This work presents a methodology for the application of differential evolution （DE） to the problem of protein folding, using the bi-dimensional hydrophobic-polar model. DE is a relatively recent evolutionary algorithm, and has been used successfully in several engineering optimization problems, usually with continuous variables. We introduce the concept of genotype-phenotype mapping in DE in order to provide a mapping between the real-valued vector and an actual folding. The methodology is detailed and several experiments with benchmarks are done. We compared the results with other similar implementations. The proposed DE has shown to be competitive, statistically consistent and very promising.     

Optimal decentralized control of large scale systems

This paper presents a new optimized decentralized controller design method for solving the tracking and disturbance rejection problems for large-scale linear time-invariant systems, using only low-order decentralized controllers. To illustrate the type of results which can be obtained using the new optimized decentralized control design method, the control of a large flexible space structure is studied and compared with the standard centralized LQR-observer controller. The order of the resultant decentralized controller is much smaller than that of the standard centralized LQR-observer controller. The proposed controller also has certain fail-safe properties and, in addition, it can be five orders of magnitude more robust than the standard LQR-observer controller based on their real stability radii. The new decentralized controller design method is applied to a large flexible space structure system with 5 inputs and 5 outputs and of order 24.     

A simplified bit metric calculation method for high-order PSK

This paper proposes a simplified bit metric calculation method for high-order PSK signal with Gray-mapping. This method calculates the bit metric recursively by the symmetry of Gray code. Analysis and simulation results show that the proposed method can significantly reduce the computational complexity and maintain similar performance compared to ML and Max-Log bit metric. Additionally, this method generates bit metrics of PSK signals with different modulation order in the same way, so it can be applied to adaptive modulation system.     

High-Accuracy and High-Speed Calculation Method for Large-Scale Multi-layer Network Designs by Integrated Decomposition Method

A multi-layer network design, which excuses light-path design and IP routing design at the same time, has attracted great attention for IP over WDM network designs. The multi-layer network design problem can be solved by using a MILP （mixed integer linear programming） problem. However, the MILP problem for a large-scale network cannot be calculated due to the huge amount of variables used in the computation. In order to cope with this problem, a calculation method, which decomposes the original MILP problem into smaller sub-problems and obtains an approximate solution by solving these smaller MILP problems, has been proposed. However, this method has a defect that the calculation accuracy is degraded. In order to cope with this problem, we propose a novel method that solves the original MILP problem using the results of the sub-problems. We evaluate our proposed method by the computational experiments and show the effectiveness of our method.     

Discretization of nonlinear non-affine time delay systems based on second-order hold

When calculating the sampled-date representation of nonlinear systems second-order hold(SOH) assumption can be applied to improving the precision of the discretization results. This paper proposes a discretization method based on Taylor series and the SOH assumption for the nonlinear systems with the time delayed non-affine input. The mathematical structure of the proposed discretization method is explored. This proposed discretization method can provide a precise and finite dimensional discretization model for the nonlinear time-delayed non-affine system by keeping the truncation order of the Taylor series. The performance of the proposed discretization method is evaluated by doing the simulation using a nonlinear system with the time-delayed non-affine input.Different input signals, time-delay values and sampling periods are considered in the simulation to investigate the proposed method.The simulation results demonstrate that the proposed method is practical and easy for time-delayed nonlinear non-affine systems.The comparison between SOH assumption with first-order hold(FOH) and zero-order hold(ZOH) assumptions is given to show the advantages of the proposed method.     

Identification schemes for unmanned excavator arm parameters

Parameter identification is a key requirement in the field of automated control of unmanned excavators （UEs）. Furthermore, the UE operates in unstructured, often hazardous environments, and requires a robust parameter identification scheme for field applications. This paper presents the results of a research study on parameter identification for UE. Three identification methods, the Newton-Raphson method, the generalized Newton method, and the least squares method are used and compared for prediction accuracy, robustness to noise and computational speed. The techniques are used to identify the link parameters （mass, inertia, and length） and friction coefficients of the full-scale UE. Using experimental data from a full-scale field UE, the values of link parameters and the friction coefficient are identified. Some of the identified parameters are compared with measured physical values. Furthermore, the joint torques and positions computed by the proposed model using the identified parameters are validated against measured data. The comparison shows that both the Newton-Raphson method and the generalized Newton method are better in terms of prediction accuracy. The Newton-Raphson method is computationally efficient and has potential for real time application, but the generalized Newton method is slightly more robust to measurement noise. The experimental data were obtained in collaboration with QinetiQ Ltd.     

Robust sliding mode control using adaptive switching gain for induction motors

A robust sliding mode approach combined with a field oriented control （FOC） for induction motor （IM） speed control is presented. The proposed sliding mode control （SMC） design uses an adaptive switching gain and an integrator. This approach guarantees the same robustness and dynamic performance of traditional SMC algorithms. And at the same time, it attenuates the chattering phenomenon, which is the main drawback in actual implementation of this technique. This approach is insensitive to uncertainties and permits to decrease the requirement for the bound of these uncertainties. The stability and robustness of the closed- loop system are proven analytically using the Lyapunov synthesis approach. The proposed method attenuates the effect of both uncertainties and external disturbances. Experimental results are presented to validate the effectiveness and the good performance of the developed method.     

Particle swarm optimization for time-optimal control design

In this paper,a particle swarm optimization(PSO)based method is proposed to obtain the time-optimal bang-bang control law for both linear and nonlinear systems.By introducing a penalty function,the method can be modified to deal with systems with constraints.Compared with existing computational methods,the proposed method can be implemented in a straightforward manner.The convergent solutions can be achieved by selecting suitable PSO parameters regardless of the initial guess of the switching times.A double integrator and a third-order nonlinear system are used to demonstrate the effectiveness and robustness of the proposed method.The method is applied to obtain the time-optimal control law for a high performance linear motion positioning system.The results show the practicality of the proposed algorithm.     

A particle swarm optimizer with chaotic self-feedback for global optimization of multimodal functions

This paper proposes an improved particle swarm optimization （PSO） with iterative chaotic map with infinite collapses （ICMIC） perturbations （ICMICPSO） for global optimization of multimodal functions. The chaotic perturbation generated by the ICMIC is incorporated into the particle＇s velocity updating rule as self-feedback to make the particles have a larger potential space to fly. With the coefficient of chaotic perturbation decaying, the dynamics of ICMICPSO algorithm is a chaotic dynamics first and then a steepest descent dynamics. The proposed ICMICPSO method as hybrid optimization is tested on several widely used multimodal functions. Numerical results of the proposed algorithm are compared with that of some other Chaotic PSO variants available in the existing literature. The performance studies demonstrate that the effectiveness and efficiency of the proposed ICMICPSO approach are comparably to or better than that of the other CPSO variants for solving the global optimization of multimodal functions.     

New Model and Algorithm for Hardware/Software Partitioning

This paper focuses on the algorithmic aspects for the hardware/software （HW/SW） partitioning which searches a reasonable composition of hardware and software components which not only satisfies the constraint of hardware area but also optimizes the execution time. The computational model is extended so that all possible types of communications can be taken into account for the HW/SW partitioning. Also, a new dynamic programming algorithm is proposed on the basis of the computational model, in which source data, rather than speedup in previous work, of basic scheduling blocks are directly utilized to calculate the optimal solution. The proposed algorithm runs in O（n·A） for n code fragments and the available hardware area A. Simulation results show that the proposed algorithm solves the HW/SW partitioning without increase in running time, compared with the algorithm cited in the literature.     

Harnessing the Power of GPUs to Speed Up Feature Selection for Outlier Detection

Acquiring a set of features that emphasize the differences between normal data points and outliers can drastically facilitate the task of identifying outliers. In our work, we present a novel non-parametric evaluation criterion for filter-based feature selection which has an eye towards the final goal of outlier detection. The proposed method seeks the subset of features that represent the inherent characteristics of the normal dataset while forcing outliers to stand out, making them more easily distinguished by outlier detection algorithms. Experimental results on real datasets show the advantage of our feature selection algorithm compared with popular and state-of-the-art methods. We also show that the proposed algorithm is able to overcome the small sample space problem and perform well on highly imbalanced datasets. Furthermore, due to the highly parallelizable nature of the feature selection, we implement the algorithm on a graphics processing unit （GPU） to gain significant speedup over the serial version. The benefits of the GPU implementation are two-fold, as its performance scales very well in terms of the number of features, as well as the number of data points.     

Self Localization Method Using Parallel Projection Model for Mobile Sensor in Navigation Applications

This paper presents a novel self localization method using parallel projection model for mobile sensor in navigation applications.The algorithm estimates the coordinate and the orientation of mobile sensor using projected references on visual image.The proposed method considers the lens non-linearity of the camera and compensates the distortion by using a calibration table.The method determines the coordinates and orientations with iterative process,which is very accurate with low computational demand.We...     

Edit Propagation via Edge-Aware Filtering

This paper presents a novel framework for efficiently propagating the stroke-based user edits to the regions with similar colors and locations in high resolution images and videos.Our framework is based on the key observation that the edit propagation intrinsically can also be achieved by utilizing recently proposed edge-preserving filters.Therefore,instead of adopting the traditional global optimization which may involve a time-consuming solution,our algorithm propagates edits with the aid of the edge-preserve filters.Such a propagation scheme has low computational complexity and supports multiple kinds of strokes for more flexible user interactions.Further,our method can be easily and efficiently implemented in GPU.The experimental results demonstrate the efficiency and user-friendliness of our approach.     

Experimental evaluation of a real-time GPU-based pose estimation system for autonomous landing of rotary wings UAVs

This paper proposes a real-time system for pose estimation of an unmanned aerial vehicle (UAV) using parallel image processing and a fiducial marker. The system exploits the capabilities of a high-performance CPU/GPU embedded system in order to provide on-board high-frequency pose estimation enabling autonomous takeoff and landing. The system is evaluated extensively with lab and field tests using a custom quadrotor. The autonomous landing is successfully demonstrated, through experimental tests, using the proposed algorithm. The results show that the system is able to provide precise pose estimation with a framerate of at least 30\,fps and an image resolution of 640?480 pixels. The main advantage of the proposed approach is in the use of the GPU for image filtering and marker detection. The GPU provides an upper bound on the required computation time regardless of the complexity of the image thereby allowing for robust marker detection even in cluttered environments.     

Modeling and identification of a PEM fuel cell humidification system

A theoretical model of a humidifier of proton exchange membrane （PEM） fuel cell systems is developed and analyzed first in this paper. The model shows that there exists a strong nonlinearity in the system. Then, the system is identified using a wavelet networks method. To avoid the curse-of-dimensionality problem, a class of wavelet networks proposed by Billings is employed. The experimental data acquired from the test bench are used for identification. The one-step-ahead predictions and the five-step-ahead predictions are compared with the real measurements, respectively. It shows that the identified model can effectively describe the real system.     

Full-parameter constrained parsimonious subspace identification with steady-state information for DC–DC converters

A full-parameter constrained parsimonious subspace identification method that incorporates the steady-state a priori information of the system is proposed to model the DC–DC converters. A parsimonious model with fewer parameters is used to represent the system, and then an optimal weighted methods is used to estimate the system parameters matrices by taking into account both dynamical data and steady-state data. Compared with traditional data-driven methods for DC–DC converters, the subspace-based method can simultaneously estimate model structure and parameter with appropriate computational complexity. Moreover, compared with the traditional full-parameter constrained subspace approach, the proposed algorithm can accurately estimate the system parameters with a smaller variance. The experimental results on a DC–DC synchronous buck converter verify the effectiveness and superiority of the proposed method.     

Fast adaptive regression-basedmodel predictive control

Model predictive control (MPC) is an optimal control method that predicts the future states of the system being controlled and estimates the optimal control inputs that drive the predicted states to the required reference. The computations of the MPC are performed at pre-determined sample instances over a finite time horizon. The number of sample instances and the horizon length determine the performance of the MPC and its computational cost. A long horizon with a large sample count allows the MPC to better estimate the inputs when the states have rapid changes over time, which results in better performance but at the expense of high computational cost. However, this long horizon is not always necessary, especially for slowly-varying states. In this case, a short horizon with less sample count is preferable as the same MPC performance can be obtained but at a fraction of the computational cost. In this paper,we propose an adaptive regression-based MPC that predicts the bestminimum horizon length and the sample count from several features extracted from the time-varying changes of the states. The proposed technique builds a synthetic dataset using the system model and utilizes the dataset to train a support vector regressor that performs the prediction. The proposed technique is experimentally compared with several state-of-the-art techniques on both linear and non-linear models. The proposed technique shows a superior reduction in computational time with a reduction of about 35–65% compared with the other techniques without introducing a noticeable loss in performance.     

A coarse-to-fine image registration method based on visual attention model

Image registration is fundamental and crucial to remote sensing. However getting highly accurate registration performance automatically and fast for large-field images consistently is a challenge. As a work around to this problem, we propose a new image registration concept based on visual attention in this paper. This concept employs the advantages of feature-based or area-based methods to improve the precision and efficiency of image registration. The key concept of proposed integrated scheme is to make optimum use of the highly prominent details in the full scene by means of visual attention computational mechanism. To testify the validation, comparisons with other classical methods are carried out on real-world images. The experimental results show that the proposed method can effectively perform on multi-view/multi-temporal remote sensing images with outstanding precision and time saving performance.     

An efficient quadrature demodulator for medical ultrasound imaging

Quadrature demodulation is used in medical ultrasound imaging to derive the envelope and instantaneous phase of the received radio-frequency(RF) signal.In quadrature demodulation,RF signal is multiplied with the sine and cosine wave reference signal and then low-pass filtered to produce the base-band complex signal,which has high computational complexity.In this paper,we propose an efficient quadrature demodulation method for B-mode and color flow imaging,in which the RF signal is demodulated by a pair of finite impulse response filters without mixing with the reference signal,to reduce the computational complexity.The proposed method was evaluated with simulation and in vivo experiments.From the simulation results,the proposed quadrature demodulation method produced similar normalized residual sum of squares(NRSS) and velocity profile compared with the conventional quadrature demodulation method.In the in vivo color flow imaging experiments,the time of the demodulation process was 5.66 ms and 3.36 ms,for the conventional method and the proposed method,respectively.These results indicated that the proposed method can maintain the performance of quadrature demodulation while reducing computational complexity.     

MEMS gyroscope control system using a band-pass continuous-time sigma-delta modulator

This paper presents a MEMS gyroscope control system of using a high-order band-pass continuoustime sigma-delta modulator.Compared with a low-pass discrete-time sigma-delta modulator based solution,the band-pass modulator can considerably decrease the sampling frequency;moreover,the continuous-time architecture has an obvious advantage on PCB prototyping and shorter lead time of the implementation in hardware.System level simulations using MATLAB/Simulink show the proposed sixth order sigma-delta modulator can achieve a high SNR of 100 dB for an angular rate input with an amplitude of 200/s and a frequency of32 Hz.A PCB circuit implementation is simulated using Orcad/PSpice to analyze the stability,and implemented in hardware.Measurement of the power spectral density of the output bitstream reveals a noise floor of90 dBV/Hz1/2.The prototype is tested on a rate table with an angular rate input,verifying that the principle of the approach of using an electro-mechanical band-pass sigma-delta modulator control system for a MEMS gyroscope.