Classification tasks in computer vision and brain-computer interface research have presented several applications such as biometrics and cognitive training. However, like in any other discipline, determining suitable representation of data has been challenging, and recent approaches have deviated from the familiar form of one vector for each data sample. This paper considers a kernel between vector sets, the mean polynomial kernel, motivated by recent studies where data are approximated by linear subspaces, in particular, methods that were formulated on Grassmann manifolds. This kernel takes a more general approach given that it can also support input data that can be modeled as a vector sequence, and not necessarily requiring it to be a linear subspace. We discuss how the kernel can be associated with the Projection kernel, a Grassmann kernel. Experimental results using face image sequences and physiological signal data show that the mean polynomial kernel surpasses existing subspace-based methods on Grassmann manifolds in terms of predictive performance and efficiency.

General purpose computing on GPU (GPGPU) has become a popular computing model for high-performance, data-intensive applications. Accordingly, there is a strong need to develop highly efficient data structures to ease the development of GPGPU applications. In this work, we proposed an efficient concurrent queue data structure for GPU computing. The GPU based provably correct, lock-free FIFO queue allows a massive number of concurrent producers and consumers. Warp-centric en-queue and de-queue procedures are introduced to better match the underlying Single-Instruction, Multiple-Thread execution model of modern GPUs. It outperforms the best previous GPU queues by up to 40 fold. The correctness of the proposed queue operations is formally validated by linearizability criteria.

Recently, there are many Japanese citizens living abroad in Asia, including developing countries. However, not many studies have been conducted regarding their mental health. The purpose of this study was to see what kinds of stress people experience when living abroad. Japanese workers living abroad, including some who are married to foreign nationals, and their families were asked seven questions in a survey, and they provided answers to questions in agreement with the intent and purpose of this study. Morphological analysis of the results and category classification by word class was carried out. This category was arranged by word classes. Additionally, the tendencies of responses were categorized according to the KJ method. In response to the question, “Do you have any trouble because of cultural differences?” these responses were classified according to common features. A fuzzy cluster analysis was carried out based on this information. Meaningful clusters were obtained by fuzzy cluster analysis. Differences in the values of stress and family culture can best be described by fuzzy cluster analysis.

In this paper, we propose a new encoding method applicable to any linear codes over arbitrary finite field whose computational complexity is O(δ*n) where δ* and n denote the maximum column weight of a parity check matrix of a code and the code length, respectively. This means that if a code has a parity check matrix with the constant maximum column weight, such as LDPC codes, it can be encoded with O(n) computation. We also clarify the relation between the proposed method and conventional methods, and compare the computational complexity of those methods. Then we show that the proposed encoding method is much more efficient than the conventional ones.

In this paper, the linear complexity and minimal polynomials of Legendre sequences over Fq have been calculated, where q = pm and p is a prime number. Our results show that Legendre sequences have high linear complexity over Fq for a large part of prime power number q so that they can resist the linear attack method.

Laser diode capable of high speed direct modulation is one of the key solution for short distance applications due to their low power consumption, low cost and small size features. Realization of high modulation bandwidth for direct modulated laser maintaining the above mentioned feature is needed to enhance the short distance, low cost data transmission. One promising approach to enhance the modulation speed is to increase the photon density to achieve high modulation bandwidth. So to achieve this target, 1.55 $mu$m InGaAsP/InGaAsP multiple quantum well (MQW) asymmetric active multimode interferometer laser diode (active MMI-LD) has been demonstrated [1]. The split pumping concept has been applied for the active MMI-LD and significant enhancement of electrical to optical 3 dB down frequency bandwidth (f$_{mathrm{3dB}})$ up to 8 GHz has been successfully confirmed. The reported high bandwidth for split pump active MMI-LD is around 3.5 times higher than the previously reported maximum 3 dB bandwidth (2.3 GHz) of active MMI-LD without split pumping section. That shows, the splitted multimode pumping section behind the electrically isolated modulation section can potentially improve the modulation bandwidth of active MMI-LD. Clear and open eye diagram had also been confirmed for 2.5 Gbps, (2$^{mathrm{7}}$-1) pseudo random bit sequence (PRBS) modulation.

Based on the non-standard generalized Boolean functions (GBFs) over Z4, we propose a new method to convert those functions into the 16-QAM Golay complementary sequences (CSs). The resultant 16-QAM Golay CSs have the upper bound of peak-to-mean envelope power ratio (PMEPR) as low as 2. In addition, we obtain multiple 16-QAM Golay CSs for a given quadrature phase shift keying (QPSK) Golay CS.

A queue layout of a graph G consists of a linear order of its vertices, and a partition of its edges into queues, such that no two edges in the same queue are nested. The queuenumber qn(G) is the minimum number of queues required in a queue layout of G. The Cartesian product of two graphs G1 = (V1,E1) and G2 = (V2,E2), denoted by G1 × G2, is the graph with {:v1 ∈ V1 and v2 ∈ V2} as its vertex set and an edge (,) belongs to G1×G2 if and only if either (u1,v1) ∈ E1 and u2 = v2 or (u2,v2) ∈ E2 and u1 = v1. Let Tk1,k2,...,kn denote the n-dimensional toroidal grid defined by the Cartesian product of n cycles with varied lengths, i.e., Tk1,k2,...,kn = Ck1 × Ck2 × … × Ckn, where Cki is a cycle of length ki ≥ 3. If k1 = k2 = … = kn = k, the graph is also called the k-ary n-cube and is denoted by Qnk. In this paper, we deal with queue layouts of toroidal grids and show the following bound: qn(Tk1,k2,...,kn) ≤ 2n-2 if n ≥ 2 and ki ≥ 3 for all i = 1,2,...,n. In particular, for n = 2 and k1,k2 ≥ 3, we acquire qn(Tk1,k2) = 2. Recently, Pai et al. (Inform. Process. Lett. 110 (2009) pp.50-56) showed that qn(Qnk) ≤ 2n-1 if n ≥1 and k ≥9. Thus, our result improves the bound of qn(Qnk) when n ≥2 and k ≥9.

This study analyzes the performance of a downlink beamformer with partitioned vector quantization under optimized feedback budget allocation. A multiuser multiple-input single-output downlink precoding system with perfect channel state information at mobile stations is considered. The number of feedback bits allocated to the channel quality indicator (CQI) and the channel direction indicator (CDI) corresponding to each partition are optimized by exploiting the quantization mean square error. In addition, the effects of equal and unequal partitioning on codebook memory and system capacity are studied and elucidated through simulations. The results show that with optimized CQI-CDI allocation, the feedback budget distributions of equal or unequal partitions are proportional to the size ratios of the partitioned subvectors. Furthermore, it is observed that for large-sized partitions, the ratio of optimal CDI to CQI is much higher than that for small-sized partitions.

In data center networks, group communication is currently playing an important role and multicast communications is an effective way to support group communication for large numbers of virtual machines. Layer-2 congestion control named QCN (Quantized Congestion Notification) has been proposed to realize the high reliability required by LAN/SAN integration in data center networking. Our preliminary evaluation in this paper shows that a multicast flow suffers lower throughput than unicast flows when conventional QCN is applied in a naive manner. This is because a sending device receives congestion feedback from multiple locations on a multicast tree and decreases transmission rate accordingly. To counter this throughput degradation of multicast flows, we propose a new Layer 2 congestion control algorithm in multicast environment, Quantized Congestion Notification with Bottleneck Selection (QCN/BS). In QCN/BS, the switch in the worst congestion level is selected and the transmission rate of the sending device is calculated exclusively according to feedback from the selected switch. Simulation results show that when conventional QCN is used, a multicast flow experiences lower and more severely unfair throughput than a unicast flow. The proposed QCN/BS resolves this problem.

The composite binary offset carrier (CBOC) modulated signal contains multi-peaks in its auto-correlation function, which brings ambiguity to the signal acquisition process of a GNSS receiver. Currently, most traditional ambiguity-removing schemes for CBOC signal acquisition approximate CBOC signal as a BOC signal, which may incur performance degradation. Based on Galileo E1 CBOC signal, this paper proposes a novel adaptive ambiguity-removing acquisition scheme which doesn't adopt the approximation used in traditional schemes. According to the energy ratio of each sub-code of CBOC signal, the proposed scheme can self-adjust its local reference code to achieve unambiguous and precise signal synchronization. Monte Carlo simulation is conducted in this paper to analyze the performance of the proposed scheme and three traditional schemes. Simulation results show that the proposed scheme has higher detection probability and less mean acquisition time than the other three schemes, which verify the superiority of the proposed scheme.

It is well known that solving randomly chosen Multivariate Quadratic equations over a finite field (MQ-Problem) is NP-hard, and the security of Multivariate Public Key Cryptosystems (MPKCs) is based on the MQ-Problem. However, this problem can be solved efficiently when the number of unknowns n is sufficiently greater than that of equations m (This is called “Underdefined”). Indeed, the algorithm by Kipnis et al. (Eurocrypt'99) can solve the MQ-Problem over a finite field of even characteristic in a polynomial-time of n when n ≥ m(m+1). Therefore, it is important to estimate the hardness of the MQ-Problem to evaluate the security of Multivariate Public Key Cryptosystems. We propose an algorithm in this paper that can solve the MQ-Problem in a polynomial-time of n when n ≥ m(m+3)/2, which has a wider applicable range than that by Kipnis et al. We will also compare our proposed algorithm with other known algorithms. Moreover, we implemented this algorithm with Magma and solved the MQ-Problem of m=28 and n=504, and it takes 78.7 seconds on a common PC.

For frequency hopping spread spectrum communication systems, the average Hamming correlation (AHC) among frequency hopping sequences (FHSs) is an important performance indicator. In this letter, a sufficient and necessary condition for a set of FHSs with optimal AHC is given. Based on interleaved technique, a new construction for optimal AHC FHS sets is also proposed, which generalizes the construction of Chung and Yang. Several optimal AHC FHS sets with more flexible parameters not covered in the literature are obtained by the new construction, which are summarized in Table 1.

One of the most important problems in web services application is the integration of different existing services into a new composite service. Existing work has the following disadvantages: (i) developers are often required to provide a composite service model first and perform formal verifications to check whether the model is correct. This makes the synthesis process of composite services semi-automatic, complex and inefficient; (ii) there is no assurance that composite services synthesized by using the fully-automatic approaches are correct; (iii) some approaches only handle simple composition problems where existing services are atomic. To address these problems, we propose a correct assurance approach for automatically synthesizing composite services based on finite state machine model. The syntax and semantics of the requirement model specifying composition requirements is also proposed. Given a set of abstract BPEL descriptions of existing services, and a composition requirement, our approach automatically generate the BPEL implementation of the composite service. Compared with existing approaches, the composite service generated by utilizing our proposed approach is guaranteed to be correct and does not require any formal verification. The correctness of our approach is proved. Moreover, the case analysis indicates that our approach is feasible and effective.

Parametric resonance based solutions for sub-gigahertz radio frequency transceiver with 0.3V supply voltage are proposed in this paper. As an implementation example, a 0.3V 720µW variation-tolerant injection-locked frequency multiplier is developed in 90nm CMOS. It features a parametric resonance based multi-phase synthesis scheme, thereby achieving the lowest supply voltage with -110dBc@ 600kHz phase noise and 873MHz-1.008GHz locking range in state-of-the-art frequency synthesizers.

Four calculation techniques for the Q-factor determination of resonant structures are compared on the basis of the influence of the VNA measurement uncertainty. The influence is evaluated using Monte Carlo calculations. On the basis of the deviation, the dispersion, and the effect of nearby resonances, the circle fitting method is the most appropriate technique. Although the 3dB method is the most popular technique, the Q-factors calculated by this method exhibit deviations, and the sign and amount of the deviation depend on the measurement setup. Comparisons using measurement data demonstrate that the uncertainty of the dielectric loss tangent calculated by the circle fitting method is less than a third of those calculated by the other three techniques.

In this letter, an improved channel-adaptive detection scheme based on condition number combined with a QRD-M and CLLL algorithms is presented for MIMO-OFDM systems. The proposed scheme estimates the channel state by using the condition number and then the number of layers for the QRD-M is changed according to the condition number of channel. After the number of layers is determined, the proposed scheme performs the combined QRD-M and CLLL. Simulation results show that the BER curves of the proposed scheme and QRD-M using CLLL have similar performance. However, the complexity of the proposed scheme is about 27% less than QRD-M detection using CLLL.

We consider the problem of optimizing the quantizer design for distributed estimation systems where all nodes located at different sites collect measurements and transmit quantized data to a fusion node, which then produces an estimate of the parameter of interest. For this problem, the goal is to minimize the amount of information that the nodes have to transmit in order to attain a certain application accuracy. We propose an iterative quantizer design algorithm that seeks to find a non-regular mapping between quantization partitions and their codewords so as to minimize global distortion such as the estimation error. We apply the proposed algorithm to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our experiments demonstrate that a significant performance gain can be achieved by our technique as compared with standard typical designs and even with distributed novel designs recently published.

Massive digital elevation models require a large number of geometric primitives that exceed the throughput of the existing graphics hardware. For the interactive visualization of these datasets, several adaptive reconstruction methods that reduce the number of primitives have been introduced over the decades. Quadtree triangulation, based on subdivision of the terrain into rectangular patches at different resolutions, is the most frequently used terrain reconstruction method. This usually accomplishes the triangulation using LOD (level-of-detail) selection and crack removal based on geometric errors. In this paper, we present bimodal vertex splitting, which performs LOD selection and crack removal concurrently on a GPU. The first mode splits each vertex for LOD selection and the second splits each vertex for crack removal. By performing these two operations concurrently on a GPU, we can efficiently accelerate the rendering speed by reducing the computation time and amount of transmission data in comparison with existing quadtree-based rendering methods.

This paper proposes an ultra-low-power 5.5-GHz PLL which employs the new divide-by-4 injection-locked frequency divider (ILFD) and a class-C VCO with linearity-compensated varactor for low supply voltage operation. A forward-body-biasing (FBB) technique can decrease threshold voltage of MOS transistors, which can improve operation frequency and can widen the lock range of the ILFD. The FBB is also employed for linear-frequency-tuning of VCO under low supply voltage of 0.5V. The double-switch injection technique is also proposed to widen the lock range of the ILFD. The digital calibration circuit is introduced to control the lock-range of ILFD automatically. The proposed PLL was fabricated in a 65nm CMOS process. With a 34.3-MHz reference, it shows a 1-MHz-offset phase noise of -106dBc/Hz at 5.5GHz output. The supply voltage is 0.54V for divider and 0.5V for other components. Total power consumption is 0.95mW.