Let T be a given tree. Each vertex of T is either a supply vertex or a demand vertex, and is assigned a positive number, called the supply or demand. Each demand vertex v must be supplied an amount of “power,” equal to the demand of v, from exactly one supply vertex through edges in T. Each edge is assigned a positive number called the capacity. One wishes to partition T into subtrees by deleting edges from T so that each subtree contains exactly one supply vertex whose supply is no less than the sum of all demands in the subtree and the power flow through each edge is no more than capacity of the edge. The “partition problem” is a decision problem to ask whether T has such a partition. The “maximum partition problem” is an optimization version of the partition problem. In this paper, we give three algorithms for the problems. First is a linear-time algorithm for the partition problem. Second is a pseudo-polynomial-time algorithm for the maximum partition problem. Third is a fully polynomial-time approximation scheme (FPTAS) for the maximum partition problem.

Pulse coupled neural network (PCNN) is a new type of artificial neural network specific for image processing applications. It is a single layer, two dimensional network with neurons which have 1:1 correspondence to the pixels of an input image. It is convenient to process the intensities and spatial locations of image pixels simultaneously by applying a PCNN. Therefore, we propose a modified PCNN with anisotropic synaptic weight matrix for image edge detection from the aspect of intensity similarities of pixels to their neighborhoods. By applying the anisotropic synaptic weight matrix, the interconnections are only established between the central neuron and the neighboring neurons corresponding to pixels with similar intensity values in a 3 by 3 neighborhood. Neurons corresponding to edge pixels and non-edge pixels will receive different input signal from the neighboring neurons. By setting appropriate threshold conditions, image step edges can be detected effectively. Comparing with conventional PCNN based edge detection methods, the proposed modified PCNN is much easier to control, and the optimal result can be achieved instantly after all neurons pulsed. Furthermore, the proposed method is shown to be able to distinguish the isolated pixels from step edge pixels better than derivative edge detectors.

We propose a novel threshold-free salient object detection approach which integrates both saliency density and edge response. The salient object with a well-defined boundary can be automatically detected by our approach. Saliency density and edge response maximization is used as the quality function to direct the salient object discovery. The global optimal window containing a salient object is efficiently located through the proposed saliency density and edge response based branch-and-bound search. To extract the salient object with a well-defined boundary, the GrabCut method is applied, initialized by the located window. Experimental results show that our approach outperforms the methods only using saliency or edge response and achieves a comparable performance with the best state-of-the-art method, while being without any threshold or multiple iterations of GrabCut.

In this paper, we develop a novel two-sample test statistic for edge detection in CT image. This test statistic involves the non-parametric estimate of the samples' probability density functions (PDF's) based on the kernel density estimator and the calculation of the mean square error (MSE) distance of the estimated PDF's. In order to extract single-pixel-wide edges, a generic detection scheme cooperated with the non-maximum suppression is also proposed. This new method is applied to a variety of noisy images, and the performance is quantitatively evaluated with edge strength images. The experiments show that the proposed method provides a more effective and robust way of detecting edges in CT image compared with other existing methods.

Given a graph G = (V,E) together with a nonnegative integer requirement on vertices r:V Z+, the annotated edge dominating set problem is to find a minimum set M ⊆ E such that, each edge in E - M is adjacent to some edge in M, and M contains at least r(v) edges incident on each vertex v ∈ V. The annotated edge dominating set problem is a natural extension of the classical edge dominating set problem, in which the requirement on vertices is zero. The edge dominating set problem is an important graph problem and has been extensively studied. It is well known that the problem is NP-hard, even when the graph is restricted to a planar or bipartite graph with maximum degree 3. In this paper, we show that the annotated edge dominating set problem in graphs with maximum degree 3 can be solved in O*(1.2721n) time and polynomial space, where n is the number of vertices in the graph. We also show that there is an O*(2.2306k)-time polynomial-space algorithm to decide whether a graph with maximum degree 3 has an annotated edge dominating set of size k or not.

We outline a number of high temperature superconducting Josephson junction-based devices including superconducting quantum interference devices (SQUIDs) developed for a wide range of applications including geophysical exploration, magnetic anomaly detection, terahertz (THz) imaging and microwave communications. All these devices are based on our patented technology for fabricating YBCO step-edge junction on MgO substrates. A key feature to the successful application of devices based on this technology is good stability, long term reliability, low noise and inherent flexibility of locating junctions anywhere on a substrate.

Given a tree T with edge lengths and edge weights, and a value B, the length-constrained heaviest path problem is to find a path in T with maximum path weight whose path length is at most B. We present a linear time algorithm for the problem when the edge lengths are uniform, i.e., all one. This algorithm with slight modification can be used to find the heaviest path of length exactly B in T in linear time.

There has recently been much research on content-based image retrieval (CBIR) that uses image features including color, shape, and texture. In CBIR, feature extraction is important because the retrieval result depends on the image feature. Query-by-sketch image retrieval is one of CBIR and query-by-sketch image retrieval is efficient because users simply have to draw a sketch to retrieve the desired images. In this type of retrieval, selecting the optimum feature extraction method is important because the retrieval result depends on the image feature. We have developed a query-by-sketch image retrieval method that uses an edge relation histogram (ERH) as a global and local feature intended for binary line images. This histogram is based on the patterns of distribution of other line pixels centered on each line pixel that have been obtained by global and local processing. ERH, which is a shift- and scale-invariant feature, focuses on the relation among the edge pixels. It is fairly simple to describe rotation- and symmetry-invariant features, and query-by-sketch image retrieval using ERH makes it possible to perform retrievals that are not affected by position, size, rotation, or mirroring. We applied the proposed method to 20,000 images in the Corel Photo Gallery. Experimental results showed that it was an effective means of retrieving images.

Precoding is an excellent choice for complementing the MIMO systems. Linear precoding techniques offer better performance at low signal-to-noise ratios (SNRs) while non-linear techniques perform better at higher SNRs. In addition, the non-linear techniques can achieve near optimal capacity at the expense of reasonable levels of complexity. However, precoding depends on the knowledge of the wireless channel. Recent work on MIMO systems have shown that channel-knowledge at the transmitter, in either full or partial forms, can increase the channel capacity and system performance considerably. Therefore, hybrid techniques should be deployed in order to obtain a better trade-off in terms of complexity and performance. In this paper, we present a hybrid precoding technique which deals with the condition of partial channel-knowledge while offering robustness against the effects of correlation and poorly scattered channels while at the same time keeping low levels of complexity and high performance.

Recent idea visualization programs still lack automatic idea summarization capabilities. This paper presents a knowledge-based method for automatically providing a short piece of English text about a topic to each idea group in idea charts. This automatic topic identification makes used Yet Another General Ontology (YAGO) and Wordnet as its knowledge bases. We propose a novel topic selection method and we compared its performance with three existing methods using two experimental datasets constructed using two idea visualization programs, i.e., the KJ Method (Kawakita Jiro Method) and mind-mapping programs. Our proposed topic identification method outperformed the baseline method in terms of both performance and consistency.

We deal with the scattering of a scalar plane wave by a half plane with a sinusoidally deformed edge from a straight edge by a physical optics approximation. The normal incidence of a plane wave to an edge is assumed. A contribution of an edge to the field integral is asymptotically evaluated and the basic properties of the scattering caused by the edge deformation is clarified. The scattering pattern has peaks at specific scattering angles, which agree with diffraction angles calculated by the well-known grating formula for normal incidence. Some numerical examples are shown and it is shown that the results are in good agreement with the results obtained by the GTD method for low angle incidence.

Due to universal frequency reuse, cell edge users in HSDPA suffer from serious inter-cell interference (ICI). In this letter we present a coordinated scheme for HSDPA which can mitigate ICI by interference avoidance in spatial domain. A system level simulation shows that our scheme can effectively improve the performance of the cell edge users.

In H.264/AVC intra frame coding, the rate-distortion optimization (RDO) is employed to select the optimal coding mode to achieve the minimum rate-distortion cost. Due to a large number of combinations of coding modes, the computational burden of Rate distortion optimization (RDO) becomes extremely high in intra prediction. In this paper, we proposed an efficient selective intra block size decision (SIB) that selects the appropriate block size for intra prediction, further proposed fast intra prediction algorithm reduces a number of modes required for RDO that significantly reduces the encoder complexity. Experimental results show that the proposed fast mode decision algorithm reduces the encoding time by up to 68% with negligible video quality degradation.

The IEEE has recently released IEEE 802.15.5 standard [3] to provide multi-hop mesh functions for low-rate wireless personal area networks (WPANs). In this paper, we extensively describe a link-layer reliable broadcast protocol referred to as timer-based reliable broadcast (TRB) [3] in the IEEE 802.15.5 standard. The TRB scheme exploits (1) bitmap based implicit ACK to effectively reduce the unnecessary error control messages and (2) randomized timer for ACK transmission to substantially reduce the possibility of contentions. Performance evaluation shows that the TRB scheme achieves 100% reliability compared with other schemes with expense of slightly increased energy consumption.

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 resetting attacks against a proof system, a prover or a verifier is reset and enforced to use the same random tape on various inputs as many times as an adversary may want. Recent deployment of cloud computing gives these attacks a new importance. This paper shows that argument systems for any NP language that are both resettably-sound and resettable zero-knowledge are possible by a constant-round protocol in the BPK model. For that sake, we define and construct a resettably-extractable conditional commitment scheme.

This letter investigates the training convergence in range-based cooperative positioning with stochastic positional knowledge. Firstly, a closed-form of squared position-error bound (SPEB) is derived with error-free ranging. Using the derived closed-form, it is proved that the SPEB reaches its minimum when at least 2 out of N (> 2) agents send training sequences. Finally, numerical results are provided to elaborate the theoretical analysis with zero-mean Gaussian ranging errors.

In this letter, we introduce a knowledge reuse method to improve the performance of a learning algorithm developed to prevent interference in multi-car elevators. This method enables the algorithm to use its previously acquired experience in new learning processes. The simulation results confirm the improvement achieved in the algorithm's performance.

Zero-knowledge arguments allows one party to prove that a statement is true, without leaking any other information than the truth of the statement. In many applications such as verifiable shuffle (as a practical application) and circuit satisfiability (as a theoretical application), zero-knowledge arguments for mathematical statements related to linear algebra are essentially used. Groth proposed (at CRYPTO 2009) an elegant methodology for zero-knowledge arguments for linear algebraic relations over finite fields. He obtained zero-knowledge arguments of the sub-linear size for linear algebra using reductions from linear algebraic relations to equations of the form z=x*'y, where x, y ∈ Fnp are committed vectors, z ∈ Fp is a committed element, and *': FnpFnpFp is a bilinear map. These reductions impose additional rounds on zero-knowledge arguments of the sub-linear size. The round complexity of interactive zero-knowledge arguments is an important measure along with communication and computational complexities. We focus on minimizing the round complexity of sub-linear zero-knowledge arguments for linear algebra. To reduce round complexity, we propose a general transformation from a t-round zero-knowledge argument, satisfying mild conditions, to a (t-2)-round zero-knowledge argument; this transformation is of independent interest.

A Fourier domain optical coherence tomography system for obtaining a two-dimensional image is constructed. Imaging characteristics of the OCT system in a transverse direction are experimentally investigated. Angle dependence of reflection intensity from a smooth surface is clearly observed and analyzed with consideration of spatial mode coupling to a fiber.