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.

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.

A social network is a useful model for identifying hidden structures and meaningful knowledge among social atoms, which have complicated interactions. In recent years, most studies have focused on the real data of the social space such as emails, tweets, and human communities. In this paper, we construct a social network from literary fiction by mapping characters to vertices and their relationship strengths to edges. The main contribution of this paper is that our model can be exploited to reveal the deep structures of fiction novels by using graph theoretic concepts, without the involvement of any manual work. Experimental evaluation showed that our model successfully classified fictional characters in terms of their importance to the plot of a novel.

Fault localization is a necessary process of locating faults in buggy programs. This paper proposes a novel approach using dynamic slicing and association analysis to improve the effectiveness of fault localization. Our approach utilizes dynamic slicing to generate a reduced candidate set to narrow the range of faults, and introduces association analysis to mine the relationship between the statements in the execution traces and the test results. In addition, we develop a prototype tool DSFL to implement our approach. Furthermore, we perform a set of empirical studies with 12 Java programs to evaluate the effectiveness of the proposed approach. The experimental results show that our approach is more effective than the compared approaches.

Detecting a variety of anomalies caused by attacks or accidents in computer networks has been one of the real challenges for both researchers and network operators. An effective technique that could quickly and accurately detect a wide range of anomalies would be able to prevent serious consequences for system security or reliability. In this article, we characterize detection techniques on the basis of learning models and propose an unsupervised learning model for real-time anomaly detection in computer networks. We also conducted a series of experiments to examine capabilities of the proposed model by employing three well-known machine learning algorithms, namely multivariate normal distribution, k-nearest neighbor, and one-class support vector machine. The results of these experiments on real network traffic suggest that the proposed model is a promising solution and has a number of flexible capabilities to detect several types of anomalies in real time.

We compared two audio output devices for augmented audio reality applications. In these applications, we plan to use speech annotations on top of the actual ambient environment. Thus, it becomes essential that these audio output devices are able to deliver intelligible speech annotation along with transparent delivery of the environmental auditory scene. Two candidate devices were compared. The first output was the bone-conduction headphone, which can deliver speech signals by vibrating the skull, while normal hearing is left intact for surrounding noise since these headphones leave the ear canals open. The other is the binaural microphone/earphone combo, which is in a form factor similar to a regular earphone, but integrates a small microphone at the ear canal entry. The input from these microphones can be fed back to the earphones along with the annotation speech. We also compared these devices to normal hearing (i.e., without headphones or earphones) for reference. We compared the speech intelligibility when competing babble noise is simultaneously given from the surrounding environment. It was found that the binaural combo can generally deliver speech signals at comparable or higher intelligibility than the bone-conduction headphones. However, with the binaural combo, we found that the ear canal transfer characteristics were altered significantly by shutting the ear canals closed with the earphones. Accordingly, if we employed a compensation filter to account for this transfer function deviation, the resultant speech intelligibility was found to be significantly higher. However, both of these devices were found to be acceptable as audio output devices for augmented audio reality applications since both are able to deliver speech signals at high intelligibility even when a significant amount of competing noise is present. In fact, both of these speech output methods were able to deliver speech signals at higher intelligibility than natural speech, especially when the SNR was low.

In this paper, we propose a superpixel based depth map generation scheme for the application to monoscopic to stereoscopic video conversion. The proposed algorithm employs four main processes to generate depth maps for all frames in the video sequences. First, the depth maps of the key frames in the input sequence are generated by superpixel merging and some user interactions. Second, the frames in the input sequences are over-segmented by Simple Linear Iterative Clustering (SLIC) or depth aided SLIC method depending on whether or not they have the depth maps. Third, each superpixel in current frame is used to match the corresponding superpixel in its previous frame. Finally, depth map is propagated with a joint bilateral filter based on the estimated matching vector of each superpixel. We show an improved performance of the proposed algorithm through experimental results.

Wireless patient monitoring is an active research area with the goal of ubiquitous health care services. This study presents a novel means of exploiting the distributed source coding (DSC) in low-complexity compression of ECG signals. We first convert the ECG data compression to an equivalent channel coding problem and exploit a linear channel code for the DSC construction. Performance is further enhanced by the use of a correlation channel that more precisely characterizes the statistical dependencies of ECG signals. Also proposed is a modified BCJR algorithm which performs symbol decoding of binary convolutional codes to better exploit the source's a priori information. Finally, a complete setup system for online ambulatory ECG monitoring via mobile cellular networks is presented. Experiments on the MIT-BIH arrhythmia database and real-time acquired ECG signals demonstrate that the proposed system outperforms other schemes in terms of encoder complexity and coding efficiency.

For the High Efficiency Video Coding (HEVC) standard, a fast transform unit (TU) decision method is proposed. HEVC defines the TU representing a region sharing the same transformation, and it supports various transform sizes from 4×4 to 32×32 by using a quadtree of TUs. The various sizes of TUs can provide good coding efficiency, whereas it may dramatically increase encoding complexity. Assuming that a TU with highly compacted energy is unlikely to be split, the proposed method determines an appropriate TU size according to the position of the last non-zero transform coefficient. Experimental results show that this reduces encoding run time by 17.2% with a negligible coding loss of 0.78% BD-rate for the random-access scenario.

Maximizing network lifetime and optimizing aggregate system utility are important but usually conflict goals in wireless multi-hop networks. For the trade-off, we present a matrix game-theoretic cross-layer optimization formulation to jointly maximize the diverse objectives in such networks with network coding. To this end, we introduce a cross-layer formulation of general network utility maximization (NUM) that accommodates routing, scheduling, and stream control from different layers in the coded networks. Specifically, for the scheduling problem and then the objective function involved, we develop a matrix game with the strategy sets of the players corresponding to hyperlink and transmission mode, and design multiple payoffs specific to lifetime and system utility, respectively. In particular, with the inherit merit that matrix game can be solved with mathematical programming, our cross-layer programming formulation actually benefits from both game-based and NUM-based approaches at the same time by cooperating the programming model for the matrix game with that for the other layers in a consistent framework. Finally, our numerical experiments quantitatively exemplify the possible performance trad-offs with respect to the two variants developed on the multiple objectives in question while qualitatively exhibiting the differences between the framework and the other related works.

Multicell cooperation is a promising technique to mitigate the inter-cell interference and improve the sum rate in cellular systems. Limited feedback design is of great importance to base station cooperation as it provides the quantized channel state information (CSI) of both the desired and interfering channels to the transmitters. Most studies on multicell limited feedback deal with scenarios of a single receive antenna at the mobile user. This paper, however, applies limited feedback to cooperative multicell multiple-input multiple-output (MIMO) systems where both base stations and users are equipped with multiple antennas. An optimized feedback strategy with random vector quantization (RVQ) codebook is proposed for interference aware coordinated beamforming that approximately maximizes the lower bound of the sum rate. By minimizing the upper-bound on the mean sum-rate loss induced by the quantization errors, we present a feedback-bit allocation algorithm to divide the available feedback bits between the desired and interfering channels for arbitrary number of transmit and receive antennas under different interfering signal strengths. Simulation results demonstrate that the proposed scheme utilizes the feedback resource effectively and achieves sum-rate performance reasonably close to the full CSI case.

A challenge faced by the video game industry is to develop believable and more intelligent Non-Playable Characters (NPCs). To tackle this problem a low-cost and simple approach has been proposed in this research, which is the development of a gossip virtual social network for NPCs. The network allows simple individual NPCs to communicate their knowledge amongst themselves. The communication within this social network is governed by social-psychological rules. These rules are categorized into four types: Contact, whether the NPC are within a contactable range of each other; Observation, whether the NPCs actually want to talk to each other based on their personal traits; Status, the current representation of the NPCs; and Relationships which determines the long term ties of the NPCs. Evaluations of the proposed gossip virtual social network was conducted, both through statistical analysis and a survey of real users. Highly satisfactory results have been achieved.

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.

To improve throughput of Electron Beam Direct Writing (EBDW) with Character Projection (CP) method, a structured routing architecture (SRA) has been proposed to restrict VIA placement and wire-track transition. It reduces possible layout patterns in the interconnect layers, and increases VIA and metal figure numbers in the EB shots while suppressing the CP character number explosion. In this paper, we discuss details of the SRA design methodology, and demonstrate the CP performance by SRA in comparison with other EBDW techniques. Our experimental results show viable CP performance for practical use, and prove SRA's feasibility in 14nm mass fabrication.

This letter considers the weighted sum-rate maximization (WSRMax) problem in downlink multicell multiuser orthogonal frequency-division multiplexing system. The WSRMax problem under per base station transmit power constraint is known to be NP-hard, and the optimal solution is computationally very expensive. We propose two less-complex suboptimal convex approximated solutions which are based on sequential parametric convex approximation approach. We derive provably faster convergent iterative convex approximation techniques that locally optimize the weighted sum-rate function. Both the iterative solutions are found to converge to the local optimal solution within a few iterations compared to other well-known techniques. The numerical results demonstrate the effectiveness and superiority of the proposed approaches.

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.

Time synchronization is important for frequency hopping, power management, scheduling, and basic operations of multi-hop ad-hoc networks. The main problem of existing time synchronization methods is that they depend on a particular node that has the fastest time information among neighbor nodes. The Cucker-Smale flocking model describes that global emergent behavior can be obtained by locally averaging the velocity of each bird. Inspired by this flocking model, we propose a time synchronization method not depending on a particular node. In the proposed method, each node revises its time information via the local-averaging procedure in a distributed manner. A self-correcting procedure is added to the proposed method in order to preserve the effect of time adjustment executed by the local-averaging procedure. The simulation results show that the proposed time synchronization method reduces the time difference among nodes, and enhances the performance of time synchronization in the context of IEEE 802.11-based ad-hoc networks.

Because dielectrics between active layers have low thermal conductivities, there is a demand to reduce the temperature increase in three-dimensional integrated circuits (3D ICs). This paper demonstrates that, in the design of 3D ICs, different layer assignments often lead to different temperature increases. Based on this observation, we are motivated to perform temperature-aware layer assignment. Our work includes two parts. Firstly, an integer linear programming (ILP) approach that guarantees a minimum temperature increase is proposed. Secondly, a polynomial-time heuristic algorithm that reduces the temperature increase is proposed. Compared with the previous work, which does not take the temperature increase into account, the experimental results show that both our ILP approach and our heuristic algorithm produce a significant reduction in the temperature increase with a very small area overhead.

This paper presents an image haze removal algorithm for a single image. The conventional Dark Channel Prior (DCP) algorithm estimates the transmission map using the dark information in an image. The haze regions are then detected using a matting algorithm. However, the resulting transmission map invariably includes some block artifacts as the DCP algorithm is based on block-based processing. Therefore, to solve this problem, an algorithm for a modified transmission map is proposed using a Hidden Markov Random Field (HMRF) algorithm. Experimental results confirm that the proposed algorithm is superior to conventional algorithms in image haze removal.

This paper presents our project of designing EdgeWrite text entry methods for Japanese language. We are developing a version of EdgeWrite text entry method for Japanese language: Katakana EdgeWrite. Katakana EdgeWrite specifies the line stroke directions and writing order of the Japanese Katakana character. The ideal corner sequence pattern of EdgeWrite for each Katakana character is designed based on its line stroke directions and writing order.