In this paper a new method for noise removal from single-trial event-related potentials recorded with a multi-channel electroencephalogram is addressed. An observed signal is separated into multiple signals with a multi-channel Wiener filter whose coefficients are estimated based on parameter estimation of a probabilistic generative model that locally models the amplitude of each separated signal in the time-frequency domain. Effectiveness of using prior information about covariance matrices to estimate model parameters and frequency dependent covariance matrices were shown through an experiment with a simulated event-related potential data set.

A non-photorealistic rendering method creates oil-film-like images, expressed with colorful, smooth curves similar to the oil films generated on the surface of glass or water, from color photo images. The proposed method generates oil-film-like images through iterative processing between a bilateral infra-envelope filter and an unsharp mask. In order to verify the effectiveness of the proposed method, tests using a Lena image were performed, and visual assessment of oil-film-like images was conducted for changes in appearance as the parameter values of the proposed method were varied. As a result of tests, the optimal value of parameters was found for generating oil-film patterns.

This paper proposes a new class of Hilbert pairs of almost symmetric orthogonal wavelet bases. For two wavelet bases to form a Hilbert pair, the corresponding scaling lowpass filters are required to satisfy the half-sample delay condition. In this paper, we design simultaneously two scaling lowpass filters with the arbitrarily specified flat group delay responses at ω=0, which satisfy the half-sample delay condition. In addition to specifying the number of vanishing moments, we apply the Remez exchange algorithm to minimize the difference of frequency responses between two scaling lowpass filters, in order to improve the analyticity of complex wavelets. The equiripple behavior of the error function can be obtained through a few iterations. Therefore, the resulting complex wavelets are orthogonal and almost symmetric, and have the improved analyticity. Finally, some examples are presented to demonstrate the effectiveness of the proposed design method.

This paper proposes a high clutter-rejection technique for wall-penetrating frequency-modulated continuous-wave (FMCW) radar. FMCW radars are widely used, as they moderate the receiver saturation problem in wall-penetrating applications by attenuating short-range clutter such as wall-clutter. However, conventional FMCW radars require a very high-order high-pass filter (HPF) to attenuate short-range clutter. A delay-line (DL) is exploited to overcome this problem. Time-delay shifts beat frequencies formed by reflection waves. This means that a proper time-delay increases the ratio of target-beat frequency to clutter-beat frequency. Consequently, low-order HPF fully attenuates short-range clutter. A third-order HPF rejects more than 20 dB and 30 dB for clutter located at 6 m and 3 m, respectively, with a target located at 9 m detection with a 10,000 GHz/s chirp rate and a 28 ns delay-line.

Maneuvering target tracking under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions has received considerable interest in the last decades. In this paper, a hierarchical interacting multiple model (HIMM) method is proposed for estimating target position under mixed LOS/NLOS conditions. The proposed HIMM is composed of two layers with Markov switching model. The purpose of the upper layer, which is composed of two interacting multiple model (IMM) filters in parallel, is to handle the switching between the LOS and the NLOS environments. To estimate the target kinetic variables (position, speed and acceleration), the unscented Kalman filter (UKF) with the current statistical (CS) model is used in the lower-layer. Simulation results demonstrate the effectiveness and superiority of the proposed method, which obtains better tracking accuracy than the traditional IMM.

Distributed compressed video sensing (DCVS), combining advantages of compressed sensing and distributed video coding, is developed as a novel and powerful system to get an encoder with low complexity. Nevertheless, it is still unclear how to explore the method to achieve an effective video recovery through utilizing realistic signal characteristics as much as possible. Based on this, we present a novel spatiotemporal dictionary learning (DL) based reconstruction method for DCVS, where both the DL model and the l1-analysis based recovery with correlation constraints are included in the minimization problem to achieve the joint optimization of sparse representation and signal reconstruction. Besides, an alternating direction method with multipliers (ADMM) based numerical algorithm is outlined for solving the underlying optimization problem. Simulation results demonstrate that the proposed method outperforms other methods, with 0.03-4.14 dB increases in PSNR and a 0.13-15.31 dB gain for non-key frames.

In this paper, a vehicular antenna design scheme that considers vehicular body effects is proposed. A wire antenna for the global positioning system (GPS) and long-term evolution (LTE) systems is implemented on a plastic plate and then mounted on a windshield of the vehicle. Common outputs are used to allow feed sharing. It is necessary to increase the GPS right-hand circularly polarization (RHCP) gain near the zenith and to reduce the axis ratio (AR). For LTE, we need to increase the horizontal polarization (HP) gain. In addition, for LTE, multiband characteristics are required. In order to achieve the specified performance, the antenna shape is optimized via a Pareto genetic algorithm (PGA). When an antenna is mounted on the body, antenna performance changes significantly. To evaluate the performance of an antenna with complex shape mounted on a windshield, a commercial electromagnetic simulator (Ansoft HFSS) is used. To apply electromagnetic results output by HFSS to the PGA algorithm operating in the MATLAB environment, a MATLAB-to-HFSS linking program via Visual BASIC (VB) script was used. It is difficult to carry out the electromagnetic analysis on the entire body because of the limitations of the calculating load and memory size. To overcome these limitations, we consider only that part of the vehicle's body that influences antenna performance. We show that a series of optimization steps can minimize the degradation caused by the vehicle`s body. The simulation results clearly show that it is well optimized at 1.575GHz for GPS, and 0.74 ∼ 0.79GHz and 2.11 ∼ 2.16GHz for LTE, respectively.

Cost aggregation is one of the most important steps in local stereo matching, while it is difficult to fulfill both accuracy and speed. In this letter, a novel cost aggregation, consisting of guidance image, fast aggregation function and simplified scan-line optimization, is developed. Experiments demonstrate that the proposed algorithm has competitive performance compared with the state-of-art aggregation methods on 32 Middlebury stereo datasets in both accuracy and speed.

The individual steps of UV imprint lithography have been explained in detail from the points of manufacturing nano-structures. The applications to photonic devices have been also introduced.

We design a new hash table for high-speed networking that reduces main memory accesses even when the ratio of inserted items to the table size is high, at which point previous schemes no longer work. This improvement comes from a new design of a summary, called expanded keys, exploiting recent multiple hash functions and Bloom filter theories.

Ultra-wideband (UWB) communications is used for medical information communication technology (MICT) as a dependable and safe communication technology in recent years. On the other hand, there are existing various UWB systems that are not used for MICT. Generally, these UWB systems use almost the same frequency band. Therefore, they interfere to each other in general transmission channel environment. In our previous work, a novel UWB pulse shape modulation using modified Hermite pulse is proposed as a multiple user access scheme. In this paper, we propose a mitigation method for inter-user interference and inter-system interference using combination of orthogonal pulse shape modulation and orthogonal matched filter (OMF) detector. The purposes of our system are to detect all signals of users in the same UWB system and to reduce the unknown interference from other UWB systems at the same time. This paper provides performance evaluation results based on both of analytical and numerical evaluation. Simulation results show that the proposed system can detect the signals that were transmitted from the same UWB system using orthogonal pulse set, while the proposed system can reduce the interference from unknown UWB systems at the same time. The theoretical analysis is expected that noise tolerance of our proposal will be deteriorated in the additive Gaussian noise channel in comparison with the conventional matched filter. It is confirmed that the numerical evaluation illustrates such noise tolerance equivalent to the theoretical analysis result.

A new design method for a high-order series-coupled microring filter using Chebyshev filter condition was proposed and its application to the design of a wavelength-selective switch (WSS) was discussed. In the proposed method, the propagation loss in a microring resonator, coupling loss at a coupler, and a free spectral ranges (FSR) in a microring resonator are considered for the first time. It was found that for high-order series coupled microring resonators, the WSS designed using Chebyshev condition has more boxlike filter responses and high extinction ratio, compared with that designed using Butterworth condition, in the case where the round-trip loss in a microring is relatively large. In addition, the fourth-order series-coupled microring WSS with boxlike responses was successfully designed, considering Vernier effect for a larger FSR and shift in resonant wavelength.

Noise and area consumption has been a trade-off in circuit design. Especially for switched-capacitor filters (SCF), kT/C noise gives a limitation to the minimum value of unit capacitance. In case of SCFs with a large capacitance spread, this limitation will result in a large area consumption due to large capacitors. This paper introduces a technique to reduce capacitance spread using charge scaling. It will be shown that this technique can reduce total capacitance of SCFs without deteriorating their noise performances. A design method to reduce the output noise of SC low-pass filters (LPF) based on the combination of cut-set scaling, charge scaling and adaptive configuration is proposed. The proposed technique can reduce the output noise voltage by 30% for small input signals.

Nowadays, with the development of online social networks (OSN), a mass of online social information has been generated in OSN, which has triggered research on social recommendation. Collaborative filtering, as one of the most popular techniques in social recommendation, faces several challenges, such as data sparsity, cold-start users and prediction quality. The motivation of our work is to deal with the above challenges by effectively combining collaborative filtering technology with social information. The trust relationship has been identified as a useful means of using social information to improve the quality of recommendation. In this paper, we propose a trust-based recommendation approach which uses GlobalTrust (GT) to represent the trust value among users as neighboring nodes. A matrix factorization based on singular value decomposition is used to get a trust network built on the GT value. The recommendation results are obtained through a modified random walk algorithm called GlobalTrustWalker. Through experiments on a real-world sparser dataset, we demonstrate that the proposed approach can better utilize users' social trust information and improve the recommendation accuracy on cold-start users.

This paper proposes a method based on modulation transfer function (MTF) to restore the power envelope of noisy reverberant speech by using a Kalman filter with linear prediction (LP). Its advantage is that it can simultaneously suppress the effects of noise and reverberation by restoring the smeared MTF without measuring room impulse responses. This scheme has two processes: power envelope subtraction and power envelope inverse filtering. In the subtraction process, the statistical properties of observation noise and driving noise for power envelope are investigated for the criteria of the Kalman filter which requires noise to be white and Gaussian. Furthermore, LP coefficients drastically affect the Kalman filter performance, and a method is developed for deriving LP coefficients from noisy reverberant speech. In the dereverberation process, an inverse filtering method is applied to remove the effects of reverberation. Objective experiments were conducted under various noisy reverberant conditions to evaluate how well the proposed Kalman filtering method based on MTF improves the signal-to-error ratio (SER) and correlation between restored power envelopes compared with conventional methods. Results showed that the proposed Kalman filtering method based on MTF can improve SER and correlation more than conventional methods.

In this paper, an iterative optimal method is proposed to design the prototype filters for a fast filter bank (FFB) with low complexity, aiming to control the optimum ripple magnitude tolerance of each filter according to the overall specifications. This problem is formulated as an optimization problem for which the total number of multiplications is to be minimized subject to the constrained ripple in the passband and stopband. In the following, an iterative solution is proposed to solve this optimization problem for the purpose of obtaining the impulse response coefficients with low complexity at each stage. Simulations are conducted to verify the performance of the proposed scheme and show that compared with the original method, the proposed scheme can reduce about 24.24% of multiplications. In addition, the proposed scheme and the original method provide similar mean square error (MSE) and the mean absolute error (MAE) of the frequency response.

3GPP Long Term Evolution (LTE) is one of the most advanced technologies in the wireless and mobility field because it provides high speed data and sophisticated applications. LTE was originally deployed by service providers on various platforms using separate dedicated hardware in Access radio layer and the Evolved Packet Core network layer (EPC), thereby limiting the system's flexibility and capacity provisioning. Thus, the concept of virtualization was introduced in the EPC hardware to solve the dedicated hardware platform limitations. It was also introduced in the IP Multimedia Subsystem (IMS) and Machine to Machine applications (M2M) for the same reason. This paper provides a simulation model of a virtualized EPC and virtualized M2M transport application server connected via an external IP network, which has significant importance in the future of mobile networks. This model studies the virtualized server connectivity problem, where two separate virtual machines communicate via the existing external legacy IP network. The simulation results show moderate performance, indicating that the selection of IP technology is much more critical than before. The paper also models MPLS technology as a replacement for the external IP routing mechanism to provide traffic engineering and achieve more efficient network performance. Furthermore, to provide a real network environment, Poisson Pareto Burst Process (PPBP) traffic source is carried over the UDP transport layer which matches the statistical properties of real-life M2M traffic. Furthermore, the paper proves End-to-End interoperability of LTE and MPLS running GTP and MPLS Label Forwarding information Base (LFIB) and MPLS traffic engineering respectively. Finally, it looks at the simulation of several scenarios using Network Simulator 3 (NS-3) to evaluate the performance improvement over the traditional LTE IP architecture under M2M traffic load.

Since digital hearing aids are sensitive to time delay and power consumption, the computational complexity of noise reduction must be reduced as much as possible. Therefore, some complicated algorithms based on the analysis of the time-frequency domain are very difficult to implement in digital hearing aids. This paper presents a new approach that yields an improved noise reduction algorithm with greatly reduce computational complexity for multi-channel digital hearing aids. First, the sub-band sound pressure level (SPL) is calculated in real time. Then, based on the calculated sub-band SPL, the noise in the sub-band is estimated and the possibility of speech is computed. Finally, a posteriori and a priori signal-to-noise ratios are estimated and the gain function is acquired to reduce the noise adaptively. By replacing the FFT and IFFT transforms by the known SPL, the proposed algorithm greatly reduces the computation loads. Experiments on a prototype digital hearing aid show that the time delay is decreased to nearly half that of the traditional adaptive Wiener filtering and spectral subtraction algorithms, but the SNR improvement and PESQ score are rather satisfied. Compared with modulation frequency-based noise reduction algorithm, which is used in many commercial digital hearing aids, the proposed algorithm achieves not only more than 5dB SNR improvement but also less time delay and power consumption.

Recently, many neural networks have been proposed for radar sea clutter suppression. However, they have poor performance under the condition of low signal to interference plus noise ratio (SINR). In this letter, we put forward a novel method to detect a small target embedded in sea clutter based on an optimal filter. The proposed method keeps the energy in the frequency cell under test (FCUT) invariant, at the same time, it minimizes other frequency signals. Finally, detect target by judging the output SINR of every frequency cell. Compared with the neural networks, the algorithm proposed can detect under lower SINR. Using real-life radar data, we show that our method can detect the target effectively when the SINR is higher than -39dB which is 23dB lower than that needed by the neural networks.

This paper describes a fast and effective algorithm for refining the parameter estimates of multicomponent third-order polynomial phase signals (PPSs). The efficiency of the proposed algorithm is accompanied by lower signal-to-noise ratio (SNR) threshold, and computational complexity. A two-step procedure is used to estimate the parameters of multicomponent third-order PPSs. In the first step, an initial estimate for the phase parameters can be obtained by using fast Fourier transformation (FFT), k-means algorithm and three time positions. In the second step, these initial estimates are refined by a simple moving average filter and singular value decomposition (SVD). The SNR threshold of the proposed algorithm is lower than those of the non-linear least square (NLS) method and the estimation refinement method even though it uses a simple moving average filter. In addition, the proposed method is characterized by significantly lower complexity than computationally intensive NLS methods. Simulations confirm the effectiveness of the proposed method.