Bilateral smoothing of gradient vector field and application to image segmentation

Medical image segmentation finds application in computer-aided diagnosis, computer-guided surgery, measuring tissue volumes, locating tumors, and pathologies. One approach to segmentation is to use active contours or snakes. Active contours start from an initialization (often manually specified) and are guided by image-dependent forces to the object boundary. Snakes may also be guided by gradient vector fields associated with an image. The first main result in this direction is that of Xu and Prince, who proposed the notion of gradient vector flow (GVF), which is computed iteratively. We propose a new formalism to compute the vector flow based on the notion of bilateral filtering of the gradient field associated with the edge map - we refer to it as the bilateral vector flow (BVF). The range kernel definition that we employ is different from the one employed in the standard Gaussian bilateral filter. The advantage of the BVF formalism is that smooth gradient vector flow fields with enhanced edge information can be computed noniteratively. The quality of image segmentation turned out to be on par with that obtained using the GVF and in some cases better than the GVF.

Common-mode filter design for PWM rectifier-based motor drives

A common-mode (CM) filter based on the LCL filter topology is proposed in this paper, which provides a parallel path for ground currents and which also restricts the magnitude of the EMI noise injected into the grid. The CM filter makes use of the components of a line to line LCL filter, which is modified to address the CM voltage with minimal additional components. This leads to a compact filtering solution. The CM voltage of an adjustable speed drive using a PWM rectifier is analyzed for this purpose. The filter design is based on the CM equivalent circuit of the drive system. The filter addresses the adverse effects of the PWM rectifier in an adjustable speed drive. Guidelines are provided on the selection of the filter components. Different variants of the filter topology are evaluated to establish the effectiveness of the proposed circuit. Experimental results based on EMI measurement on the grid side and the CM current measurement on the motor side are presented. These results validate the effectiveness of the filter.

Integrated higher-order pulse-width modulation filter-transformer structure for single-phase static compensator

A power filter is necessary to connect the output of a power converter to the grid so as to reduce the harmonic distortion introduced in the line current and voltage by the power converter. Many a times, a transformer is also present before the point of common coupling. Magnetic components often constitute a significant part of the overall weight, size and cost of the grid interface scheme. So, a compact inexpensive design is desirable. A higher-order LCL-filter and a transformer are increasingly being considered for grid interconnection of the power converter. This study proposes a design method based on a three-winding transformer, that generates an integrated structure that behaves as an LCL-filter, with both the filter inductances and the transformer that are merged into a single electromagnetic component. The parameters of the transformer are derived analytically. It is shown that along with a filter capacitor, the transformer parameters provide the filtering action of an LCL-filter. A single-phase full-bridge power converter is operated as a static compensator for performance evaluation of the integrated filter transformer. A resonant integrator-based single-phase phase locked loop and stationary frame AC current controller are employed for grid frequency synchronisation and line current control, respectively.

Dynamic state estimation for identifying earthquake support motions in instrumented structures

The problem of identification of multi-component and (or) spatially varying earthquake support motions based on measured responses in instrumented structures is considered. The governing equations of motion are cast in the state space form and a time domain solution to the input identification problem is developed based on the Kalman and particle filtering methods. The method allows for noise in measured responses, imperfections in mathematical model for the structure, and possible nonlinear behavior of the structure. The unknown support motions are treated as hypothetical additional system states and a prior model for these motions are taken to be given in terms of white noise processes. For linear systems, the solution is developed within the Kalman filtering framework while, for nonlinear systems, the Monte Carlo simulation based particle filtering tools are employed. In the latter case, the question of controlling sampling variance based on the idea of Rao-Blackwellization is also explored. Illustrative examples include identification of multi-component and spatially varying support motions in linear/nonlinear structures.

A scaled unscented transformation based directed Gaussian sum filter for nonlinear dynamic system identification

Impoverishment of particles, i.e. the discretely simulated sample paths of the process dynamics, poses a major obstacle in employing the particle filters for large dimensional nonlinear system identification. A known route of alleviating this impoverishment, i.e. of using an exponentially increasing ensemble size vis-a-vis the system dimension, remains computationally infeasible in most cases of practical importance. In this work, we explore the possibility of unscented transformation on Gaussian random variables, as incorporated within a scaled Gaussian sum stochastic filter, as a means of applying the nonlinear stochastic filtering theory to higher dimensional structural system identification problems. As an additional strategy to reconcile the evolving process dynamics with the observation history, the proposed filtering scheme also modifies the process model via the incorporation of gain-weighted innovation terms. The reported numerical work on the identification of structural dynamic models of dimension up to 100 is indicative of the potential of the proposed filter in realizing the stated aim of successfully treating relatively larger dimensional filtering problems. (C) 2013 Elsevier Ltd. All rights reserved.

Reach task-associated excitatory overdrive of motor cortical neurons following infusion with ALS-CSF

Converging evidence from transgenic animal models of amyotrophic lateral sclerosis (ALS) and human studies suggest alterations in excitability of the motor neurons in ALS. Specifically, in studies on human subjects with ALS the motor cortex was reported to be hyperexcitable. The present study was designed to test the hypothesis that infusion of cerebrospinal fluid from patients with sporadic ALS (ALS-CSF) into the rat brain ventricle can induce hyperexcitability and structural changes in the motor cortex leading to motor dysfunction. A robust model of sporadic ALS was developed experimentally by infusing ALS-CSF into the rat ventricle. The effects of ALS-CSF at the single neuron level were examined by recording extracellular single unit activity from the motor cortex while rats were performing a reach to grasp task. We observed an increase in the firing rate of the neurons of the motor cortex in rats infused with ALS-CSF compared to control groups. This was associated with impairment in a specific component of reach with alterations in the morphological characteristics of the motor cortex. It is likely that the increased cortical excitability observed in the present study could be the result of changes in the intrinsic properties of motor cortical neurons, a dysfunctional inhibitory mechanism and/or an underlying structural change culminating in a behavioral deficit.

Iterated gain-based stochastic filters for dynamic system identification

We propose a novel form of nonlinear stochastic filtering based on an iterative evaluation of a Kalman-like gain matrix computed within a Monte Carlo scheme as suggested by the form of the parent equation of nonlinear filtering (Kushner-Stratonovich equation) and retains the simplicity of implementation of an ensemble Kalman filter (EnKF). The numerical results, presently obtained via EnKF-like simulations with or without a reduced-rank unscented transformation, clearly indicate remarkably superior filter convergence and accuracy vis-a-vis most available filtering schemes and eminent applicability of the methods to higher dimensional dynamic system identification problems of engineering interest. (C) 2013 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Active Dendrites Regulate Spectral Selectivity in Location-Dependent Spike Initiation Dynamics of Hippocampal Model Neurons

How does the presence of plastic active dendrites in a pyramidal neuron alter its spike initiation dynamics? To answer this question, we measured the spike-triggered average (STA) from experimentally constrained, conductance-based hippocampal neuronal models of various morphological complexities. We transformed the STA computed from these models to the spectral and the spectrotemporal domains and found that the spike initiation dynamics exhibited temporally localized selectivity to a characteristic frequency. In the presence of the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, the STA characteristic frequency strongly correlated with the subthreshold resonance frequency in the theta frequency range. Increases in HCN channel density or in input variance increased the STA characteristic frequency and its selectivity strength. In the absence of HCN channels, the STA exhibited weak delta frequency selectivity and the characteristic frequency was related to the repolarization dynamics of the action potentials and the recovery kinetics of sodium channels from inactivation. Comparison of STA obtained with inputs at various dendritic locations revealed that nonspiking and spiking dendrites increased and reduced the spectrotemporal integration window of the STA with increasing distance from the soma as direct consequences of passive filtering and dendritic spike initiation, respectively. Finally, the presence of HCN channels set the STA characteristic frequency in the theta range across the somatodendritic arbor and specific STA measurements were strongly related to equivalent transfer-impedance-related measurements. Our results identify explicit roles for plastic active dendrites in neural coding and strongly recommend a dynamically reconfigurable multi-STA model to characterize location-dependent input feature selectivity in pyramidal neurons.

A Kushner-Stratonovich Monte Carlo filter applied to nonlinear dynamical system identification

A Monte Carlo filter, based on the idea of averaging over characteristics and fashioned after a particle-based time-discretized approximation to the Kushner-Stratonovich (KS) nonlinear filtering equation, is proposed. A key aspect of the new filter is the gain-like additive update, designed to approximate the innovation integral in the KS equation and implemented through an annealing-type iterative procedure, which is aimed at rendering the innovation (observation prediction mismatch) for a given time-step to a zero-mean Brownian increment corresponding to the measurement noise. This may be contrasted with the weight-based multiplicative updates in most particle filters that are known to precipitate the numerical problem of weight collapse within a finite-ensemble setting. A study to estimate the a-priori error bounds in the proposed scheme is undertaken. The numerical evidence, presently gathered from the assessed performance of the proposed and a few other competing filters on a class of nonlinear dynamic system identification and target tracking problems, is suggestive of the remarkably improved convergence and accuracy of the new filter. (C) 2013 Elsevier B.V. All rights reserved.

Common-Mode and Differential-Mode Active Damping for PWM Rectifiers

Modern pulse-width-modulated (PWM) rectifiers use LC L filters that can be applied in both the common mode and differential mode to obtain high-performance filtering. Interaction between the passive L and C components in the filter leads to resonance oscillations. These oscillations need to be damped either by the passive damping or active damping. The passive damping increases power loss and can reduce the effectiveness of the filter. Methods of active damping, using control strategy, are lossless while maintaining the effectiveness of the filters. In this paper, an active damping strategy is proposed to damp the oscillations in both line-to-line and line-to-ground. An approach based on pole placement by the state feedback is used to actively damp both the differential-and common-mode filter oscillations. Analytical expressions for the state-feedback controller gains are derived for both continuous and discrete-time model of the filter. Tradeoff in selection of the active damping gain on the lower order power converter harmonics is analyzed using a weighted admittance function. Experimental results on a 10-kVA laboratory prototype PWM rectifier are presented. The results validate the effectiveness of the active damping method, and the tradeoff in the settings of the damping gain.

Threshold-Independent QRS Detection Using the Dynamic Plosion Index

Detection of QRS serves as a first step in many automated ECG analysis techniques. Motivated by the strong similarities between the signal structures of an ECG signal and the integrated linear prediction residual (ILPR) of voiced speech, an algorithm proposed earlier for epoch detection from ILPR is extended to the problem of QRS detection. The ECG signal is pre-processed by high-pass filtering to remove the baseline wandering and by half-wave rectification to reduce the ambiguities. The initial estimates of the QRS are iteratively obtained using a non-linear temporal feature, named the dynamic plosion index suitable for detection of transients in a signal. These estimates are further refined to obtain a higher temporal accuracy. Unlike most of the high performance algorithms, this technique does not make use of any threshold or differencing operation. The proposed algorithm is validated on the MIT-BIH database using the standard metrics and its performance is found to be comparable to the state-of-the-art algorithms, despite its threshold independence and simple decision logic.

A Contraction Mapping Approach for Robust Estimation of Lagged Autocorrelation

We consider the zero-crossing rate (ZCR) of a Gaussian process and establish a property relating the lagged ZCR (LZCR) to the corresponding normalized autocorrelation function. This is a generalization of Kedem's result for the lag-one case. For the specific case of a sinusoid in white Gaussian noise, we use the higher-order property between lagged ZCR and higher-lag autocorrelation to develop an iterative higher-order autoregressive filtering scheme, which stabilizes the ZCR and consequently provide robust estimates of the lagged autocorrelation. Simulation results show that the autocorrelation estimates converge in about 20 to 40 iterations even for low signal-to-noise ratio.

Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning

It is well known that the impulse response of a wide-band wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this paper, we consider the estimation of the unknown channel coefficients and its support in OFDM systems using a sparse Bayesian learning (SBL) framework for exact inference. In a quasi-static, block-fading scenario, we employ the SBL algorithm for channel estimation and propose a joint SBL (J-SBL) and a low-complexity recursive J-SBL algorithm for joint channel estimation and data detection. In a time-varying scenario, we use a first-order autoregressive model for the wireless channel and propose a novel, recursive, low-complexity Kalman filtering-based SBL (KSBL) algorithm for channel estimation. We generalize the KSBL algorithm to obtain the recursive joint KSBL algorithm that performs joint channel estimation and data detection. Our algorithms can efficiently recover a group of approximately sparse vectors even when the measurement matrix is partially unknown due to the presence of unknown data symbols. Moreover, the algorithms can fully exploit the correlation structure in the multiple measurements. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the mean-square error and bit error rate performance.

On-line Estimation of Fundamental and Ripple Components of Line Currents in a Voltage-Source Inverter Operated at Low Switching Frequency

High-power voltage-source inverters (VSI) are often switched at low frequencies due to switching loss constraints. Numerous low-switching-frequency PWM techniques have been reported, which are quite successful in reducing the total harmonic distortion under open-loop conditions at such low operating frequencies. However, the line current still contains low-frequency components (though of reduced amplitudes), which are fed back to the current loop controller during closed-loop operation. Since the harmonic frequencies are quite low and are not much higher than the bandwidth of the current loop, these are amplified by the current controller, causing oscillations and instability. Hence, only the fundamental current should be fed back. Filtering out these harmonics from the measured current (before feeding back) leads to phase shift and attenuation of the fundamental component, while not eliminating the harmonics totally. This paper proposes a method for on-line extraction of the fundamental current in induction motor drives, modulated with low-switching-frequency PWM. The proposed method is validated through simulations on MATLAB/Simulink. Further, the proposed algorithm is implemented on Cyclone FPGA based controller board. Experimental results are presented for an R-L load.

MULTI-PITCH TRACKING USING GAUSSIAN MIXTURE MODEL WITH TIME VARYING PARAMETERS AND GRATING COMPRESSION TRANSFORM

Grating Compression Transform (GCT) is a two-dimensional analysis of speech signal which has been shown to be effective in multi-pitch tracking in speech mixtures. Multi-pitch tracking methods using GCT apply Kalman filter framework to obtain pitch tracks which requires training of the filter parameters using true pitch tracks. We propose an unsupervised method for obtaining multiple pitch tracks. In the proposed method, multiple pitch tracks are modeled using time-varying means of a Gaussian mixture model (GMM), referred to as TVGMM. The TVGMM parameters are estimated using multiple pitch values at each frame in a given utterance obtained from different patches of the spectrogram using GCT. We evaluate the performance of the proposed method on all voiced speech mixtures as well as random speech mixtures having well separated and close pitch tracks. TVGMM achieves multi-pitch tracking with 51% and 53% multi-pitch estimates having error <= 20% for random mixtures and all-voiced mixtures respectively. TVGMM also results in lower root mean squared error in pitch track estimation compared to that by Kalman filtering.