Considering diversified HTTP types, the performance bottleneck of signature-based classification must be resolved. We define a signature model classifying the traffic in multiple dimensions and suggest a hierarchical signature structure to remove signature redundancy and minimize search space. Our experiments on campus traffic demonstrated 1.8 times faster processing speed than the Aho-Corasick matching algorithm in Snort.

Point spread function (PSF) estimation plays a paramount role in image deblurring processing, and traditionally it is solved by parameter estimation of a certain preassumed PSF shape model. In real life, the PSF shape is generally arbitrary and complicated, and thus it is assumed in this manuscript that a PSF may be decomposed as a weighted sum of a certain number of Gaussian kernels, with weight coefficients estimated in an alternating manner, and an l1 norm-based total variation (TVl1) algorithm is adopted to recover the latent image. Experiments show that the proposed method can achieve satisfactory performance on synthetic and realistic blurred images.

The conical area evolutionary algorithm (CAEA) has a very high run-time efficiency for bi-objective optimization, but it can not tackle problems with more than two objectives. In this letter, a conical hypervolume evolutionary algorithm (CHEA) is proposed to extend the CAEA to a higher dimensional objective space. CHEA partitions objective spaces into a series of conical subregions and retains only one elitist individual for every subregion within a compact elitist archive. Additionally, each offspring needs to be compared only with the elitist individual in the same subregion in terms of the local hypervolume scalar indicator. Experimental results on 5-objective test problems have revealed that CHEA can obtain the satisfactory overall performance on both run-time efficiency and solution quality.

This study improves the compression efficiency of Lee's colorization-based coding framework by introducing a novel colorization matrix construction and an adaptive color conversion. Colorization-based coding methods reconstruct color components in the decoder by colorization, which adds color to a base component (a grayscale image) using scant color information. The colorization process can be expressed as a linear combination of a few column vectors of a colorization matrix. Thus it is important for colorization-based coding to make a colorization matrix whose column vectors effectively approximate color components. To make a colorization matrix, Lee's colorization-based coding framework first obtains a base and color components by RGB-YCbCr color conversion, and then performs a segmentation method on the base component. Finally, the entries of a colorization matrix are created using the segmentation results. To improve compression efficiency on this framework, we construct a colorization matrix based on a correlation of base-color components. Furthermore, we embed an edge-preserving smoothing filtering process into the colorization matrix to reduce artifacts. To achieve more improvement, our method uses adaptive color conversion instead of RGB-YCbCr color conversion. Our proposed color conversion maximizes the sum of the local variance of a base component, which resulted in increment of the difference of intensities at region boundaries. Since segmentation methods partition images based on the difference, our adaptive color conversion leads to better segmentation results. Experiments showed that our method has higher compression efficiency compared with the conventional method.

To accomplish secure communication in vehicular networks, public key infrastructure (PKI) can be employed. However, traditional PKI systems are not suitable because a unique certificate is assigned to each vehicle and thus no anonymity is guaranteed. In the combinatorial certificate schemes, each vehicle is assigned multiple certificates from a shared certificate pool and each certificate in the pool is assigned to multiple vehicles to achieve a level of anonymity. When a certificate assigned to a misbehaving vehicle is revoked, a certificate replacement procedure is executed to all vehicles sharing the certificate. To replace the revoked certificate, a randomized certificate replacement scheme probabilistically assigns different certificates to different vehicles, which can reduce collateral damage caused by repeatedly misusing a certificate and its replacement certificates. Unfortunately, previous randomized certificate replacement schemes allow unbounded collateral damage; a finite number of certificate replacements cannot detect the misbehaving vehicle with certainty. To address this problem, we propose a new randomized certificate replacement scheme with bounded collateral damage.

Template tracking has been extensively studied in Computer Vision with a wide range of applications. A general framework is to construct a parametric model to predict movement and to track the target. The difference in intensity between the pixels belonging to the current region and the pixels of the selected target allows a straightforward prediction of the region position in the current image. Traditional methods track the object based on the assumption that the relationship between the intensity difference and the region position is linear or non-linear. They will result in bad tracking performance when just one model is adopted. This paper proposes a method, called as Mixture Hyperplanes Approximation, which is based on finite mixture of generalized linear regression models to perform robust tracking. Moreover, a fast learning strategy is discussed, which improves the robustness against noise. Experiments demonstrate the performance and stability of Mixture Hyperplanes Approximation.

Isosurface extraction is one of the most popular techniques for visualizing scalar volume data. However, volume data contains infinitely many isosurfaces. Furthermore, a single isosurface might contain many connected components, or contours, with each representing a different object surface. Hence, it is often a tedious and time-consuming manual process to find and extract contours that are interesting to users. This paper describes a novel method for automatically extracting salient contours from volume data. For this purpose, we propose a contour gradient tree (CGT) that contains the information of salient contours and their saliency magnitude. We organize the CGT in a hierarchical way to generate a sequence of contours in saliency order. Our method was applied to various medical datasets. Experimental results show that our method can automatically extract salient contours that represent regions of interest in the data.

In recent years, many variants of key point based image descriptors have been designed for the image matching, and they have achieved remarkable performances. However, to some images, local features appear to be inapplicable. Since theses images usually have many local changes around key points compared with a normal image, we define this special image category as the image with local changes (IL). An IL pair (ILP) refers to an image pair which contains a normal image and its IL. ILP usually loses local visual similarities between two images while still holding global visual similarity. When an IL is given as a query image, the purpose of this work is to match the corresponding ILP in a large scale image set. As a solution, we use a compressed HOG feature descriptor to extract global visual similarity. For the nearest neighbor search problem, we propose random projection indexed KD-tree forests (rKDFs) to match ILP efficiently instead of exhaustive linear search. rKDFs is built with large scale low-dimensional KD-trees. Each KD-tree is built in a random projection indexed subspace and contributes to the final result equally through a voting mechanism. We evaluated our method by a benchmark which contains 35,000 candidate images and 5,000 query images. The results show that our method is efficient for solving local-changes invariant image matching problems.

We have proposed a new Bayesian network model (BNM) framework for single-trial-EEG-based Brain-Computer Interface (BCI). The BNM was constructed in the following. In order to discriminate between left and right hands to be imaged from single-trial EEGs measured during the movement imagery tasks, the BNM has the following three steps: (1) independent component analysis (ICA) for each of the single-trial EEGs; (2) equivalent current dipole source localization (ECDL) for projections of each IC on the scalp surface; (3) BNM construction using the ECDL results. The BNMs were composed of nodes and edges which correspond to the brain sites where ECDs are located, and their connections, respectively. The connections were quantified as node activities by conditional probabilities calculated by probabilistic inference in each trial. The BNM-based BCI is compared with the common spatial pattern (CSP) method. For ten healthy subjects, there was no significant difference between the two methods. Our BNM might reflect each subject's strategy for task execution.

This paper studies a privacy-preserving decision tree learning protocol (PPDT) for vertically partitioned datasets. In vertically partitioned datasets, a single class (target) attribute is shared by both parities or carefully treated by either party in existing studies. The proposed scheme allows both parties to have independent class attributes in a secure way and to combine multiple class attributes in arbitrary boolean function, which gives parties some flexibility in data-mining. Our proposed PPDT protocol reduces the CPU-intensive computation of logarithms by approximating with a piecewise linear function defined by light-weight fundamental operations of addition and constant multiplication so that information gain for attributes can be evaluated in a secure function evaluation scheme. Using the UCI Machine Learning dataset and a synthesized dataset, the proposed protocol is evaluated in terms of its accuracy and the sizes of trees*.

The pilot contamination is a serious problem which hinders the capacity increasing in the massive MIMO system. Similar to Fractional Frequency Reuse (FFR) in the OFDMA system, Fractional Pilot Reuse (FPR) is proposed for the massive MIMO system. The FPR can be further classified as the strict FPR and soft FPR. Meanwhile, the detailed FPR schemes with pilot assignment and the mathematical models are provided. With FPR, the capacity and the transmission quality can be improved with metrics such as the higher Signal to Interference and Noise Ratio (SINR) of the pilots, the higher coverage probability, and the higher system capacity.

Multi-tenant datacenter networking, with which multiple customer networks (tenants) are virtualized and consolidated in a single shared physical infrastructure, has recently become a promising approach to reduce device cost, thanks to advances of virtualization technologies for various networking devices (e.g., switches, firewalls, load balancers). Since network devices are configured with low-level commands (no context of tenants), network engineers need to manually manage the context of tenants in different stores such as spreadsheet and/or configuration management database (CMDB). The use of CMDB is also effective in increasing the ‘visibility’ of tenant configurations (e.g., information sharing among various teams); However, different from the ideal use, only limited portion of network configuration are stored in CMDB in order to reduce the amount of ‘double configuration management’ between device settings (running information) and CMDB (stored information). In this present work, we aim to bridge the gap between CDMB and device status. Our basic approach is to automatically analyze per-device configuration settings to recover per-tenant network-wide configuration (running information) based on a graph-traversal technique applied over abstracted graph representation of device settings (to handle various types of vendor-specific devices); The recovered running information of per-tenant network configurations is automatically uploaded to CMDB. An implementation of this methodology is applied to a datacenter environment that management of about 100 tenants involves approximately 5,000 CMDB records, and our practical experiences are that this methodology enables to double the amount of CMDB records. We also discuss possible use cases enabled with this methodology.

Multi-layer transport networks that utilize sub-lambda paths over a wavelength path have been shown to be effective in accommodating traffic with various levels of granularity. For different service requirements, a virtualized network was proposed where the infrastructure is virtually sliced to accommodate different levels of reliability. On the other hand, network reconfiguration is a promising candidate for quasi-dynamic and multi-granular traffic. Reconfiguration, however, incurs some risks such as service disruption and fluctuations in delay. There has not yet been any study on accommodating and reconfiguring paths according to different service classes in multi-layer transport networks. In this paper, we propose differentiated reconfiguration to address the trade-off relationship between accommodation efficiency and disruption risk in virtualized multi-layer transport networks that considers service classes defined as a combination of including or excluding a secondary path and allowing or not allowing reconfiguration. To implement the proposed network, we propose a multi-layer redundant path accommodation design and reconfiguration algorithm. A reliability evaluation algorithm is also introduced. Numerical evaluations show that when all classes are divided equally, equipment cost can be reduced approximately by 6%. The proposed reconfigurable networks are shown to be a cost effective solution that maintains reliability.

The hop-limited adaptive routing (HLAR) mechanism and its enhancement (EHLAR), both tailored for the packet-switched non-geostationary (NGEO) satellite networks, are proposed and evaluated. The proposed routing mechanisms exploit both the predictable topology and inherent multi-path property of the NGEO satellite networks to adaptively distribute the traffic via all feasible neighboring satellites. Specifically, both mechanisms assume that a satellite can send the packets to their destinations via any feasible neighboring satellites, thus the link via the neighboring satellite to the destination satellite is assigned a probability that is proportional to the effective transmission to the destination satellites of the link. The satellite adjusts the link probability based on the packet sending information observed locally for the HLAR mechanism or exchanged between neighboring satellites for the EHLAR mechanism. Besides, the path of the packets are bounded by the maximum hop number, thus avoiding the unnecessary over-detoured packets in the satellite networks. The simulation results corroborate the improved performance of the proposed mechanisms compared with the existing in the literature.

An L2-in-L3 tunneling technology plays an important role in network virtualization based on the concept of Software-Defined Networking (SDN). VXLAN (Virtual eXtensible LAN) and NVGRE (Network Virtualization using Generic Routing Encapsulation) protocols are being widely used in public cloud datacenters. These protocols resolve traditional VLAN problems such as a limitation of the number of virtual networks, however, their network performances are low without dedicated hardware acceleration. Although STT (Stateless Transport Tunneling) achieves far better performance, it has pragmatic problems in that STT packets can be dropped by network middleboxes like stateful firewalls because of modified TCP header semantics. In this paper, we propose yet another layer 4 protocol (Segment-oriented Connection-less Protocol, SCLP) for existing tunneling protocols. Our previous study revealed that the high-performance of STT mainly comes from 2-level software packet pre-reassembly before decapsulation. The SCLP header is designed to take advantage of such processing without modifying existing protocol semantics. We implement a VXLAN over SCLP tunneling and evaluate its performance by comparing with the original VXLAN (over UDP), NVGRE, Geneve, and STT. The results show that the throughput of the proposed method was comparable to STT and almost 70% higher than that of other protocols.

Linear codes with locality r and availability t have a wide application in distribution storage because they permit local repair and parallel accesses of hot data. In this letter, the locality and availability of some linear codes based on finite geometry are given. According to these results, we give some linear codes that have higher rate than known codes with the same locality and availability.

The advanced front-end (AFE) for automatic speech recognition (ASR) was standardized by the European Telecommunications Standards Institute (ETSI). The AFE provides speech enhancement realized by an iterative Wiener filter (IWF) in which a smoothed FFT spectrum over adjacent frames is used to design the filter. We have previously proposed robust time-varying complex Auto-Regressive (TV-CAR) speech analysis for an analytic signal and evaluated the performance of speech processing such as F0 estimation and speech enhancement. TV-CAR analysis can estimate more accurate spectrum than FFT, especially in low frequencies because of the nature of the analytic signal. In addition, TV-CAR can estimate more accurate speech spectrum against additive noise. In this paper, a time-invariant version of wide-band TV-CAR analysis is introduced to the IWF in the AFE and is evaluated using the CENSREC-2 database and its baseline script.

This paper proposes the MIMO MC-CDMA channel estimation method for the various mobile environments. The distinctive feature of the proposed method is possible to robustly estimate with respect to the mobile velocity using the Kalman filter with the colored driving source. Effectiveness of the proposed method are shown by computer simulations.

This paper analyzes the impact of directional antennas in improving the transmission capacity, defined as the maximum allowable spatial node density of successful transmissions multiplied by their data rate with a given outage constraint, in wireless networks. We consider the case where the gain Gm for the mainlobe of beamwidth can scale at an arbitrarily large rate. Under the beamwidth scaling model, the transmission capacity is analyzed for all path-loss attenuation regimes for the following two network configurations. In dense networks, in which the spatial node density increases with the antenna gain Gm, the transmission capacity scales as Gm4/α, where α denotes the path-loss exponent. On the other hand, in extended networks of fixed node density, the transmission capacity scales logarithmically in Gm. For comparison, we also show an ideal antenna model where there is no sidelobe beam. In addition, computer simulations are performed, which show trends consistent with our analytical behaviors. Our analysis sheds light on a new understanding of the fundamental limit of outage-constrained ad hoc networks operating in the directional mode.

The dramatic growth in wireless data traffic has triggered the investigation of fifth generation (5G) wireless communication systems. Small cells will play a very important role in 5G to meet the 5G requirements in spectral efficiency, energy savings, etc. In this paper, we investigate low complexity millimeter-wave communication systems with uniform circular arrays (UCAs) in line-of-sight (LOS) multiple-input multiple-output (MIMO) channels, which are used in fixed wireless access such as small cell wireless backhaul for 5G. First, we demonstrate that the MIMO channel matrices for UCAs in LOS-MIMO channels are circulant matrices. Next, we provide a detailed derivation of the unified optimal antenna placement which makes MIMO channel matrices orthogonal for 3×3 and 4×4 UCAs in LOS channels. We also derive simple analytical expressions of eigenvalues and capacity as a function of array design (link range and array diameters) for the concerned systems. Finally, based on the properties of circulant matrices, we propose a high performance low complexity LOS-MIMO precoding system that combines forward error correction (FEC) codes and spatial interleaver with the fixed IDFT precoding matrix. The proposed precoding system for UCAs does not require the channel knowledge for estimating the precoding matrix at the transmitter under the LOS condition, since the channel matrices are circulant ones for UCAs. Simulation results show that the proposed low complexity system is robust to various link ranges and can attain excellent performance in strong LOS environments and channel estimation errors.