Adaptive 1-D and 2-D Filtering with Internally Passive Structures

Internally passive filters such as wave digital filters (WDF) and orthogonal filters, which have desirable properties, are developed as filter structures for adaptive filters. The equations for updating the multiplier coefficients as well as for computing the gradients are derived for these filters. The stability monitoring during the adaptation process for these filters is simple to implement. Simulation results are included to demonstrate the capability of these filters in system identification and noise cancellation. Moreover, motivated by applications of multidimensional adaptive filters in video compression and image enhancement, the proposed adaptive wave digital filters have been extended to two dimensions and applied as adaptive fan filters. Computer simulations are also conducted to show their potential applications to two-dimensional system identification and image enhancement.     

Quadratic filters for signal processing

Polynomial (or Volterra) filters are introduced, and the quadratic filters are presented as the simplest example of such filters. The principle aspects and properties of quadratic filters are derived in the framework of the discrete Volterra expansion. Fixed as well as adaptive filters are considered in one-dimensional and multidimensional environments. Such issues as design and efficient realizations are thoroughly addressed, and standard and advanced adaptation algorithms are presented. Several examples of signal processing applications requiring quadratic filters are discussed     

Crystal, ceramic, and mechanical filters in Japan

In Japan, there has been considerable activity in the research and manufacturing of quartz crystal filters, ceramic filters and mechanical filters. This paper reviews some of these contributions. An emphasis is placed on filters actually used. First multiple-mode resonators are reviewed; this concept having had profound effects on all types of filters. Quartz-crystal filters as well as other crystal filters are discussed. Then follow reviews on ceramic and mechanical filters.     

A new class of nonlinear filters-neural filters

A class of nonlinear filters based on threshold decomposition and neural networks is defined. It is shown that these neural filters include all filters defined either by continuous functions, such as linear finite impulse response (FIR) filters, or by Boolean functions, such as generalized stack filters. Adaptive least-mean-absolute-error and adaptive least-mean-square-error algorithms are derived for determining optimal neural filters. As special cases, adaptive generalized stack and adaptive generalized weighted order statistic filtering algorithms under both error criteria are derived. Experimental results in 1D and 2D signal processing are presented to compare the performances of the adaptive neural filters and other widely used filters     

小型射频LC低通滤波器设计

研究射频LC低通滤波器的设计，通过仿真计算研究了以螺旋电感和平板电容作为基本元件的滤波器设计制作在不同介电常数的介质基板上时性能的变化情况，结果表明采用高介电常数的介质基板时滤波器的尺寸可以减小，同时性能指标也可以获得一定的改善。     

Rank conditioned rank selection filters for signal restoration

A class of nonlinear filters called rank conditioned rank selection (RCRS) filters is developed and analyzed in this paper. The RCRS filters are developed within the general framework of rank selection (RS) filters, which are filters constrained to output an order statistic from the observation set. Many previously proposed rank order based filters can be formulated as RS filters. The only difference between such filters is in the information used in deciding which order statistic to output. The information used by RCRS filters is the ranks of selected input samples, hence the name rank conditioned rank selection filters. The number of input sample ranks used is referred to as the order of the RCRS filter. The order can range from zero to the number of samples in the observation window, giving the filters valuable flexibility. Low-order filters can give good performance and are relatively simple to optimize and implement. If improved performance is demanded, the order can be increased but at the expense of filter simplicity. In this paper, many statistical and deterministic properties of the RCRS filters are presented. A procedure for optimizing over the class of RCRS filters is also presented. Finally, extensive computer simulation results that illustrate the performance of RCRS filters in comparison with other techniques in image restoration applications are presented.     

基于光纤Bragg光栅的滤波技术

介绍了国际上最新的基于光纤Bragg光栅的光纤滤波技术发展情况，详细阐述了分布反馈式光纤Bragg光栅滤波器、多波长选择光纤Bragg光栅滤波器、应用光纤Bragg光栅的Michelson干涉型（MI）滤波器以及光纤Bragg光栅滤波耦合器的理论难点、基本原理及其应用。     

Nonlinear filtering by threshold decomposition

A new threshold decomposition architecture is introduced to implement stack filters. The architecture is also generalized to a new class of nonlinear filters known as threshold decomposition (TD) filters which are shown to be equivalent to the class of L1-filters under certain conditions. Another new class of filters known as linear and order statistic (LOS) filters result from the intersection of the class of TD and L1-filters. Performance comparisons among several filters are then presented. It was found that TD is compatible with L1, LOS, and linear filters in suppressing Gaussian noise, and is superior in suppressing salt-and-pepper noise. LOS filters, however, provide a better compromise in performance and complexity.     

The classification and associative memory capability of stackfilters

The notion of the on-set of a positive Boolean function is used to classify stack filters into three different types, called decreasing, increasing, and mixed. The associative memory capability of each of these three types of stack filters is then investigated. The associative memory of a stack filter is defined to be the set of root signals of that filter. In a class of stack filters in which each filter's root set contains a desired set of patterns, those filters whose root sets have the smallest cardinality are said to be minimal among all filters in that class for that set of patterns. Some learning schemes are proposed to find minimal decreasing and increasing stack filters. It is also shown that, for any specified set of patterns, there is always a mixed stack filter which is minimal when one considers all stack filters which preserve those patterns. In this sense, mixed stack filters are always at least as good as decreasing or increasing stack filters     

Vector median filters

Two nonlinear algorithms for processing vector-valued signals are introduced. The algorithms, called vector median operations, are derived from two multidimensional probability density functions using the maximum-likelihood-estimate approach. The underlying probability densities are exponential, and the resulting operations have properties very similar to those of the median filter. In the vector median approach, the samples of the vector-valued input signal are processed as vectors. The operation inherently utilizes the correlation between the signal components, giving the filters some desirable properties. General properties as well as the root signals of the vector median filters are studied. The vector median operation is combined with linear filtering, resulting in filters with improved noise attenuation and filters with very good edge response. An efficient algorithm for implementing long vector median filters is presented. The noise attenuation of the filters is discussed, and an application to velocity filtering is shown     

Precision of morphological-representation estimators for translation-invariant binary filters: Increasing and nonincreasing

Mean-absolute-error-optimal, finite-observation, translations, invariant, binary-image filters have previously been characterized in terms of morphological representations: increasing filters as unions of erosions and nonincreasing filters as unions of hit-or-miss operators. Based on these characterizations, (sub)optimal filters have been designed via image-process realizations. The present paper considers the precision of filter estimation via realizations. The following problems are considered: loss of performance owing to employing erosion filters limited by basis size, precision in the estimation of erosion bases, and precision in the estimation of union-of-hit-or-miss filters. A key point: while precision deteriorates for both erosion and hit-or-miss filters as window size increases, the number of image realizations required to obtain good estimation in erosion-filter design can be much less than the number required for hit-or-miss-filter design. Thus, while in theory optimal hit-or-miss filtering is better because the unconstrained optimal hit-or-miss filter is the conditional expectation, owing to estimation error it is very possible that estimated optimal erosion filters are better than estimated optimal hit-or-miss filters.     

Wave active filters

A method is described and implemented for the derivation of active RC filters from analogue passive filters of the lossless ladder doubly terminated kind. The method is based on scattering-parameter formulation and the active RC filters thus derived are referred to as wave active filters.     

Compact Partial$H$-Plane Filters

In this paper, three types of partial H-plane filters, having advantages of compact, low cost, and mass-producible properties, are presented as compact waveguide filters, which are implemented by partial H-plane waveguide. Two types of partial H-plane filters have the same frequency responses as those of conventional E- and H-plane filters, respectively, while their cross sections are one-quarter. To further reduce the length, the last type of partial H-plane filters is designed by using quarter-wavelength resonators. The length of this filter is shorter by 29.2% than that of the conventional E-plane filter. In addition, it has a superior spurious frequency response     

Some representation properties of stack filters

Stack filters, a class of nonlinear filters which are based on positive Boolean functions as the window operators, are considered. The representations of these window operations are presented via the structures of on-set and off-set of the positive Boolean functions, which can be expressed as a Boolean expression containing no complements of the input variables. A fast algorithm for finding the representation of stack filters is designed. This algorithm can be easily extended to find the representation of morphological filters mentioned by P. Maragos (1989)     

The Application of Genetic Algorithm in E-Plane Waveguide Filter Design

Traditional optimization methods, such as Powell's method and the Simplex method, are generally very difficult to design high performance filters with the relative band-width less than 0.15% or more than 10%. In this paper, Genetic Algorithm (GA) is used to design filters that meet these requirements. Two special filters are designed in this paper with the relative band-width as 0.15% and 25%, respectively. The numerical results demonstrate that GA can be used to design filters of superior quality when compared to the filters designed by using Powell's method or Simplex method.     

Permutation weighted order statistic filter lattices

We introduce and analyze a new class of nonlinear filters called permutation weighted order statistic (PWOS) filters. These filters extend the concept of weighted order statistic (WOS) filters, in which filter weights associated with the input samples are used to replicate the corresponding samples, and an order statistic is chosen as the filter output. PWOS filters replicate each input sample according to weights determined by the temporal-order and rank-order of samples within a window. Hence, PWOS filters are in essence time-varying WOS filters. By varying the amount of temporal-rank order information used in selecting the output for a given observation window size, we obtain a wide range of filters that are shown to comprise a complete lattice structure. At the simplest level in the lattice, PWOS filters reduce to the well-known WOS filter, but for higher levels in the lattice, the obtained selection filters can model complex nonlinear systems and signal distortions. It is shown that PWOS filters are realizable by a N! piecewise linear threshold logic gate where the coefficients within each partition can be easily optimized using stack filter theory. Simulations are included to show the advantages of PWOS filters for the processing of image and video signals.     

可重构滤波器研究进展综述

随着通信、雷达等无线电系统向高频、宽带和小型化方向发展,出现了多种形式的可重构微波滤波器。本文综述了各种典型可重构滤波器的发展现状,对比分析了不同种类可重构滤波器的优缺点。得出的结论是:在毫米波频段,基于液晶和铁氧体等新材料的可重构滤波器具有低损耗的优势,而基于MEMS技术的可重构滤波器兼具适应高频段和易于集成的优点,这将是未来可重构滤波器研究的重要方向。     

Extended permutation filters and their application to edge enhancement.

Extended permutation (EP) filters are defined and analyzed. In particular, we focus on extended permutation rank selection (EPRS) filters. These filters are constrained to output an order statistic from an extended observation vector. This extended vector includes N observation samples and K statistics that are functions of the observation samples. The rank permutations from selected samples in this extended observation vector are used as the basis for selecting an order statistic output. We show that by including the sample mean in the extended observation vector, the filters exhibit excellent edge enhancement properties. We also show that several previously defined classes of rank-order-based edge enhancers (CS, LUM, and WMMR sharpeners) can be formulated as subclasses of EPRS filters. These sharpening subclasses are in addition to the smoothing subclasses, which include rank conditioned rank selection, permutation stack, and weighted order statistic filters. Thus, this novel class of filters provides a broad framework within which many rank-order-based smoothers and edge enhancers can be unified. Edge enhancement properties are developed and an L(n) norm EPRS filter optimization procedure is presented. Finally, extensive computer simulation results are presented, comparing the performance of EPRS and other sharpening filters in edge enhancement applications.     

Current mode continuous time wave active filters

Continuous time wave active filters using current mode processing are presented. The circuits derived are well suited for implementation as high frequency integrated filters. It is demonstrated that the important tuning process is simpler, and that current mode wave active filters are inherently adaptable.<>     

Nonlinear order statistic filters for image filtering and edge detection

A novel class of nonlinear filters for image processing is proposed. This class is a combination of nonlinear mean and order statistic filters. Median, homomorphic, α-trimmed mean, nonlinear mean, order statistic, and linear filters can be considered as special cases of this class. The properties of these filters in the presence of different kinds of noise are investigated. It is shown that these filters can be used for the reduction of additive white noise, signal-dependent noise, and impulse noise. It is also shown that they preserve edges better than linear filters. Such filters can successfully be used as edge detectors, by appropriate adjustment of some of their parameters. Edge information can be used as an input to these filters to perform in an adaptive manner, changing their behaviour near the edges of an image. It is finally shown that many of the filters proposed have a reasonable (and in certain cases small) computational complexity.