The proper allocation of network resources from a common physical substrate to a set of virtual networks (VNs) is one of the key technical challenges of network virtualization. While a variety of state-of-the-art algorithms have been proposed in an attempt to address this issue from different facets, the challenge still remains in the context of large-scale networks as the existing solutions mainly perform in a centralized manner which requires maintaining the overall and up-to-date information of the underlying substrate network. This implies the restricted scalability and computational efficiency when the network scale becomes large. This paper tackles the virtual network mapping problem and proposes a novel hierarchical algorithm in conjunction with a substrate network decomposition approach. By appropriately transforming the underlying substrate network into a collection of sub-networks, the hierarchical virtual network mapping algorithm can be carried out through a global virtual network mapping algorithm (GVNMA) and a local virtual network mapping algorithm (LVNMA) operated in the network central server and within individual sub-networks respectively with their cooperation and coordination as necessary. The proposed algorithm is assessed against the centralized approaches through a set of numerical simulation experiments for a range of network scenarios. The results show that the proposed hierarchical approach can be about 5-20 times faster for VN mapping tasks than conventional centralized approaches with acceptable communication overhead between GVNCA and LVNCA for all examined networks, whilst performs almost as well as the centralized solutions.

In this paper, we propose a switchable linear prediction (LP)/warped linear prediction (WLP) hybrid scheme for the transform coded excitation (TCX) coder, which is adopted as a core codec in AMR-WB+ and USAC. The proposed algorithm selects either an LP or WLP filter on a per-frame basis. To provide a smooth transitions between LP and WLP frames, a window switching scheme is developed using sine and rectangular windows. In addition, a Gaussian Mixture Model (GMM)-based classification module is used to determine the prediction mode. Through a subjective listening test it was confirmed that the proposed LP/WLP switching scheme offers improved sound quality.

Recently, a flexible loss recovery scheme, called Active Caching (AC) has been proposed to accomplish a Desired Communication Reliability (DCR) about the whole data packets at a source depending on the various applications. However, since AC does not consider the packet delivery rate of each wireless link on multi-hop forwarding paths, it increases the number of totally transmitted packets to achieve a DCR and thus grows the energy consumption of sensor nodes. Thus, this letter proposes a novel recovery scheme which can minimize the number of totally transmitted packets while satisfying a DCR. By geometric programming, the proposed scheme allocates an optimized one-hop packet transmission rate of each wireless link on the multi-hop forwarding path.

The conventional semi-orthogonal user pairing algorithm in uplink virtual MIMO systems can be used to improve the total system throughput but it usually fails to maintain good throughput performance for users experiencing relatively poor channel conditions. A novel user paring algorithm is presented in this paper to solve this fairness issue. Based on our analysis of the MMSE receiver, a new criterion called “inverse selection” is proposed for use in conjunction with the semi-orthogonal user selection. Simulation results show that the proposed algorithm can significantly improve the throughput of users with poor channel condition at only a small reduction of the overall throughput.

This letter presents a model with queueing theory to analyze the performance of non-saturated IEEE 802.11 DCF networks. We use the closed queueing network model and derive an approximate representation of throughput which can reveal the relationship between the throughput and the total offered load under finite traffic load conditions. The accuracy of the model is verified by extensive simulations.

We present the theory and experiment of metal-cavity nanolasers and nanoLEDs flip-chip bonded to silicon under electrical injection at room temperature. We first review the recent progress on micro- and nanolasers. We then present the design rule and our theoretical model. We show the experimental results of our metal-cavity surface-emitting microlasers and compare with our theoretical results showing an excellent agreement. We found the important contributions of the nonradiative recombination currents including Auger recombination, surface recombination, and leakage currents. Finally, experimental demonstration of electrical injection nanoLEDs toward subwavelength nanoscale lasers is reported.

In disaster sites of 2011 Tohoku Earthquake, digital communication was virtually unavailable due to the serious damage to the existing Internet and ICT resources. Thus there were urgent demands for recovering the Internet connectivity and first aid communication tools. This paper describes the design and deployment of networking systems that provide Internet connectivity using 3G mobile links or VSAT satellite links. In this paper we examine two approaches for post-disaster networking: quickly deployable package and on-demand networking. Based on a comparison of their characteristics and deployment experiences, this paper tries to extract lessons that contribute to improving the preparedness to another disaster. This paper also shares our significant operational experience acquired through supporting a maximum of 54 sites in Tohoku area including evacuation shelters, temporary hospitals and local government offices.

In this comment, an inequality of algebraic immunity of the sum of two Boolean functions is pointed out to be generally incorrect. Then we present some results on how to impose conditions such that the inequality is true. Finally, complete proofs of two existing results are given.

In this paper, we propose a novel gait recognition framework - Spherical Space Model with Human Point Clouds (SSM-HPC) to recognize front view of human gait. A new gait representation - Marching in Place (MIP) gait is also introduced which preserves the spatiotemporal characteristics of individual gait manner. In comparison with the previous studies on gait recognition which usually use human silhouette images from image sequences, this research applies three dimensional (3D) point clouds data of human body obtained from stereo camera. The proposed framework exhibits gait recognition rates superior to those of other gait recognition methods.

In this paper, an improved hybrid LUT-based architecture for low-error and efficient fixed-width squarer circuits is presented in which LUT-based and conventional logic circuits are employed together to achieve the good trade-off between hardware complexity and performance. By exploiting the mathematical identities and hybrid architecture, the mean error and mean squarer error of the proposed squarer are reduced by up to 40%, compared with the best previous method presented in literature. Moreover, the proposed method can improve the speed and reduce the area of the squarer circuit. The implementation and chip measurement results in 0.18-µm CMOS technology are also presented and discussed.

In this paper, time-difference estimation of filtered random signals passed through multipath channels is discussed. First, we reformulate the approach based on innovation-rate sampling (IRS) to fit our random signal model, then use the IRS results to drive the nonlinear least-squares (NLS) minimization algorithm. This hybrid approach (referred to as the IRS-NLS method) provides consistent estimates even for cases with sub-Nyquist sampling assuming the use of compactly-supported sampling kernels that satisfies the recently-developed nonaliasing condition in the frequency domain. Numerical simulations show that the proposed NLS-IRS method can improve performance over the straight-forward IRS method, and provides approximately the same performance as the NLS method with reduced sampling rate, even for closely-spaced time delays. This enables, given a fixed observation time, significant reduction in the required number of samples, while maintaining the same level of estimation performance.

We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose – the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum- and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data.

This paper presents a novel algorithm to generate homogeneous superpixels from Markov random walks. We exploit Markov clustering (MCL) as the methodology, a generic graph clustering method based on stochastic flow circulation. In particular, we introduce a graph pruning strategy called compact pruning in order to capture intrinsic local image structure. The resulting superpixels are homogeneous, i.e. uniform in size and compact in shape. The original MCL algorithm does not scale well to a graph of an image due to the square computation of the Markov matrix which is necessary for circulating the flow. The proposed pruning scheme has the advantages of faster computation, smaller memory footprint, and straightforward parallel implementation. Through comparisons with other recent techniques, we show that the proposed algorithm achieves state-of-the-art performance.

In this paper, we propose a method capable of shortening the distance from a noise detection microphone to a loudspeaker in active noise control system with non-minimum phase secondary path. The distance can be basically shortened by forming the noise control filter, which produces the secondary noise provided by the loudspeaker, with the cascade connection of a non-recursive filter and a recursive filter. The output of the recursive filter, however, diverges even when the secondary path includes only a minimum phase component. In this paper, we prevent the divergence by utilizing MINT (multi-input/output inverse theorem) method increasing the number of secondary paths than that of primary paths. MINT method, however, requires a large scale inverse matrix operation, which increases the processing cost. We hence propose a method reducing the processing cost. Actually, MINT method has only to be applied to the non-minimum phase components of the secondary paths. We hence extract the non-minimum phase components and then apply MINT method only to those. The order of the inverse matrix thereby decreases and the processing cost can be reduced. We finally show a simulation result demonstrating that the proposed method successfully works.

In this letter, a new method is proposed to solve the direction-of-arrivals (DOAs) estimation problem of coherently distributed sources based on the block-sparse signal model of compressed sensing (CS) and the convex optimization theory. We make use of a certain number of point sources and the CS array architecture to establish the compressive version of the discrete model of coherently distributed sources. The central DOA and the angular spread can be estimated simultaneously by solving a convex optimization problem which employs a joint norm constraint. As a result we can avoid the two-dimensional search used in conventional algorithms. Furthermore, the multiple-measurement-vectors (MMV) scenario is also considered to achieve robust estimation. The effectiveness of our method is confirmed by simulation results.

This paper presents a single-cycle shared output buffered router for Networks-on-Chip. In output ports, each input port always has an output virtual-channel (VC) which can be exchanged by VC swapper. Its critical path is only 24 logic gates, and it reduces 9.4% area overhead compared with the classical router.

In this study, we show that the motion blur is caused by exposure time of video camera as well as the characteristics of LCD system. Also, we suggest that evaluation method of motion picture quality according to the frequency response of video camera and LCD systems of hold and scanning backlight type.