A Signal-Processing Adaptive Algorithm for Selective Current Harmonic Cancellation in Active Power Filters

Selective harmonic cancellation has become of primary importance in a wide range of power electronics applications, for example, uninterrupted power systems, regenerative converters, and active power filters (APFs). In such applications, the primary objectives are an accurate cancellation of selected harmonics and a quick speed of response under transients. This paper provides a novel signal-processing algorithm for selective harmonic identification based on heterodyning, moving average finite-impulse response filters, and phase-locked loop (PLL). The algorithm is applied over the current of a nonlinear load in the feedforward-based control of an APF. The PLL tracks the phase and frequency of the fundamental component. Then, the fundamental phase is multiplied by the order of the selected harmonic, and two random unitary orthogonal “axis waves” are generated. These unitary waves, rotating at the harmonic frequency, are multiplied by the input load current, thereby “moving” the Fourier series coefficients of the selected harmonic to dc (heterodyning). Moving average FIR filters are used to filter the harmonics generated in the heterodyning process from the dc signal; moving average FIR filters are very suitable for most of the power quality applications, thanks to their “comb-type” frequency response and their quick transient response. Experimental results confirm good performance for steady-state harmonic cancellation and an optimal system response to load transients. The theory of the algorithm has been developed for single- and three-phase systems.      

A New Control Technique for Three-Phase Shunt Hybrid Power Filter

This paper presents a nonlinear control technique for a three-phase shunt hybrid power filter (SHPF) to enhance its dynamic response when it is used to compensate for harmonic currents and reactive power. The dynamic model of the SHPF system is first elaborated in the stationary “abc” reference frame and then transformed into the synchronous orthogonal “dq” reference frame. The “dq” frame model is divided into two separate loops, namely, the two current dynamic inner loops and the dc-voltage dynamic outer loop. Proportional–integral (PI) controllers are utilized to control the SHPF input currents and dc-bus voltage. The currents track closely their references so that the SHPF behaves as a quasi-ideal current source connected in parallel with the load. It provides the reactive power and harmonic currents required by the nonlinear load, thereby achieving sinusoidal supply currents in phase with supply voltages under dynamic and steady-state conditions. The SHPF consists of a small-rating voltage-source inverter (VSI) in series with a fifth-harmonic tuned $LC$ passive filter. The rating of the VSI in the SHPF system is much smaller than that in the conventional shunt active power filter because the passive filter takes care of the major burden of compensation. The effectiveness of the control technique is demonstrated through simulation and experimentation under steady-state and dynamic operating conditions.      

Parametric Velocity Synthetic Aperture Radar:Signal Modeling and Optimal Methods

Velocity synthetic aperture radar (VSAR) is equipped with a linear array to receive the echoes from a radar illuminating area via multiple channels, each of which can reconstruct a reflectivity image for the same stationary scene. Based on analysis of pixel vector sampled among multi-images, VSAR may effectively suppress the strong ground clutter and improve moving target detection and location. In this paper, different Doppler-distributed properties are derived for the moving target and clutter, respectively. Then, we propose a novel parametric statistical model for VSAR by dividing the pixel vector into three components, namely, target, clutter, and noise. Furthermore, a method of adaptive implementation of optimal processing (AIOP-VSAR) is presented for moving target detection. It is shown that the optimum detection performance may be obtained via AIOP-VSAR, particularly for the slowly moving target in an inhomogeneous clutter environment. Also, the Cramer–Rao bounds (CRBs) are derived for the estimation of unknown model parameters, as well as the azimuth locations of moving targets, and the maximum-likelihood methods are proposed to reach these CRBs. Based on the proposed target detection and parameter estimation methods, we present a complete parametric flowchart for VSAR. It is demonstrated that the proposed flowchart may effectively mitigate the “azimuth location ambiguity” of VSAR and has the super-resolution ability to resolve “velocity layover” for multiple targets. Finally, some detailed numerical experiments and scene simulations are provided to show the effectiveness of the proposed methods.      

DT-MRI fiber tracking: a shortest paths approach

We derive a new fiber tracking algorithm for DT-MRI that parts with the locally “greedy” paradigm intrinsic to conventional tracking algorithms. We demonstrate the ability to precisely reconstruct a diverse range of fiber trajectories in authentic and computer-generated DT-MRI data, for which well-known conventional tracking algorithms are shown to fail. Our approach is to pose fiber tracking as a problem in computing shortest paths in a weighted digraph. Voxels serve as vertices, and edges are included between neighboring voxels. We assign probabilities (weights) to edges using a Bayesian framework. Higher probabilities are assigned to edges that are aligned with fiber trajectories in their close proximity. We compute optimal paths of maximum probability using computationally scalable shortest path algorithms. The salient features of our approach are: global optimality—unlike conventional tracking algorithms, local errors do not accumulate and one “wrong-turn” does not spell disaster; a target point is specified a priori; precise reconstruction is demonstrated for extremely low signal-to-noise ratio; impartiality to which of two endpoints is used as a seed; and, faster computation times than conventional all-paths tracking. We can use our new tracking algorithm in either a single-path tracking mode (deterministic tracking) or an all-paths tracking mode (probabilistic tracking).      

MCMC粒子滤波算法研究及应用

粒子滤波算法是一种基于贝叶斯估计的蒙特卡罗方法,适用于非线性非高斯系统的分析,被广泛应用于跟踪、定位等问题的研究中。为了解决粒子滤波算法在重采样后,丧失粒子多样性的问题,本文在粒子滤波算法的重采样步骤后,加入了马尔可夫链蒙特卡罗（Markov Chain Monte Carlo,简称MCMC）移动步骤,增加粒子的多样性。利用粒子滤波算法和MCMC粒子滤波算法对目标跟踪问题进行了仿真,并且通过分析仿真实验结果,比较了两种算法的性能,结果说明加入MCMC粒子滤波算法的性能优于粒子滤波算法。     

Active Mask Segmentation of Fluorescence Microscope Images

We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the “contour” to that of “inside and outside,” or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively.      

A "twisting and bending" model-based nonrigid image registration technique for 3-D ultrasound carotid images

Atherosclerosis at the carotid bifurcation resulting in cerebral emboli is a major cause of ischemic stroke. Most strokes associated with carotid atherosclerosis can be prevented by lifestyle/dietary changes and pharmacological treatments if identified early by monitoring carotid plaque changes. Registration of 3-D ultrasound (US) images of carotid plaque obtained at different time points is essential for sensitive monitoring of plaque changes in volume and surface morphology. This registration technique should be nonrigid, since different head positions during image acquisition sessions cause relative bending and torsion in the neck, producing nonlinear deformations between the images. We modeled the movement of the neck using a “twisting and bending” model with only six parameters for nonrigid registration. We evaluated the algorithm using 3-D US carotid images acquired at two different head positions to simulate images acquired at different times. We calculated the mean registration error (MRE) between the segmented vessel surfaces in the target image and the registered image using a distance-based error metric after applying our “twisting and bending” model-based nonrigid registration algorithm. We achieved an average registration error of $0.80 pm 0.26$ mm using our nonrigid registration technique, which was a significant improvement in registration accuracy over rigid registration, even with reduced degrees-of-freedom compared to the other nonrigid registration algorithms.      

Optical Phase Add–Drop for Format Conversion Between DQPSK and DPSK and its Application in Optical Label Switching Systems

We propose and experimentally demonstrate an optical phase “add–drop” scheme to enable the format conversion between a 20-Gb/s differential quadrature phase-shift keying (DQPSK) and 10-Gb/s differential phase-shift keying. Meanwhile, the incoming DQPSK could be directly forwarded to another wavelength through the proposed optical phase “drop” scheme, which is implemented by four-wave-mixing effect in highly nonlinear fiber. By orthogonally encoding the label and payload of the optical packet on the in-phase ( $I$) and quadrature ( $ Q $) components of DQPSK, the proposed scheme could be applied in the optical label switching networks. The label erasing and rewriting of optical packets can be achieved through the proposed optical phase “drop” and “add” schemes, respectively.      

Application of Representation Theory to Dual-Mode Microwave Bandpass Filters

This paper discusses the physics behind the operation of dual-mode bandpass filters from a field theoretical point of view. It is argued that the two degenerate modes of the empty dual-mode cavity, commonly taken as the vertical and horizontal polarizations, become nonphysical when coupling and tuning elements are inserted. Instead, the original degenerate modes are rotated, or modified in a complex way, to generate two new modes whose characteristics depend on the coupling and tuning elements. It is shown that the tuning elements, as placed in existing dual-mode filter designs, act as both tuning and coupling elements. A working dual-mode filter can be designed with only “tuning” elements present. A physical representation of dual-mode filters in terms of the eigenresonances of the dual-mode cavities, with the tuning and coupling elements present, is introduced. Two fourth-order dual-mode rectangular cavity filters with the same response in the passband and its vicinity are also presented to demonstrate the similar role played by “tuning” and coupling elements in dual-mode cavities. The first filter uses only “tuning” elements, while the second is based only on “coupling” elements.      

Properties of the Stimulus Router System, a Novel Neural Prosthesis

Various types of neural prostheses (NPs) have been developed to restore motor function after neural injury. Surface NPs are noninvasive and inexpensive, but are often poorly selective, activating nontargeted muscles and cutaneous sensory nerves that can cause discomfort or pain. Implantable NPs are highly selective, but invasive and costly. The stimulus router system (SRS) is a novel NP consisting of fully implanted leads that “capture” and route some of the current flowing between a pair of surface electrodes to the vicinity of a target nerve. An SRS lead consists of a “pick-up” terminal that is implanted subcutaneously under one of the surface electrodes and a “delivery” terminal that is secured on or near the target nerve. We have published a preliminary report on the basic properties of the SRS [L. S. Gan , “A new means of transcutaneous coupling for neural prostheses,” IEEE Trans. Biomed. Eng., vol. 54, no. 3, pp. 509–517, Mar. 2007]. Here, we further characterize the SRS and identify aspects that maximize its performance as a motor NP. The surface current needed to activate nerves with an SRS, was found to depend on the proximity of the delivery terminal(s) to the nerve, electrode configurations, contact areas of the surface electrodes and implanted terminals, and the distance between the surface anode and the delivery terminal.      

An Active Contour Model for Segmenting and Measuring Retinal Vessels

This paper presents an algorithm for segmenting and measuring retinal vessels, by growing a “Ribbon of Twins” active contour model, which uses two pairs of contours to capture each vessel edge, while maintaining width consistency. The algorithm is initialized using a generalized morphological order filter to identify approximate vessels centerlines. Once the vessel segments are identified the network topology is determined using an implicit neural cost function to resolve junction configurations. The algorithm is robust, and can accurately locate vessel edges under difficult conditions, including noisy blurred edges, closely parallel vessels, light reflex phenomena, and very fine vessels. It yields precise vessel width measurements, with subpixel average width errors. We compare the algorithm with several benchmarks from the literature, demonstrating higher segmentation sensitivity and more accurate width measurement.      

PSD – probabilistic algorithm for mobile robot 6D localization without natural and artificial landmarks based on 2.5D map and a new type of laser scanner in GPS-denied scenarios

This paper presents an approach to mobile robot 6D localization based on a 3D laser scanner in GPS-denied scenarios. Commonly, 6D localization using laser scanners is performed with the use of extraction and association of the features or by comparison of the whole scans (very often off-line) using the ICP algorithm or its modifications. However, in some unstructured non-urbanized rough terrain environments, feature extraction does not seem to be reliable enough. For such kind of environment, we present a new method to mobile robot localization in GPS-denied applications, called PSD (Point-to-Surfel Distance). Unlike state of the art localization methods using laser scanners, we consider every single laser scanner measurement as an observation and use Point-to-Surfel Distance for correction of position and orientation of the robot. Mobile robot localization is based on a specific representation of the terrain in the 2.5D surfel map (terrain height and inclination). The simulation tests compared our method using extended Kalman filter (EKF) and single laser scanner measurements with an up-to-date method using particle filter (PF) and comparing the scan lines with the reference map and with another method using Gaussian mixture maps. The tests confirmed that the proposed method provides satisfying results for GPS-denied scenarios in rough terrain without extractable landmarks and our method is thirty times faster than the PF method (serial implementation). KITTI benchmark tests and real terrain experiments confirmed its usefulness and advantages as well.     

非线性高斯系统边缘分布多目标贝叶斯滤波器

为解决存在数据关联不确定、检测不确定和杂波情况下的多目标跟踪问题,提出了一种新的多目标贝叶斯滤波器.代替维持多目标状态的联合后验密度,所提出的贝叶斯滤波器联合传递各个目标状态的边缘分布和它们的存在概率.为了处理目标运动和传感器测量模型中的非线性,利用无迹变换技术提出了一种非线性高斯条件下边缘分布贝叶斯滤波器的近似实现算法.仿真实验结果表明,与PHD(Probability Hypothesis Density)滤波器相比,所提出的滤波器具有更好的多目标跟踪能力.     

Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction

This letter presents a novel two-stage noise adaptive fuzzy switching median (NAFSM) filter for salt-and-pepper noise detection and removal. Initially, the detection stage will utilize the histogram of the corrupted image to identify noise pixels. These detected “noise pixels” will then be subjected to the second stage of the filtering action, while “noise-free pixels” are retained and left unprocessed. Then, the NAFSM filtering mechanism employs fuzzy reasoning to handle uncertainty present in the extracted local information as introduced by noise. Simulation results indicate that the NAFSM is able to outperform some of the salt-and-pepper noise filters existing in literature.      

Image-Driven Population Analysis Through Mixture Modeling

We present iCluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The output of the algorithm is a small number of template images that represent different modes in a population. This is in contrast with traditional, hypothesis-driven computational anatomy approaches that assume a single template to construct an atlas. We derive the algorithm based on a generative model of an image population as a mixture of deformable template images. We validate and explore our method in four experiments. In the first experiment, we use synthetic data to explore the behavior of the algorithm and inform a design choice on parameter settings. In the second experiment, we demonstrate the utility of having multiple atlases for the application of localizing temporal lobe brain structures in a pool of subjects that contains healthy controls and schizophrenia patients. Next, we employ iCluster to partition a data set of 415 whole brain MR volumes of subjects aged 18 through 96 years into three anatomical subgroups. Our analysis suggests that these subgroups mainly correspond to age groups. The templates reveal significant structural differences across these age groups that confirm previous findings in aging research. In the final experiment, we run iCluster on a group of 15 patients with dementia and 15 age-matched healthy controls. The algorithm produces two modes, one of which contains dementia patients only. These results suggest that the algorithm can be used to discover subpopulations that correspond to interesting structural or functional “modes.”      

Robust Reduced-Rank Adaptive Algorithm Based on Parallel Subgradient Projection and Krylov Subspace

In this paper, we propose a novel reduced-rank adaptive filtering algorithm exploiting the Krylov subspace associated with estimates of certain statistics of input and output signals. We point out that, when the estimated statistics are erroneous (e.g., due to sudden changes of environments), the existing Krylov-subspace-based reduced-rank methods compute the point that minimizes a “wrong” mean-square error (MSE) in the subspace. The proposed algorithm exploits the set-theoretic adaptive filtering framework for tracking efficiently the optimal point in the sense of minimizing the “true” MSE in the subspace. Therefore, compared with the existing methods, the proposed algorithm is more suited to adaptive filtering applications. A convergence analysis of the algorithm is performed by extending the adaptive projected subgradient method (APSM). Numerical examples demonstrate that the proposed algorithm enjoys better tracking performance than the existing methods for system identification problems.      

Miniaturized Bandpass Filters With Double-Folded Substrate Integrated Waveguide Resonators in LTCC

This paper proposes miniaturized bandpass filters with double-folded substrate integrated waveguide (SIW) resonators using multilayer low-temperature co-fired ceramic (LTCC) technology. Formed by inserting a metal plate with two orthogonal slots into the cavity, the double-folded SIW resonator is used for the circuit size reduction with its footprint about a quarter of the conventional ${rm TE}_{101}$ mode. With LTCC technology, there is more flexibility to organize the cavities of filters because of the 3-D arrangement. The vertically stacked cavities are coupled by “L”- or “U”-shaped slots, and if arranged horizontally, by an inductive window on the common sidewall or a suspended stripline between the cavities. Through experimental measurements and simulations at both the $Ka$- $V$ -bands, it has been demonstrated that the proposed filter has compact sizes and good frequency responses. The area of the fully stacked Chebyshev filter has 88% size reduction in comparison with a three-pole planar waveguide filter, while the vertically stacked quasi-elliptic filter has 74% size reduction in comparison with a four-pole planar waveguide filter.      

一种融合地图与传感器信息的室内地图匹配新算法

基于粒子滤波技术,提出了融合地图信息与传感器信息的室内地图匹配算法,对于在室内定位中由状态空间模型描述的非线性系统,通过非参数化的蒙特卡洛( Monte Carlo)模拟方法来实现递推贝叶斯滤波,将室内地理信息数据、传感器信息、无线定位信息融入到粒子的权重值中,对观测值进行不断修正。实验证明,所提出的基于粒子滤波的地图匹配技术有效解决了由于无线定位结果穿墙、错定至隔壁房间而造成的用户体验差等问题,同时对室内定位结果进行了修正,提高了室内定位精度。     

Bootstrap Particle Filtering

This article provides an overview of nonlinear statistical signal processing based on the Bayesian paradigm. The next-generation processors are well founded on MC simulation-based sampling techniques. The development of the sequential Bayesian processor is reviewed using the state-space models. The popular bootstrap algorithm was outlined and applied to an ocean acoustic synthetic aperture towed array target tracking problem to test the performance of a particle filtering technique.     

Encoding Probabilistic Brain Atlases Using Bayesian Inference

This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. Probabilistic atlases are typically constructed by counting the relative frequency of occurrence of labels in corresponding locations across the training images. However, such an “averaging” approach generalizes poorly to unseen cases when the number of training images is limited, and provides no principled way of aligning the training datasets using deformable registration. In this paper, we generalize the generative image model implicitly underlying standard “average” atlases, using mesh-based representations endowed with an explicit deformation model. Bayesian inference is used to infer the optimal model parameters from the training data, leading to a simultaneous group-wise registration and atlas estimation scheme that encompasses standard averaging as a special case. We also use Bayesian inference to compare alternative atlas models in light of the training data, and show how this leads to a data compression problem that is intuitive to interpret and computationally feasible. Using this technique, we automatically determine the optimal amount of spatial blurring, the best deformation field flexibility, and the most compact mesh representation. We demonstrate, using 2-D training datasets, that the resulting models are better at capturing the structure in the training data than conventional probabilistic atlases. We also present experiments of the proposed atlas construction technique in 3-D, and show the resulting atlases' potential in fully-automated, pulse sequence-adaptive segmentation of 36 neuroanatomical structures in brain MRI scans.