Correlation filter-based approaches achieve competitive results in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the videos. To address this issue, we propose a novel tracker combined with color features in a correlation filter framework, which extracts not only gray but also color information as the feature maps to compute the maximum response location via multi-channel correlation filters. In particular, we modify the label function of the conventional classifier to improve positioning accuracy and employ a discriminative correlation filter to handle scale variations. Experiments are performed on 35 challenging benchmark color sequences. And the results clearly show that our method outperforms state-of-the-art tracking approaches while operating in real-time.

Case studies have reported that pedestrian detection methods using vehicle radar are not complete systems because each system has specific limitations at the cost of the calculating amounts, the system complexity or the range resolution. In this letter, we proposed a novel pedestrian detection method by template matching using Gabor filter bank, which was evaluated based on the data observed by 24GHz UWB radar.

We study a use of Gaussian kernels with a wide range of scales for nonlinear function estimation. The estimation task can then be split into two sub-tasks: (i) model selection and (ii) learning (parameter estimation) under the selected model. We propose a fully-adaptive and all-in-one scheme that jointly carries out the two sub-tasks based on the multikernel adaptive filtering framework. The task is cast as an asymptotic minimization problem of an instantaneous fidelity function penalized by two types of block l1-norm regularizers. Those regularizers enhance the sparsity of the solution in two different block structures, leading to efficient model selection and dictionary refinement. The adaptive generalized forward-backward splitting method is derived to deal with the asymptotic minimization problem. Numerical examples show that the scheme achieves the model selection and learning simultaneously, and demonstrate its striking advantages over the multiple kernel learning (MKL) method called SimpleMKL.

In this paper, we propose a filtering approach based on global motion estimation (GME) and global motion compensation (GMC) for pre- and postprocessing of video codecs. For preprocessing a video codec, group of pictures (GOP), which is a basic unit for GMC, and reference frames are first defined for an input video sequence. Next, GME and GMC are sequentially performed for every frame in each GOP. Finally, a block-based adaptive temporal filter is applied between the GMC frames before video encoding. For postprocessing a video codec at the decoder end, every decoded frame is inversely motion-compensated using the transmitted global motion information. The holes generated during inverse motion compensation can be filled with the reference frames. The experimental results show that the proposed algorithm provides higher Bjontegaard-delta peak signal-to-noise ratios (BD-PSNRs) of 0.63 and 0.57 dB on an average compared with conventional H.264 and HEVC platforms, respectively.

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.

Sound source localization is an essential technique in many applications, e.g., speech enhancement, speech capturing and human-robot interaction. However, the performance of traditional methods degrades in noisy or reverberant environments, and it is sensitive to the spatial location of sound source. To solve these problems, we propose a sound source localization framework based on bi-direction interaural matching filter (IMF) and decision weighting fusion. Firstly, bi-directional IMF is put forward to describe the difference between binaural signals in forward and backward directions, respectively. Then, a hybrid interaural matching filter (HIMF), which is obtained by the bi-direction IMF through decision weighting fusion, is used to alleviate the affection of sound locations on sound source localization. Finally, the cosine similarity between the HIMFs computed from the binaural audio and transfer functions is employed to measure the probability of the source location. Constructing the similarity for all the spatial directions as a matrix, we can determine the source location by Maximum A Posteriori (MAP) estimation. Compared with several state-of-the-art methods, experimental results indicate that HIMF is more robust in noisy environments.

For the nonlinear acoustic echo cancellation, we present an algorithm to estimate the threshold of the clipping effect and the room impulse response vector by suppressing their time-varying cost function. A common way to suppress the time-varying cost function of a pair of parameters is to alternatingly minimize the function with respect to each parameter while keeping the other fixed, which we refer to as adaptive alternating minimization. However, since the cost function for the threshold is nonconvex, the conventional methods approximate the exact minimizations by gradient descent updates, which causes serious degradation of the estimation accuracy in some occasions. In this paper, by exploring the fact that the cost function for the threshold becomes piecewise quadratic, we propose to exactly minimize the cost function for the threshold in a closed form while suppressing the cost function for the impulse response vector in an online manner, which we call exact-online adaptive alternating minimization. The proposed method is expected to approximate more efficiently the adaptive alternating minimization strategy than the conventional methods. Numerical experiments demonstrate the efficacy of the proposed method.

In this paper, a new scheme for personalized web page recommendation using multi-user search engine query information is proposed. Our contribution is a scheme that improves the accuracy of personalization for various types of contents (e.g., documents, images and music) without increasing user burden. The proposed scheme combines “preference footprints” for browsed pages with collaborative filtering. We acquire user interest using words that are relevant to queries submitted by users, attach all user interests to a page as a footprint when it is browsed, and evaluate the relevance of web pages in relation to words in footprints. The performance of the scheme is evaluated experimentally. The results indicate that the proposed scheme improves the precision and recall of previous schemes by 1%-24% and 80%-107%, respectively.

The previous researches on the chaotic CDMA have theoretically derived the chaotic sequences having the minimum asynchronous cross-correlation. To minimize the asynchronous cross-correlation, autocorrelation of each sequence have to be C(τ)≈C×rτ, r=-2+√3, dumped oscillation with increase of the lag τ. There are several methods to generate such sequences, using a chaotic map, using the Lebesgue spectrum filter (LSF) and so on. In this paper, such lowest cross-correlation found in the chaotic CDMA researches is applied to solution search algorithms for combinatorial optimization problems. In combinatorial optimization, effectiveness of the chaotic search has already been clarified. First, an importance of chaos and autocorrelation with dumped oscillation for combinatorial optimization is shown. Next, in order to realize ideal solution search, the LSF is applied to the Hopfield-Tank neural network, the 2-opt method and the 2-exchange method. Effectiveness of the LSF is clarified even for the large problems for the traveling salesman problems and the quadratic assignment problems.

In this paper, we study a novel method to avoid a local minimum stagnation in the design problem of IIR (Infinite Impulse Response) filters using PSO (Particle Swarm Optimization). Although PSO is appropriate to solve nonlinear optimization problems, it is reported that a local minimum stagnation occurs due to a strong intensification of particles during the search. Then, multi-swarm PSO based on the particle reallocation strategy is proposed to avoid the local minimum stagnation. In this method, a reallocation space is determined by using some global bests. In this paper, the relationship between the number of swarms and the best value of design error is shown and the effectiveness of the proposed method is shown through several design examples.

When using a wireless capsule endoscope (WCE), it is important to know WCE location. In this paper, we focus on a time of arrival (TOA)-based localization technique, as it has better location estimation performance than other radio frequency-based techniques. However, the propagation speed of signals transmitted from inside of a human body varies depending on which biological tissues they pass through. For this reason, almost all of conventional TOA-based methods have to obtain the relative permittivity of the passed biological tissues or the propagation speed beforehand through another measurement system, i.e., magnetic resonance imaging (MRI) or computational tomography (CT). To avoid such troublesome pre-measurement, we propose a hybrid TOA/received signal strength indicator (RSSI)-based method, which can simultaneously estimate the WCE location and the averaged relative permittivity of the human body. First, we derive the principle of RSSI-based relative permittivity estimation from an finite difference time domain (FDTD) simulation. Second, we combine the TOA-based localization and the proposed RSSI-based relative permittivity estimation, and add them to the particle filter tracking technique. Finally, we perform computer simulations to evaluate the estimation accuracy of the proposed method. The simulation results show that the proposed method can accomplish good localization performance, 1.3mm, without pre-measurement of the human body structure information.

In this paper, a novel method for an effective allocation of non-zero digits in design of CSD (Canonic Signed-Digit) coefficient FIR (Finite Impulse Response) filters is proposed. The design problem can be formulated as a mixed integer programming problem, which is well-known as a NP-hard problem. Recently, a heuristic approach using the PSO (Particle Swarm Optimization) for solving the problem has been proposed, in which the maximum number of non-zero digits was limited in each coefficient. On the other hand, the maximum number of non-zero digits is limited in total in the proposed method and 0-1PSO is applied. It enables an effective allocation of non-zero digits, and provides a good design. Several examples are shown to present the efficiency of the proposed method.

Design of lumped-element immittance inverters which support dual-frequency operation and tuning of both operational frequencies is presented. Unique properties of the dual-composite right/left-handed transmission lines (D-CRLH TL) give an opportunity to design immittance inverters with two non-multiple operational frequencies and a stop band between them. Replacement of capacitors of D-CRLH TL unit cells with variable ones enables inverter tunability. Tunability analysis of such immittance inverters is given. It is shown that a tuning range of the operational frequencies is limited by a tolerable variation of the inverter parameter. The design concept is verified by results of electromagnetic simulation and measured frequency characteristics of fixed (non-tunable) as well as tunable dual-frequency immittance inverters and dual-band filters using the inverters.

This paper presents a wideband digital predistortion (DPD) architecture suitable for wideband wireless systems, such as IEEE 802.11ad/WiGig, where low oversampling ratio of the digital-to-analog converter (DAC) is a bottleneck for available linearization bandwidth. In order to overcome the bandwidth limitation in the conventional DPD, the proposed DPD introduces a complex coefficient filter in the DPD signal processing, which enables it to achieve asymmetric linearization. This approach effectively suppresses one side of adjacent channel leakages with twice the bandwidth as compared to the conventional DPD. The concept is verified through system simulation and measurements. Using a scaled model of a 2 GHz RF carrier frequency, the measurement shows a 4.2 dB advantage over the conventional DPD in terms of adjacent channel leakage.

This paper presents a K-band fully reconfigurable waveguide resonator filter with a new negative coupling structure. A pair of transmission zeros as well as the center frequency and bandwidth of the presented filter can be adjusted. The filter adopts the concept of a frequency-tunable coupling resonator in designing the coupling structure, which allows for controlling the coupling coefficient. All coupling values in the filter structure can be tuned by adjusting the resonant frequency of each frequency-tunable coupling resonator. This work also presents a design method in detail for the coupling resonator with a negative coupling coefficient. In addition, the approach for separating the resonant peak produced by the coupling resonator with a negative coupling value from the passband for the purpose of improving the stopband performance is described. For verifying the presented filter structure, a fourth-order waveguide filter has been fabricated and measured. The fabricated filter has the center frequency tuning range from 18.34GHz to 18.75GHz, the bandwidth tuning ratio of 1.94 : 1.

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has emerged as a promising tool for tracking a time-varying number of targets. However, the standard CBMeMBer filter may perform poorly when measurements are coupled with sensor biases. This paper extends the CBMeMBer filter for simultaneous target tracking and sensor biases estimation by introducing the sensor translational biases into the multi-Bernoulli distribution. In the extended CBMeMBer filter, the biases are modeled as the first order Gauss-Markov process and assumed to be uncorrelated with target states. Furthermore, the sequential Monte Carlo (SMC) method is adopted to handle the non-linearity and the non-Gaussian conditions. Simulations are carried out to examine the performance of the proposed filter.

A novel virtual sensors-based positioning method has been presented in this paper, which can make use of both direct paths and indirect paths. By integrating the virtual sensor idea and Bayesian state and observation framework, this method models the indirect paths corresponding to persistent virtual sensors as virtual direct paths and further reformulates the wireless positioning problem as the maximum likelihood estimation of both the mobile terminal's positions and the persistent virtual sensors' positions. Then the method adopts the EM (Expectation Maximization) and the particle filtering schemes to estimate the virtual sensors' positions and finally exploits not only the direct paths' measurements but also the indirect paths' measurements to realize the mobile terminal's positions estimation, thus achieving better positioning performance. Simulation results demonstrate the effectiveness of the proposed method.

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.

We propose an edge-preserving multiscale image decomposition method using filters for non-equispaced signals. It is inspired by the domain transform, which is a high-speed edge-preserving smoothing method, and it can be used in many image processing applications. One of the disadvantages of the domain transform is sensitivity to noise. Even though the proposed method is based on non-equispaced filters similar to the domain transform, it is robust to noise since it employs a multiscale decomposition. It uses the Laplacian pyramid scheme to decompose an input signal into the piecewise-smooth components and detail components. We design the filters by using an optimization based on edge-preserving smoothing with a conversion of signal distances and filters taking into account the distances between signal intervals. In addition, we also propose construction methods of filters for non-equispaced signals by using arbitrary continuous filters or graph spectral filters in order that various filters can be accommodated by the proposed method. As expected, we find that, similar to state-of-the-art edge-preserving smoothing techniques, including the domain transform, our approach can be used in many applications. We evaluated its effectiveness in edge-preserving smoothing of noise-free and noisy images, detail enhancement, pencil drawing, and stylization.