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.

This paper proposes a visual secret sharing (VSS) scheme with efficient pixel expansion which prevents malicious share holders from deceiving an honest share holder. A VSS scheme encrypts a secret image into pieces referred to as shares where each party keeps a share so that stacking a sufficient number of shares recovers the secret image. A cheat prevention VSS scheme gives another piece to each party for verifying whether the share presented by another party is genuine. The proposed scheme improves the contrast of the recovered image and cheat-prevention functionality by introducing randomness in producing pieces for verification. Experimental results show the effectiveness of the proposed scheme.

Channel modeling, which is quite important for wireless communications system design, is difficult to be statistically generated from experimental results due to the expense and time constraints. However, with the computational electromagnetics method, the Electro-Magnetic (EM) field can be emulated and the corresponding EM wave propagation scenario can be analyzed. In this letter, the Finite Integration Technique (FIT) method is utilized to calculate the EM wave propagation of the onboard mobile communications in the cabin of an aircraft. With the simulation results, the channel model is established. Compared with Finite-Difference Time-Domain (FDTD), the proposed scheme is more accurate, which is promising to be used in the cabin channel modeling for onboard mobile system design.

In this paper, we propose a jointly optimized predictive-adaptive partitioned block transform to exploit the spatial characteristics of intra residuals and improve video coding performance. Under the assumptions of traditional Markov representations, the asymmetric discrete sine transform (ADST) can be combined with a discrete cosine transform (DCT) for video coding. In comparison, the interpolative Markov representation has a lower mean-square error for images or regions that have relatively high contrast, and is insensitive to changes in image statistics. Hence, we derive an even discrete sine transform (EDST) from the interpolative Markov model, and use a coding scheme to switch between EDST and DCT, depending on the prediction direction and boundary information. To obtain an implementation independent of multipliers, we also propose an orthogonal 4-point integer EDST, which consists solely of adds and bit-shifts. We implement our hybrid transform coding scheme within the H.264/AVC intra-mode framework. Experimental results show that the proposed scheme significantly outperforms standard DCT and ADST. It also greatly reduces the blocking artifacts typically observed around block edges, because the new transform is more adaptable to the characteristics of intra-prediction residuals.

In this paper, we propose the construction of quasi-cyclic (QC) LDPC codes based on the modified progressive edge-growth (PEG) algorithm to achieve the maximum local girth. Although the previously designed QC-LDPC codes based on the PEG algorithm has more flexible code rates than the conventional QC-LDPC code, in the design process, multiple choices of the edges may be chosen. In the proposed algorithm, we aim to maximize the girth property by choosing the suitable edges and thus improve the error correcting performance. Simulation results show that the QC-LDPC codes constructed from the proposed method give higher proportion of high local girths than other methods, particularly, at high code rates. In addition, the proposed codes offer superior bit error rate and block error rate performances to the previous PEG-QC codes over the additive white Gaussian noise (AWGN) channel.

A new theoretical formulation based on BIBO (Bounded Input Bounded Output) operators is proposed for a general feedback amplifier circuit. Several fundamental theorems are derived in this letter. The main theorem provides a basis for a realization of an inverse of a feedback-branch linear or nonlinear BIBO operator satisfying the associative law.

In 2001, Boneh and Franklin realized the first Identity-Based Encryption (IBE), and at the same time they proposed a simple way to revoke users from the system. Later, Boldyreva et al. pointed out that Boneh-Franklin's revocation method is not scalable well and they proposed the first IBE scheme with efficient revocation. Recently, Tseng and Tsai [Computer Journal, Vol.55 No.4, page 475-486, 2012] claimed that Boldyreva et al.'s scheme requires a secure channel between each user and the key generation center in the key update phase, and proposed a new revocable IBE (RIBE) with a public channel by extending the Boneh-Franklin scheme. In this paper, we revisit Tseng and Tsai's result; we first point out that secure channels (except for the initial key setup) are not mandatory in the definition of RIBE scheme formalized by Boldyreva et al. Next, we show that Boldyreva et al.'s scheme does not require any secure channels (except for the initial key setup), which is different from what Tseng and Tsai claimed and so invalidates their contribution of the first RIBE with a public channel. Moreover, we point out that there are simple techniques to remove secure channels from the Boneh-Franklin RIBE. Interestingly, we show that the secure-channel-free Boneh-Franklin RIBE scheme is secure against decryption key exposure, whereas the Tseng-Tsai RIBE scheme is vulnerable to this attack.

This paper addresses the sensing-throughput tradeoff problem by using cluster-based cooperative spectrum sensing (CSS) schemes in two-layer hierarchical cognitive radio networks (CRNs) with soft data fusion. The problem is formulated as a combinatorial optimization problem involving both discrete and continuous variables. To simplify the solution, a reasonable weight fusion rule (WFR) is first optimized. Thus, the problem devolves into a constrained discrete optimization problem. In order to efficiently and effectively resolve this problem, a lexicographical approach is presented that solving two optimal subproblems consecutively. Moreover, for the first optimal subproblem, a closed-form solution is deduced, and an optimal clustering scheme (CS) is also presented for the second optimal subproblem. Numerical results show that the proposed approach achieves a satisfying performance and low complexity.

In this paper, we examine additive homomorphic encryptions in the discrete logarithm setting. Recently, Wang et al. proposed an additive homomorphic encryption scheme by modifying the ElGamal encryption scheme [Information Sciences 181(2011) 3308-3322]. We show that their scheme allows only limited number of additions among encrypted messages, which is different from what they claimed.

To understand human emotion, it is necessary to be aware of the surrounding situation and individual personalities. In most previous studies, however, these important aspects were not considered. Emotion recognition has been considered as a classification problem. In this paper, we attempt new approaches to utilize a person's situational information and personality for use in understanding emotion. We propose a method of extracting situational information and building a personalized emotion model for reflecting the personality of each character in the text. To extract and utilize situational information, we propose a situation model using lexical and syntactic information. In addition, to reflect the personality of an individual, we propose a personalized emotion model using KBANN (Knowledge-based Artificial Neural Network). Our proposed system has the advantage of using a traditional keyword-spotting algorithm. In addition, we also reflect the fact that the strength of emotion decreases over time. Experimental results show that the proposed system can more accurately and intelligently recognize a person's emotion than previous methods.

Localization in wireless sensor networks is the problem of estimating the geographical locations of wireless sensor nodes. We propose a framework to utilizing multiple data sources for localization scheme based on support vector machines. The framework can be used with both classification and regression formulation of support vector machines. The proposed method uses only connectivity information. Multiple hop count data sources can be generated by adjusting the transmission power of sensor nodes to change the communication ranges. The optimal choice of communication ranges can be determined by evaluating mutual information. We consider two methods for integrating multiple data sources together; unif method and align method. The improved localization accuracy of the proposed framework is verified by simulation study.

In this paper, we consider the optimization of measurement matrix in Compressed Sensing (CS) framework. Based on the boundary constraint, we propose a novel algorithm to make the “mutual coherence” approach a lower bound. This algorithm is implemented by using an iterative strategy. In each iteration, a neighborhood interval of the maximal off-diagonal entry in the Gram matrix is scaled down with the same shrinkage factor, and then a lower mutual coherence between the measurement matrix and sparsifying matrix is obtained. After many iterations, the magnitudes of most of off-diagonal entries approach the lower bound. The proposed optimization algorithm demonstrates better performance compared with other typical optimization methods, such as t-averaged mutual coherence. In addition, the effectiveness of CS can be used for the compression of complex synthetic aperture radar (SAR) image is verified, and experimental results using simulated data and real field data corroborate this claim.

The relationship between different brain areas is characterized by functional networks through correlations of time series obtained from neuroimaging experiments. Due to its high spatial resolution, functional MRI data is commonly used for generating functional networks of the entire brain. These networks are comprised of the measurement points/channels as nodes and links are established if there is a correlation in the measured time series of these nodes. However, since the evaluation of correlation becomes more accurate with the length of the underlying time series, we construct in this paper functional networks from MEG data, which has a much higher time resolution than fMRI. We study in particular how the network topologies change in an experiment on ambiguity of words, where the subject first receives a priming word before being presented with an ambiguous or unambiguous target word.

This letter presents a method of adding a virtual halo effect to an object of interest in video sequences. A modified graph-cut segmentation algorithm extracts object layers. The halo is modeled by the accumulation of gradually changing Gaussians. With a synthesized blooming effect, the experimental results show that the proposed method conveys realistic halo effect.

This paper presents a new scalable method to considerably reduce the rollback propagation effect of the conventional optimistic message logging by utilizing positive features of reliable FIFO group communication links. To satisfy this goal, the proposed method forces group members to replicate different receive sequence numbers (RSNs), which they assigned for each identical message to their group respectively, into their volatile memories. As the degree of redundancy of RSNs increases, the possibility of local recovery for each crashed process may significantly be higher. Experimental results show that our method can outperform the previous one in terms of the rollback distance of non-faulty processes with a little normal time overhead.

Considering the real existent fact of the ideal edge and the learning style of image analogy without reference parameters, a blind image recovery algorithm using a self-adaptive learning method is proposed in this paper. We show that a specific local image patch with degradation characteristic can be utilized for restoring the whole image. In the training process, a clear counterpart of the local image patch is constructed based on the ideal edge assumption so that identification of the Point Spread Function is no longer needed. Experiments demonstrate the effectiveness of the proposed method on remote sensing images.

The most important advantage of HMM-based TTS is its highly intelligible. However, speech synthesized by HMM-based TTS is muffled and far from natural, especially under limited data conditions, which is mainly caused by its over-smoothness. Therefore, the motivation for this paper is to improve the naturalness of HMM-based TTS trained under limited data conditions while preserving its intelligibility. To achieve this motivation, a hybrid TTS between HMM-based TTS and the modified restricted Temporal Decomposition (MRTD), named HTD in this paper, was proposed. Here, TD is an interpolation model of decomposing a spectral or prosodic sequence of speech into sparse event targets and dynamic event functions, and MRTD is one simplified version of TD. With a determination of event functions close to the concept of co-articulation in speech, MRTD can synthesize smooth speech and the smoothness in synthesized speech can be adjusted by manipulating event targets of MRTD. Previous studies have also found that event functions of MRTD can represent linguistic information of speech, which is important to perceive speech intelligibility, while sparse event targets can convey the non-linguistics information, which is important to perceive the naturalness of speech. Therefore, prosodic trajectories and MRTD event functions of the spectral trajectory generated by HMM-based TTS were kept unchanged to preserve the high and stable intelligibility of HMM-based TTS. Whereas MRTD event targets of the spectral trajectory generated by HMM-based TTS were rendered with an original speech database to enhance the naturalness of synthesized speech. Experimental results with small Vietnamese datasets revealed that the proposed HTD was equivalent to HMM-based TTS in terms of intelligibility but was superior to it in terms of naturalness. Further discussions show that HTD had a small footprint. Therefore, the proposed HTD showed its strong efficiency under limited data conditions.

In this letter, by using Whiteman's generalized cyclotomy of order 2 over Zpq, where p, q are twin primes, we construct new perfect Gaussian integer sequences of period pq.

Logging the execution process of a program is a popular activity for practical program understanding. However, understanding the behavior of a program from a complete execution trace is difficult because a system may generate a substantial number of runtime events. To focus on a small subset of runtime events, a dynamic object process graph (DOPG) has been proposed. Although a DOPG can potentially facilitate program understanding, the logging process has not been adapted for DOPGs. If a developer is interested in the behavior of a particular object, only the runtime events related to the object are necessary to construct a DOPG. The vast majority of runtime events in a complete execution trace are irrelevant to the interesting object. This paper analyzes actual DOPGs and reports that a logging tool can be optimized to record only the runtime events related to a particular object specified by a developer.

The abundance of information published on the Internet makes filtering of hazardous Web pages a difficult yet important task. Supervised learning methods such as Support Vector Machines (SVMs) can be used to identify hazardous Web content. However, scalability is a big challenge, especially if we have to train multiple classifiers, since different policies exist on what kind of information is hazardous. We therefore propose two different strategies to train multiple SVMs for personalized Web content filters. The first strategy identifies common data clusters and then performs optimization on these clusters in order to obtain good initial solutions for individual problems. This initialization shortens the path to the optimal solutions and reduces the training time on individual training sets. The second approach is to train all SVMs simultaneously. We introduce an SMO-based kernel-biased heuristic that balances the reduction rate of individual objective functions and the computational cost of kernel matrix. The heuristic primarily relies on the optimality conditions of all optimization problems and secondly on the pre-calculated part of the whole kernel matrix. This strategy increases the amount of information sharing among learning tasks, thus reduces the number of kernel calculation and training time. In our experiments on inconsistently labeled training examples, both strategies were able to predict hazardous Web pages accurately (> 91%) with a training time of only 26% and 18% compared to that of the normal sequential training.