This paper describes Path-Moose, a scalable tree-based shortest path bridging protocol. Both ARP-Path and Path-Moose protocols belong to a new category of bridges that we name All-path, because all paths of the network are explored simultaneously with a broadcast frame distributed over all network links to find a path or set a multicast tree. Path-Moose employs the ARP-based low latency routing mechanism of the ARP-Path protocol on a bridge basis instead of a per-single-host basis. This increases scalability by reducing forwarding table entries at core bridges by a factor of fifteen times for big data center networks and achieves a faster reconfiguration by an approximate factor of ten. Reconfiguration time is significantly shorter than ARP-Path (zero in many cases) because, due to the sharing of network paths by the hosts connected to same edge bridges, when a host needs the path it has already been recovered by another user of the path. Evaluation through simulations shows protocol correctness and confirms the theoretical evaluation results.

In recent years, technologies and markets related to data centers have been rapidly changing and growing. Data centers are playing an important role in ICT infrastructure deployment and promise to become common platforms for almost all social infrastructures. Even though research has focused on networking technologies, various technologies are needed to develop high-performance, cost-efficient, and flexible large-scale data centers. To understand those technologies better, this paper surveys recent research and development efforts and results in accordance with a data center network taxonomy that the authors defined.

This paper presents a new technique to smooth speech feature vectors for text-independent speaker verification using an adaptive band-pass IIR filer. The filter is designed by considering the probability density of modulation-frequency components of an M-dimensional feature vector. Each dimension of the feature vector is processed and filtered separately. Initial filter parameters, low-cut-off and high-cut-off frequencies, are first determined by the global mean of the probability densities computed from all feature vectors of a given speech utterance. Then, the cut-off frequencies are adapted over time, i.e. every frame vector, in both low-frequency and high-frequency bands based also on the global mean and the standard deviation of feature vectors. The filtered feature vectors are used in a SVM-GMM Supervector speaker verification system. The NIST Speaker Recognition Evaluation 2006 (SRE06) core-test is used in evaluation. Experimental results show that the proposed technique clearly outperforms a baseline system using a conventional RelAtive SpecTrA (RASTA) filter.

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.

In our previous work [2], we proposed a new concept of utility functions for rate control in communication networks. Unlike conventional utility-based rate control in which the utility function of each user is defined as a function of its transmitting data rate, in [2], we defined the utility function of each user as a function of not only its transmitting data rate but also it receiving data rate. The former is called a session-level utility function and the latter is called a user-level utility function. The user-level utility function reflects the satisfaction with the service of a user with two-way communication, which consists of transmitting and receiving sessions, better than the session-level utility function, since user's satisfaction depends on not only the satisfaction with its transmitting session but also that for its receiving session. In [2], an algorithm that required each user to know the exact utility function of its correspondent was developed. However, in some cases, this information might not be available due to some reasons such as security and privacy issues, and in such cases, the algorithm developed in [2] cannot be used. Hence, in this paper, we develop a new distributed algorithm that does not require each user to know the utility function of its correspondent. Numerical results show that our new algorithm, which does not require the utility information of the correspondent, converges to the same solution to that with the algorithm that requires the utility information of the correspondent.

We humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation.

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.

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.

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.

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 direction-of-arrival estimation (DoA) scheme that uses a uniform circular array (UCA) is proposed for near-field sources, where multiple pairs-of-subarrays exist with central symmetry. First, multiple generalized ESPRIT (G-ESPRIT) spectrums are obtained by applying the conventional G-ESPRIT algorithm to each of multiple pairs-of-subarrays. Second, a parallel spectrum is found by adding up the reciprocals of these G-ESPRIT spectrums and taking the reciprocal of the total. The locations of peaks in the parallel spectrum give the DoAs being estimated. When a DoA approaches the translation direction of two subarrays, the conventional G-ESPRIT spectrum is broken by a false peak. Since the translation directions of pairs-of-subarrays are different from each other, the false peak, due to the DoA approaching one of translation directions, does not exist simultaneously in all G-ESPRIT spectrums. The parallel concatenation of the spectrums suppresses the false peak and enhances the true DoA peaks. Simulation shows that the proposed scheme reduces the root mean square error of the DoA estimation, compared with the conventional G-ESPRIT algorithm.

We propose a novel technique for the estimation of device-parameters suitable for postfabrication performance compensation and adaptive delay testing, which are effective means to improve the yield and reliability of LSIs. The proposed technique is based on Bayes' theorem, in which the device-parameters of a chip, such as the threshold voltage of transistors, are estimated by current signatures obtained in a regular IDDQ testing framework. Neither additional circuit implementation nor additional measurement is required for the purpose of parameter estimation. Numerical experiments demonstrate that the proposed technique can achieve 10-mV accuracy in threshold voltage estimations.

We propose a practical local and global path-planning algorithm for an autonomous vehicle or a car-like robot in an unknown semi-structured (or unstructured) environment, where obstacles are detected online by the vehicle's sensors. The algorithm utilizes a probabilistic method based on particle filters to estimate the dynamic obstacles' locations, a support vector machine to provide the critical points and Bezier curves to smooth the generated path. The generated path safely travels through various static and moving obstacles and satisfies the vehicle's movement constraints. The algorithm is implemented and verified on simulation software. Simulation results demonstrate the effectiveness of the proposed method in complicated scenarios that posit the existence of multi moving objects.

Object-oriented languages have recently become common, making register indirect jumps more important than ever. In object-oriented languages, virtual functions are heavily used because they improve programming productivity greatly. Virtual function calls usually consist of register indirect jumps, and consequently, programs written in object-oriented languages contain many register indirect jumps. The prediction of the targets of register indirect jumps is more difficult than the prediction of the direction of conditional branches. Many predictors have been proposed for register indirect jumps, but they cannot predict the jump targets with high accuracy or require very complex hardware. We propose a method that resolves jump targets by forwarding execution results. Our proposal dynamically finds the producers of register indirect jumps in virtual function calls. After the execution of the producers, the execution results are forwarded to the processor's front-end. The jump targets can be resolved by the forwarded execution results without requiring prediction. Our proposal improves the performance of programs that include unpredictable register indirect jumps, because it does not rely on prediction but instead uses actual execution results. Our evaluation shows that the IPC improvement using our proposal is as high as 5.4% on average and 9.8% at maximum.

The recent increase in mobile data traffic has resulted in service quality problems. Although an economic approach to control congestion can be achieved by pricing, the current pricing schedule of mobile data services instead causes smartphone users to create more traffic. We establish a pricing model based on the distribution of demand types among heterogeneous users to improve the current tariff structure; our method mixes usage-based and fixed-fee pricing schemes. The results derived from the application of this model to survey data on willingness-to-pay for mobile data service demonstrate that the provider can decrease the amount of data traffic and increase the expected revenue by lowering the price for a unit of data and raising the fixed-fee level for unlimited service. The model also explains the changing weight of usage-based and fixed-fee pricing schemes by considering shifts in the type distribution through service evolution and proposes pricing strategies for future communications services.

The quantum dot optical frequency comb laser (QD-CML) is an attractive photonic device for generating a stable emission of fine multiple-wavelength peaks. In the present paper, 1.0-GHz and 10-ps-order short optical pulsation is successfully demonstrated from a hybrid mode-locked QD-CML with an ultrabroadband wavelength tuning range in the T+O band. In addition, 10-GHz high-repetition intensity-stable short optical pulse generation with a high S/N ratio is successfully demonstrated using an external-cavity QD-CML with a 10th-harmonic mode-locking technique.