In this letter, we propose an effective method to retrieve images from a 2D shape image database using discrete shock graphs combined with an adaptive selection algorithm. Experimental results show that our method is more accurate and fast than conventional approaches and reduces computational complexity.

In this study, we demonstrate an acceleration of flexible generalized minimal residual algorithm (FGMRES) implemented with the method of moments and the fast multipole method (FMM), based on a combined tangential formulation. For the implementation of the FGMRES incorporated with the FMM concept, we propose a new definition of the truncation number for the FMM operator within the inner solver. The proposed truncation number provides an optimal variable preconditioner by controlling the accuracy and computational cost of the inner iteration. Moreover, to further accelerate the convergence, we introduce the concept of a multistage preconditioner. Numerical experiments reveal that our new version of FGMRES, based on the proposed truncation number for the inner solver and the multistage preconditioner, achieves outstanding acceleration of the convergence for large-scale and practical electromagnetic scattering and radiation problems with several levels of geometrical complexity.

In this paper, an on-line nonnegative matrix factorization (NMF) algorithm for acoustic signal processing systems is developed based on the recursive least squares (RLS) method. In order to develop the on-line NMF algorithm, we reformulate the NMF problem into multiple least squares (LS) normal equations, and solve the reformulated problems using RLS methods. In addition, we eliminate the irrelevant calculations based on the NMF model. The proposed algorithm has been evaluated with a well-known dataset used for NMF performance evaluation and with real acoustic signals; the results show that the proposed algorithm performs better than the conventional algorithm in on-line applications.

With Wireless Sensor Networks (WSNs) involving in diverse applications, the realistic analysis of energy consumption of a sensor node in an error-prone network environment is emerging as an elementary research issue. In this paper, we introduce a Distributed Communication Model (DCM) that can accurately determine the energy consumption through data communication from source to destination in error-prone network environments. The energy consumption is affected with the quality of link, which is characterized by symmetry, directivity, instability, and irregularity of the communication range of a sensor node. Due to weak communication links, significant packet loss occurs that affects the overall energy consumption. While other models unable to determine energy consumption due to lossy links in error-prone and unstable network environments, DCM can accurately estimate the energy consumption in such situations. We also perform comprehensive analysis of overheads caused by data propagation through multi-hop distributed networks. We validate DCM through both simulations and experiments using MICAz motes. Similarity of the results from energy consumption analysis with both simulations and experimentations shows that DCM is realistic, compared to other models in terms of accuracy and diversity of the environments.

This paper presents a low-power FPGA based on mixed synchronous/asynchronous design. The proposed FPGA consists of several sections which consist of logic blocks, and each section can be used as either a synchronous circuit or an asynchronous circuit according to its workload. An asynchronous circuit is power-efficient for a low-workload section since it does not require the clock tree which always consumes the power. On the other hand, a synchronous circuit is power-efficient for a high-workload section because of its simple hardware. The major consideration is designing an area-efficient synchronous/asynchronous hybrid logic block. This is because the hardware amount of the asynchronous circuit is about double that of the synchronous circuit, and the typical implementation wastes half of the hardware in synchronous mode. To solve this problem, we propose a hybrid logic block that can be used as either a single asynchronous logic block or two synchronous logic blocks. The proposed FPGA is fabricated using a 65-nm CMOS process. When the workload of a section is below 22%, asynchronous mode is more power-efficient than synchronous mode. Otherwise synchronous mode is more power-efficient.

A MB-OFDM UWB transmitter with on-chip transformer and LO leakage calibration for WiMedia bandgroup 1 is presented. The measurements show a gain-flatness of 1 dB, an LOLRR of -53 dBc/-43 dBc (wi/o cali), an EVM of 2.2% with a power consumption of 22 mW and an area of 1.26 mm2.

In this paper, we study how exploiting multiple differential characteristics with a common initial difference and different output differences improves the complexity of differential cryptanalysis attack. We call such an approach Multiple Differential Cryptanalysis. We describe such an attack rigorously by studying the probability distribution of multiple differential characteristics and giving an attack algorithm based on LLR statistic. We also present a statistical analysis on the attack complexity based on LLR probabilistic technique. Our analysis shows that the data complexity of the proposed attack decreases as the number of characteristics increases. We do an experiment with the described method to show its improvements through cryptanalyzing a reduced round PRESENT block cipher with 5 rounds.

In network coding, for the case that the network topology is unknown completely, random linear network coding has been proposed as an acceptable coding technique. In this paper, we define average failure probability of random linear network coding in order to characterize the performance of random network coding, and then analyze this failure probability for different known topological information of network. We obtain several upper bounds on the failure probabilities, and further show that, for some networks, these upper bounds are tight or asymptotically tight. Moreover, if the more topological information of the network is utilized, the better upper bounds are acquired.

The contribution of this paper is two-fold. Firstly, we conduct a large-scale real-world evaluation of the effectiveness of integrating an automatic transliteration system with a machine translation system. A human evaluation is usually preferable to an automatic evaluation, and in the case of this evaluation especially so, since the common machine translation evaluation methods are affected by the length of the translations they are evaluating, often being biassed towards translations in terms of their length rather than the information they convey. We evaluate our transliteration system on data collected in field experiments conducted all over Japan. Our results conclusively show that using a transliteration system can improve machine translation quality when translating unknown words. Our second contribution is to propose a novel Bayesian model for unsupervised bilingual character sequence segmentation of corpora for transliteration. The system is based on a Dirichlet process model trained using Bayesian inference through blocked Gibbs sampling implemented using an efficient forward filtering/backward sampling dynamic programming algorithm. The Bayesian approach is able to overcome the overfitting problem inherent in maximum likelihood training. We demonstrate the effectiveness of our Bayesian segmentation by using it to build a translation model for a phrase-based statistical machine translation (SMT) system trained to perform transliteration by monotonic transduction from character sequence to character sequence. The Bayesian segmentation was used to construct a phrase-table and we compared the quality of this phrase-table to one generated in the usual manner by the state-of-the-art GIZA++ word alignment process used in combination with phrase extraction heuristics from the MOSES statistical machine translation system, by using both to perform transliteration generation within an identical framework. In our experiments on English-Japanese data from the NEWS2010 transliteration generation shared task, we used our technique to bilingually co-segment the training corpus. We then derived a phrase-table from the segmentation from the sample at the final iteration of the training procedure, and the resulting phrase-table was used to directly substitute for the phrase-table extracted by using GIZA++/MOSES. The phrase-table resulting from our Bayesian segmentation model was approximately 30% smaller than that produced by the SMT system's training procedure, and gave an increase in transliteration quality measured in terms of both word accuracy and F-score.

In this letter, a novel physical layer signaling transmission scheme is proposed, where the signaling information is conveyed by a pair of training sequences located in the odd and even subcarriers of an orthogonal frequency division multiplexing (OFDM) training symbol. At the receiver side, only a single correlator is required to detect the signaling information. Computer simulations verify the proposed signaling could outperform the S1 signaling and achieve similar robustness as the S2 signaling of the DVB-T2 standard.

Microwave/millimeter-wave phase and amplitude characteristics of the optically controlled phased array antenna with a different SMF for each antenna feed were measured. Suitable phases for the beam steering can be realized by the adjustment of the LD wavelength independently with multiple SMFs. In addition to the phase, amplitude of each antenna feed can be controlled stably using LD current without phase variation. Furthermore, effectiveness of the calibration method of the phased array using multiple SMFs by LD wavelength adjustment is experimentally verified. Excellent microwave/millimeter-wave phase characteristics using 2- and 3-element optically controlled phased array feed were experimentally demonstrated with calibration of the phases. Phase characteristics of the array using multiple SMFs were also compared with that using a single SMF experimentally.

We propose two types of active directional couplers to assure high TX cancellation: an asymmetric type and a symmetric type. For attaining low receiving through loss, coupling capacitors used in conventional couplers are replaced by amplifiers in the proposed active directional couplers. The asymmetric active directional coupler is composed of a small number of components and simple structure. The symmetric active directional coupler has wide-bandwidth TX cancellation. Measurement results show that receiving through loss of -5.3 dB and the TX cancellation of -67.6 dB are obtained in the asymmetric active directional coupler, and receiving through loss of -6.7 dB and the TX cancellation of -66.4 dB are obtained in the symmetric active directional coupler. Compared to the asymmetric active directional coupler, the symmetric active directional coupler has advantage of wider bandwidth of 1.25 MHz to reduce TX leakage of less than -55 dB. Both the proposed active directional couplers achieve high TX cancellation, and the symmetric active directional coupler can be applied in a UHF RFID system with 10-m communication range.

In this paper, we propose a novel feeding structure for a coaxial-excited compact waveguide filter, which is composed of planar resonators called frequency-selective surfaces (FSSs). In our proposed feeding structure, new FSSs located at the input and output ports are directly excited by the coaxial line. By using the FSSs, the transition from the TEM mode to the TE10 mode is realized by the resonance of the FSSs. Therefore, the backshort length from the coaxial probe to the shorted waveguide end can be made much shorter than one-quarter of the guided wavelength. Additionally, the coaxial-excited FSS provides one transmission zero at each stopband. As a design example, a three-stage bandpass filter with 4% bandwidth at the X band is demonstrated. The designed filter has a very compact size of one cavity and has high skirt selectivity with six transmission zeros. The effectiveness of the design is confirmed by the comparison of frequency characteristics obtained by the simulation and measurement.

On the huge-scale array antenna for SSPS (space solar power systems), the problem of faulty elements and effect of mutual coupling between array elements should be considered in practice. In this paper, the effect of faulty elements as well as mutual coupling on the performance of the huge-scale array antenna are analyzed by using the proposed IEM/LAC. The result shows that effect of faulty elements and mutual coupling on the actual gain of the huge-scale array antenna are significant.

In cellular networks, maximizing the energy efficiency (EE) while satisfying certain QoS requirements is challenging. In this article, we utilize effective capacity (EC) theory as an effective means of meeting these challenges. Based on EC and taking a realistic base station (BS) power consumption model into account, we develop a novel energy efficiency (EE) metric: effective energy efficiency (EEE), to represent the delivered service bit per energy consumption at the upper layer with QoS constraints. Maximizing the EEE problem with EC constraints is addressed and then an optimal power control scheme is proposed to solve it. After that, the EEE and EC tradeoff is discussed and the effects of diverse QoS parameters on EEE are investigated through simulations, which provides insights into the quality of service (QoS) provision, and helps the system power consumption optimization.

Histogram-based image features such as HoG, SIFT and histogram of visual words are generally represented as high-dimensional, non-negative vectors. We propose a supervised method of reducing the dimensionality of histogram-based features by using non-negative matrix factorization (NMF). We define a cost function for supervised NMF that consists of two terms. The first term is the generalized divergence term between an input matrix and a product of factorized matrices. The second term is the penalty term that reflects prior knowledge on a training set by assigning predefined constants to cannot-links and must-links in pairs of training data. A multiplicative update rule for minimizing the newly-defined cost function is also proposed. We tested our method on a task of scene classification using histograms of visual words. The experimental results revealed that each of the low-dimensional basis vectors obtained from the proposed method only appeared in a single specific category in most cases. This interesting characteristic not only makes it easy to interpret the meaning of each basis but also improves the power of classification.

To reduce the error of channel estimation caused by noise, a novel noise suppression method based on the degree of confidence is proposed in this paper. The false alarm and false dismissal probabilities, corresponding to noise being taken as part of channel impulse response (CIR) and part of the CIR being mis-detected as noise, respectively, are also investigated. A false alarm reduction method is therefore presented to reduce the false alarms in the estimated CIR while the mis-detection ratio still remains low. Simulation results show the effectiveness of the proposed method.

A new direction of arrival (DOA) estimation method is introduced with arbitrary array geometry when uncorrelated and coherent signals coexist. The DOAs of uncorrelated signals are first estimated via subspace-based high resolution DOA estimation technique. Then a matrix that only contains the information of coherent signals can be formulated by eliminating the contribution of uncorrelated signals. Finally a subspace block sparse reconstruction approach is taken for DOA estimations of the coherent signals.

Scale-invariant features are widely used for image retrieval and shape classification. The curvature of a planar curve is a fundamental feature and it is geometrically invariant with respect it the coordinate system. The curvature-based feature varies in position when multi-scale analysis is performed. Therefore, it is important to recognize the scale in order to detect the feature point. Numerous shape descriptors based on contour shapes have been developed in the field of pattern recognition and computer vision. A curvature scale-space (CSS) representation cannot be applied to a contour fragment and requires the tracking of feature points. In a gradient-based curvature computation, although the gradient computation considers the scale, the curvature is normalized with respect to not the scale but the contour length. The scale-invariant feature transform algorithm that detects feature points from an image solves similar problems by using the difference of Gaussian (DoG). It is difficult to apply the SIFT algorithm to a planar curve for feature extraction. In this paper, an automatic scale detection method for a contour fragment is proposed. The proposed method detects the appropriate scales and their positions on the basis of the difference of curvature (DoC) without the tracking of feature points. To calculate the differences, scale-normalized curvature is introduced. An advantage of the DoC algorithm is that the appropriate scale can be obtained from a contour fragment as a local feature. It then extends the application area. The validity of the proposed method is confirmed by experiments. The proposed method provides the most stable and robust scales of feature points among conventional methods such as curvature scale-space and gradient-based curvature.

Main bottleneck of photomosaic algorithm is a search for a best matched image. Unlike several techniques which use fast approximation search for increasing the speed, we propose a parallel framework for fast photomosaic using a programmable GPU. This paper suggests a design of vertex structure for a best match searching on each cell of photomosaic grid and shows a texture representation of image database. The shader programs which are used for searching a best match and rendering image tiles into a display are presented. In addition, a simple duplicate reduction and color correction methods are proposed. Our algorithm not only offers dramatic enhancement of speed, but also always guarantees the 'exact' result.