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.

We propose an exponent-based partitioning broadcast protocol (EPBP) to promise the prompt dissemination of emergency message (EM) in vehicular networks. EPBP divides the communication range into segments with different widths iteratively. The width varies corresponding to the exponential curve. The design makes the farther no-empty segment thinner, as a result of which the collision rate of candidates' contention for the relay node decreases and the one-hop message progress increases efficiently. In addition, we adjust the interval of back-off timers to avoid the spurious forwarding problem, and develop more accurate analytical models for the performance. Our simulation verifies these models and show a significant increase of EPBP compared with the state-of-the-art protocols. EM dissemination speed can be improved as 55.94% faster in dense vehicle networks, and packet delivery ratio has risen to higher than 99.99%.

In the present paper we show how to obtain rapidly the spectral BRDF and BTDF of different display components or transparent displays using Fourier optics system under different illumination configurations. Results can be used to simulate the entire structure of a LCD display or to predict transparent display performances under various illuminations.

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.

Tor's hidden services provide both sender privacy and recipient privacy to users. A hot topic in security of Tor is how to deanonymize its hidden services. Existing works proved that the recipient privacy could be revealed, namely a hidden server's real IP address could be located. However, the hidden service's circuit is bi-directionally anonymous, and the sender privacy can also be revealed. In this letter, we propose a novel approach that can transparently discover the client of the hidden service. Based on extensive analysis on the hidden service protocol, we find a combination of cells which can be used to generate a special traffic feature with the cell-padding mechanism of Tor. A user can implement some onion routers in Tor networks and monitor traffic passing through them. Once the traffic feature is discovered, the user confirms one of the controlled routers is chosen as the entry router, and the adjacent node is the client. Compared with the existing works, our approach does not disturb the normal communication of the hidden service. Simulations have demonstrated the effectiveness of our method.

This paper proposes an efficient lossless coding scheme for color video in RGB 4:4:4 format. For the R signal that is encoded before the other signals at each frame, we employ a block-adaptive prediction technique originally developed for monochrome video. The prediction technique used for the remaining G and B signals is extended to exploit inter-color correlations as well as inter- and intra-frame ones. In both cases, multiple predictors are adaptively selected on a block-by-block basis. For the purpose of designing a set of predictors well suited to the local properties of video signals, we also explore an appropriate setting for the spatiotemporal partitioning of a video volume.

In this paper, a measurement method for the impedance and mutual coupling of multi-antennas that we have proposed is summarized. Impedance and mutual coupling characteristics are obtained after reducing the influence of the coaxial cables by synthesizing the measured S-parameters under the condition that unbalanced currents on the outside of the coaxial cables are canceled at feed points. We apply the proposed method to two closely positioned monopole antennas mounted on a small ground plane and demonstrate the validity and effectiveness of the proposed method by simulation and experiment. The proposed method is significantly better in terms of the accuracy of the mutual coupling data. In the presented case, the errors at the resonant frequency of the antennas are only 0.5dB in amplitude and 1.8° in phase.

The most commonly used scattering parameters (S parameters) are normalized to a real reference resistance, typically 50Ω. In some cases, the use of S parameters normalized to some complex reference impedance is essential or convenient. But there are different definitions of complex-referenced S parameters that are incompatible with each other and serve different purposes. To make matters worse, different simulators implement different ones and which ones are implemented is rarely properly documented. What are possible scenarios in which using the right one matters? This tutorial-style paper is meant as an informal and not overly technical exposition of some such confusing aspects of S parameters, for those who have a basic familiarity with the ordinary, real-referenced S parameters.

This paper presents a novel statistical sample-based approach for Gaussian Mixture Model (GMM)-based Voice Conversion (VC). Although GMM-based VC has the promising flexibility of model adaptation, quality in converted speech is significantly worse than that of natural speech. This paper addresses the problem of inaccurate modeling, which is one of the main reasons causing the quality degradation. Recently, we have proposed statistical sample-based speech synthesis using rich context models for high-quality and flexible Hidden Markov Model (HMM)-based Text-To-Speech (TTS) synthesis. This method makes it possible not only to produce high-quality speech by introducing ideas from unit selection synthesis, but also to preserve flexibility of the original HMM-based TTS. In this paper, we apply this idea to GMM-based VC. The rich context models are first trained for individual joint speech feature vectors, and then we gather them mixture by mixture to form a Rich context-GMM (R-GMM). In conversion, an iterative generation algorithm using R-GMMs is used to convert speech parameters, after initialization using over-trained probability distributions. Because the proposed method utilizes individual speech features, and its formulation is the same as that of conventional GMM-based VC, it makes it possible to produce high-quality speech while keeping flexibility of the original GMM-based VC. The experimental results demonstrate that the proposed method yields significant improvements in term of speech quality and speaker individuality in converted speech.

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 presents an innovative fabrication process for a planar circuits at millimeter-wave frequency. Screen printing technology provides low cost and high performance coplanar waveguides (CPW) lines in planar devices operated at millimeter-wave frequency up to 110GHz. Printed transmission lines provide low insertion losses of 0.30dB/mm at 110GHz and small return loss like as impedance standard lines. In the paper, Multiline Thru-Reflect-Line (TRL) calibration was also demonstrated by using the impedance standard substrates (ISS) fabricated by screen printing. Regarding calibration capability validation, verification devices were measured and compare the results to the result obtained by the TRL calibration using commercial ISS. The comparison results obtained by calibration of screen printing ISS are almost the same as results measured based on conventional ISS technology.

In this paper, we propose a compressed sensing scheme using sparse-graph codes and peeling decoder (SGPD). By using a mix method for construction of sensing matrices proposed by Pawar and Ramchandran, it generates local sensing matrices and implements sensing and signal recovery in an adaptive manner. Then, we show how to optimize the construction of local sensing matrices using the theory of sparse-graph codes. Like the existing compressed sensing schemes based on sparse-graph codes with “good” degree profile, SGPD requires only O(k) measurements to recover a k-sparse signal of dimension n in the noiseless setting. In the presence of noise, SGPD performs better than the existing compressed sensing schemes based on sparse-graph codes, still with a similar implementation cost. Furthermore, the average variable node degree for sensing matrices is empirically minimized for SGPD among various existing CS schemes, which can reduce the sensing computational complexity.

In this paper, we address the problem of non-parametric template matching which does not assume any specific deformation models. In real-world matching scenarios, deformation between a template and a matching result usually appears to be non-rigid and non-linear. We propose a novel approach called local rigidity constraints (LRC). LRC is built based on an assumption that the local rigidity, which is referred to as structural persistence between image patches, can help the algorithm to achieve better performance. A spatial relation test is proposed to weight the rigidity between two image patches. When estimating visual similarity under an unconstrained environment, high-level similarity (e.g. with complex geometry transformations) can then be estimated by investigating the number of LRC. In the searching step, exhaustive matching is possible because of the simplicity of the algorithm. Global maximum is given out as the final matching result. To evaluate our method, we carry out a comprehensive comparison on a publicly available benchmark and show that our method can outperform the state-of-the-art method.

In this research, we investigated the reliability of a 1-out-of-2 system with two-stage repair comprising hardware restoration and data reconstruction modes. Hardware restoration is normally independently executed by two modules. In contrast, we assumed that one of the modules could omit data reconstruction by replicating the data from the module during normal operation. In this 1-out-of-2 system, the two modules mutually cooperated in the recovery mode. As a first step, an evaluation model using Markov chains was constructed to derive a reliability measure: “unavailability in steady state.” Numerical examples confirmed that the reliability of the system was improved by the use of two cooperating modules. As the data reconstruction time increased, the gains in terms of system reliability also increased.

Considering an uncertain multi-objective optimization system with interval coefficients, this letter proposes an interval multi-objective particle swarm optimization algorithm. In order to improve its performance, a crowding distance measure based on the distance and the overlap degree of intervals, and a method of updating the archive based on the acceptance coefficient of decision-maker, are employed. Finally, results show that our algorithm is capable of generating excellent approximation of the true Pareto front.

This paper proposes a method of incrementally constructing semantic representations. Our method is based on Steedman's Combinatory Categorial Grammar (CCG), which has a transparent correspondence between syntax and semantics. In our method, a derivation for a sentence is constructed in an incremental fashion and the corresponding semantic representation is derived synchronously. Our method uses normal form CCG derivation. This is the difference between our approach and previous ones. Previous approaches use most left-branching derivation called incremental derivation, but they cannot process coordinate structures incrementally. Our method overcomes this problem.

In this letter, standard particle swarm optimization (PSO) with the center-symmetric trimmed correlation matrix and the orthogonal projection technique is firstly presented for blind carrier frequency offset estimation under interleaved orthogonal frequency division multiple access (OFDMA) uplink systems. It doesn't require eigenvalue decomposition and only needs a single OFDMA data block. Second, this letter also presents adaptive multiple inertia weights with Newton method to speed up the convergence of standard PSO iteration process. Meanwhile, the advantage of inherent interleaved OFDMA signal structure also is exploited to conquer the problems of local optimization and the effect of ambiguous peaks for the proposed approaches. Finally, several simulation results are provided for illustration and comparison.

This paper proposes a blind, inaudible, robust digital-audio watermarking scheme based on singular-spectrum analysis, which relates to watermarking techniques based on singular value decomposition. We decompose a host signal into its oscillatory components and modify amplitudes of some of those components with respect to a watermark bit and embedding rule. To improve the sound quality of a watermarked signal and still maintain robustness, differential evolution is introduced to find optimal parameters of the proposed scheme. Test results show that, although a trade-off between inaudibility and robustness still persists, the difference in sound quality between the original and the watermarked one is considerably smaller. This improved scheme is robust against many attacks, such as MP3 and MP4 compression, and band-pass filtering. However, there is a drawback, i.e., some music-dependent parameters need to be shared between embedding and extraction processes. To overcome this drawback, we propose a method for automatic parameter estimation. By incorporating the estimation method into the framework, those parameters need not to be shared, and the test results show that it can blindly decode watermark bits with an accuracy of 99.99%. This paper not only proposes a new technique and scheme but also discusses the singular value and its physical interpretation.

A self-adaptive scaled min-sum algorithm for LDPC decoding based on the difference between the first two minima of the check node messages (Δmin) is proposed. Δmin is utilized for adjusting the scaling factor of the check node messages, and simulation results show that the proposed algorithm improves the error correcting performance compared to existing algorithms.

Bistatic multi-input multi-output (MIMO) radar has the capability of measuring the transmit angle from the receiving array, which means the existence of information redundancy and benefits data association. In this paper, a data association decision for bistatic MIMO radar is proposed and the performance advantages of bistatic MIMO radar in data association is analyzed and evaluated. First, the parameters obtained by receiving array are sent to the association center via coordinate conversion. Second, referencing the nearest neighbor association (NN) algorithm, an improved association decision is proposed with the transmit angle and target range as association statistics. This method can evade the adverse effects of the angle system errors to data association. Finally, data association probability in the presence of array directional error is derived and the correctness of derivation result is testified via Monte Carlo simulation experiments. Besides that performance comparison with the conventional phased array radar verifies the excellent performance of bistatic MIMO Radar in data association.