Packet pacing is a well-known technique for reducing the short-time-scale burstiness of traffic, and software-based packet pacing has been categorized into two approaches: the timer interrupt-based approach and the gap packet-based approach. The former was originally hard to implement for Gigabit class networks because it requires the operating system to handle too frequent periodic timer interrupts, thus incurring a large overhead. On the other hand, a gap packet-based packet pacing mechanism achieves precise pacing without depending on the timer resolution. However, in order to guarantee the accuracy of rate control, the system must be able to transmit packets at the wire rate. In this paper, we propose a high-resolution timer-based packet pacing mechanism that determines the transmission timing of packets by using a sub-microsecond resolution timer. The high-resolution timer is a light-weight mechanism compared to the traditional low-resolution periodic timer. With recent progress in hardware protocol offload technologies and multicore-aware network protocol stacks, we believe high-resolution timer-based packet pacing has become practical. Our experimental results show that the proposed mechanism can work on a wider range of systems without degrading the accuracy of rate control. However, a higher CPU load is observed when the number of traffic classes increases, compared to a gap packet-based pacing mechanism.

Quantization is an important operation in digital communications systems. It not only introduces quantization noise but also changes the statistical properties of the quantized signal. Furthermore, quantization noise cannot be always considered as an additive source of Gaussian noise as it depends on the input signal probability density function. In orthogonal-frequency-division-multiplexing transmission the signal undergoes different operations which change its statistical properties. In this paper we analyze the statistical transformations of the signal from the transmitter to the receiver and determine how these effect the quantization. The discussed process considers the transceiver parameters and the channel properties to model the quantization noise. Simulation results show that the model agrees well with the simulated transmissions. The effect of system and channel properties on the quantization noise and its effect on bit-error-rate are shown. This enables the design of a quantizer with an optimal resolution for the required performance metrics.

Efficient resource allocation for delay-sensitive traffic, such as telephony and video streaming, in Orthogonal Frequency Division Multiple Access (OFDMA) networks is needed to increase system performance. In our system, users try to achieve a low queuing delay and buffer space usage by competing for transmission over the subchannels. We formulate this problem as a bargaining game and use the Nash Bargaining Solution (NBS) to realize a fair and efficient subchannel allocation for the users. Simulation results show performance improvements, with regard to packet dropping and delay distribution, over other algorithms.

In this paper, we propose a novel coding scheme for the geometry of the triangular mesh model. The geometry coding schemes can be classified into two groups: schemes with perfect reconstruction property that maintains their connectivity, and schemes without it in which the remeshing procedure is performed to change the mesh to semi-regular or regular mesh. The former schemes have good coding performance at higher coding rate, while the latter give excellent coding performance at lower coding rate. We propose a geometry coding scheme that maintains the connectivity and has a perfect reconstruction property. We apply a method that successively structures on 2-D plane the surrounding vertices obtained by expanding vertex sequences neighboring the previous layer. Non-separable component decomposition is applied, in which 2-D structured data are decomposed into four components depending on whether their location was even or odd on the horizontal and vertical axes in the 2-D plane. And a prediction and update are performed for the decomposed components. In the prediction process the predicted value is obtained from the vertices, which were not processed, neighboring the target vertex in the 3-D space. And the zero-tree coding is introduced in order to remove the redundancies between the coefficients at similar positions in different resolution levels. SFQ (Space-Frequency Quantization) is applied, which gives the optimal combination of coefficient pruning for the descendant coefficients of each tree element and a uniform quantization for each coefficient. Experiments applying the proposed method to several polygon meshes of different resolutions show that the proposed method gives a better coding performance at lower bit rate when compared to the conventional schemes.

In this study, we extracted articles describing problems, articles describing their solutions, and articles describing their causes from a Japanese Q&A style Web forum using a supervised machine learning with 0.70, 0.86, and 0.56 F values, respectively. We confirmed that these values are significantly better than their baselines. This extraction will be useful to construct an application that can search for problems provided by users and display causes and potential solutions.

In this paper, effective Chinese definite anaphora resolution is addressed by using feature weight learning and Web-based knowledge acquisition. The presented salience measurement is based on entropy-based weighting on selecting antecedent candidates. The knowledge acquisition model is aimed to extract more semantic features, such as gender, number, and semantic compatibility by employing multiple resources and Web mining. The resolution is justified with a real corpus and compared with a classification-based model. Experimental results show that our approach yields 72.5% success rate on 426 anaphoric instances. In comparison with a general classification-based approach, the performance is improved by 4.7%.

This paper presents a syntax-based framework for gap resolution in analytic languages. CCG, reputable for dealing with deletion under coordination, is extended with a memory mechanism similar to the slot-and-filler mechanism, resulting in a wider coverage of syntactic gaps patterns. Though our grammar formalism is more expressive than the canonical CCG, its generative power is bounded by Partially Linear Indexed Grammar. Despite the spurious ambiguity originated from the memory mechanism, we also show that its probabilistic parsing is feasible by using the dual decomposition algorithm.

In reconstruction-based super resolution, a high-resolution image is estimated using multiple low-resolution images with sub-pixel misalignments. Therefore, when only one low-resolution image is available, it is generally difficult to obtain a favorable image. This letter proposes a method for overcoming this difficulty for single- image super resolution. In our method, after interpolating pixel values at sub-pixel locations on a patch-by-patch basis by support vector regression, in which learning samples are collected within the given image based on local similarities, we solve the regularized reconstruction problem with a sufficient number of constraints. Evaluation experiments were performed for artificial and natural images, and the obtained high-resolution images indicate the high-frequency components favorably along with improved PSNRs.

The required bit resolution of digital-analog-converter (DAC) for optical OFDM was evaluated through simulation and experiment. We found that the signal degradation caused by quantization depends on the number of subcarriers theoretically, but the other noise generated in experiment suppresses the dependency. Additionally, signal degradation caused by quantization is independent of subcarrier modulation, and the performance improvement saturates with increasing bit resolution. With the criteria for the BER of 110-3, the required DAC resolution by simulation with ideal analog-digital-converter at receiver is 3 bits for QPSK, 4 bits for 8-QAM and 5 bits for 16-QAM and 32-QAM. From the experimental result, the required bit resolution is increased as 6 bits for 32-QAM.

This paper proposes a method to incorporate the concept of time for the inclusion of dynamics of signaling pathway in a Petri net model, i.e., to use timed Petri nets. Incorporation of delay times into a Petri net model makes it possible to conduct quantitative evaluation on a target signaling pathway. However, experimental data describing detailed reactions are not available in most cases. An algorithm given in this paper determines delay times of a timed Petri net only from the structural information of it. The suitability of this algorithm has been confirmed by the results of an application to the IL-1 signaling pathway.

Portable sensory devices such as sensors equipped mobile phones enable convenient sensing and monitoring of urban areas. Such devices, which are always carried by humans, are referred to as Human Probes. Instead of carrying out sensing activities independently, cooperation of Human Probes helps in realizing efficient urban sensing environments. In this paper, we propose an Architecture of Qualitative Urban Information Blending and Acquisition (Aquiba), in which the sensing activities are adjusted autonomously according to cooperation of Human Probes. Aquiba introduces a cooperative sensing approach which aims to maintain desired sensing resolution efficiently while minimizing overall energy consumption. To study the performance of Aquiba, we have conducted comprehensive simulations ranging from small- to large-scale scenarios along with applying three different movement patterns of human. The simulation results demonstrate that Aquiba is capable of providing high sensing resolution and reducing overall energy consumption.

A novel method for single image super resolution without any training samples is presented in the paper. By sparse representation, the method attempts to recover at each pixel its best possible resolution increase based on the self similarity of the image patches across different scale and rotation transforms. The experiments indicate that the proposed method can produce robust and competitive results.

This paper presents an approach for improving proximity and diversity in multiobjective evolutionary algorithms (MOEAs). The idea is to discover new nondominated solutions in the promising area of search space. It can be achieved by applying mutation only to the most converged and the least crowded individuals. In other words, the proximity and diversity can be improved because new nondominated solutions are found in the vicinity of the individuals highly converged and less crowded. Empirical results on multiobjective knapsack problems (MKPs) demonstrate that the proposed approach discovers a set of nondominated solutions much closer to the global Pareto front while maintaining a better distribution of the solutions.

We propose a modification of the generalized gradient vector flow field techniques based on multiresolution analysis and phase portrait techniques. The original image is subjected to mutliresolutional analysis to create a sequence of approximation and detail images. The approximations are converted into an edge map and subsequently into a gradient field subjected to the generalized gradient vector flow transformation. The procedure removes noise and extends large gradients. At every iteration the algorithm obtains a new, improved vector field being filtered using the phase portrait analysis. The phase portrait is applied to a window with a variable size to find possible boundary points and the noise. As opposed to previous phase portrait techniques based on binary rules our method generates a continuous adjustable score. The score is a function of the eigenvalues of the corresponding linearized system of ordinary differential equations. The salient feature of the method is continuity: when the score is high it is likely to be the noisy part of the image, but when the score is low it is likely to be the boundary of the object. The score is used by a filter applied to the original image. In the neighbourhood of the points with a high score the gray level is smoothed whereas at the boundary points the gray level is increased. Next, a new gradient field is generated and the result is incorporated into the iterative gradient vector flow iterations. This approach combined with multiresolutional analysis leads to robust segmentations with an impressive improvement of the accuracy. Our numerical experiments with synthetic and real medical ultrasound images show that the proposed technique outperforms the conventional gradient vector flow method even when the filters and the multiresolution are applied in the same fashion. Finally, we show that the proposed algorithm allows the initial contour to be much farther from the actual boundary than possible with the conventional methods.

In this paper, VLSI architecture of a joint parameter decoder is proposed to realize the calculation of motion vector (MV), intra prediction mode (IPM) and boundary strength (BS) for ultra high definition H.264/AVC applications. For this architecture, a 64-cycle-per-MB pipeline with simplified control modes is designed to increase system throughput and reduce hardware cost. Moreover, in order to save memory bandwidth, the data which includes the motion information for the co-located picture and the last decoded line, is pre-processed before being stored to DRAM. A partition based storage format is applied to condense the MB level data, while variable length coding based compression method is utilized to reduce the data size in each partition. Experimental results show our design is capable of real-time 38402160@60 fps decoding at less than 133 MHz, with 37.2 k logic gates. Meanwhile, by applying the proposed scheme, 85-98% bandwidth saving is achieved, compared with storing the original information for every 44 block to DRAM.

In this paper, optimization and verification of the current-mode multiple-valued digit ORNS arithmetic circuits are presented. The multiple-valued digit ORNS is the redundant number system using digit values in the multiple-valued logic and it realizes the full-parallel calculation without any ripple carry propagation. First, the 4-bit addition and multiplication algorithms employing the multiple-valued digit ORNS are optimized through logic-level analyses. In the multiplier, the maximum digit value and the number of modulo operations in series are successfully reduced from 49 to 29 and from 3 to 2, respectively, by the arrangement of addition lines. Next, circuit components such as a current mirror are verified using HSPICE. The proposed switched current mirror which has functions of a current mirror and an analog switch is effective to reduce the minimum operation voltage by about 0.13 volt. Besides an ordinary strong-inversion region, the circuit components operated under the weak-inversion region show good simulation results with the unit current of 10 nanoamperes, and it brings both of the lower power dissipation and the stable operation under the lower supply voltage.

Virtual Private Network (VPN) is a cost effective method to provide integrated multimedia services. Usually heterogeneous multimedia data can be categorized into different types according to the required Quality of Service (QoS). Therefore, VPN should support the prioritization among different services. In order to support multiple types of services with different QoS requirements, efficient bandwidth management algorithms are important issues. In this paper, I employ the Kalai-Smorodinsky Bargaining Solution (KSBS) for the development of an adaptive bandwidth adjustment algorithm. In addition, to effectively manage the bandwidth in VPNs, the proposed control paradigm is realized in a dynamic online approach, which is practical for real network operations. The simulations show that the proposed scheme can significantly improve the system performances.

This paper presents a wide range DCO with fine discrete tuning steps using a ΣΔ modulation technique for UWB frequency synthesizer. The proposed discrete tuning scheme provides a low effective frequency resolution without any degradation of the phase noise performance. With its three step discrete tunings, the DCO simultaneously has a wide tuning range and fine tuning steps. The frequency synthesizer was implemented using 0.13 µm CMOS technology. The tuning range of the DCO is 5.8-6.8 GHz with an effective frequency resolution of 3.9 kHz. It achieves a measured phase noise of -108 dBc/Hz at 1 MHz offset and a tuning range of 16.8% with the power consumption of 5.9 mW. The figure of merit with the tuning range is -181.5 dBc/Hz.

In this paper, we propose an inconsistency resolution method based on a new concept, insecure backtracking role mapping. By analyzing the role graph, we prove that the root cause of security inconsistency in distributed interoperation is the existence of insecure backtracking role mapping. We propose a novel and efficient algorithm to detect the inconsistency via finding all of the insecure backtracking role mappings. Our detection algorithm will not only report the existence of inconsistency, but also generate the inconsistency information for the resolution. We reduce the inconsistency resolution problem to the known Minimum-Cut problem, and based on the results generated by our detection algorithm we propose an inconsistency resolution algorithm which could guarantee the security of distributed interoperation. We demonstrate the effectiveness of our approach through simulated tests and a case study.

This letter proposes closed form solutions to the L2-sensitivity minimization of second-order state-space digital filters with real poles. We consider two cases of second-order digital filters: distinct real poles and multiple real poles. In case of second-order digital filters, we can express the L2-sensitivity of second-order digital filters by a simple linear combination of exponential functions and formulate the L2-sensitivity minimization problem by a simple polynomial equation. As a result, the minimum L2-sensitivity realizations can be synthesized by only solving a fourth-degree polynomial equation, which can be analytically solved.