Conventional adaptive notch filter based on an infinite impulse response (IIR) filter is well known. However, this kind of adaptive notch filter has a problem of stability due to its adaptive IIR filter. In addition, tap coefficients of this notch filter converge to solutions with bias error. In order to solve these problems, an adaptive notch filter using Fourier sine series (ANFF) is proposed. The ANFF is stable because an adaptive IIR filter is not used as an all-pass filter. Further, the proposed adaptive notch filter is robust enough to overcome effects of a disturbance signal, due to a structure of the notch filter based on an exponential filter and line symmetry of auto correlation.

Ultra-wideband pulse radar exhibits high range resolution, and excellent capability in penetrating dielectric media. With that, it has great potential as an innovative non-destructive inspection technique for objects such as human body or concrete walls. For suitability in such applications, we have already proposed an accurate permittivity estimation method for a 2-dimensional dielectric object of arbitrarily shape and clear boundary. In this method, the propagation path estimation inside the dielectric object is calculated, based on the geometrical optics (GO) approximation, where the dielectric boundary points and its normal vectors are directly reproduced by the range point migration (RPM) method. In addition, to compensate for the estimation error incurred using the GO approximation, a waveform compensation scheme employing the finite-difference time domain (FDTD) method was incorporated, where an initial guess of the relative permittivity and dielectric boundary are employed for data regeneration. This study introduces the 3-dimensional extension of the above permittivity estimation method, aimed at practical uses, where only the transmissive data are effectively extracted, based on quantitative criteria that considers the spatial relationship between antenna locations and the dielectric object position. Results from a numerical simulation verify that our proposed method accomplishes accurate permittivity estimations even for 3-dimensional dielectric medium of wavelength size.

We propose an adaptive reversible data hiding method with superior visual quality and capacity in which an adaptive generalized difference expansion (AGDE) method is applied to an integer-to-integer subband transform (I2I-ST). I2I-ST performs the reversible subband transform and the AGDE method is a state-of-the-art method of reversible data hiding. The results of experiments we performed objectively and perceptually show that the proposed method has better visual quality than conventional methods at the same embedding rate due to low variance in the frequency domain.

The notion of pseudo-free groups was first introduced and formalized by Hohenberger and Rivest in order to unify cryptographic assumptions. Catalano, Fiore and Warinschi proposed a generalized notion called adaptive pseudo-free groups, and showed that the RSA group $Z_N^ imes$ is adaptive pseudo-free with some specific parametric distribution under the strong RSA assumption. In this paper, we develop an alternative parametric distribution and show that the RSA group $Z_N^ imes$ is adaptive pseudo-free with the parametric distribution under the RSA assumption rather than the strong RSA assumption.

This letter improves two adaptive steganographic methods in Refs. [5], [6], which utilize the remainders of two consecutive pixels to record the information of secret data. Through analysis, we point out that they perform mistakenly under some conditions, and the recipient cannot extract the secret data exactly. We correct these by enlarging the adjusting range of the remainders of two consecutive pixels within the block in the embedding procedure. Furthermore, the readjusting phase in Ref. [6] is improved by allowing every two-pixel block to be fully modified, and then the sender can select the best choice that introduces the smallest embedding distortion. Experimental results show that the improved method not only extracts secret data exactly but also reduces the embedding distortion.

We analyze a power adaptation method to maximize the achievable rate under the finite block length regime, for MIMO block fading channel with channel state information available at both the transmitter and receiver side. We find a convex approximation to the lower bound of the achievable rate, and it leads to a simple power and rate adaptation method. We show that the method achieves near optimal channel rate under the finite block length regime. Compared to the classical waterfilling method, the proposed method can further improve achievable rate especially for short block lengths.

The long-term degradation due to aging such as NBTI (Negative Bias Temperature Instability) is a hot issue in the current circuit design using nanometer process technologies, since it causes a delay fault in the field. In order to resolve the problem, we must estimate delay variation caused by long-term degradation in design stage, but over estimation must be avoided so as to make timing design easier. If we can treat such a variation statistically, and if we treat it together with delay variations due to process variability, then we can reduce over margin in timing design. Moreover, such a statistical static timing analyzer treating process variability and long-term degradation together will help us to select an appropriate set of paths for which field testing are conducted to detect delay faults. In this paper, we propose a new delay model with a half triangular distribution, which is introduced for handling a random factor with unknown distribution such as long term degradation. Then, we show an algorithm for finding the statistical maximum, which is one of key operations in statistical static timing analysis. We also show a few experimental results demonstrating the effect of the proposed model and algorithm.

In this paper, we propose a chromatic adaptation model based on the adapting degree according to the level of adapting luminance and chromaticity in various surround illuminants. In the proposed model, first maximum adapted cone responses are calculated through the estimation of adapting degree for viewing conditions then corresponding colors are reproduced from original colors using the ratio of maximum adapted cone responses between different viewing conditions. The purpose of this study is to produce chromatic adaptation transform applied to environment-adaptive color display system. As a result, our proposed model can give better estimation performance than prior models and be embodied easily as a linear model in display systems. So it is confirmed that the implemented system can predict corresponding-color data very well under a variety of viewing conditions.

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.

Ocean surface current radar is a Doppler radar to observe oceanographic information using the Bragg scattering resonance mechanism. In this paper, we consider angular resolution improvement of the radar. The radar employs an antenna array with FMICW operation, then it can resolve angular distribution by Digital Beam Forming (DBF) and distance by Fourier transform of the beat signal obtained by the FMICW radar. In order to obtain sufficient angular resolution, large array length or aperture with increasing the number of elements is needed, that is often difficult to realize in the HF/VHF ocean surface current radar. In this paper we propose to apply the Khatri-Rao (KR) product array processing to the radar. To verify effectiveness of the KR product array processing in angular resolution enhancement for the ocean surface current radar, we apply the KR product array to actual experimental data set of the radar, and show that the method is available to angular resolution enhancement and Doppler spectrum improvement.

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.

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.

The classification of human motion is an important aspect of monitoring pedestrian traffic. This requires the development of advanced surveillance and monitoring systems. Methods to achieve this have been proposed using micro-Doppler radars. However, reliable long-term data and/or complicated procedures are needed to classify motion accurately with these conventional methods because their accuracy and real-time capabilities are invariably inadequate. This paper proposes an accurate and real-time method for classifying the movements of pedestrians using ultra wide-band (UWB) Doppler radar to overcome these problems. The classification of various movements is achieved by extracting feature parameters based on UWB Doppler radar images and their radial velocity distributions. Experiments were carried out assuming six types of pedestrian movements (pedestrians swinging both arms, swinging only one arm, swinging no arms, on crutches, pushing wheelchairs, and seated in wheelchairs). We found they could be classified using the proposed feature parameters and a k-nearest neighbor algorithm. A classification accuracy of 96% was achieved with a mean calculation time of 0.55s. Moreover, the classification accuracy was 99% using our proposed method for classifying three groups of pedestrian movements (normal walkers, those on crutches, and those in wheelchairs).

A microwave mammography setup for clinical testing was developed and used to successfully carry out an initial clinical test. The equipment is based on multistatic ultra wideband (UWB) radar, which features a multistatic microwave imaging via space time (MS-MIST) algorithm for high resolution and a conformal array with an aspirator for fixing the breast in place. In this paper, an outline of the equipment, a numerical simulation, and clinical test results are presented.

In this paper, a multi-temporal analysis of polarimetric synthetic aperture radar (Pol-SAR) data over the sandbank and oyster farm area is presented. Specifically, a four-component scattering model, being able to identify single bounce, double bounce, volume, and helix scattering power contributions, has been employed to retrieve information. Decomposition results of a time series RADARSAT Pol-SAR images acquired over the western Taiwan coast indicate that the coastal tide level plays a key role in the sandbank and oyster farm monitoring. At high tide levels, the underlying sandbank creates a shallow area with an increased roughness of the above sea surface, leading to an enhanced surface scattering power as compared to the ambient water. Contrarily, at low tide levels, the exposed sandbank appears to be a smooth scatterer, generating decreased backscattering power than the surrounding area. On the other hand, the double-bounce scattering power is shown to be highly correlated with the tide level in the oyster farms due to their vertical structures. This also demonstrates a promising potential of the four-component scattering power decomposition for coastal tide level monitoring applications.

We consider uplink multi-carrier code-division multiple access (MC-CDMA) systems in a multi-cell environment. It is assumed that all intra-cell users employ Alamouti's simple space-time block coding (STBC), which is known to the base station receiver, but the receiver has no information on whether inter-cell users employ STBC or not. In this case we propose a blind adaptive minimum output energy (MOE) receiver for uplink STBC MC-CDMA, which is designed to perfectly remove the interference from intra-cell users by using the spreading sequence information on all intra-cell users and to reduce the interference from inter-cell users by minimizing the constrained output energy. Analysis and simulation results show that the proposed adaptive receiver has a faster convergence rate and higher steady-state signal-to-interference plus noise ratio (SINR) than a conventional scheme in which only the spreading code information of the desired user is utilized.

We address the problem of processing graph pattern matching queries over a massive set of data graphs in this letter. As the number of data graphs is growing rapidly, it is often hard to process such queries with serial algorithms in a timely manner. We propose a distributed graph querying algorithm, which employs feature-based comparison and a filter-and-verify scheme working on the MapReduce framework. Moreover, we devise an efficient scheme that adaptively tunes a proper feature size at runtime by sampling data graphs. With various experiments, we show that the proposed method outperforms conventional algorithms in terms of scalability and efficiency.

We present an acoustic model adaptation method where the transformation matrix for a new speaker is given by the product of bases and a weight matrix. The bases are built from the parallel factor analysis 2 (PARAFAC2) of training speakers' transformation matrices. We perform continuous speech recognition experiments using the WSJ0 corpus.

This letter proposes a new delivery format in order to realize unified transmissions of stereoscopic video contents over a dynamic adaptive streaming scheme. With the proposed delivery format, various forms of stereoscopic video contents regardless of their encoding and composition types can be delivered over the current dynamic adaptive streaming scheme. In addition, the proposed delivery format supports dynamic and efficient switching between 2D and 3D sequences in an interoperable manner for both 2D and 3D digital devices, regardless of their capabilities. This letter describes the designed delivery format and shows dynamic interoperable applications for 2D and 3D mixed contents with the implemented system in order to verify its features and efficiency.

This paper reports an advanced millimeter-wave radar system to enable detection of vehicles and pedestrians in wide areas around the radar site such as an intersection. We focus on a pulse coding scheme using complementary codes to reduce range sidelobe for discriminating vehicles from pedestrians with high accuracy. In order to suppress sidelobe increase created by RF circuit imperfections, a π/2 shift pulse modulation method with a complementary code pair cycle is presented. Moreover, in order to improve the angular resolution, a high-resolution direction of arrival estimation involving Tx beam scanning is presented. Experiments on a prototype confirm its range sidelobe suppression exceeds 40dB and its angular resolution is 5° for two human's separation at the distance of about 10m in an anechoic chamber. In a trial intersection experiment, a pedestrian detection rate of 95% was achieved at the false alarm rate of 10% in the range from 5m to 40m. The results prove the system's feasibility for future automotive safety application.