This study examines the dialog-based language learning game (DB-LLG) realized in a 3D environment built with game contents. We designed the DB-LLG to communicate with users who can conduct interactive conversations with game characters in various immersive environments. From the pilot test, we found that several technologies were identified as essential in the construction of the DB-LLG such as dialog management, hint generation, and grammar error detection and feedback. We describe the technical details of our system POSTECH immersive English study (Pomy). We evaluated the performance of each technology using a simulator and field tests with users.

Break arcs are generated in a DC48V and 12A resistive circuit. Silver electrical contacts are separated at constant opening speed. The cathode contact surface is irradiated by a blue LED. The center wavelength of the emission of the LED is 470nm. There is no spectral line of the light emitted from the break arcs. Only the images of contact surface are observed by a high-speed camera and an optical band pass filter. Another high-speed camera observes only the images of the break arc. Time evolutions of the cathode surface morphology being eroded by the break arcs and the motion of the break arcs are observed with these cameras, simultaneously. The images of the cathode surface are investigated by the image analysis technique. The results show that the moments when the expanded regions on the cathode surface are formed during the occurrence of the break arcs. In addition, it is shown that the expanded regions are not contacted directly to the cathode roots of the break arcs.

Parametric resonance based solutions for sub-gigahertz radio frequency transceiver with 0.3V supply voltage are proposed in this paper. As an implementation example, a 0.3V 720µW variation-tolerant injection-locked frequency multiplier is developed in 90nm CMOS. It features a parametric resonance based multi-phase synthesis scheme, thereby achieving the lowest supply voltage with -110dBc@ 600kHz phase noise and 873MHz-1.008GHz locking range in state-of-the-art frequency synthesizers.

Computer systems with GPUs are expected to become a strong methodology for high-speed encryption processing. Moreover, power consumption has remained a primary deterrent for such processing on devices of all sizes. However, GPU vendors are currently announcing their future roadmaps of GPU architecture development: Nvidia Corp. promotes the Kepler architecture and AMD Corp. emphasizes the GCN architecture. Therefore, we evaluated throughput and power efficiency of three 128-bit block ciphers on GPUs with recent Nvidia Kepler and AMD GCN architectures. From our experiments, whereas the throughput and per-watt throughput of AES-128 on Radeon HD 7970 (2048 cores) with GCN architecture are 205.0Gbps and 1.3Gbps/Watt respectively, those on Geforce GTX 680 (1536 cores) with Kepler architecture are, respectively, 63.9Gbps and 0.43Gbps/W; an approximately 3.2 times throughput difference occurs between AES-128 on the two GPUs. Next, we investigate the reasons for the throughput difference using our micro-benchmark suites. According to the results, we speculate that to ameliorate Kepler GPUs as co-processor of block ciphers, the arithmetic and logical instructions must be improved in terms of software and hardware.

Yield enhancements and quality improvements must be considered as factors of the utmost importance in VLSI (Very Large Scale Integration circuits) manufacturing in order to reduce cost and ensure customer satisfaction. This paper will present a study of the yield theory, an analysis of actual manufacturing data, and the challenges of yield enhancement.

In this paper, a novel White-RGB (WRGB) color filter array-based imaging system for cell phone is presented to reduce noise and reproduce color in low illumination. The core process is based on adaptive diagonal color separation to recover color components from a white signal using diagonal reference blocks and location-based color ratio estimation in the luminance space. The experiments, which are compared with the RGB and state-of-the-art WRGB approaches, show that our imaging system performs well for various spatial frequency images and color restoration in low-light environments.

Dynamic spectrum access is the key approach in cognitive wireless regional area networks, and it is adopted by secondary users to access the licensed radio spectrum opportunistically. In order to realize real-time secondary spectrum usage, a dynamic spectrum access model based on stochastic differential games is proposed to realize dynamic spectrum allocation; a Nash equilibrium solution to the model is given and analyzed in this paper. From an overall perspective, the relationships between available spectrum percentage and the spectrum access rate are studied. Changes in the available spectrum percentage of the cognitive wireless regional area networks involve a deterministic component and a stochastic component which depends upon an r-dimensional Wiener process. The Wiener process represents an accumulation of random influences over the interval, and it reflects stochastic and time-varying properties of the available spectrum percentage. Simulation results show that the dynamic spectrum access model is efficient, and it reflects the time-varying radio frequency environment. Differential games are useful tools for the spectrum access and management in the time-varying radio environment.

Recently, the wavelet-based estimation method has gradually been becoming popular as a new tool for software reliability assessment. The wavelet transform possesses both spatial and temporal resolution which makes the wavelet-based estimation method powerful in extracting necessary information from observed software fault data, in global and local points of view at the same time. This enables us to estimate the software reliability measures in higher accuracy. However, in the existing works, only the point estimation of the wavelet-based approach was focused, where the underlying stochastic process to describe the software-fault detection phenomena was modeled by a non-homogeneous Poisson process. In this paper, we propose an interval estimation method for the wavelet-based approach, aiming at taking account of uncertainty which was left out of consideration in point estimation. More specifically, we employ the simulation-based bootstrap method, and derive the confidence intervals of software reliability measures such as the software intensity function and the expected cumulative number of software faults. To this end, we extend the well-known thinning algorithm for the purpose of generating multiple sample data from one set of software-fault count data. The results of numerical analysis with real software fault data make it clear that, our proposal is a decision support method which enables the practitioners to do flexible decision making in software development project management.

The large and complicated safety-critical systems today need to keep changing to accommodate ever-changing objectives and environments. Accordingly, runtime analysis for safe reconfiguration or evaluation is currently a hot topic in the field, whereas information acquisition of external environment is crucial for runtime safety analysis. With the rapid development of web services, mobile networks and ubiquitous computing, abundant realtime information of environment is available on the Internet. To integrate these public information into runtime safety analysis of critical systems, this paper brings forward a framework, which could be implemented with open source and cross platform modules and encouragingly, applicable to various safety-critical systems.

In this paper, we present a high frame rate (HFR) vision system that can automatically control its exposure time by executing brightness histogram-based image processing in real time at a high frame rate. Our aim is to obtain high-quality HFR images for robust image processing of high-speed phenomena even under dynamically changing illumination, such as lamps flickering at 100 Hz, corresponding to an AC power supply at 50 / 60 Hz. Our vision system can simultaneously calculate a 256-bin brightness histogram for an 8-bit gray image of 512×512 pixels at 2000 fps by implementing a brightness histogram calculation circuit module as parallel hardware logic on an FPGA-based high-speed vision platform. Based on the HFR brightness histogram calculation, our method realizes automatic exposure (AE) control of 512×512 images at 2000 fps using our proposed AE algorithm. The proposed AE algorithm can maximize the number of pixels in the effective range of the brightness histogram, thus excluding much darker and brighter pixels, to improve the dynamic range of the captured image without over- and under-exposure. The effectiveness of our HFR system with AE control is evaluated according to experimental results for several scenes with illumination flickering at 100 Hz, which is too fast for the human eye to see.

We propose a parameterized multisurface fitting method for multi-frame super-resolution (SR) processing. A parameter assumed for the unknown high-resolution (HR) pixel is used for multisurface fitting. Each surface fitted at each low-resolution (LR) pixel is an expression of the parameter. Final SR result is obtained by fusing the sampling values from these surfaces in the maximum a posteriori fashion. Experimental results demonstrate the superiority of the proposed method.

We consider Monte Carlo tree search problem, a variant of Min-Max tree search problem where the score of each leaf is the expectation of some Bernoulli variables and not explicitly given but can be estimated through (random) playouts. The goal of this problem is, given a game tree and an oracle that returns an outcome of a playout, to find a child node of the root which attains an approximate min-max score. This problem arises in two player games such as computer Go. We propose a simple and efficient algorithm for Monte Carlo tree search problem.

Due to the recent progress made in camera and network environments, on-line video services enable people around the world to watch or share high-quality HD videos that can record a wider angle without losing objects' details in each image. As a result, users of these services can watch videos in different ways with different ROIs (Regions of Interest), especially when there are multiple objects in a scene, and thus there are few common ways for them to transfer their impressions for each scene directly. Posting messages is currently the usual way but it does not sufficiently enable all users to transfer their impressions. To transfer a user's impressions directly and provide users with a richer video watching experience, we propose a system that enables them to extract their favorite parts of videos as ROI trajectories through simple and intuitive manipulation of their tablet device. It also enables them to share a recorded trajectory with others after stabilizing it in a manner that should be satisfactory to every user. Using statistical analysis of user manipulations, we have demonstrated an approach to trajectory stabilization that can eliminate undesirable or uncomfortable elements due to tablet-specific manipulations. The system's validity has been confirmed by subjective evaluations.

In this paper, we present a new four parameter estimator of sampled sinusoidal signals that does not require iteration. Mathematically, the four parameters (frequency, phase, magnitude, and dc offset) of sinusoidal signals can be obtained when four data points are given. In general, the parameters have to be calculated with iteration since the equations are nonlinear. In this paper, we point out that the four parameters can be obtained analytically if the four data points given are measured using a fixed sampling interval. Analytical expressions for the four parameters are derived using the signal differences. Based on this analysis, we suggest an algorithm of estimating the four parameters from N data samples corrupted by noise without iteration. When comparing with the IEEE-1057 method which is based on the least-square method, the proposed algorithm does not require the initial guess of the parameters for iteration and avoid the convergence problem. Also, the number of required numerical operations for estimation is fixed if N is determined. As a result, the processing time of parameter estimation is much faster than the least-square method which has been confirmed by numerical simulations. Simulation results and the quantitative analysis show that the estimation error of the estimated parameters is less than 1.2 times the square root of the Cramer-Rao bounds when the signal to noise ratio is larger than 20dB.

Text Categorization (TC) is a task of classifying a set of documents into one or more predefined categories. Centroid-based method, a very popular TC method, aims to make classifiers simple and efficient by constructing one prototype vector for each class. It classifies a document into the class that owns the prototype vector nearest to the document. Many studies have been done on constructing prototype vectors. However, the basic philosophies of these methods are quite different from each other. It makes the comparison and selection of centroid-based TC methods very difficult. It also makes the further development of centroid-based TC methods more challenging. In this paper, based on the observation of its general procedure, the centroid-based text classification is treated as a kind of ranking task, and a unified framework for centroid-based TC methods is proposed. The goal of this unified framework is to classify a text via ranking all possible classes by document-class similarities. Prototype vectors are constructed based on various loss functions for ranking classes. Under this framework, three popular centroid-based methods: Rocchio, Hypothesis Margin Centroid and DragPushing are unified and their details are discussed. A novel centroid-based TC method called SLRCM that uses a smoothing ranking loss function is further proposed. Experiments conducted on several standard databases show that the proposed SLRCM method outperforms the compared centroid-based methods and reaches the same performance as the state-of-the-art TC methods.

Regularized forward selection is viewed as a method for obtaining a sparse representation in a nonparametric regression problem. In regularized forward selection, regression output is represented by a weighted sum of several significant basis functions that are selected from among a large number of candidates by using a greedy training procedure in terms of a regularized cost function and applying an appropriate model selection method. In this paper, we propose a model selection method in regularized forward selection. For the purpose, we focus on the reduction of a cost function, which is brought by appending a new basis function in a greedy training procedure. We first clarify a bias and variance decomposition of the cost reduction and then derive a probabilistic upper bound for the variance of the cost reduction under some conditions. The derived upper bound reflects an essential feature of the greedy training procedure; i.e., it selects a basis function which maximally reduces the cost function. We then propose a thresholding method for determining significant basis functions by applying the derived upper bound as a threshold level and effectively combining it with the leave-one-out cross validation method. Several numerical experiments show that generalization performance of the proposed method is comparable to that of the other methods while the number of basis functions selected by the proposed method is greatly smaller than by the other methods. We can therefore say that the proposed method is able to yield a sparse representation while keeping a relatively good generalization performance. Moreover, our method has an advantage that it is free from a selection of a regularization parameter.

Recently, there have been many studies on constructing cryptographic primitives that are secure even if some secret information leaks. In this paper, we consider the problem of constructing public-key encryption schemes that are resilient to leaking the randomness used in the encryption algorithm. In particular, we consider the case in which public-key encryption schemes are constructed from the KEM/DEM framework, and the leakage of randomness in the encryption algorithms of KEM and DEM occurs independently. For this purpose, we define a new security notion for KEM. Then we provide a generic construction of a public-key encryption scheme that is resilient to randomness leakage from any KEM scheme satisfying this security. Also we construct a KEM scheme that satisfies the security from hash proof systems.

In signal restoration problems, we expect to improve the restoration performance with a priori information about unknown target signals. In this paper, the parametric Wiener filter with linear constraints for unknown target signals is discussed. Since the parametric Wiener filter is usually defined as the minimizer of the criterion not for the unknown target signal but for the filter, it is difficult to impose constraints for the unknown target signal in the criterion. To overcome this difficulty, we introduce a criterion for the parametric Wiener filter defined for the unknown target signal whose minimizer is equivalent to the solution obtained by the original formulation. On the basis of the newly obtained criterion, we derive a closed-form solution for the parametric Wiener filter with linear constraints.

We first find simple characterizations of $rac{1}{N} mathbb{Z}$-invariance of arbitrary principal shift-invariant space $V(phi)$. Then we find several equivalent conditions for $V(phi)$ to admit periodic oversampling for a class of continuous frame generators $phi$. In particular, when $phi$ is band-limited and $hat{phi}$ is piecewise continuous, we find very simple and general sufficient conditions for $V(phi)$ to admit periodic oversampling, which involve the extra invariance of $V(phi)$, together with an illustrating example.

In this paper, we propose a method for deciding the parameters to satisfy the experiment values, and also checked the effectiveness of this method based on Kramers-Kronig (K.K.) relation. In our proposed method, we are expressed as matrix the Sellmeier's formula, and are solved the simulatenaous equation until the satisfied the experiment value. Numerical results are given for the influence of pulse responses using the medium constants which can be found by proposed method. Also, numerical technique of pulse responses is employed the fast inversion of Laplace transform (FILT).