A new class of nonlinear Rauch–Tung–Striebel cubature Kalman smoothers

In this paper, a new Rauch–Tung–Striebel type of nonlinear smoothing method is proposed based on a class of high-degree cubature integration rules. This new class of cubature Kalman smoothers generalizes the conventional third-degree cubature Kalman smoother using the combination of Genz׳s or Mysovskikh׳s high-degree spherical rule with the moment matching based arbitrary-degree radial rule, which considerably improves the estimation accuracy. A target tracking problem is utilized to demonstrate the performance of this new smoother and to compare it with other Gaussian approximation smoothers. It will be shown that this new cubature Kalman smoother enhances the filtering accuracy and outperforms the extended Kalman smoother, the unscented Kalman smoother, and the conventional third-degree cubature Kalman smoother. It also maintains close performance to the Gauss–Hermite quadrature smoother with much less computational cost.     

A novel method for estimating the track-soil parameters based on Kalman and improved strong tracking filters

A tracked vehicle has been widely used in exploring unknown environments and military fields. In current methods for suiting soil conditions, soil parameters need to be given and the traction performance cannot always be satisfied on soft soil. To solve the problem, it is essential to estimate track-soil parameters in real-time. Therefore, a detailed mathematical model is proposed for the first time. Furthermore, a novel algorithm which is composed of Kalman filter (KF) and improved strong tracking filter (STF) is developed for online track-soil estimation and named as KF–ISTF. By this method, the KF is used to estimate slip parameters, and the ISTF is used to estimate motion states. Then the key soil parameters can be estimated by using a suitable soil model. The experimental results show that equipped with the estimation algorithm, the proposed model can be used to estimate the track-soil parameters, and make the traction performance satisfied with soil conditions.     

Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints

In this article, a novel approach for 2-channel linear phase quadrature mirror filter (QMF) bank design based on a hybrid of gradient based optimization and optimization of fractional derivative constraints is introduced. For the purpose of this work, recently proposed nature inspired optimization techniques such as cuckoo search (CS), modified cuckoo search (MCS) and wind driven optimization (WDO) are explored for the design of QMF bank. 2-Channel QMF is also designed with particle swarm optimization (PSO) and artificial bee colony (ABC) nature inspired optimization techniques. The design problem is formulated in frequency domain as sum of L₂ norm of error in passband, stopband and transition band at quadrature frequency. The contribution of this work is the novel hybrid combination of gradient based optimization (Lagrange multiplier method) and nature inspired optimization (CS, MCS, WDO, PSO and ABC) and its usage for optimizing the design problem. Performance of the proposed method is evaluated by passband error (ϕ_p), stopband error (ϕ_s), transition band error (ϕ_t), peak reconstruction error (PRE), stopband attenuation (A_s) and computational time. The design examples illustrate the ingenuity of the proposed method. Results are also compared with the other existing algorithms, and it was found that the proposed method gives best result in terms of peak reconstruction error and transition band error while it is comparable in terms of passband and stopband error. Results show that the proposed method is successful for both lower and higher order 2-channel QMF bank design. A comparative study of various nature inspired optimization techniques is also presented, and the study singles out CS as a best QMF optimization technique.     

Verification of Iron Loss Calculation Method Using a High‐Precision Iron Loss Analyzer

Because of the improved performance of power devices, the volume of the ac filter inductors used in high‐frequency PWM inverters has been reduced. However, the temperature rise in the filter inductor due to this miniaturization has become more pronounced. Therefore, we have proposed an iron loss calculation method for the ac filter inductor. However, the accuracy of the value calculated via the loss map method cannot be verified, because the iron loss arising during each switching period cannot be measured with conventional power measuring instruments. In order to resolve this problem, we developed an inductor loss analyzer (ILA), which allows precise measurement of the iron loss in the inductor during each switching period. The accuracy of the calculation of iron loss in the filter inductor by the loss map method was verified with the ILA. We found that the value calculated by the loss map method differed slightly from the value measured with the ILA. However, these differences can be reduced if we take into account the accurate flux density calculation and the effect of the duty ratio of PWM pulses on the loss. Finally, we verified that the loss map method can provide accurate iron loss calculations.     

An improved adaptive unscented Kalman filter for estimating the states of in‐wheel‐motored electric vehicle

Vehicle state is essential for active safety stability control. However, the accurate measurement of some vehicle states is difficult to achieve without the use of expensive equipment. To improve estimation accuracy in real time, this paper proposes an estimator of vehicle velocity based on the adaptive unscented Kalman filter (AUKF) for an in‐wheel‐motored electric vehicle (IWMEV). Given the merits of an independent drive structure, the tire forces of the IWMEV can be directly calculated through a vehicle dynamic model. Additionally, by means of the normalized innovation square, the validity of vehicle velocity estimation can be detected, and the sliding window length can be adjusted adaptively; thus, the steady‐state error and the dynamic performance of the IWMEV are demonstrated to be simultaneously improved over an alternative approach in comparisons. Then, an adaptive adjustment strategy for the noise covariance matrices is introduced to overcome the impact of parameter uncertainties. The numerically simulated and experimental results prove that the proposed vehicle velocity estimator based on AUKF not only improves estimation accuracy but also possesses strong robustness against parameter uncertainties. The deployment of the estimation algorithm by using a single‐chip microcomputer verifies the strong real‐time performance and easy‐to‐implement characteristics of the proposed algorithm.     

Sensor interpixel correlation analysis and reduction for color filter array high dynamic range image reconstruction

High dynamic range imaging (HDRI) by bracketing of low dynamic range (LDR) images is demanding, as the sensor is deliberately operated at saturation. This exacerbates any crosstalk, interpixel capacitance, blooming and smear, all causing interpixel correlations (IC) and a deteriorated modulation transfer function (MTF). Established HDRI algorithms exclude saturated pixels, but generally overlook IC. This work presents a calibration method to estimate the affected region from saturated pixels for a color filter array (CFA) sensor, using the native CFA as a matched filter. The method minimizes color crosstalk given a set of candidates for proximity regions, and requires no special setup. Results are shown for a 21‐bit HDR output image with improved color fidelity and reduced noise. The calibration reduces IC in the LDR images and is performed only once for a given sensor. The improvement is applicable to any HDRI algorithm based on CFA image bracketing, irrespective of sensor technology. Generalizations to subsaturated and supersaturated pixels are described, facilitating a suggested irradiance‐exposure dependent point spread function charge repatriation strategy.     

Carbon nanotube filter for heavy metal ion adsorption

This study investigated carbon nanotube filtration technology using catalyst particles supported on silicalite-1–biomorphic carbon materials (BCMs). Aqueous solutions of Mn(II), Cu(II), Cr(III), Cd(II), and Pb(II) were used to test the efficiency of heavy metal ions removal. Carbon nanotubes (CNTs) were synthesized and grown on BCMs by the chemical vapor deposition method catalyzed with the catalyst (Co, Fe, and Ni). The synthesized CNTs with Co– and Fe– nanoparticles were typically multi-walled carbon nanotubes, and they showed good crystallinity (I_D/I_G = 1.05) and yield of (11.10 and 8.86) %. The removal efficiency of Mn(II), Cu(II), Cr(III), Cd(II), and Pb(II) ions using Co-catalyzed CNT filter was 97.57%, 98.01%, 97.89%, 97.42%, and 99.99%, respectively.     

Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.     

面向长距离量子通信的双光子关联成像技术

量子时域鬼成像利用光子对的时间-频率关联性实现了50km光纤链路上的图像传送,有望应用于量子通信的协议中。然而,现有方案的成像质量较差,成像速度较慢,因此其应用性在一定程度上受到了限制。文章对已有成像方案进行了较大的改进,利用可编程滤波器数字设定待成像信息,进而实现了二维图像信息的自动化实时传送。实验结果证明,改进方案的成像质量和速度均有显著改善,有利于推动量子关联成像在量子通信领域的应用。