In this study, 2-location distance-based registration (2DBR) is proposed to improve the performance of traditional distance-based registration. In distance-based registration, when a mobile station (MS) enters a new cell, the MS calculates the distance from the last registered cell and registers its location if the calculated distance reaches a prescribed distance threshold D. In 2DBR, an MS stores not only the last registered location area (LA) but also the second-to-last LA, and then no registration is performed when the MS crosses the two stored LAs. The 2DBR may increase paging cost but it may decrease registration cost. Simulation results show that our proposed 2DBR outperforms current distance-based registration in most cases.

We demonstrated the reduced surface roughness of poly (3-hexylthiophene) (P3HT):(6,6)-phenyl-C61-butyric acid methyl ester (PCBM) thin films with different ratios fabricated by the electrospray deposition (ESD) method. Aggregated structures were observed at the lower voltage, and the uniformity became bad at the higher voltage. Anyway, the minimum root mean square (RMS) roughness was 1.46 nm by optimizing the applied voltage.

A two-dimensional microarray of ten thousand (100100) chondrocyte-spheroids was successfully constructed with a 100-µm spacing on a micropatterned gold electrodes that were coated with poly(ethylene glycol) (PEG) hydrogels. The PEGylated surface as a cytophobic region was regulated by controlling the gel structure through photolithography. In this way, a PEG hydrogel was modulated enough to inhibit outgrowth of chondrocytes from cell adhering region in the horizontal direction. These structural control of PEG hydrogel was critical for inducing formation of three-dimensional chondrocyte condensations (spheroids) within 24 hours. We report noninvasive monitoring of the cellular functional change at the cell membrane using a chondrocyte-based field effect transistor (FET), which is based on detection of extracellular potential change induced as a result of the interaction between extracellular matrix (ECM) protein secreted from spheroid and substrate at the cell membrane. The interface potential change at the cell membrane/gate insulator interface can be monitored during the uptake of substrate without any labeling materials. Our findings on the time course of the interface potential would provide important information to understand the uptake kinetics for cellular differentiation.

This study investigated the relationship between social skills and facial asymmetry in facial expressions. Three-dimensional facial landmark data of facial expressions (neutral, happy, and angry) were obtained from Japanese participants (n = 62). Following a facial expression task, each participant completed KiSS-18 (Kikuchi's Scale of Social Skills; Kikuchi, 2007). Using a generalized Procrustes analysis, faces and their mirror-reversed versions were represented as points on a hyperplane. The asymmetry of each individual face was defined as Euclidian distance between the face and its mirror reversed face on this plane. Subtraction of the asymmetry level of a neutral face of each individual from the asymmetry level of a target emotion face was defined as the index of “expression asymmetry” given by a particular emotion. Correlation coefficients of KiSS-18 scores and expression asymmetry scores were computed for both happy and angry expressions. Significant negative correlations between KiSS-18 scores and expression asymmetries were found for both expressions. Results indicate that the symmetry in facial expressions increases with higher level of social skills.

We show a phase transition of the first eigenvalue of random (c,d)-regular graphs, whose instance of them consists of one vertex with degree c and the other vertices with degree d for c > d. We investigate a reduction from the first eigenvalue analysis of a general (c,d)-regular graph to that of a tree, and prove that, for any fixed c and d, and for a graph G chosen from the set of all (c,d)-regular graphs with n vertices uniformly at random, the first eigenvalue of G is approximately with high probability.

Subgraph matching is a fundamental operation for querying graph-structured data. Due to potential errors and noises in real-world graph data, exact subgraph matching is sometimes inappropriate in practice. In this paper we consider an approximate subgraph matching model that allows missing edges. Based on this model, approximate subgraph matching finds all occurrences of a given query graph in a database graph, allowing missing edges. A straightforward approach is to first generate query subgraphs of a given query graph by deleting edges and then perform exact subgraph matching for each query subgraph. In this paper we propose a sharing-based approach to approximate subgraph matching, called SASUM. Our method is based on the fact that query subgraphs are highly overlapped. Due to this overlapping nature of query subgraphs, the matches of a query subgraph can be computed from the matches of a smaller query subgraph, which results in reducing the number of query subgraphs that require expensive exact subgraph matching. Our method uses a lattice framework to identify sharing opportunities between query subgraphs. To further reduce the number of graphs that need exact subgraph matching, SASUM generates small base graphs that are shared by query subgraphs and chooses the minimum number of base graphs whose matches are used to derive the matching results of all query subgraphs. A comprehensive set of experiments shows that our approach outperforms the state-of-the-art approach by orders of magnitude in terms of query execution time.

For simply-typed term rewriting systems (STRSs) and higher-order rewrite systems (HRSs) a la Nipkow, we proposed a method for proving termination, namely the static dependency pair method. The method combines the dependency pair method introduced for first-order rewrite systems with the notion of strong computability introduced for typed λ-calculi. This method analyzes a static recursive structure based on definition dependency. By solving suitable constraints generated by the analysis, we can prove termination. In this paper, we extend the method to rewriting systems for functional programs (RFPs) with product, algebraic data, and ML-polymorphic types. Although the type system in STRSs contains only product and simple types and the type system in HRSs contains only simple types, our RFPs allow product types, type constructors (algebraic data types), and type variables (ML-polymorphic types). Hence, our RFPs are more representative of existing functional programs than STRSs and HRSs. Therefore, our result makes a large contribution to applying theoretical rewriting techniques to actual problems, that is, to proving the termination of existing functional programs.

We study the computational complexity of the following two-player game. The instance is a graph G = (V,E), an initial vertex s ∈ V, and a target set T ⊆ V. A “cat” is initially placed on s. Player 1 chooses a vertex in the graph and removes it and its incident edges from the graph. Player 2 moves the cat from the current vertex to one of the adjacent vertices. Players 1 and 2 alternate removing a vertex and moving the cat, respectively. The game continues until either the cat reaches a vertex of T or the cat cannot be moved. Player 1 wins if and only if the cat cannot be moved before it reaches a vertex of T. It is shown that deciding whether player 1 has a forced win on the game on G is PSPACE-complete.

A simple and efficient reduced-reference video quality assessment method based on the activity-difference of DCT coefficients is proposed. The method provides better accuracy, monotonicity, and consistent predictions than the PSNR full-reference metric and comparable results with the full-reference SSIM. It also shows an improved performance to a similar VQ technique based on the calculation of the pixel luminance differences performed in the spatial-domain.

Due to security demand of society development, real-time face recognition has been receiving more and more attention nowadays. In this paper, a real-time face recognition system via Local Binary Pattern (LBP) plus Improved Biomimetic Pattern Recognition (BPR) has been proposed. This system comprises three main steps: real-time color face detection process, feature extraction process and recognition process. Firstly, a color face detector is proposed to detect face with eye alignment and simultaneous performance; while in feature extraction step, LBP method is adopted to eliminate the negative effect of the light heterogeneity. Finally, an improved BPR method with Selective Sampling construction is applied to the recognition system. Experiments on our established database named WYU Database, PUT Database and AR Database show that this real-time face recognition system can work with high efficiency and has achieved comparable performance with the state-of-the-art systems.

The size and complexity of software in computer systems and even in consumer electronics is drastically and continuously increasing, thus increasing the compilation time. For example, the compilation time for building some of mobile phones' platform software takes several hours. In order to reduce the compilation time, this paper proposes a Distributed Scalable Compilation Tool, called DiSCo where full compilation passes such as preprocessing, compilation, and even linking are performed at remote machines, i.e. in parallel. To the best of our knowledge DiSCo is the first distributed compiler to support complete distributed processing in all the compilation passes. We use an extensive dependency analysis in parsing compilation commands for exploiting higher command-level parallelism, and we apply a file caching method and a network-drive protocol for reducing the remote compilation overhead and simplifying the implementation. Lastly, we minimize load imbalance and remote machine management overhead with our heuristic static scheduling method by predicting compilation time and considering the overheads invoked by the compilation process. Our evaluation using four large mobile applications and eight GNU applications shows that the performance of DiSCo is scalable and the performance is close to a profile scheduling.

Estimating a proper location of vanishing point from a single road image without any prior known camera parameters is a challenging problem due to limited information from the input image. Most edge-based methods for vanishing point detection only work well for structured roads with clear painted lines or distinct boundaries, while they usually fail in unstructured roads lacking sharply defined, smoothly curving edges. In order to overcome this limitation, texture-based methods for vanishing point detection have been widely published. Authors of these methods often calculate the texture orientation at every pixel of the road image by using directional filter banks such as Gabor wavelet filter, and seek the vanishing point by a voting scheme. A local adaptive soft voting method for obtaining the vanishing point was proposed in a previous study. Although this method is more effective and faster than prior texture-based methods, the associated computational cost is still high due to a large number of scanning pixels. On the other hand, this method leads to an estimation error in some images, in which the radius of the proposed half-disk voting region is not large enough. The goal of this paper is to reduce the computational cost and improve the performance of the algorithm. Therefore, we propose a novel local soft voting method, in which the number of scanning pixels is much reduced, and a new vanishing point candidate region is introduced to improve the estimation accuracy. The proposed method has been implemented and tested on 1000 road images which contain large variations in color, texture, lighting condition and surrounding environment. The experimental results demonstrate that this new voting method is both efficient and effective in detecting the vanishing point from a single road image and requires much less computational cost when compared to the previous voting method.

In this study, a novel forest method based on specific random trees (SRT) was proposed for a multiclass classification problem. The proposed SRT was built on one specific class, which decides whether a sample belongs to a certain class. The forest can make a final decision on classification by ensembling all the specific trees. Compared with the original random forest, our method has higher strength, but lower correlation and upper error bound. The experimental results based on 10 different public datasets demonstrated the efficiency of the proposed method.

With the increased deployment of wireless sensor networks (WSNs) in location-based services, the need for accurate localization of sensor nodes is gaining importance. Sensor nodes in a WSN localize themselves with the help of anchors that know their own positions. Some anchors may be malicious and provide incorrect information to the sensor nodes. In this case, accurate localization of a sensor node may be severely affected. In this study, we propose a secure and lightweight localization method. In the proposed method, uncertainties in the estimated distance between the anchors and a sensor node are taken into account to improve localization accuracy. That is, we minimize the weighted summation of the residual squares. Simulation results show that our method is very effective for accurate localization of sensor nodes. The proposed method can accurately localize a sensor node in the presence of malicious anchors and it is computationally efficient.

This letter presents the ideas and methods of the winning solution* for the Kaggle Algorithmic Trading Challenge. This analysis challenge took place between 11th November 2011 and 8th January 2012, and 264 competitors submitted solutions. The objective of this competition was to develop empirical predictive models to explain stock market prices following a liquidity shock. The winning system builds upon the optimal composition of several models and a feature extraction and selection strategy. We used Random Forest as a modeling technique to train all sub-models as a function of an optimal feature set. The modeling approach can cope with highly complex data having low Maximal Information Coefficients between the dependent variable and the feature set and provides a feature ranking metric which we used in our feature selection algorithm.

In this letter, we propose a novel search approach to blur kernel estimation for defocused image restoration. An adaptive binary search on consensus is the main contribution of our research. It is based on binary search and random sample consensus set (RANSAC). Moreover an evaluating function which uses a histogram of gradient distribution is proposed for assessing restored images. Simulations on an image benchmark dataset shows that the proposed algorithm can estimate, on average, the blur kernels 15.14% more accurately than other defocused image restoration algorithms.

This paper presents a novel scale-rotation invariant generative model (SRIGM) and a kernel sparse representation classification (KSRC) method for scene categorization. Recently the sparse representation classification (SRC) methods have been highly successful in a number of image processing tasks. Despite its popularity, the SRC framework lucks the abilities to handle multi-class data with high inter-class similarity or high intra-class variation. The kernel random coordinate descent (KRCD) algorithm is proposed for 1 minimization in the kernel space under the KSRC framework. It allows the proposed method to obtain satisfactory classification accuracy when inter-class similarity is high. The training samples are partitioned in multiple scales and rotated in different resolutions to create a generative model that is invariant to scale and rotation changes. This model enables the KSRC framework to overcome the high intra-class variation problem for scene categorization. The experimental results show the proposed method obtains more stable performances than other existing state-of-art scene categorization methods.

We propose a jitter amplifier architecture for an oscillator-based true random number generator (TRNG). Two types of latency-controllable (LC) buffer, which are the key components of the proposed jitter amplifier, are presented. We derive an equation to estimate the gain of the jitter amplifier, and analyze sufficient conditions for the proposed circuit to work properly. The proposed jitter amplifier was fabricated with a 65 nm CMOS process. The jitter amplifier with the two-voltage LC buffer occupied 3,300 µm2 and attained 8.4x gain, and that with the single-voltage LC buffer achieved 2.2x gain with an 1,700 µm2 area. The jitter amplification of the sampling clock increased the entropy of a bit stream and improved the results of the NIST test suite so that all the tests passed whereas TRNGs with simple correctors failed. The jitter amplifier attained higher throughput per area than a frequency divider when the required amount of jitter was more than two times larger than the inherent jitter in our test-chip implementations.

A scalable pattern discovery by compression is proposed. A string is representable by a context-free grammar deriving the string deterministically. In this framework of grammar-based compression, the aim of the algorithm is to output as small a grammar as possible. Beyond that, the optimization problem is approximately solvable. In such approximation algorithms, the compressor based on edit-sensitive parsing (ESP) is especially suitable for detecting maximal common substrings as well as long frequent substrings. Based on ESP, we design a linear time algorithm to find all frequent patterns in a string approximately and prove several lower bounds to guarantee the length of extracted patterns. We also examine the performance of our algorithm by experiments in biological sequences and other compressible real world texts. Compared to other practical algorithms, our algorithm is faster and more scalable with large and repetitive strings.

Many broadcast encryption schemes have been proposed for conventional networks. However, those schemes are not suitable for wireless sensor networks, which have very limited resources such as communication, computation, and storage. In this paper, we propose an efficient and practical identity-based broadcast encryption scheme for sensor networks by exploiting the characteristics of sensor networks: in the deployment stage, the set of neighboring sensor nodes are determined and most communications are conducted among the neighbors due to radio power limitations of the nodes. The proposed scheme features the following achievements: (1) all of the public keys and private keys are of constant size; (2) it satisfies all the security requirements for sensor networks. The proposed scheme is optimal in the sense that it requires no pairing operation when adopting pre-computation.