An augmented PAKE (Password-Authenticated Key Exchange) protocol is said to be secure against server-compromise impersonation attacks if an attacker who obtained password verification data from a server cannot impersonate a client without performing off-line dictionary attacks on the password verification data. There are two augmented PAKE protocols where the first one [12] was proposed in the IEEE Communications Letters and the second one [15] was submitted to the IEEE P1363.2 standard working group [9]. In this paper, we show that these two augmented PAKE protocols [12], [15] (claimed to be secure) are actually insecure against server-compromise impersonation attacks. More specifically, we present generic server-compromise impersonation attacks on these augmented PAKE protocols [12],[15].

This paper describes a nonlinear filter that can extract the image feature from noise corrupted image labeled self-quotient ε-filter (SQEF). SQEF is an improved self-quotient filter (SQF) to extract the image feature from noise corrupted image. Although SQF is a simple approach for feature extraction from the images, it is difficult to extract the feature when the image includes noise. On the other hand, SQEF can extract the image feature not only from clear images but also from noise corrupted images with uniform noise, Gaussian noise and impulse noise. We show the algorithm of SQEF and describe its feature when it is applied to uniform noise corrupted image, Gaussian noise corrupted image and impulse noise corrupted image. Experimental results are also shown to confirm the effectiveness of the proposed method.

Binary sequence pairs as a class of mismatched filtering of binary sequences can be applied in radar, sonar, and spread spectrum communication system. Binary sequence pairs with two-level periodic autocorrelation function (BSPT) are considered as the extension of usual binary sequences with two-level periodic autocorrelation function. Each of BSPT consists of two binary sequences of which all out-phase periodic crosscorrelation functions, also called periodic autocorrelation functions of sequence pairs, are the same constant. BSPT have an equivalent relationship with difference set pairs (DSP), a new concept of combinatorial mathematics, which means that difference set pairs can be used to research BSPT as a kind of important tool. Based on the equivalent relationship between BSPT and DSP, several families of BSPT including perfect binary sequence pairs are constructed by recursively constructing DSP on the integer ring. The discrete Fourier transform spectrum property of BSPT reveals a necessary condition of BSPT. By interleaving perfect binary sequence pairs and Hadamard matrix, a new family of binary sequence pairs with zero correlation zone used in quasi-synchronous code multiple division address is constructed, which is close to the upper theoretical bound with sequence length increasing.

We present a novel RFID middleware architecture, Otedama, which makes use of a unique property of RFID information to improve performance. RFID tags are bound to items. New information related to an RFID tag is generated at the site where the ID exists, and the entity most interested in the history and the item itself is in close proximity to the RFID tag. To exploit this property, we propose a scheme which bundles information related to a specific ID into one object and moves that bundle to a nearby server as the RFID tag moves from place to place. By using this scheme, information is always accessible by querying a system near the physical location of the tag, providing better query performance. Additionally, the volume of records that must be kept by a repository manager is reduced, because the relocation naturally migrates data away as physical objects move. We show the effectiveness of this architecture by analyzing data from a major retailer, finding that information retrieval performance will be six times better, and the cost of search is possibly several times cheaper.

In this letter, we first study the impact of the basic reference frame jitter on the digital image stabilization. Next, a method for stabilizing the digital image sequence based on the correction for basic reference frame jitter is proposed. The experimental results show that our proposed method can effectively decrease the excessive undefined areas in the stable image sequence resulting from the basic reference frame jitter.

A new best-relay selection scheme is proposed in this letter in order to maintain a reliable cooperative communications for ubiquitous sensor networks in indoor environments. The suggested technique relies on eigenvalue decomposition to select the best relay. The simulation results confirm that the performance of the proposed approach is better than that of the previous scheme in indoor environments.

Fractional sampling (FS) and Doppler diversity equalization in OFDM receivers can achieve two types of diversity (path diversity and frequency diversity) simultaneously on time-varying multipath channels. However FS with a higher sampling rate requires the large amount of complexity in demodulation. In this paper, a novel sampling point selection (SPS) scheme with MMSE equalization in FS-OFDM receivers is proposed. On fast time-varying multipath channels, the proposed scheme selects the appropriate samples from the fractionally sampled signals. Through the computer simulation, it is demonstrated that with the proposed scheme, both path diversity gain and Doppler diversity gain can increase as compared to a conventional non-SPS scheme.

This paper combines the LBP operator and the active contour model. It introduces a salient gradient vector flow snake (SGVF snake), based on a novel edge map generated from the salient boundary point image (SBP image). The MDGVM criterion process helps to reduce feature detail and background noise as well as retaining the salient boundary points. The resultant SBP image as an edge map gives powerful support to the SGVF snake because of the inherent combination of the intensity, gradient and texture cues. Experiments prove that the MDGVM process has high efficiency in reducing outliers and the SGVF snake is a large improvement over the GVF snake for contour detection, especially in natural images with low contrast and small texture background.

A new type of digital filter for removing impulsive noise in color images is proposed using interactive evolutionary computing. This filter is realized as a rule-based system containing switching median filters. This filter detects impulsive noise in color images with rules and applies switching median filters only at the noisy pixel. Interactive evolutionary computing (IEC) is adopted to optimize the filter parameters, considering the subjective assessment by human vision. In order to detect impulsive noise precisely, complicated rules with multiple parameters are required. Here, the relationship between color components and the degree of peculiarity of the pixel value are utilized in the rules. Usually, optimization of such a complicated rule-based system is difficult, but IEC enables such optimization easily. Moreover, human taste and subjective sense are highly considered in the filter performance. Computer simulations are shown for noisy images to verify its high performance.

In this paper, we emphasize the need for data cleansing when clustering large-scale transaction databases and propose a new data cleansing method that improves clustering quality and performance. We evaluate our data cleansing method through a series of experiments. As a result, the clustering quality and performance were significantly improved by up to 165% and 330%, respectively.

We propose a new parameter-extraction approach based on a mixed-mode genetic algorithm (GA), including the efficient search-space separation and local-minima-convergence prevention process. The technique, substantially extended from our previous work, allows the designed figures-of-merit, such as internal quantum efficiency (ηi) as well as transparency current density (Jtr) of lasers and minimum noise figure (NFmin) as well as associated available gain (GA,assoc) of low-noise amplifiers (LNAs), extracted by an analytical equation-based methodology combined with an evolutionary numerical tool. Extraction results, which agree well with actually measured data, for both state-of-the-art InGaAs quantum-well lasers and advanced SiGe LNAs are presented for the first time to demonstrate this multi-parameter analysis and high-accuracy optimization.

We consider the backlight calculation of local dimming as an optimization problem. The luminance produced by many LEDs at each pixel considered is calculated which should cover the gray value of each pixel, while the sum of LED currents is to be minimized. For this purpose a specific approach called as "Sorted Sector Covering" (SSC) was developed and is described in this paper. In our pre-processing unit called condenser the source image is reduced to a matrix of much lower resolution so that the computation effort of the SSC algorithm is drastically reduced. During this preprocessing phase, filter functions can be integrated so that a further reduction of the power consumption is achieved. Our processing system allows high power saving and high visual quality at low processor cost. We approach the local dimming problem in the physical viewing direction -- from LED to pixel. The luminance for the pixel is based on the light spread function (LSF) and the PWM values of the LEDs. As the physical viewing direction is chosen, this method is universal and can be applied for any kind of LED arrangement -- direct-lit as well as edge-lit. It is validated on prototypes, e.g., a locally dimmed edge-lit TV.

In this paper, we propose a new 3D sound rendering method for multiple sound sources with limited computational resources. The method is based on fuzzy clustering, which achieves dual benefits of two general methods based on amplitude-panning and hard clustering. In embedded systems where the number of reproducible sound sources is restricted, the general methods suffer from localization errors and/or serious quality degradation, whereas the proposed method settles the problems by executing clustering-process and amplitude-panning simultaneously. Computational cost evaluation based on DSP implementation and subjective listening test have been performed to demonstrate the applicability for embedded systems and the effectiveness of the proposed method.

As described in this paper, construction and blind estimation methods of phase sequences are proposed for subcarrier-phase control based peak-to-average power ratio (PAPR) reduction in low-density parity-check (LDPC)-coded orthogonal frequency division multiplexing (OFDM) systems. On the transmitter side, phase sequence patterns are constructed based on a given parity-check matrix. The PAPR of the OFDM signal is reduced by multiplying the constructed phase sequence selected from the same number of candidates as the number of weighting factor (WF) combinations in a partial transmit sequence (PTS) method. On the receiver side, the phase sequence is estimated blindly using the decoding function, i.e., the most likely phase sequence among a limited number of possible phase sequence candidates is inferred by comparing the sum-product calculation results of each candidate. Computer simulation results show that PAPR of QPSK-OFDM and 16QAM-OFDM signals can be reduced respectively by about 3.7 dB and 4.0 dB without marked degradation of the block error rate (BLER) performance as compared to perfect estimation in an attenuated 12-path Rayleigh fading condition.

In this letter, we propose a novel motion detection method in order to accurately perform the detection of moving objects in the automatic video surveillance system. Based on the proposed Background Generation Mechanism, the presence of either moving object or background information is firstly checked in order to supply the selective updating of the high-quality adaptive background model, which facilitates the further motion detection using the Laplacian distribution model. The overall results of the detection accuracy will be demonstrated that our proposed method attains a substantially higher degree of efficacy, outperforming the state-of-the-art method by average Similarity accuracy rates of up to 56.64%, 27.78%, 50.04%, 43.33%, and 44.09%, respectively.

Several papers have presented measured function to handle multi-criteria fuzzy decision-making problems based on interval-valued intuitionistic fuzzy sets. However, in some cases, the proposed function cannot give sufficient information about alternatives. Consequently, in this paper, we will overcome previous insufficient problem and provide a novel accuracy function to measure the degree of the interval-valued intuitionistic fuzzy information. And a practical example has been provided to demonstrate our proposed approach. In addition, to make computing and ranking results easier and to increase the recruiting productivity, a computer-based interface system has been developed for decision makers to make decisions more efficiently.

We present a new method that can represent the reflectance of metallic paints accurately using a two-layer reflectance model with sampled microfacet distribution functions. We model the structure of metallic paints simplified by two layers: a binder surface that follows a microfacet distribution and a sub-layer that also follows a facet distribution. In the sub-layer, the diffuse and the specular reflectance represent color pigments and metallic flakes respectively. We use an iterative method based on the principle of Gauss-Seidel relaxation that stably fits the measured data to our highly non-linear model. We optimize the model by handling the microfacet distribution terms as a piecewise linear non-parametric form in order to increase its degree of freedom. The proposed model is validated by applying it to various metallic paints. The results show that our model has better fitting performance compared to the models used in other studies. Our model provides better accuracy due to the non-parametric terms employed in the model, and also gives efficiency in analyzing the characteristics of metallic paints by the analytical form embedded in the model. The non-parametric terms for the microfacet distribution in our model require densely measured data but not for the entire BRDF(bidirectional reflectance distribution function) domain, so that our method can reduce the burden of data acquisition during measurement. Especially, it becomes efficient for a system that uses a curved-sample based measurement system which allows us to obtain dense data in microfacet domain by a single measurement.

This letter treats lossy source coding of binary sources with short linear block codes. It is numerically shown that very simple basis-reprocessing approach yields good rate-distortion performance with manageable computational complexity for small block lengths. The result for code lengths up to 100 outperforms the message-passing based encoding for low-density codes which are several times longer.

In this paper, a human detection method is developed. An appearance based detector and a motion based detector are proposed respectively. A multi scale block histogram of template feature (MB-HOT) is used to detect human by the appearance. It integrates the gray value information and the gradient value information, and represents the relationship of three blocks. Experiment on INRIA dataset shows that this feature is more discriminative than other features, such as histogram of orientation gradient (HOG). A motion based feature is also proposed to capture the relative motion of human body. This feature is calculated in optical flow domain and experimental result in our dataset shows that this feature outperforms other motion based features. The detection responses obtained by two features are combined to reduce the false detection. Graphic process unit (GPU) based implementation is proposed to accelerate the calculation of two features, and make it suitable for real time applications.

We address the problem of perceived age estimation from face images, and propose a new semi-supervised approach involving two novel aspects. The first novelty is an efficient active learning strategy for reducing the cost of labeling face samples. Given a large number of unlabeled face samples, we reveal the cluster structure of the data and propose to label cluster-representative samples for covering as many clusters as possible. This simple sampling strategy allows us to boost the performance of a manifold-based semi-supervised learning method only with a relatively small number of labeled samples. The second contribution is to take the heterogeneous characteristics of human age perception into account. It is rare to misjudge the age of a 5-year-old child as 15 years old, but the age of a 35-year-old person is often misjudged as 45 years old. Thus, magnitude of the error is different depending on subjects' age. We carried out a large-scale questionnaire survey for quantifying human age perception characteristics, and propose to utilize the quantified characteristics in the framework of weighted regression. Consequently, our proposed method is expressed in the form of weighted least-squares with a manifold regularizer, which is scalable to massive datasets. Through real-world age estimation experiments, we demonstrate the usefulness of the proposed method.