A simple form synthesis of linear time-variant digital filter via spectral decomposition of its impulse response

The paper describes a new technique to synthesize a finite impulse response of linear time-variant (LTV) digital filter. First, a finite impulse response is decomposed into a sum of products of two orthogonal sequences. The direct implementation of the decomposed impulse response leads to the parallel connection of linear time-invariant (LTI) digital filters, followed by time-varying multipliers. A simple filter structure is obtained by properly modifying the sequences to realize the parallel form structure as a cascade connection of first- or second-order recursive LTI filters. The structure is easy to implement on a computer and saves computation time. Numerical examples illustrating the technique are included.     

A simple design method for the cosine-modulated filter banks using weighted constrained least square technique

This paper presents a simple and efficient design method for cosine-modulated filter banks with prescribed stopband attenuation, passband ripple, and channel overlap. The method casts the design problem as a linear minimization of filter coefficients such that their value at ω=π/2M is 0.707, which results in a simpler, more direct design procedure. The weighted constrained least squares technique is exploited for designing the prototype filter for cosine modulation (CM) filter banks. Several design examples are included to show the increased efficiency and flexibility of the proposed method over the exiting methods. An application of the proposed method is considered in the area of sub-band coding of the ECG and speech signals.     

参量匹配滤波器和向量自回归滤波器的等价性(英文)

分析了自适应匹配滤波器和向量自回归(VAR)时域白化滤波器.结果表明,通过最小化用误差平方之和估计的均方误差得到的参量滤波器系数和通过相同阶数的多通道最小二乘法得到的VAR滤波器系数是等价的.此外,还分析了VAR滤波器最小二乘估计器的渐进性能,分析了滤波器的运算量和杂波抑制性能.     

Chaotic synchronization based on neural filter

Because of the high sensitivity of chaotic systems to their initial conditions, synchronization of chaotic systems with uncertain parameters has been a challenging problem especially in noisy environment. Since synchronization of the transmitter and receiver systems involves recursive estimation, recursive nonlinear filters are called for and the extended Kalman (EKF) filter and unscented Kalman (UKF) filter have been applied. However, such suboptimal filters incur high synchronization errors and provide no capacity for uncertain environment, which motivated the use of the neural filter for chaotic synchronization in this paper. The neural filter, which is a recurrent neural network, can approximate the minimum-variance to any degree. Furthermore, the neural filter can adapt to a uncertain environment without online filter weight adjustment, which is computationally efficient. Numerical experiments show that the chaotic synchronization scheme based on the neural filter outperforms those based on EKF and UKF by a large margin.     

Conditionally bilinear filter with tracking application

A recursive nonlinear filter and tracking methodology is developed here for a class of partially observable processes. The method is based on an approximation of a nonlinear system by a system which is linear in the unobservable states and has the unobservables conditionally Gaussian with respect to the observations initially. Model smoothness, such as required with most approximating filters is not required here, and applications to simulated tracking problems show the filter to be considerably more accurate than the modified second-order filter which in general sense includes the extended Kalman filter.     

A delay-derivative-dependent approach to robust H∞ filter design for uncertain systems with time-varying distributed delays

This paper focuses on the problem of robust H∞ filter design for uncertain systems with time-varying state and distributed delays. System uncertainties are considered as norm-bounded time-varying parametric uncertainties. The delays are assumed to be time-varying delays being differentiable uniformly bounded with delay-derivative bounded by a constant, which may be greater than one. A new delay-derivative-dependent approach of filter design for the systems is proposed. A novel Lyapunov-Krasovskii functional (LKF) is employed, and a tighter upper bound of its derivative is obtained by employing an inequality and using free-weighting matrices technique, then the proposed result has advantages over some existing results, in that it has less conservatism and it enlarges the application scope. An improved sufficient condition for the existence of such a filter is established in terms of linear matrix inequality (LMI). Finally, illustrative examples are given to show the effectiveness and reduced conservatism of the proposed method.     

A new type of wave digital filter

This paper proposes a new wave digital filter derived from doubly terminated LC-ladder networks by replacing each series or shunt arm element of the ladder by its equivalent digital two-port. It is shown that such two-ports may be cascaded without the use of adapters defined by Fettweis (1). A number of realizations of the wave digital two-ports, which are canonic with respect to both multipliers and delays, have been obtained. Also a realization which is canonic with respect to multipliers only is given and an example considered using this realization. The sensitivity of this filter with respect to the multiplier coefficient changes due to finite word length is compared with the conventional cascaded digital filter and also the one proposed by Renner and Gupta. It is found that the proposed filter appears to be a more desirable form of implementation than the conventional cascade form and comparable to that of Renner and Gupta (2).     

On the construction of transitional filter nomographs

The construction of nomographs for transitional classical filters is described. Gain functions of classical filters are related to filter requirements resulting in a formulation for the general gain nomograph. The transitional filters that are products of approximating polynomials are incorporated into the general gain nomograph resulting in transitional filter nomographs that are sums of the individual nomographs. Nomographs for transitional filters using alternative forms where poles are interpolated are also considered. The resulting nomographs allow for quick optimization of transitional filter frequency response in many cases. Design examples are submitted and discussed. The proposed transitional filter nomographs provide the engineer with increased insight into the selection of classical transitional filters with optimum frequency response.     

利用Filter Solutions辅助设计滤波器

介绍一种滤波器辅助设计软件Filter Solutions,利用其设计一款常用的带通滤波器,并将仿真曲线与实测结果进行比较。     

Hierarchical recursive least squares parameter estimation of non-uniformly sampled Hammerstein nonlinear systems based on Kalman filter

This paper focuses on parameter estimation problems for non-uniformly sampled Hammerstein nonlinear systems. By combining the lifting technique and state space transformation, we derive a nonlinear regression identification model with different input and output updating rates. Furthermore, the unmeasurable state vector is estimated by Kalman filter, and by using the hierarchical identification principle, we develop a hierarchical recursive least squares algorithm for estimating the unknown parameters of the identification model. Finally, illustrative examples are given to indicate that the proposed algorithm is effective.     

Sliding mode mean square filter design for linear stochastic time delay systems

This paper investigates the mean-square filtering problem for a linear time delay systems with Gaussian white noises. The obtained solution contains a sliding mode term, signum of the innovations process. It is demonstrated that the estimate produced by the designed filter generates the mean-square estimate, which has the same minimum estimation-error variance as the best estimate given by the classical Kalman–Bucy filter. The theoretical result is applied to an illustrative example: the tryptophan operon of E. coli, verifying the performance of the designed filter. It is demonstrated that the estimates produced by the designed sliding-mode mean-square filter and the Kalman–Bucy filter yield the same estimation-error variance. Simulation graphs demonstrate the better performance of the designed sliding-mode filter and show the potential of the proposed new filter.     

Dielectrophoretic manipulation of particles in a modified microfluidic H filter with multi-insulating blocks

The conventional microfluidic H filter is modified with multi-insulating blocks to achieve a flow-through manipulation and separation of microparticles. The device transports particles by exploiting electro-osmosis and electrophoresis, and manipulates particles by utilizing dielectrophoresis (DEP). Polydimethylsiloxane (PDMS) blocks fabricated in the main channel of the PDMS H filter induce a nonuniform electric field, which exerts a negative DEP force on the particles. The use of multi-insulating blocks not only enhances the DEP force generated, but it also increases the controllability of the motion of the particles, facilitating their manipulation and separation. Experiments were conducted to demonstrate the controlled flow direction of particles by adjusting the applied voltages and the separation of particles by size under two different input conditions, namely (i) a dc electric field mode and (ii) a combined ac and dc field mode. Numerical simulations elucidate the electrokinetic and hydrodynamic forces acting on a particle, with theoretically predicted particle trajectories in good agreement with those observed experimentally. In addition, the flow field was obtained experimentally with fluorescent tracer particles using the microparticle image velocimetry (μ-PIV) technique.     

Breaking the filter bubble: democracy and design

It has been argued that the Internet and social media increase the number of available viewpoints, perspectives, ideas and opinions available, leading to a very diverse pool of information. However, critics have argued that algorithms used by search engines, social networking platforms and other large online intermediaries actually decrease information diversity by forming so-called “filter bubbles”. This may form a serious threat to our democracies. In response to this threat others have developed algorithms and digital tools to combat filter bubbles. This paper first provides examples of different software designs that try to break filter bubbles. Secondly, we show how norms required by two democracy models dominate the tools that are developed to fight the filter bubbles, while norms of other models are completely missing in the tools. The paper in conclusion argues that democracy itself is a contested concept and points to a variety of norms. Designers of diversity enhancing tools must thus be exposed to diverse conceptions of democracy.     

基于ADS的微带抽头式发夹型带通滤波器的设计

微带滤波器在通信系统中起着非常重要的作用,其性能的好坏直接影响着整个通信系统的性能。传统的设计滤波器的方法要通过理论进行大量的计算,计算过程繁杂。文章简要介绍了微带抽头式发夹型带通滤波器的设计理论以及如何通过ADS软件进行设计和优化仿真,并结合具体实例验证其可行性。     

Novel event-triggered filter design for nonlinear networked control systems

This paper investigates the problem of event-triggered filter design for nonlinear networked control systems (NCSs) in the framework of interval type-2 (IT2) fuzzy systems. A novel IT2 fuzzy filter for ensuring asymptotic stability and H_∞ performance of filtering error system is proposed, where the premise variables are different from those of the fuzzy model. Attention is focused on solving the problem of event-triggered filter design subject to parameter uncertainties, data quantization, and communication delay in a unified frame. It is shown that the proposed event-triggered filter design communication mechanism for IT2 fuzzy NCSs has the advantage of the existing event-triggered approaches to reduce the utilization of limited network resources and provides flexibility in balancing the tracking error and the utilization of network resources. Finally, simulation example is given to validate the advantages of the presented results.     

M-estimation based sparse grid quadrature filter and stochastic stability analysis

In this study, a novel M-estimation based sparse grid quadrature filter (MSGQF) is proposed to improve the robust performance of the nonlinear system. We present a systematic formulation of the sparse grid quadrature filter (SGQF), and extend it to the discrete-time nonlinear system with abnormal measurement values. The M-estimation method is introduced in the SGQF, which uses the Huber’s cost function to update the measurement covariance. Convergence on the modified robust SGQF is established and proved. The sufficient conditions are shown to ensure stochastic stability of the MSGQF. A target tracking problem has been conducted to demonstrate the accuracy of the MSGQF. When measurement abnormal values appear, it outperforms the unscented Kalman filter (UKF), the cubature Kalman filter (CKF) and the SGQF. Theoretical analysis and simulation results prove that the MSGQF provides significant performance improvement in the robustness of the nonlinear system.     

A new type of transitional filter based on summation of polynomials

A new transitional filter based on the sum of filter polynomials is proposed and discussed. It shall be shown that the approach allows for greater flexibility in adjusting passband and stopband gain shapes. The sensitivity of the filter transfer function is analyzed and related to the approximation problem. Some new modified classical filters are also derived including maximally flat (Butterworth) and equiripple (Chebyshev) types. Several examples of combining Butterworth, Chebyshev and sync-tuned filters are presented and compared to those obtained using the conventional product method. The new transitional filter provides an alternative to other forms and provides a means for isolating the effects of non-uniformly distributed pole angles.     

A family of proportionate normalized subband adaptive filter algorithms

In this paper, the concept of proportionate adaptation is extended to the normalized subband adaptive filter (NSAF), and seven proportionate normalized subband adaptive filter algorithms are established. The proposed algorithms are proportionate normalized subband adaptive filter (PNSAF), μ‐law PNSAF (MPNSAF), improved PNSAF (IPNSAF), the improved IPNSAF (IIPNSAF), the set-membership IPNSAF (SM-IPNSAF), the selective partial update IPNSAF (SPU-IPNSAF), and SM-SPU-IPNSAF which are suitable for sparse system identification in network echo cancellation. When the impulse response of the echo path is sparse, the PNSAF has initial faster convergence than NSAF but slows down dramatically after initial convergence. The MPNSAF algorithm has fast convergence speed during the whole adaptation. The IPNSAF algorithm is suitable for both sparse and dispersive impulse responses. The SM-IPNSAF exhibits good performance with significant reduction in the overall computational complexity compared with the ordinary IPNSAF. In SPU-IPNSAF, the filter coefficients are partially updated rather than the entire filter at every adaptation. In SM-SPU-IPNSAF algorithm, the concepts of SM and SPU are combined which leads to a reduction in computational complexity. The simulation results show good performance of the proposed algorithms.     

基于ADS的微带低通滤波器设计

使用传统的理论计算的方法设计滤波器过程较为繁琐,文章使用ADS软件快速设计微带型低通滤波器,并在HFSS中建模仿真,在实际工程应用中取得了良好的效果。