This paper presents a self-clustering algorithm to detect symmetry in images. We combine correlations of orientations, scales and descriptors as a triple feature vector to evaluate each feature pair while low confidence pairs are regarded as outliers and removed. Additionally, all confident pairs are preserved to extract potential symmetries since one feature point may be shared by different pairs. Further, each feature pair forms one cluster and is merged and split iteratively based on the continuity in the Cartesian and concentration in the polar coordinates. Pseudo symmetric axes and outlier midpoints are eliminated during the process. Experiments demonstrate the robustness and accuracy of our algorithm visually and quantitatively.

Question and Answering (Q&A) sites are recently gaining popularity on the Web. People using such sites are like a community-anyone can ask, anyone can answer, and everyone can share, since all of the questions and answers are public and searchable immediately. This mechanism can reduce the time and effort to find the most relevant answer. Unfortunately, the users suffer from answer quality problem due to several reasons including limited knowledge about the question domain, bad intentions (e.g. spam, making fun of others), limited time to prepare good answers, etc. In order to identify the credible users to help people find relevant answer, in this paper, we propose a ranking algorithm, InfluenceRank, which is basis of analyzing relationship in terms of users' activities and their mutual trusts. Our experimental studies show that the proposed algorithm significantly outperforms the baseline algorithms.

In this paper, we propose a novel in-network aggregation scheduling scheme for forest fire monitoring in a wireless sensor network. This adaptively configures both the timeout and the collecting period according to the potential level of a fire occurrence. At normal times, the proposed scheme decreases a timeout that is a wait time for packets sent from child nodes and makes the collecting period longer. That reduces the dissipated energy of the sensor node. Conversely, the proposed scheme increases the timeout and makes the collecting period shorter during fire occurrences in order to achieve more accurate data aggregation and early fire detection.

In the literature, two connectivity-based distributed clustering schemes exist: CDC (Connectivity-based Distributed node Clustering scheme) and SDC (SCM-based Distributed Clustering). While CDC and SDC have mechanisms for maintaining clusters against nodes joining and leaving, neither method assumes that frequent changes occur in the network topology. In this paper, we propose a lightweight distributed clustering method that we term SBDC (Schelling-Based Distributed Clustering) since this scheme is derived from Schelling's model – a popular segregation model in sociology. We evaluate the effectiveness of the proposed SBDC in an environment where frequent changes arise in the network topology. Our simulation results show that SBDC outperforms CDC and SDC under frequent changes in network topology caused by high node mobility.

We investigate in detail the scattering properties and heating characteristics in various commercially available optical fibers and fiber cables when a bubble train forms in the middle of the fiber as a result of the fiber fuse phenomenon that occurs when a high power signal is launched into the fiber. We found theoretically and experimentally that almost all the optical light is scattered at the top of the bubble train. The scattered light heats UV coated fiber, nylon jacketed silica fiber, fire-retardant jacketed fiber (PVC or FRPE jacketed fiber) and fire-retardant fiber cable (PVC or FRPE fiber cable), to around 100, over 200 and over 600, respectively, and finally the fiber burns and is destroyed at a launched optical power of 3 W. Furthermore, it is confirmed that the combustion does not spread when we use fire retardant jacketed fibers.

We propose a novel scheme that aims to determine the optimal number of clusters based on the field conditions and the positions of mobile sink nodes. In addition, we merge algorithms of tree-based index structures to form an energy-efficient cluster structure. A performance evaluation shows that the proposed method produces highly-balanced clusters that are energy efficient and achieves up to 1.4 times higher survival rates than the previous clustering schemes, under various operational conditions.

Dynamic resource management has become an active area of research in the Cloud Computing paradigm. Cost of resources varies significantly depending on configuration for using them. Hence efficient management of resources is of prime interest to both Cloud Providers and Cloud Users. In this work we suggest a probabilistic resource provisioning approach that can be exploited as the input of a dynamic resource management scheme. Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations. We show that the resulting model verifies a Large Deviation Principle that statistically characterizes extreme rare events, such as the ones produced by “buzz/flash crowd effects” that may cause workload overflow in the VoD context. This analysis provides valuable insight on expectable abnormal behaviors of systems. We exploit the information obtained using the Large Deviation Principle for the proposed Video on Demand use-case for defining policies (Service Level Agreements). We believe these policies for elastic resource provisioning and usage may be of some interest to all stakeholders in the emerging context of cloud networking.

A low power and high performance with third order delta-sigma modulator for audio applications, fabricated in a 0.18 µm CMOS process, is presented. The modulator utilizes a third order noise shaping with only one opamp by using an opamp sharing technique. The opamp sharing among three integrator stages is achieved through the optimal operation timing, which makes use of the load capacitance differences between the three integrator stages. The designed modulator achieves 101.1 dB signal-to-noise ratio (A-weighted) and 101.5 dB dynamic range (A-weighted) with 7.5 mW power consumption from a 3.3 V supply. The die area is 1.27 mm2. The fabricated delta-sigma modulator achieves the highest figure-of-merit among published high performance low power audio delta-sigma modulators.

To reduce the cost of fault management in all-optical networks, it is a promising approach to detect the degradation of optical signal quality solely at the terminal points of all-optical monitoring paths. The all-optical monitoring paths must be routed so that all single-link failures can be localized using route information of monitoring paths where signal quality degradation is detected. However, route computation for the all-optical monitoring paths that satisfy the above condition is time consuming. This paper proposes a procedure for deriving the lower bounds of the required number of monitoring paths to localize all single-link failures, and proposes an efficient monitoring path computation method based on the derived lower bounds. The proposed method repeats the route computation for the monitoring paths until feasible routes can be found, while the assumed number of monitoring paths increases, starting from the lower bounds. With the proposed method, the minimum number of monitoring paths with the overall shortest routes can be obtained quickly by solving several small-scale integer linear programming problems when the possible terminal nodes of monitoring paths are arbitrarily given. Thus, the proposed method can minimize the required number of monitors for detecting the degradation of signal quality and the total overhead traffic volume transferred through the monitoring paths.

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.

In our previous research, to achieve unequal error protection (UEP), we proposed a scheme which encodes the data by randomly switching between several codes which use different signal constellations and showed the effectiveness in AWGN channels. In this letter, we propose our UEP system using MMSE-FDE for fast and selective fading by using the fact that importance levels are changed every few symbols, i.e., every block, in the proposed system. We confirmed the improvement in BER performance and the effectiveness of adaptive equalization for the proposed system in fading channels. Moreover, in fading channels we confirmed the validity of the theoretical tradeoff shown in static conditions.

An important concept in secret sharing scheme is the access structure. However, determining the access structure of the secret sharing scheme based on a linear code is a very difficult problem. In this work, we provide a method to construct a class of two-weight linear codes over finite rings. Based on the two-weight codes, we present an access structure of a secret sharing scheme.

Execution performance is critical for large-scale and data-intensive workflows. This paper proposes DISWOP, a novel scheduling algorithm for data-intensive workflow optimizations; it consists of three main steps: workflow process generation, task & resource mapping, and task clustering. To evaluate the effectiveness and efficiency of DISWOP, a comparison evaluation of different workflows is conducted a prototype workflow platform. The results show that DISWOP can speed up execution performance by about 1.6-2.3 times depending on the task scale.

Some optical components have polarization dependent loss (PDL), which degrades the performance of optical measurement systems. Various PDL suppression methods have been proposd, most of them have rather complicated structures. In this paper we propose a new simple method for PDL suppression, in which a single birefringent element is concatenated to a PDL device with their birefringent axes offset by π/4. The effectiveness of the proposed method is verified by experiments, that is, polarization dependent loss variation amplitude V of a device relative to its average loss is reduced from 90% to 2% by using a 2 m long PANDA fiber for an LED light source whose central wavelength λ0 and spectral width Δλ are 847 nm and 55 nm, respectively. Furthermore, for an SLD light source with λ0=1539 nm and Δλ=71 nm, V as much as 80% is reduced to 2% by using the same PANDA fiber.

In this paper, we present a supervised learning method to seek out answers to the most frequently asked descriptive questions: reason, method, and definition questions. Most of the previous systems for question answering focus on factoids, lists or definitional questions. However, descriptive questions such as reason questions and method questions are also frequently asked by users. We propose a system for these types of questions. The system conducts an answer search as follows. First, we analyze the user's question and extract search keywords and the expected answer type. Second, information retrieval results are obtained from an existing search engine such as Yahoo or Google . Finally, we rank the results to find snippets containing answers to the questions based on a ranking SVM algorithm. We also propose features to identify snippets containing answers for descriptive questions. The features are adaptable and thus are not dependent on answer type. Experimental results show that the proposed method and features are clearly effective for the task.

In this paper, a new swept-frequency method for the measurement of the complex permittivity and permeability of materials is proposed. The method is based on the S-parameters measurement of a cylindrical material placed inside a rectangular waveguide, where the axis of the cylinder is perpendicular to the narrow waveguide walls. The usage of cylinders in measurement is beneficial because they are easy to fabricate and handle. A novel exact solution of the field scattered by the cylinder is developed. The solution is based on expanding the field in a sum of orthogonal modes in cylindrical coordinates. Excitation coefficients relating the cylindrical scattered field to the waveguide modes are derived, and are used to rigorously formulates the S-parameters. Measurement are performed in the S-band with two dielectric materials (PTFE, nylon), and in the X-band with one magnetic material (ferrite epoxy). The measurement results agree with those from the literature.

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.

With the wide usage of multispectral images, a fast efficient multidimensional clustering method becomes not only meaningful but also necessary. In general, to speed up the multidimensional images' analysis, a multidimensional feature vector should be transformed into a lower dimensional space. The Hilbert curve is a continuous one-to-one mapping from N-dimensional space to one-dimensional space, and can preserves neighborhood as much as possible. However, because the Hilbert curve is generated by a recurve division process, 'Boundary Effects' will happen, which means data that are close in N-dimensional space may not be close in one-dimensional Hilbert order. In this paper, a new efficient approach based on the space-filling curves is proposed for classifying multispectral satellite images. In order to remove 'Boundary Effects' of the Hilbert curve, multiple Hilbert curves, z curves, and the Pseudo-Hilbert curve are used jointly. The proposed method extracts category clusters from one-dimensional data without computing any distance in N-dimensional space. Furthermore, multispectral images can be analyzed hierarchically from coarse data distribution to fine data distribution in accordance with different application. The experimental results performed on LANDSAT data have demonstrated that the proposed method is efficient to manage the multispectral images and can be applied easily.

In this paper, we propose a novel architecture for large-scale regular expression matching, called dynamically reconfigurable bit-parallel NFA architecture (Dynamic BP-NFA), which allows dynamic loading of regular expressions on-the-fly as well as efficient pattern matching for fast data streams. This is the first dynamically reconfigurable hardware with guaranteed performance for the class of extended patterns, which is a subclass of regular expressions consisting of union of characters and its repeat. This class allows operators such as character classes, gaps, optional characters, and bounded and unbounded repeats of character classes. The key to our architecture is the use of bit-parallel pattern matching approach, in which the information of an input non-deterministic finite automaton (NFA) is first compactly encoded in bit-masks stored in a collection of registers and block RAMs. Then, the NFA is efficiently simulated by a fixed circuitry using bitwise Boolean and arithmetic operations consuming one input character per clock regardless of the actual contents of an input text. Experimental results showed that our hardwares for both string and extended patterns were comparable to previous dynamically reconfigurable hardwares in their performances.

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.