In a field of biomedical engineering, not only low-pass filters for high frequency elimination but also notch filters for suppressing powerline interference are necessary to process low-frequency biosignals. For integration of low-frequency filters, chip implementation of large capacitances is major difficulty. As methods to enhance capacitances with small chip area, use of capacitance multipliers is effective. This letter describes design consideration of integrated low-frequency low-pass notch filter employing capacitance multipliers. Two main points are presented. Firstly, a new floating capacitance multiplier is proposed. Secondly, a technique to reduce the number of capacitance multipliers is proposed. By this technique, power consumption is reduced. The proposed techniques are applied a 3rd order low-pass notch filter. Simulation results show the effectiveness of the proposed techniques.

This paper proposes a dual-polarized filtering antenna with extracted-pole unit (EPU) using LTCC substrate. The EPU realizes the high skirt characteristic of the bandpass filter with transmission zeros (TZs) located near the passband without cross coupling. The filtering antenna with EPU is designed and fabricated in 28GHz band for 5G Band-n257 (26.5-29.5GHz). The measured S11 is less than -10.6dB in Band-n257, and the isolation between two ports for dual polarization is greater than 20.0dB. The measured peak antenna gain is 4.0dBi at 28.8GHz and the gain is larger than 2.5dBi in Band-n257. The frequency characteristics of the measured antenna gain shows the high skirt characteristic out of band, which are in good agreement with electromagnetic (EM)-simulated results.

A surrogate-based electromagnetic (EM) optimization using neural networks (NNs) is presented for computationally efficient microwave bandpass filter (BPF) design. This paper first describes the forward problem (EM analysis) and the inverse problems (EM design), and the two fundamental issues in BPF designs. The first issue is that the EM analysis is a time-consuming task, and the second one is that EM design highly depends on the structural optimization performed with the help of EM analysis. To accelerate the optimization design, two surrogate models of forward and inverse models are introduced here, which are built with the NNs. As a result, the inverse model can instantaneously guess initial structural parameters with high accuracy by simply inputting synthesized coupling-matrix elements into the NN. Then, the forward model in conjunction with optimization algorithm enables designers to rapidly find optimal structural parameters from the initial ones. The effectiveness of the surrogate-based EM optimization is verified through the structural designs of a typical fifth-order microstrip BPF with multiple couplings.

Recent trends in designing filters involve development of sparse filters with coefficients that not only have real but also zero values. These sparse filters can achieve a high performance through optimizing the selection of the zero coefficients and computing the real (non-zero) coefficients. Designing an infinite impulse response (IIR) sparse filter is more challenging than designing a finite impulse response (FIR) sparse filter. Therefore, studies on the design of IIR sparse filters have been rare. In this study, we consider IIR filters whose coefficients involve zero value, called sparse IIR filter. First, we formulate the design problem as a linear programing problem without imposing any stability condition. Subsequently, we reformulate the design problem by altering the error function and prepare several possible denominator polynomials with stable poles. Finally, by incorporating these methods into successive thinning algorithms, we develop a new design algorithm for the filters. To demonstrate the effectiveness of the proposed method, its performance is compared with that of other existing methods.

A non-photorealistic rendering method has been proposed for generating oil-film-like images from photographic images by bilateral infra-envelope filter. The conventional method has a disadvantage that it takes much time to process. We propose a method for generating oil-film-like images that can be processed faster than the conventional method. The proposed method uses an iterative process with upper and lower smoothing filters. To verify the effectiveness of the proposed method, we conduct experiments using Lenna image. As a result of the experiments, we show that the proposed method can process faster than the conventional method.

A pupil function of aperture in image capturing systems is theoretically derived such that one can perfectly reconstruct all-in-focus image through linear filtering of the focal stack. The perfect reconstruction filters are also designed based on the derived pupil function. The designed filters are space-invariant; hence the presented method does not require region segmentation. Simulation results using synthetic scenes shows effectiveness of the derived pupil function and the filters.

Visual tracking has been studied for several decades but continues to draw significant attention because of its critical role in many applications. This letter handles the problem of fixed template size in Kernelized Correlation Filter (KCF) tracker with no significant decrease in the speed. Extensive experiments are performed on the new OTB dataset.

Block-state realization of state-space digital filters offers reduced implementation complexity relative to canonical state-space filters while filter's internal structure remains accessible. In this paper, we present a quantitative analysis on l2 coefficient sensitivity of block-state digital filters. Based on this, we develop two techniques for minimizing average l2-sensitivity subject to l2-scaling constraints. One of the techniques is based on a Lagrange function and some matrix-theoretic techniques. The other solution method converts the problem at hand into an unconstrained optimization problem which is solved by using an efficient quasi-Newton algorithm where the key gradient evaluation is done in closed-form formulas for fast and accurate execution of quasi-Newton iterations. A case study is presented to demonstrate the validity and effectiveness of the proposed techniques.

Multiple notch filters are used to suppress narrow-band or sinusoidal interferences in digital signals. In this paper, we propose a novel optimization design technique of an infinite impulse response (IIR) multiple notch filter. It is based on the Nelder-Mead simplex method. Firstly, the system function of the desired notch filter is constructed to form the objective function of the optimization technique. Secondly, the design parameters of the desired notch filter are optimized by Nelder-Mead simplex method. A weight function is also introduced to improve amplitude response of the notch filter. Thirdly, the convergence and amplitude response of the proposed technique are compared with other Nelder-Mead based design methods and the cascade-based design method. Finally, the practicability of the proposed notch filter design technique is demonstrated by some practical applications.

Current signature-based antivirus solutions have three limitations such as the large volume of signature database, privacy preservation, and computation overheads of signature matching. In this paper, we propose LigeroAV, a light-weight, performance-enhanced antivirus, suitable for pervasive environments such as mobile phones. LigeroAV focuses on detecting MD5 signatures which are more than 90% of signatures. LigeroAV offloads matching computation in the cloud server with up-to-dated signature database while preserving privacy level using the Bloom filter.

This paper proposes a new and simple microstrip bandpass filter structure for the design of a fully canonical transversal array filter. The filter is constructed by the parallel arrangement of microstrip even- and odd-mode half-wavelength resonators. In this filter, transmission zeros (TZs) are not produced by cross couplings used in conventional filter designs, but by an intrinsic negative coupling of the odd-mode resonators having open ends with respect to the even-mode resonators with shorted ends. Thus, the control of the resonant frequency and the external Q factor of each resonator makes it possible to form both a specified passband and TZs. As an example, a fully canonical bandpass filter with 2-GHz center frequency, 6% bandwidth, and four TZs is synthesized with a coupling-matrix optimization, and its structural parameters are designed. The designed filter achieves a rapid roll-off and low-loss passband response, which can be confirmed numerically and experimentally.

This study investigates a real-time joint channel and hyperparameter estimation method for orthogonal frequency division multiplexing mobile communications. The channel frequency response of the pilot subcarrier and its fixed hyperparameters (such as channel statistics) are estimated using a Liu and West filter (LWF), which is based on the state-space model and sequential Monte Carlo method. For the first time, to our knowledge, we demonstrate that the conventional LWF biases the hyperparameter due to a poor estimate of the likelihood caused by overfitting in noisy environments. Moreover, this problem cannot be solved by conventional smoothing techniques. For this, we modify the conventional LWF and regularize the likelihood using a Kalman smoother. The effectiveness of the proposed method is confirmed via numerical analysis. When both of the Doppler frequency and delay spread hyperparameters are unknown, the conventional LWF significantly degrades the performance, sometimes below that of least squares estimation. By avoiding the hyperparameter estimation failure, our method outperforms the conventional approach and achieves good performance near the lower bound. The coding gain in our proposed method is at most 10 dB higher than that in the conventional LWF. Thus, the proposed method improves the channel and hyperparameter estimation accuracy. Derived from mathematical principles, our proposal is applicable not only to wireless technology but also to a broad range of related areas such as machine learning and econometrics.

Two plasmonic band-bass filters are analyzed: one is a grating-type filter and the other is a slit-type filter. The former shows a band-pass characteristic with a high transmission for a two-dimensional structure, while the latter exhibits a high transmission even for a three-dimensional structure with a thin metal layer.

Visual tracking has been studied for several decades but continues to draw significant attention because of its critical role in many applications. Recent years have seen greater interest in the use of correlation filters in visual tracking systems, owing to their extremely compelling results in different competitions and benchmarks. However, there is still a need to improve the overall tracking capability to counter various tracking issues, including large scale variation, occlusion, and deformation. This paper presents an appealing tracker with robust scale estimation, which can handle the problem of fixed template size in Kernelized Correlation Filter (KCF) tracker with no significant decrease in the speed. We apply the discriminative correlation filter for scale estimation as an independent part after finding the optimal translation based on the KCF tracker. Compared to an exhaustive scale space search scheme, our approach provides improved performance while being computationally efficient. In order to reveal the effectiveness of our approach, we use benchmark sequences annotated with 11 attributes to evaluate how well the tracker handles different attributes. Numerous experiments demonstrate that the proposed algorithm performs favorably against several state-of-the-art algorithms. Appealing results both in accuracy and robustness are also achieved on all 51 benchmark sequences, which proves the efficiency of our tracker.

In this letter, an effective acoustic feedback cancellation algorithm is proposed based on the normalized sub-band adaptive filter (NSAF). To improve the confliction between fast convergence rate and low misalignment in the NSAF algorithm, a variable step size is designed to automatically vary according to the update state of the filter. The update state of the filter is adaptively detected via the normalized distance between the long term average and the short term average of the tap-weight vector. Simulation results demonstrate that the proposed algorithm has superior performance in terms of convergence rate and misalignment.

In this Letter, a new iris recognition approach based on local Gabor orientation feature is proposed. On one hand, the iris feature extraction method using the traditional Gabor filters can cause time-consuming and high-feature dimension. On the other hand, we can find that the changes of original iris texture in angle and radial directions are more obvious than the other directions by observing the iris images. These changes in the preprocessed iris images are mainly reflected in vertical and horizontal directions. Therefore, the local directional Gabor filters are constructed to extract the horizontal and vertical texture characteristics of iris. First, the iris images are preprocessed by iris and eyelash location, iris segmentation, normalization and zooming. After analyzing the variety of iris texture and 2D-Gabor filters, we construct the local directional Gabor filters to extract the local Gabor features of iris. Then, the Gabor & Fisher features are obtained by Linear Discriminant Analysis (LDA). Finally, the nearest neighbor method is used to recognize the iris. Experimental results show that the proposed method has better iris recognition performance with less feature dimension and calculation time.

This invited paper aims to present an overview of our recent research and development (R&D) of advanced microwave planar filters, in particular with miniaturization and/or electronically tunable/ reconfigurable functionalities, which are in demand for future communication/radar systems as well as emerging wireless applications.

This paper proposes a compact three-mode H-shaped resonator bandpass filter fed by antiparallel coupled input/output lines. To investigate the resonant behavior of the H-shaped resonator, even/odd-mode resonance conditions of the resonator are first derived analytically. The multimode resonances of the H-shaped resonator filter are modeled by a multipath circuit formed with resonance paths. Moreover, a direct source/load coupling path is connected in parallel, of which the value shows a frequency dependency because of the antiparallel coupled feeding lines, thereby generating four transmission zeros (TZs) greater than the number of a theoretical limitation. The H-shaped resonator bandpass filter is synthesized, developed, and tested, showing a third-order passband response with four TZs located near the passband, and a wide stopband property.

For two-dimensional IIR digital filters described by the Fornasini-Marchesini second model, the problem of jointly optimizing high-order error feedback and realization to minimize the effects of roundoff noise at the filter output subject to l2-scaling constraints is investigated. The problem at hand is converted into an unconstrained optimization problem by using linear-algebraic techniques. The unconstrained optimization problem is then solved iteratively by applying an efficient quasi-Newton algorithm with closed-form formulas for key gradient evaluation. Finally, a numerical example is presented to illustrate the validity and effectiveness of the proposed technique.

Many kinds of data can be represented as a network or graph. It is crucial to infer the latent structure underlying such a network and to predict unobserved links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data. Latent variables and unknown parameters in MMSB have been estimated through Bayesian inference with the entire network; however, it is important to estimate them online for evolving networks. In this paper, we first develop online inference methods for MMSB through sequential Monte Carlo methods, also known as particle filters. We then extend them for time-evolving networks, taking into account the temporal dependency of the network structure. We demonstrate through experiments that the time-dependent particle filter outperformed several baselines in terms of prediction performance in an online condition.