Showing papers on "Filter design published in 2008"

An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images

Abstract: A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image quality and to improve the performance of all the tasks needed for quantitative imaging analysis. The method proposed in this paper is based on a 3-D optimized blockwise version of the nonlocal (NL)-means filter (Buades, , 2005). The NL-means filter uses the redundancy of information in the image under study to remove the noise. The performance of the NL-means filter has been already demonstrated for 2-D images, but reducing the computational burden is a critical aspect to extend the method to 3-D images. To overcome this problem, we propose improvements to reduce the computational complexity. These different improvements allow to drastically divide the computational time while preserving the performances of the NL-means filter. A fully automated and optimized version of the NL-means filter is then presented. Our contributions to the NL-means filter are: 1) an automatic tuning of the smoothing parameter; 2) a selection of the most relevant voxels; 3) a blockwise implementation; and 4) a parallelized computation. Quantitative validation was carried out on synthetic datasets generated with BrainWeb (Collins, , 1998). The results show that our optimized NL-means filter outperforms the classical implementation of the NL-means filter, as well as two other classical denoising methods [anisotropic diffusion (Perona and Malik, 1990)] and total variation minimization process (Rudin, , 1992) in terms of accuracy (measured by the peak signal-to-noise ratio) with low computation time. Finally, qualitative results on real data are presented.

Weighted sum-rate maximization using weighted MMSE for MIMO-BC beamforming design

Abstract: This paper studies linear transmit filter design for Weighted Sum-Rate (WSR) maximization in the multiple input multiple output broadcast channel (MIMO-BC). The problem of finding the optimal transmit filter is non-convex and intractable to solve using low complexity methods. Motivated by recent results highlighting the relationship between mutual information and minimum mean square error (MMSE), this paper establishes a relationship between weighted sum-rate and weighted MMSE in the MIMO-BC. The relationship is used to propose two low complexity algorithms for finding a local weighted sum-rate optimum based on alternating optimization. Numerical results studying sum-rate show that the proposed algorithms achieve high performance with few iterations.

Multiresolution Bilateral Filtering for Image Denoising

Abstract: The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges; it has shown to be an effective image denoising technique. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. There are two main contributions of this paper. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising applications. The second contribution is an extension of the bilateral filter: multiresolution bilateral filter, where bilateral filtering is applied to the approximation (low-frequency) subbands of a signal decomposed using a wavelet filter bank. The multiresolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. Experimental results with both simulated and real data are provided.

Signal Synthesis and Receiver Design for MIMO Radar Imaging

Abstract: Multiple-input-multiple-output (MIMO) radar is an emerging technology that has significant potential for advancing the state-of-the-art of modern radar. When orthogonal waveforms are transmitted, with M+N (N transmit and M receive) antennas, an MN-element filled virtual array can be obtained. To successfully utilize such an array for high-resolution MIMO radar imaging, constant-modulus transmit signal synthesis and optimal receive filter design play critical roles. We present in this paper a computationally attractive cyclic optimization algorithm for the synthesis of constant-modulus transmit signals with good auto- and cross-correlation properties. Then we go on to discuss the use of an instrumental variables approach to design receive filters that can be used to minimize the impact of scatterers in nearby range bins on the received signals from the range bin of interest (the so-called range compression problem). Finally, we present a number of numerical examples to demonstrate the effectiveness of the proposed approaches.

Nonlinear Estimation and Multiple Sensor Fusion Using Unscented Information Filtering

Abstract: This letter represents a new unscented information filtering algorithm for nonlinear estimation and multiple sensor information fusion. The proposed information fusion algorithm is derived by embedding the unscented transformation method used in the sigma point filter into the extended information filtering architecture. The new information filter achieves not only the accuracy and robustness of the sigma point filter but also the flexibility of the information filter for multiple sensor estimation. Performance comparison of the proposed filter with the extended information filter is demonstrated through a target-tracking simulation study.

First-order filters generalized to the fractional domain

Abstract: Traditional continuous-time filters are of integer order. However, using fractional calculus, filters may also be represented by the more general fractional-order differential equations in which case integer-order filters are only a tight subset of fractional-order filters. In this work, we show that low-pass, high-pass, band-pass, and all-pass filters can be realized with circuits incorporating a single fractance device. We derive expressions for the pole frequencies, the quality factor, the right-phase frequencies, and the half-power frequencies. Examples of fractional passive filters supported by numerical and PSpice simulations are given.

An Embedded Common-Mode Suppression Filter for GHz Differential Signals Using Periodic Defected Ground Plane

Abstract: A novel low-cost filter design for common-mode noise suppression in high-speed differential signals is proposed. It is realized by periodically etching the dumbbell-shape defected ground structure (DGS) to perturb the return current of the common- mode noise. A transmission-line model for the proposed structure is also developed with good agreement to the full-wave simulation and measurement result. It is found that over 20 dB suppression of common-mode noise can be achieved over a wide frequency range from 3.3 to 5.7 GHz with 3 cascaded DGS cells, while the differential signals still keep good signal integrity in eye-pattern observation. The common-mode current, which generally results in common-mode EMI, on the attached input/output cable is also proved to be efficiently suppressed (15 dB in average) within the stopband by the proposed filter.

The design of an optimal filter for monthly GRACE gravity models

Abstract: SUMMARY Most applications of the publicly released Gravity Recovery and Climate Experiment monthly gravity field models require the application of a spatial filter to help suppressing noise and other systematic errors present in the data The most common approach makes use of a simple Gaussian averaging process, which is often combined with a ‘destriping’ technique in which coefficient correlations within a given degree are removed As brute force methods, neither of these techniques takes into consideration the statistical information from the gravity solution itself and, while they perform well overall, they can often end up removing more signal than necessary Other optimal filters have been proposed in the literature; however, none have attempted to make full use of all information available from the monthly solutions By examining the underlying principles of filter design, a filter has been developed that incorporates the noise and full signal variance–covariance matrix to tailor the filter to the error characteristics of a particular monthly solution The filter is both anisotropic and nonsymmetric, meaning it can accommodate noise of an arbitrary shape, such as the characteristic stripes The filter minimizes the mean-square error and, in this sense, can be considered as the most optimal filter possible Through both simulated and real data scenarios, this improved filter will be shown to preserve the highest amount of gravity signal when compared to other standard techniques, while simultaneously minimizing leakage effects and producing smooth solutions in areas of low signal

On Nonlinear $H_{\infty }$ Filtering for Discrete-Time Stochastic Systems With Missing Measurements

Abstract: In this paper, the H infin filtering problem is investigated for a general class of nonlinear discrete-time stochastic systems with missing measurements. The system under study is not only corrupted by state-dependent white noises but also disturbed by exogenous inputs. The measurement output contains randomly missing data that is modeled by a Bernoulli distributed white sequence with a known conditional probability. A filter of very general form is first designed such that the filtering process is stochastically stable and the filtering error satisfies H infin performance constraint for all admissible missing observations and nonzero exogenous disturbances under the zero-initial condition. The existence conditions of the desired filter are described in terms of a second-order nonlinear inequality. Such an inequality can be decoupled into some auxiliary ones that can be solved independently by taking special form of the Lyapunov functionals. As a consequence, a linear time-invariant filter design problem is discussed for the benefit of practical applications, and some simplified conditions are obtained. Finally, two numerical simulation examples are given to illustrate the main results of this paper.

Salt-and-pepper noise detection and reduction using fuzzy switching median filter

Abstract: This paper presents a new fuzzy switching median (FSM) filter employing fuzzy techniques in image processing. The proposed filter is able to remove salt-and-pepper noise in digital images while preserving image details and textures very well. By incorporating fuzzy reasoning in correcting the detected noisy pixel, the low complexity FSM filter is able to outperform some well known existing salt-and-pepper noise fuzzy and classical filters.

Differential evolution particle swarm optimization for digital filter design

Abstract: In this paper, swarm and evolutionary algorithms have been applied for the design of digital filters. Particle swarm optimization (PSO) and differential evolution particle swarm optimization (DEPSO) have been used here for the design of linear phase finite impulse response (FIR) filters. Two different fitness functions have been studied and experimented, each having its own significance. The first study considers a fitness function based on the passband and stopband ripple, while the second study considers a fitness function based on the mean squared error between the actual and the ideal filter response. DEPSO seems to be promising tool for FIR filter design especially in a dynamic environment where filter coefficients have to be adapted and fast convergence is of importance.

An enhanced memetic differential evolution in filter design for defect detection in paper production

Abstract: This article proposes an Enhanced Memetic Differential Evolution (EMDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. Defect detection is handled by means of two Gabor filters and their design is performed by the EMDE. The EMDE is a novel adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution with the exploitative features of three local search algorithms employing different pivot rules and neighborhood generating functions. These local search algorithms are the Hooke Jeeves Algorithm, a Stochastic Local Search, and Simulated Annealing. The local search algorithms are adaptively coordinated by means of a control parameter that measures fitness distribution among individuals of the population and a novel probabilistic scheme. Numerical results confirm that Differential Evolution is an efficient evolutionary framework for the image processing problem under investigation and show that the EMDE performs well. As a matter of fact, the application of the EMDE leads to a design of an efficiently tailored filter. A comparison with various popular metaheuristics proves the effectiveness of the EMDE in terms of convergence speed, stagnation prevention, and capability in detecting solutions having high performance.

A Square-Root Nyquist (M) Filter Design for Digital Communication Systems

Abstract: Designing matched transmit and receive filters whose combination satisfies the Nyquist condition is a classical problem in digital communication systems. In this correspondence, we propose a novel method for designing such filters. The proposed method is based on a cost function whose minimization leads to designs that can strike a balance between the stopband attenuation, the residual intersymbol interference (ISI), robust sensitivity to timing jitter, and/or reduced peak-to-average power ratio (PAR). An iterative algorithm for optimizing the proposed cost function is suggested and its excellent performance is shown by presenting a variety of design examples. Compared to the published works, the proposed method offers the following unique advantages. By introducing a symmetry in the filter coefficients, filters with reduced computational complexity can be designed. We also introduce a design parameter that allows one to strike a balance between the PAR and other features of the desired filter.

A New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography

Abstract: This paper introduces a new approximate solution of the optimal nonlinear filter suitable for nonlinear oceanic and atmospheric data assimilation problems. The method is based on a local linearization in a low-rank kernel representation of the state's probability density function. In the resulting low-rank kernel particle Kalman (LRKPK) filter, the standard (weight type) particle filter correction is complemented by a Kalman-type correction for each particle using the covariance matrix of the kernel mixture. The LRKPK filter's solution is then obtained as the weighted average of several low-rank square root Kalman filters operating in parallel. The Kalman-type correction reduces the risk of ensemble degeneracy, which enables the filter to efficiently operate with fewer particles than the particle filter. Combined with the low-rank approximation, it allows the implementation of the LRKPK filter with high-dimensional oceanic and atmospheric systems. The new filter is described and its relevance demonstrated through applications with the simple Lorenz model and a realistic configuration of the Princeton Ocean Model (POM) in the Mediterranean Sea.

$H_{\infty} $ Filter Design for Nonlinear Systems With Time-Delay Through T–S Fuzzy Model Approach

Abstract: This paper is concerned with the Hinfin filter design for nonlinear systems with time-varying delay via Takagi-Sugeno fuzzy model approach. Delay-dependent design method is proposed in terms of linear matrix inequalities (LMIs), which forms the main contribution of this paper. The main technique used is the free-weighting matrix method combined with a matrix decoupling approach. The results for rate-independent case, delay-independent case, and delay-free case are also given as easy corollaries. An illustrative example is given to show the effectiveness of the present method.

A New Common Subexpression Elimination Algorithm for Realizing Low-Complexity Higher Order Digital Filters

Abstract: The complexity of linear-phase finite-impulse-response (FIR) filters is dominated by the complexity of coefficient multipliers. The number of adders (subtractors) used to implement the multipliers determines the complexity of the FIR filters. It is well known that common subexpression elimination (CSE) methods based on canonical signed digit (CSD) coefficients reduce the number of adders required in the multipliers of FIR filters. A new CSE algorithm using binary representation of coefficients for the implementation of higher order FIR filters with a fewer number of adders than CSD-based CSE methods is presented in this paper. We show that the CSE method is more efficient in reducing the number of adders needed to realize the multipliers when the filter coefficients are represented in the binary form. Our observation is that the number of unpaired bits (bits that do not form CSs) is considerably few for binary coefficients compared to CSD coefficients, particularly for higher order FIR filters. As a result, the proposed binary-coefficient-based CSE method offers good reduction in the number of adders in realizing higher order filters. The reduction of adders is achieved without much increase in critical path length of filter coefficient multipliers. Design examples of FIR filters show that our method offers an average adder reduction of 18% over the best known CSE method, without any increase in the logic depth.

A Three-Phase Four-Wire Power Filter Comprising a Three-Phase Three-Wire Active Power Filter and a Zig–Zag Transformer

Abstract: Active power filters (APFs) have been developed to solve the problems of harmonic suppression and reactive power compensation simultaneously, In this paper, a three-phase four-wire power filter comprising a three-phase three-wire APF and a Zig-Zag transformer is developed. A hardware prototype is implemented and tested to verify the performance of the proposed power filter under various ideal and non-ideal power conditions. Experimental results show that this three-phase four-wire power filter has the desired performance under limited zero sequence utility voltage.

Noise cancellation system with lower rate emulation

Abstract: A noise cancellation system, comprising: an input for a digital signal, the digital signal having a first sample rate; a digital filter, connected to the input to receive the digital signal; a decimator, connected to the input to receive the digital signal and to generate a decimated signal at a second sample rate lower than the first sample rate; and a processor. The processor comprises: an emulation of the digital filter, connected to receive the decimated signal and to generate an emulated filter output; and a control circuit, for generating a control signal on the basis of the emulated filter output. The control signal is applied to the digital filter to control a filter characteristic thereof.

An Algorithm for the Design of Low-Power Hardware-Efficient FIR Filters

Abstract: A novel algorithm for designing low-power and hardware-efficient linear-phase finite-impulse response (FIR) filters is presented. The algorithm finds filter coefficients with reduced number of signed-power-of-two (SPT) terms given the filter frequency response characteristics. The algorithm is a branch-and-bound-based algorithm that fixes a coefficient to a certain value. The value is determined by finding the boundary values of the coefficient using linear programming. Although the worst case run time of the algorithm is exponential, its capability to find appreciably good solutions in a reasonable amount of time makes it a desirable CAD tool for designing low-power and hardware-efficient filters. The superiority of the algorithm on existing methods in terms of SPT term count, design time, hardware complexity, and power performance is shown with several design examples. Up to 30% reduction in the number of SPT terms is achieved over unoptimized Remez coefficients, which is 20% better than compared optimization methods. The average power saving is 20% over unoptimized coefficients, which is up to 14% better than optimized coefficients obtained with existing methods.

Optimal Full-Order and Reduced-Order Estimators for Discrete-Time Systems With Multiple Packet Dropouts

Abstract: This paper is concerned with the estimation problem for discrete-time stochastic linear systems with multiple packet dropouts. Based on a recently developed model for multiple-packet dropouts, the original system is transferred to a stochastic parameter system by augmentation of the state and measurement. The optimal full-order linear filter of the form of employing the received outputs at the current and last time instants is investigated. The solution to the optimal linear filter is given in terms of a Riccati difference equation governed by packet arrival rate. The optimal filter is reduced to the standard Kalman filter when there are no packet dropouts. The steady-state filter is also studied. A sufficient condition for the existence of the steady-state filter is given and the asymptotic stability of the optimal filter is analyzed. At last, a reduced-order filter is investigated.

Adaptive interpolation filters for video coding

Abstract: In digital video image encoding and decoding, a filter type is selected based on symmetrical properties of the images and coefficient values of an interpolation filter are calculated based on the selected filter type. Coefficient values, filter tap-length and selected filter-type are provided in the encoded video data. Coefficient values are also calculated based on a prediction signal representative of the different between a video frame and a reference image. The prediction signal is calculated from the reference image based on a predefined base filter and motion estimation performed on the video frame. The predefined base filter has fixed coefficient values. Coefficient values are selected from interpolation of pixel values in a selected image segment in the video frame. Symmetry properties of images can be a vertical symmetry, a horizontal symmetry and a combination thereof, so that only a portion of the filter coefficients are coded.

Regularization for spectral matched filter and RX anomaly detector

Abstract: This paper describes a new adaptive spectral matched filter and a modified RX-based anomaly detector that incorporates the idea of regularization (shrinkage). The regularization has the effect of restricting the possible matched filters (models) to a subset which are more stable and have better performance than the non-regularized adaptive spectral matched filters. The effect of regularization depends on the form of the regularization term and the amount of regularization is controlled by so called regularization coefficient. In this paper the sum-of-squares of the filter coefficients is used as the regularization term and several different values for the regularization coefficient are tested. A Bayesian-based derivation of the regularized matched filter is also provided. Experimental results for detecting and recognizing targets in hyperspectral imagery are presented for regularized and non-regularized spectral matched filters and RX algorithm.