In tooth contour extraction there is insufficient intensity difference in x-ray images between the tooth and dental bone. This difference must be enhanced in order to improve the accuracy of tooth segmentation. This paper proposes a method to improve the intensity between the tooth and dental bone. This method consists of an estimation of tooth orientation (intensity projection, smoothing filter, and peak detection) and PCA-Stacked Gabor with ellipse Gabor banks. Tooth orientation estimation is performed to determine the angle of a single oriented tooth. PCA-Stacked Gabor with ellipse Gabor banks is then used, in particular to enhance the border between the tooth and dental bone. Finally, active contour extraction is performed in order to determine tooth contour. In the experiment, in comparison with the conventional active contour without edge (ACWE) method, the average mean square error (MSE) values of extracted tooth contour points are reduced from 26.93% and 16.02% to 19.07% and 13.42% for tooth x-ray type I and type H images, respectively.

This paper proposes a robust bilateral filter which can handle mixed Gaussian and impulsive noise by hybridizing the conventional bilateral filter and the switching median filter. The effectiveness of the proposed method is verified in comparison with other conventional methods by some experiments using the natural digital images.

This paper presents a unified treatment of the tracking analysis of adaptive filters with data normalization and error nonlinearities. The approach we develop is based on the celebrated energy-conservation framework, which investigates the energy flow through each iteration of an adaptive filter. Aside from deriving earlier results in a unified manner, we obtain new performance results for more general filters without restricting the regression data to a particular distribution. Simulations show good agreement with the theoretical findings.

In this paper, online sparse Volterra system identification is proposed. For that purpose, the conventional adaptive projection-based algorithm with weighted l1 balls (APWL1) is revisited for nonlinear system identification, whereby the linear-in-parameters nature of Volterra systems is utilized. Compared with sparsity-aware recursive least squares (RLS) based algorithms, requiring higher computational complexity and showing faster convergence and lower steady-state error due to their long memory in time-invariant cases, the proposed approach yields better tracking capability in time-varying cases due to short-term data dependence in updating the weight. Also, when N is the number of sparse Volterra kernels and q is the number of input vectors involved to update the weight, the proposed algorithm requires O(qN) multiplication complexity and O(Nlog 2N) sorting-operation complexity. Furthermore, sparsity-aware least mean-squares and affine projection based algorithms are also tested.

The optimal design of an extrapolated impulse response (EIR) filter (in the mini-max sense) is a non-linear programming problem. In this paper, the optimal design of the EIR filter by the semi-infinite programming (SIP) is investigated and an iterative technique for optimally designing the EIR filter is proposed. The simulation experiment validates the effectiveness of the SIP technique and the proposed iterative technique in the optimal design of the EIR filter.

MIMO transmission technologies have become an essential component of cellular systems such as Long Term Evolution (LTE) and LTE-Advanced. Recently, evaluating the communication performance of mobile users in cellular MIMO systems has become an urgent requirement. In this paper, we propose dynamic MIMO channel modeling for the urban environment. Our proposal is based on Geometry-based Stochastic Channel Modeling (GSCM). The cluster parameters such as the local scatterer locations around the measurement course are estimated by applying the particle filtering to measured data. We carried out radio propagation measurements in an urban environment at 3.35GHz band, and generated the dynamic channel from the measured data. The experiments showed that both the spreads and auto-correlation of Time of Arrival (ToA), Angle of Arrival (AoA) and Angle of Departure (AoD) were reconstructed within the acceptable error range in our dynamic channel model.

In terms of the transmission-line theory, a general synthesis of a new class of optimum Chebyshev-type ultra-wideband bandpass (UWB) filter prototype composed of multistage stepped-impedance resonators (SIRs) and two short-circuited shunt stubs positioned at input- and output- ports is presented. By the comparison of the real and theoretical transfer functions, the design/characteristic equations are obtained for the design of the proposed filter prototype rather than the traditional design tables. The explicit expressions of one-stage and two-stage filters are then derived and reported. Accordingly, bandpass filters with an arbitrary FBW (Fractional Bandwidth) and passband ripple can be easily designed by solving the design equations. As an example, a 10-degree Chebyshev distributed filter (two-stage filter) with an FBW of 110% is synthesized to meet FCC's outdoor mask. The synthesized circuit model are confirmed by a commercial circuit simulator and then optimized by an EM simulator, fabricated in microstrip line and characterized by the network analyzer. The good agreements between the measured and predicted frequency responses validate the effectiveness of newly proposed filter prototype and the corresponding synthesis technique. In addition, the designed filter exhibits good characteristics of comparatively low insertion loss, quite sharp skirt, very flat group delay and good stopband (especially in lower one) as well. It should be also highlighted that, compared with the conventional filters composed merely of parallel-coupled SIRs or shunt short-circuit-stubs, the new prototype can reduce the overall length of the filter by more than 3/4λg. Moreover, in terms of the presented design technique, the proposed filter prototype can be also used to easily realize the UWB filters with an FBW even greater than 110%.

In this paper, we propose a use of the group sparsity in adaptive learning of second-order Volterra filters for the nonlinear acoustic echo cancellation problem. The group sparsity indicates sparsity across the groups, i.e., a vector is separated into some groups, and most of groups only contain approximately zero-valued entries. First, we provide a theoretical evidence that the second-order Volterra systems tend to have the group sparsity under natural assumptions. Next, we propose an algorithm by applying the adaptive proximal forward-backward splitting method to a carefully designed cost function to exploit the group sparsity effectively. The designed cost function is the sum of the weighted group l1 norm which promotes the group sparsity and a weighted sum of squared distances to data-fidelity sets used in adaptive filtering algorithms. Finally, Numerical examples show that the proposed method outperforms a sparsity-aware algorithm in both the system-mismatch and the echo return loss enhancement.

This paper describes a small size broadband fractional-N RF synthesizer for an RF test module with a high throughput and multiple resources installed in RF Automated Test Equipment (ATE) systems. The core device is the PLL-LSI composed of the 13-band asymmetrical tournament form voltage-controlled oscillators (VCOs) and the proposed 48-bit ΔΣ modulator with the infinite impulse response (IIR) filter. The single-loop PLL RF synthesizer is constructed in the form of systems in package (SiP) including the PLL-LSI and the active loop filter. The RF synthesizer SiP features a small size of 20mm × 20mm × 3mm, a high frequency resolution of smaller than 50µHz, and a phase noise of better than -110dBc/Hz at offset frequency of 1MHz across a frequency range of 100MHz to 13.4GHz. In addition, a frequency settling time of 150 µs that is faster than our conventional dual-loop PLL synthesizers using the discrete VCOs or the YIG-tuned oscillators (YTOs) is achieved. The synthesizer SiP significantly contributes to the realization of small size, high throughput RF test modules for RF ATEs.

This paper proposes an enhancement to a previously reported adaptive peak-to-average power ratio (PAPR) reduction method based on clipping and filtering (CF) for eigenmode multiple-input multiple-output (MIMO) — orthogonal frequency division multiplexing (OFDM) signals. We enhance the method to accommodate the case with adaptive modulation and channel coding (AMC). Since the PAPR reduction process degrades the signal-to-interference and noise power ratio (SINR), the AMC should take into account this degradation before PAPR reduction to select accurately the modulation scheme and coding rate (MCS) for each spatial stream. We use the lookup table-based prediction of SINR after PAPR reduction, in which the interference caused by the PAPR reduction is obtained as a function of the stream index, frequency block index, clipping threshold for PAPR reduction, and input backoff (IBO) of the power amplifier. Simulation results show that the proposed PAPR reduction method increases the average throughput compared to the conventional CF method for a given adjacent channel leakage power ratio (ACLR) when we assume practical AMC.

This letter introduces innovative VAD based on horizontal spectral entropy with long-span of time (HSELT) feature sets to improve mobile ASR performance in low signal-to-noise ratio (SNR) conditions. Since the signal characteristics of nonstationary noise change with time, we need long-term information of the noisy speech signal to define a more robust decision rule yielding high accuracy. We find that HSELT measures can horizontally enhance the transition between speech and non-speech segments. Based on this finding, we use the HSELT measures to achieve high accuracy for detecting speech signal form various stationary and nonstationary noises.

This paper presents a new adaptive peak-to-average power ratio (PAPR) reduction method based on clipping and filtering (CF) for precoded orthogonal frequency division multiplexing (OFDM)-multiple-input multiple-output (MIMO) transmission. While the conventional CF method adds roughly the same interference power to each of the transmission streams, the proposed method suppresses the addition of interference power to the streams with good channel conditions. Since the sum capacity is dominated by the capacity of the streams under good channel conditions and the interference caused by the PAPR reduction process severely degrades the achievable capacity for these streams, the proposed method significantly improves the achievable sum capacity compared to the conventional CF method for a given PAPR. Simulation results show the capacity gain by using the proposed method compared to the conventional method.

Based on sample-pair refinement and local optimization, this paper proposes a high-accuracy and quick matting algorithm. First, in order to gather foreground/background samples effectively, we shoot rays in hybrid (gradient and uniform) directions. This strategy utilizes the prior knowledge to adjust the directions for effective searching. Second, we refine sample-pairs of pixels by taking into account neighbors'. Both high confidence sample-pairs and usable foreground/background components are utilized and thus more accurate and smoother matting results are achieved. Third, to reduce the computational cost of sample-pair selection in coarse matting, this paper proposes an adaptive sample clustering approach. Most redundant samples are eliminated adaptively, where the computational cost decreases significantly. Finally, we convert fine matting into a de-noising problem, which is optimized by minimizing the observation and state errors iteratively and locally. This leads to less space and time complexity compared with global optimization. Experiments demonstrate that we outperform other state-of-the-art methods in local matting both on accuracy and efficiency.

In this paper, we propose a new class of two dimensional (2D) M-channel (M-ch) non-separable filter banks (FBs) based on cosine modulated filter banks (CMFBs) via a new diagonally modulation scheme. Until now, many researchers have proposed 2D non-separable CMFBs. Nevertheless, efficient direction-selective CMFBs have not been yet. Thanks to our new modulations with diagonal shifts, proposed CMFBs have several frequency supports including direction-selective ones which cannot be realized by conventional ones. In a simulation, we show design examples of proposed CMFBs and their various directional frequency supports.

This paper proposes a novel high-temperature superconducting dual-band bandpass filter (HTS-DBPF), that employs a broadside coupling structure, in which quarter-wavelength resonators are formed on opposite sides of each substrate. This structure provides a dual-band operation of the BPF and flexibility, in the sense of having a wide range in selecting two center passband frequencies of the HTS-DBPF. This paper employs the ratio of the lower and higher center passband frequencies, α, as a criterion for evaluating the flexibility. The obtained α ranges are from 1 to 4.7, which are the widest for DBPFs for mobile communications applications, to the best knowledge of the authors. This paper presents a 2.4-/2.9-GHz band HTS-DBPF, as an experimental example, using a YBCO film deposited on an MgO substrate. The measured frequency responses of the HTS-DBPF agree with the electromagnetic simulated results. Measurement and simulation results confirm that the proposed filter architecture is effective in configuring a DBPF that can set each center passband frequency widely.

Many successful methods for recognizing human action are spatio-temporal interest point (STIP) based methods. Given a test video sequence, for a matching-based method using a voting mechanism, each test STIP casts a vote for each action class based on its mutual information with respect to the respective class, which is measured in terms of class likelihood probability. Therefore, two issues should be addressed to improve the accuracy of action recognition. First, effective STIPs in the training set must be selected as references for accurately estimating probability. Second, discriminative STIPs in the test set must be selected for voting. This work uses ε-nearest neighbors as effective STIPs for estimating the class probability and uses a variance filter for selecting discriminative STIPs. Experimental results verify that the proposed method is more accurate than existing action recognition methods.

We propose a non-photorealistic rendering method for generating moire-picture-like color images from color photographs. The proposed method is performed in two steps. First, images with a staircasing effect are generated by a bilateral filter. Second, moire patterns are generated with an improved bilateral filter called an anti-bilateral filter. The characteristic of the anti-bilateral filter is to emphasize gradual boundaries.

We have succeeded in demonstrating high-performance four-channel 25 Gb/s integrated receiver for 100 Gb/s Ethernet with a built-in spatial Demux optics and an integrated PD array. All components which configure to the Demux optics adhered to a prism. Because of the shaping accuracy for prism, the insertion loss was able to suppress to 0.8 dB with small size. The connection point of the package for high speed electrical signals was improved to decrease the transmission loss. The small size of 12 mm 17 mm 7 mm compact package with a side-wall electrical connector has been achieved, which is compatible with the assembly in CFP2 form-factor. We observed the sensitivity at average power of -12.1 dBm and the power penalty of sensitivity due to the crosstalk of less than 0.1 dB.

In this letter, we present a fast image/video super resolution framework using edge and nonlocal constraint. The proposed method has three steps. First, we improve the initial estimation using content-adaptive bilateral filtering to strengthen edge. Second, the high resolution image is estimated by using classical back projection method. Third, we use joint content-adaptive nonlocal means filtering to get the final result, and self-similarity structures are obtained by the low resolution image. Furthermore, content-adaptive filtering and fast self-similarity search strategy can effectively reduce computation complexity. The experimental results show the proposed method has good performance with low complexity and can be used for real-time environment.

We propose a motor speed ripple elimination method using a state dependent disturbance observer (SDDOB). The SDDOB eliminates the state dependent disturbance in the system regardless of the operation frequency, input time delay and output time delay. The SDDOB and a main proportional integral (PI) controller constitute a robust motor speed controller. Experimental results show the effectiveness of the proposed method.