This paper introduces a novel approach of feature description by integrating the intensity order and textures in different support regions into a compact vector. We first propose the Intensity Order Local Binary Pattern (IO-LBP) operator, which simultaneously encodes the gradient and texture information in the local neighborhood of a pixel. We divide each region of interest into segments according to the order of pixel intensities, build one histogram of IO-LBP patterns for each segment, and then concatenate all histograms to obtain a feature descriptor. Furthermore, multi support regions are adopted to enhance the distinctiveness. The proposed descriptor effectively describes a region at both local and global levels, and thus high performance is expected. Experimental results on the Oxford benchmark and images of cast shadows show that our approach is invariant to common photometric and geometric transformations, such as illumination change and image rotation, and robust to complex lighting effects caused by shadows. It achieves a comparable accuracy to that of state-of-art methods while performs considerably faster.

Light source estimation and virtual lighting must be believable in terms of appearance and correctness in augmented reality scenes. As a result of illumination complexity in an outdoor scene, realistic lighting for augmented reality is still a challenging problem. In this paper, we propose a framework based on an estimation of environmental lighting from well-defined objects, specifically human faces. The method is tuned for outdoor use, and the algorithm is further enhanced to illuminate virtual objects exposed to direct sunlight. Our model can be integrated into existing mobile augmented reality frameworks to enhance visual perception.

The next generation of information technology demands both high capacity and mobility for applications such as high speed wireless access capable of supporting broadband services. The transport of wireless and wireline signals is converging into a common telecommunication infrastructure. In this paper, we will present the Marie Curie Framework Program 7 project “Wireless and wireline service convergence in next generation optical access networks” (WISCON), which focuses on the conception and study of novel architectures for wavelength-division-multiplexing (WDM) optical multi-modulation format radio-over-fiber (RoF) systems; this is a promising solution to implement broadband seamless wireless -wireline access networks. This project successfully concluded in autumn 2013, and is being follow up by another Marie Curie project entitled “flexible edge nodes for dynamic optical interconnection of access and core networks” (FENDOI), which will be also briefly described.

Digital coherent receivers have gained significant attention in the last decade. The reason for this is that coherent detection, along with digital signal processing (DSP) allows for substantial increase of the channel capacity by employing advanced detection techniques. In this paper, we first review coherent detection technique employed in the receiver as well as the required receiver structure. Subsequently, we describe the core part of the receiver — DSP algorithms — that are used for data processing. We cover all basic elements of a conventional coherent receiver DSP chain: deskew, orthonormaliation, chromatic dispersion compensation/nonlinear compensation, resampling and timing recovery, polarization demultiplexing and equalization, frequency and phase recovery, digital demodulation. We also describe novel subsystems of a digital coherent receiver: modulation format recognition and impairment mitigation via expectation maximization, which may gain popularity with increasing importance of autonomous networks.

In the Identity-Based Encryption (IBE) setting, the rejoin functionality seems to be impossible since each user has the unique identity as its public key. Moreover, sometimes these identities are unchangeable, e.g., biological information (finger print iris, and so on) is regarded as the identity. Even if changeable value is indicated as an identity, e.g., e-mail address, it is preferable that the same identity can be used after a secret key is leaked. In this paper, we give a formal security definition of RIBE with the rejoin functionality, and also show that the Seo-Emura RIBE scheme [PKC 2013] (with a slight modification) has the rejoin functionality.

Dubé and Beaudoin proposed a lossless data compression called compression via substring enumeration (CSE) in 2010. We evaluate an upper bound of the number of bits used by the CSE technique to encode any binary string from an unknown member of a known class of k-th order Markov processes. We compare the worst case maximum redundancy obtained by the CSE technique for any binary string with the least possible value of the worst case maximum redundancy obtained by the best fixed-to-variable length code that satisfies the Kraft inequality.

Complex-valued Hopfield associative memory (CHAM) is one of the most promising neural network models to deal with multilevel information. CHAM has an inherent property of rotational invariance. Rotational invariance is a factor that reduces a network's robustness to noise, which is a critical problem. Here, we proposed complex-valued bipartite auto-associative memory (CBAAM) to solve this reduction in noise robustness. CBAAM consists of two layers, a visible complex-valued layer and an invisible real-valued layer. The invisible real-valued layer prevents rotational invariance and the resulting reduction in noise robustness. In addition, CBAAM has high parallelism, unlike CHAM. By computer simulations, we show that CBAAM is superior to CHAM in noise robustness. The noise robustness of CHAM decreased as the resolution factor increased. On the other hand, CBAAM provided high noise robustness independent of the resolution factor.

We consider a unified approach to the tracking analysis of adaptive filters with error and matrix data nonlinearities. Using energy-conservation arguments, we not only derive earlier results in a unified manner, but we also obtain new performance results for more general adaptive algorithms without requiring the restriction of the regression data to a particular distribution. Numerical simulations support the theoretical results.

Digital map systems can be categorized, based on the support they provide, into map navigation systems and map touring systems. Map navigation systems put more focus on helping travelers finding routes or directions instantly. By contrast, map touring systems such as Google Maps running on desktop computers are built to support users in developing their routes and survey knowledge before they go for travel. In this paper, traveler conceptual models are proposed as an interaction paradigm to enhance user immersion and interaction experience on map touring systems. A map touring system, MapXplorer, is also introduced as a proof of concept with its system design and implementation explained in detail. Twenty participants were invited to join the user study that investigates users' performance and preferences on navigation and exploration tasks. The results of experiments show that the proposed system surpasses traditional map touring systems on both navigation and exploration tasks for about 50 percent on average, and provides better user experience.

A novel auto-tuning digital DC--DC converter is presented. In order to reduce the recovery time and undershoot, the auto-tuning control combines LnL, conventional PID and a predictive PID with a configurable predictive coefficient. A switch module is used to select an algorithm from the three control algorithms, according to the difference between the error signal and the two initially predefined thresholds. The detection and control logic is designed for both window delay line ADC and $Sigma Delta$ DPWM to correct the delay deviation. When the output of the converter exceeds the quantization range, the digital output of ADC is set at 0 or 1, and the delay line stops working to reduce power consumption. Theoretical analysis and simulations in the CSMC CMOS 0.5,$mu$m process are carried out to verify the proposed DC--DC converter. It is found that the converter achieves a power efficiency of more than 90% at heavy load, and reduces the recovery time and undershoot.

This paper studies an underlay-based cognitive two-way relay network which consists of a primary network (PN) and a secondary network (SN). Two secondary users (SUs) exchange information with the aid of multiple single-antenna amplify-and-forward relays while a primary transmitter communicates with a primary receiver in the same spectrum. Unlike the existing contributions, the transmit powers of the SUs and the distributed beamforming weights of the relays are jointly optimized to minimize the sum interference power from the SN to the PN under the quality-of-service (QoS) constraints of the SUs determined by their output signal-to-interference-plus-noise ratio (SINR) and the transmit power constraints of the SUs and relays. This approach leads to a non-convex optimization problem which is computationally intractable in general. We first investigate two necessary conditions that optimal solutions should satisfy. Then, the non-convex minimization problem is solved analytically based on the obtained conditions for single-relay scenarios. For multi-relay scenarios, an iterative numerical algorithm is proposed to find suboptimal solutions with low computational complexity. It is shown that starting with an arbitrarily initial feasible point, the limit point of the solution sequence derived from the iterative algorithm satisfies the two necessary conditions. To apply this algorithm, two approaches are developed to find an initial feasible point. Finally, simulation results show that on average, the proposed low-complexity solution considerably outperforms the scheme without source power control and performs close to the optimal solution obtained by a grid search technique which has prohibitively high computational complexity.

This paper proposes a QoS-aware differential processing control (QADPC) scheme for OpenFlow-based mobile networks. QADPC classifies the input packets to the control plane by considering end terminal mobility and service type. Then, different capacities are assigned to each classified packet for prioritized processing. By means of Markov chains, QADPC is evaluated in terms of blocking probability and waiting time in the control plane. Analytical results demonstrate that QADPC offers high priority packets both lower blocking probability and less waiting time.

This study analyzes emotions established between people while interacting in face-to-face conversation. By focusing on empathy and antipathy, especially the process by which they are perceived by external observers, this paper aims to elucidate the tendency of their perception and from it develop a computational model that realizes the automatic inference of perceived empathy/antipathy. This paper makes two main contributions. First, an experiment demonstrates that an observer's perception of an interacting pair is affected by the time lags found in their actions and reactions in facial expressions and by whether their expressions are congruent or not. For example, a congruent but delayed reaction is unlikely to be perceived as empathy. Based on our findings, we propose a probabilistic model that relates the perceived empathy/antipathy of external observers to the actions and reactions of conversation participants. An experiment is conducted on ten conversations performed by 16 women in which the perceptions of nine external observers are gathered. The results demonstrate that timing cues are useful in improving the inference performance, especially for perceived antipathy.

We discuss a new high performance computing service (HPCS) platform that has been developed to provide domain-neutral computing service under the governmental support from “EDucation-research Integration through Simulation On the Net” (EDISON) project. With a first focus on technical features, we not only present in-depth explanations of the implementation details, but also describe the strengths of the EDISON platform against the successful nanoHUB.org gateway. To validate the performance and utility of the platform, we provide benchmarking results for the resource virtualization framework, and prove the stability and promptness of the EDISON platform in processing simulation requests by analyzing several statistical datasets obtained from a three-month trial service in the initiative area of computational nanoelectronics. We firmly believe that this work provides a good opportunity for understanding the science gateway project ongoing for the first time in Republic of Korea, and that the technical details presented here can be served as an useful guideline for any potential designs of HPCS platforms.

In this paper we present a cognitive architecture inspired on the biological functioning of the motor system in humans. To test the model, we built a robotic hand with a Lego Mindstorms™ kit. Then, through communication between the architecture and the robotic hand, the latter was able to perform the movement of the fingers, which therefore allowed it to perform grasping of some objects. In order to obtain these results, the architecture performed a conversion of the activation of motor neuron pools into specific degrees of servo motor movement. In this case, servo motors acted as muscles, and degrees of movement as exerted muscle force. Finally, this architecture will be integrated with high-order cognitive functions towards getting automatic motor commands generation, through planning and decision making mechanisms.

An integrated 2×28Gb/s dual-channel duobinary driver IC is presented. Each channel has integrated coding blocks, transforming a non-return-to-zero input signal into a 3-level electrical duobinary signal to achieve an optical duobinary modulation. To the best of our knowledge this is the fastest modulator driver including on-chip duobinary encoding and precoding. Moreover, it only consumes 652mW per channel at a differential output swing of 6Vpp.

With the development of personal electronic equipment, the use of a smartphone with a tri-axial accelerometer to detect human physical activity is becoming popular. In this paper, we propose a new feature based on FFT for activity recognition from tri-axial acceleration signals. To improve the classification performance, two fusion methods, minimal distance optimization (MDO) and variance contribution ranking (VCR), are proposed. The new proposed feature achieves a recognition rate of 92.41%, which outperforms six traditional time- or frequency-domain features. Furthermore, the proposed fusion methods effectively improve the recognition rates. In particular, the average accuracy based on class fusion VCR (CFVCR) is 97.01%, which results in an improvement in accuracy of 4.14% compared with the results without any fusion. Experiments confirm the effectiveness of the new proposed feature and fusion methods.

This paper covers new architectures, technologies, and performance benchmarking together with prospects for high productivity and high performance computing enabled by photonics. The exponential and sustained increases in computing and data center needs are driving the demands for exascale computing in the future. Power-efficient and parallel computing with balanced system design is essential for reaching that goal as should support ∼billion total concurrencies and ∼billion core interconnections with ∼exabyte/second bisection bandwidth. Photonic interconnects offer a disruptive technology solution that fundamentally changes the computing architectural design considerations. Optics provide ultra-high throughput, massive parallelism, minimal access latencies, and low power dissipation that remains independent of capacity and distance. In addition to the energy efficiency and many of the fundamental physical problems, optics will bring high productivity computing where programmers can ignore locality between billions of processors and memory where data resides. Repeaterless interconnection links across the entire computing system and all-to-all massively parallel interconnection switch will significantly transform not only the hardware aspects of computing but the way people program and harness the computing capability. This impacts programmability and productivity of computing. Benchmarking and optimization of the configuration of the computing system is very important. Practical and scalable deployment of photonic interconnected computing systems are likely to be aided by emergence of athermal silicon photonics and hybrid integration technologies.

Coalition Structure Generation (CSG) means partitioning agents into exhaustive and disjoint coalitions so that the sum of values of all the coalitions is maximized. Solving this problem could be facilitated by employing some compact representation schemes, such as marginal contribution network (MC-net). In MC-net, the CSG problem is represented by a set of rules where each rule is associated with a real-valued weights, and the goal is to maximize the sum of weights of rules under some constraints. This naturally leads to a combinatorial optimization problem that could be solved with weighted partial MaxSAT (WPM). In general, WPM deals with only positive weights while the weights involved in a CSG problem could be either positive or negative. With this in mind, in this paper, we propose an extension of WPM to handle negative weights and take advantage of the extended WPM to solve the MC-net-based CSG problem. Specifically, we encode the relations between each pair of agents and reform the MC-net as a set of Boolean formulas. Thus, the CSG problem is encoded as an optimization problem for WPM solvers. Furthermore, we apply this agent relation-based WPM with minor revision to solve the extended CSG problem where the value of a coalition is affected by the formation of other coalitions, a coalition known as externality. Experiments demonstrate that, compared to the previous encoding, our proposed method speeds up the process of solving the CSG problem significantly, as it generates fewer number of Boolean variables and clauses that need to be examined by WPM solver.

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.