Let r be an odd prime, such that r≥5 and r≠p, m be the order of r modulo p. Then, there exists a 2pth root of unity in the extension field Frm. Let G(x) be the generating polynomial of the considered quaternary sequences over Fq[x] with q=rm. By explicitly computing the number of zeros of the generating polynomial G(x) over Frm, we can determine the degree of the minimal polynomial, of the quaternary sequences which in turn represents the linear complexity. In this paper, we show that the minimal value of the linear complexity is equal to $ rac{1}{2}(3p-1) $ which is more than p, the half of the period 2p. According to Berlekamp-Massey algorithm, these sequences viewed as enough good for the use in cryptography.

In recent years, applications of complex-valued neural networks have become wide spread. Quaternions are an extension of complex numbers, and neural networks with quaternions have been proposed. Because quaternion algebra is non-commutative algebra, we can consider two orders of multiplication to calculate weighted input. However, both orders provide almost the same performance. We propose hybrid quaternionic Hopfield neural networks, which have both orders of multiplication. Using computer simulations, we show that these networks outperformed conventional quaternionic Hopfield neural networks in noise tolerance. We discuss why hybrid quaternionic Hopfield neural networks improve noise tolerance from the standpoint of rotational invariance.

The mode of intra prediction in H.264/AVC is encoded based on the most probable mode (MPM). To increase coding efficiency, the probability of the case that MPM is equal to coding mode of the current block should increase. In this paper we propose an efficient scheme to make MPM which is matched for the spatial direction property of pixels in the current block. Simulation results show that the proposed scheme gives significant coding gains when compared with the conventional techniques.

The number of states is a very important matter for model checking approach in Petri net's analysis. We first gave a formal definition of state number calculation problem: For a Petri net with an initial state (marking), how many states does it have? Next we showed the problem cannot be solved in polynomial time for a popular subclass of Petri nets, known as free choice workflow nets, if P≠NP. Then we proposed a polynomial time algorithm to solve the problem by utilizing a representational bias called as process tree. We also showed effectiveness of the algorithm through an application example.

This paper proposes a novel greedy algorithm, called Creditability-Estimation based Matching Pursuit (CEMP), for the compressed sensing signal recovery. As proved in the algorithm of Stagewise Orthogonal Matching Pursuit (StOMP), two Gaussian distributions are followed by the matched filter coefficients corresponding to and without corresponding to the actual support set of the original sparse signal, respectively. Therefore, the selection for each support point is interpreted as a process of hypothesis testing, and the preliminarily selected support set is supposed to consist of rejected atoms. A hard threshold, which is controlled by an input parameter, is used to implement the rejection. Because the Type I error may happen during the hypothesis testing, not all the rejected atoms are creditable to be the true support points. The creditability of each preliminarily selected support point is evaluated by a well-designed built-in mechanism, and the several most creditable ones are adaptively selected into the final support set without being controlled by any extra external parameters. Moreover, the proposed CEMP does not necessitate the sparsity level to be a priori control parameter in operation. In order to verify the performance of the proposed algorithm, Gaussian and Pulse Amplitude Modulation sparse signals are measured in the noiseless and noisy cases, and the experiments of the compressed sensing signal recoveries by several greedy algorithms including CEMP are implemented. The simulation results show the proposed CEMP can achieve the best performances of the recovery accuracy and robustness as a whole. Besides, the experiment of the compressed sensing image recovery shows that CEMP can recover the image with the highest Peak Signal to Noise Ratio (PSNR) and the best visual quality.

The mappings from independent binary variables to quadrature amplitude modulation (QAM) symbols are developed. Based the proposed mappings and the existing binary mutually uncorrelated complementary sequence sets (MUCSSs), a construction producing QAM periodic complementary sequence sets (PCSSs) is presented. The resultant QAM PCSSs have the same numbers and periods of sub-sequences as the binary MUCSSs employed, and the family size of new sequence sets is increased with exponent of periods of sub-sequences. The proposed QAM PCSSs can be applied to CDMA or OFDM communication systems so as to suppress multiple access interference (MAI) or to reduce peak-to-mean envelope power ratio (PMEPR), respectively.

The linear complexity of binary sequences plays a fundamental part in cryptography. In the paper, we construct more general forms of generalized cyclotomic binary sequences with period 2pm+1qn+1. Furthermore, we establish the formula of the linear complexity of proposed sequences. The results reveal that such sequences with period 2pm+1qn+1 have a good balance property and high linear complexity.

A term tree pattern is a rooted ordered tree pattern which consists of ordered tree structures with edge labels and structured variables with labels. All variables with the same label in a term tree pattern can be simultaneously replaced with ordered trees isomorphic to the same rooted ordered tree. Then, a term tree pattern is suitable for representing structural features common to tree structured data such as XML documents on the web, the secondary structures of RNA in biology and parse trees describing grammatical structures of natural languages. Let $ott$ be the set of all term tree patterns which have one or more variables with the same label. Let $lott$ be the set of all term tree patterns t such that all variables in t have distinct labels. We remark that $lottsubsetneq ott$ holds. In this paper, we consider a problem, called Matching problem for term tree patterns, of deciding whether or not a given rooted ordered tree T is obtained from a given term tree pattern t by replacing variables in t with rooted ordered trees. We show that Matching problem for term tree patterns in $ott$ is NP-complete, by giving a reduction from the string pattern matching problem, which is NP-complete. Next, by giving operations on an interval, which is a set containing all integers between two given integers representing vertex identifiers, we propose an efficient algorithm for solving Matching problem for term tree patterns in $lottsubsetneq ott$. Then, we show that, when an ordered tree having N vertices and a term tree pattern $t in lott$ having n vertices are given, the proposed matching algorithm correctly solves this problem in O(nN) time.

We propose a novel bias-free adaptive beamformer employing an affine projection algorithm with the optimal regularization parameter. The generalized sidelobe canceller affine projection algorithm suffers from a bias of a weight vectors under the condition of no reference signals for output of an array in the beamforming application. First, we analyze the bias in the algorithm and prove that the bias can be eliminated through a large regularization parameter. However, this causes slow convergence at the initial state, so the regularization parameter should be controlled. Through the optimization of the regularization parameter, the proposed method achieves fast convergence without the bias at the steady-state. Experimental results show that the proposed beamformer not only removes the bias but also achieves both fast convergence and high steady-state output signal-to-interference-plus-noise ratio.

We study the problem of transforming one list (vertex) coloring of a graph into another list coloring by changing only one vertex color assignment at a time, while at all times maintaining a list coloring, given a list of allowed colors for each vertex. This problem is known to be PSPACE-complete for bipartite planar graphs. In this paper, we first show that the problem remains PSPACE-complete even for bipartite series-parallel graphs, which form a proper subclass of bipartite planar graphs. We note that our reduction indeed shows the PSPACE-completeness for graphs with pathwidth two, and it can be extended for threshold graphs. In contrast, we give a polynomial-time algorithm to solve the problem for graphs with pathwidth one. Thus, this paper gives sharp analyses of the problem with respect to pathwidth.

In this letter, an enhanced detection scheme using threshold and lattice-reduction algorithm is proposed. The first step of the proposed detection scheme finds another basis channel matrix H' which has good properties from the channel matrix H by using lattice-reduction algorithm. And QRD-M detection scheme using threshold algorithm is executed in the next step. Simulation results show that the proposed method has better performance than the conventional QRD-M detection scheme at high SNR. Also, it reduces candidate symbols because of the threshold algorithm.

What should be the ultimate form of the cloud computing environment? The solution should have two important features; “Fine-Granularity” and “Participation.” To realize an attractive and feasible solution with these features, we propose a “participating fine-grained cloud computing platform” that a large number of personal or small-company resource suppliers participate in, configure and provide cloud computing on. This enables users to be supplied with smaller units of resources such as computing, memory, content, and applications, in comparison with the traditional Infrastructure as a Service (IaaS). Furthermore, to search for nearby resources efficiently among the many available on the platform, we also propose Resource Breadcrumbs (RBC) as a key technology of our proposed platform to provide in-network guidance capability autonomously for users' queries. RBC allows supplier-nodes to distribute guidance information directed to themselves with dedicated control messages; in addition, the information can be logged along the trail of message from supplier to user. With this distributed information, users can to autonomously locate nearby resources. Distributed management also reduces computational load on the central database and enables a participating fine-grained cloud platform at lower cost.

In recent years, researchers have tried to create unhindered human-computer interaction by giving virtual agents human-like conversational skills. Predicting backchannel feedback for agent listeners has become a novel research hot-spot. The main goal of this paper is to identify appropriate features and methods for backchannel prediction in Mandarin conversations. Firstly, multimodal Mandarin conversations are recorded for the analysis of backchannel behaviors. In order to eliminate individual difference in the original face-to-face conversations, more backchannels from different listeners are gathered together. These data confirm that backchannels occurring in the speakers' pauses form a vast majority in Mandarin conversations. Both prosodic and visual features are used in backchannel prediction. Four types of models based on the speakers' pauses are built by using support vector machine classifiers. An evaluation of the pause-based prediction model has shown relatively high accuracy in consideration of the optional nature of backchannel feedback. Finally, the results of the subjective evaluation validate that the conversations performed between humans and virtual listeners using backchannels predicted by the proposed models is more unhindered compared to other backchannel prediction methods.

Assume that Alice, Bob, and Carol, each of whom privately holds a one-bit input, want to learn the output of some Boolean function, say the majority function, of their inputs without revealing more of their own secret inputs than necessary. In this paper, we show that such a secure three-input function evaluation can be performed with a deck of real cards; specifically, the three players can learn only the output of the function using eight physical cards — four black and four red cards — with identical backs.

Cloud computing is a widely used computing platform in business and academic communities. Performance is an important issue when a user runs an application in the cloud. The user may want to estimate the application-execution time beforehand to guarantee the application performance or to choose the most suitable cloud. Moreover, the cloud system architect and the designer need to understand the application performance characteristics, such as the scalability or the utilization of cloud platforms, to improve performance. However, because the application performance in clouds sometime fluctuates, estimation of the application performance is difficult. In this paper, we discuss the performance fluctuation of Hadoop jobs in both a public cloud and a community cloud for one to three months. The experimental results indicate phenomena that we cannot see without long-term experiments and phenomena inherent in Hadoop. The results suggest better ways to estimate Hadoop application performances in clouds. For example, we should be aware of application characteristics (CPU intensive or communication intensive), datacenter characteristics (busy or not), and time frame (time of day and day of the week) to estimate the performance fluctuation due to workload congestion in cloud platforms. Furthermore, we should be aware of performance degradation due to task re-execution in Hadoop applications.

Tonal signals are shown as spectral peaks in the frequency domain. When the number of spectral peaks is small and the spectral signal is sparse, Compressive Sensing (CS) can be adopted to locate the peaks with a low-cost sensing system. In the CS scheme, a time domain signal is modelled as $oldsymbol{y}=Phi F^{-1}oldsymbol{s}$, where y and s are signal vectors in the time and frequency domains. In addition, F-1 and $Phi$ are an inverse DFT matrix and a random-sampling matrix, respectively. For a given y and $Phi$, the CS method attempts to estimate s with l0 or l1 optimization. To generate the peak candidates, we adopt the frequency-domain information of $ esmile{oldsymbol{s}}$ = $oldsymbol{F} esmile{oldsymbol{y}}$, where $ esmile{y}$ is the extended version of y and $ esmile{oldsymbol{y}}left(oldsymbol{n} ight)$ is zero when n is not elements of CS time instances. In this paper, we develop Gaussian statistics of $ esmile{oldsymbol{s}}$. That is, the variance and the mean values of $ esmile{oldsymbol{s}}left(oldsymbol{k} ight)$ are examined.

Tracking algorithms for arbitrary objects are widely researched in the field of computer vision. At the beginning, an initialized bounding box is given as the input. After that, the algorithms are required to track the objective in the later frames on-the-fly. Tracking-by-detection is one of the main research branches of online tracking. However, there still exist two issues in order to improve the performance. 1) The limited processing time requires the model to extract low-dimensional and discriminative features from the training samples. 2) The model is required to be able to balance both the prior and new objectives' appearance information in order to maintain the relocation ability and avoid the drifting problem. In this paper, we propose a real-time tracking algorithm called coupled randomness tracking (CRT) which focuses on dealing with these two issues. One randomness represents random projection, and the other randomness represents online random forests (ORFs). In CRT, the gray-scale feature is compressed by a sparse measurement matrix, and ORFs are used to train the sample sequence online. During the training procedure, we introduce a tree discarding strategy which helps the ORFs to adapt fast appearance changes caused by illumination, occlusion, etc. Our method can constantly adapt to the objective's latest appearance changes while keeping the prior appearance information. The experimental results show that our algorithm performs robustly with many publicly available benchmark videos and outperforms several state-of-the-art algorithms. Additionally, our algorithm can be easily utilized into a parallel program.

Coordinated multi-point (CoMP) transmission and reception is a promising technique for interference mitigation in cellular systems. The scheduling algorithm for CoMP has a significant impact on the network processing complexity and performance. Performing exhaustive search permits centralized scheduling and thus the optimal global solution; however, it incurs a high level of computational complexity and may be impractical or lead to high cost as well as network instability. In order to provide a more realistic scheduling method while balancing performance and complexity, we propose a low complexity centralized scheduling scheme that adaptively selects users for single-cell transmission or different CoMP scheme transmission to maximize the system weighted sum capacity. We evaluate the computational complexity and system-level simulation performance in this paper. Compared to the optimal scheduling method with exhaustive search, the proposed scheme has a much lower complexity level and achieves near optimal performance.

This paper proposes an ultra-low power fully on-chip CMOS relaxation oscillator (ROSC) for a real-time clock application. The proposed ROSC employs a compensation circuit of a comparator's non-idealities caused by offset voltage and delay time. The ROSC can generate a stable, and 32-kHz oscillation clock frequency without increasing power dissipation by using a low reference voltage and employing a novel compensation architecture for comparators. Measurement results in a 0.18-$mu$m CMOS process demonstrated that the circuit can generate a stable clock frequency of 32.55,kHz with low power dissipation of 472,nW at 1.8-V power supply. Measured line regulation and temperature coefficient were 1.1%/V and 120,ppm/$^{circ}$C, respectively.

A renal biopsy is a procedure to get a small piece of kidney for microscopic examination. With the development of tissue sectioning and medical imaging techniques, microscope renal biopsy image sequences are consequently obtained for computer-aided diagnosis. This paper proposes a new context-based segmentation algorithm for acquired image sequence, in which an improved genetic algorithm (GA) patching method is developed to segment different size target. To guarantee the correctness of first image segmentation and facilitate the use of context information, a boundary fusion operation and a simplified scale-invariant feature transform (SIFT)-based registration are presented respectively. The experimental results show the proposed segmentation algorithm is effective and accurate for renal biopsy image sequence.