Head-mounted displays (HMDs) and augmented reality (AR) are actively being studied. However, ordinary AR HMDs for visual assistance have a problem in which users have difficulty simultaneously focusing their eyes on both the real target object and the displayed image because the image can only be displayed at a fixed distance from an user's eyes in contrast to where the real object three-dimensionally exists. Therefore, we considered incorporating a holographic technology, an ideal three-dimensional (3D) display technology, into an AR HMD system. A few studies on holographic HMDs have had technical problems, and they have faults in size and weight. This paper proposes a compact holographic AR HMD system with the purpose of enabling an ideal 3D AR HMD system which can correctly reconstruct the image at any depth. In this paper, a Fourier transform optical system (FTOS) was implemented using only one lens in order to achieve a compact and lightweight structure, and a compact holographic AR HMD system was constructed. The experimental results showed that the proposed system can reconstruct sharp images at the correct depth for a wide depth range. This study enabled an ideal 3D AR HMD system that enables simultaneous viewing of both the real target object and the reconstructed image without feeling visual fatigue.

The definitions of the Lee complete ρ weight enumerator and the exact complete ρ weight enumerator over Mn×s(F2[u,v]/) are introduced, and the MacWilliams identities with respect to the RT metric for these two weight enumerators of linear codes over Mn×s(F2[u,v]/) are obtained. Finally, we give two examples to illustrate the results we obtained.

In this letter, we investigate the price-based power allocation with rate proportional fairness constraint in downlink non-orthogonal multiple access (NOMA) systems. The Stackelberg game is utilized to model the interaction between the base station (BS) and users. The revenue maximization problem of the BS is first converted to rate allocation problem, then the optimal rate allocation for each user is obtained by variable substitution. Finally, a price-based power allocation with rate proportional fairness (PAPF) algorithm is proposed based on the relationship between rate and transmit power. Simulation results show that the proposed PAPF algorithm is superior to the previous price-based power allocation algorithm in terms of fairness index and minimum normalized user (MNU) rate.

Soft-thresholding is a sparse modeling method typically applied to wavelet denoising in statistical signal processing. It is also important in machine learning since it is an essential nature of the well-known LASSO (Least Absolute Shrinkage and Selection Operator). It is known that soft-thresholding, thus, LASSO suffers from a problem of dilemma between sparsity and generalization. This is caused by excessive shrinkage at a sparse representation. There are several methods for improving this problem in the field of signal processing and machine learning. In this paper, we considered to extend and analyze a method of scaling of soft-thresholding estimators. In a setting of non-parametric orthogonal regression problem including discrete wavelet transform, we introduced component-wise and data-dependent scaling that is indeed identical to non-negative garrote. We here considered a case where a parameter value of soft-thresholding is chosen from absolute values of the least squares estimates, by which the model selection problem reduces to the determination of the number of non-zero coefficient estimates. In this case, we firstly derived a risk and construct SURE (Stein's unbiased risk estimator) that can be used for determining the number of non-zero coefficient estimates. We also analyzed some properties of the risk curve and found that our scaling method with the derived SURE is possible to yield a model with low risk and high sparsity compared to a naive soft-thresholding method with SURE. This theoretical speculation was verified by a simple numerical experiment of wavelet denoising.

The aim of this research is realizing a high resolution and a fast color switching of electronic papers. In this report, we realized basis of electric papers comprised on magnetic Janus particles was established. Colored and magnetic Janus particles were successfully prepared, and magnetic Janus particles were introduced into honeycomb matrices. Introduced magnetic Janus particles quickly respond to an external magnetic field.

Image steganalysis can determine whether the image contains the secret messages. In practice, the number of the cover images is far greater than that of the secret images, so it is very important to solve the detection problem in imbalanced image sets. Currently, SMOTE, Borderline-SMOTE and ADASYN are three importantly synthesized algorithms used to solve the imbalanced problem. In these methods, the new sampling point is synthesized based on the minority class samples. But this research is seldom seen in image steganalysis. In this paper, we find that the features of the majority class sample are similar to those of the minority class sample based on the distribution of the image features in steganalysis. So the majority and minority class samples are both used to integrate the new sample points. In experiments, compared with SMOTE, Borderline-SMOTE and ADASYN, this approach improves detection accuracy using the FLD ensemble classifier.

We examined the novel aggregation control of the LC and monomer during formation of the polymer walls from a LC/monomer mixture in order to suppress the presence of the residual monomers and polymer networks in the pixel areas. The method is utilization of the differing wettabilities among LC and monomer molecules on a substrate surface. We patterned a substrate surface with a fluororesin and a polyimide film, and promoted phase separation of the LC and monomer by cooling process. This resulted in the LC and monomer aggregates primarily existing in the pixel areas and non-pixel areas, respectively. Moreover, the polymer-walls structure which was formed in this method partitioned into individual pixels in a lattice region and prevented the LC from flowing. This polymer-walls formation technique will be useful for developing high-quality flexible LCDs.

As devices continue to shrink, the parameter shift due to process variation and aging effects has an increasing impact on the circuit yield and reliability. However, predicting how long a circuit can maintain its design yield above the design specification is difficult because the design yield changes during the aging process. Moreover, performing Monte Carlo (MC) simulation iteratively during aging analysis is infeasible. Therefore, most existing approaches ignore the continuity during simulations to obtain high speed, which may result in accumulation of extrapolation errors with time. In this paper, an incremental simulation technique is proposed for lifetime yield analysis to improve the simulation speed while maintaining the analysis accuracy. Because aging is often a gradual process, the proposed incremental technique is effective for reducing the simulation time. For yield analysis with degraded performance, this incremental technique also reduces the simulation time because each sample is the same circuit with small parameter changes in the MC analysis. When the proposed dynamic aging sampling technique is employed, 50× speedup can be obtained with almost no decline accuracy, which considerably improves the efficiency of lifetime yield analysis.

In ASIACRYPT2015, a new model for the analysis of block cipher against side-channel attack and a dedicated attack, differential bias attack, were proposed by Bogdanov et al. The model assumes an adversary who has leaked values whose positions are unknown and randomly chosen from internal states (random leakage model). This paper improves the security analysis on AES under the random leakage model. In the previous method, the adversary requires at least 234 chosen plaintexts; therefore, it is hard to recover a secret key with a small number of data. To consider the security against the adversary given a small number of data, we reestimate complexity. We propose another hypothesis-testing method which can minimize the number of required data. The proposed method requires time complexity more than t>260 because of time-data tradeoff, and some attacks are tractable under t≤280. Therefore, the attack is a threat for the long-term security though it is not for the short-term security. In addition, we apply key enumeration to the differential bias attack and propose two evaluation methods, information-theoretic evaluation and experimental one with rank estimation. From the evaluations on AES, we show that the attack is a practical threat for the long-term security.

Establishing local visual correspondences between images taken under different conditions is an important and challenging task in computer vision. A common solution for this task is detecting keypoints in images and then matching the keypoints with a feature descriptor. This paper proposes a robust and low-dimensional local feature descriptor named Adaptively Integrated Gradient and Intensity Feature (AIGIF). The proposed AIGIF descriptor partitions the support region surrounding each keypoint into sub-regions, and classifies the sub-regions into two categories: edge-dominated ones and smoothness-dominated ones. For edge-dominated sub-regions, gradient magnitude and orientation features are extracted; for smoothness-dominated sub-regions, intensity feature is extracted. The gradient and intensity features are integrated to generate the descriptor. Experiments on image matching were conducted to evaluate performances of the proposed AIGIF. Compared with SIFT, the proposed AIGIF achieves 75% reduction of feature dimension (from 128 bytes to 32 bytes); compared with SURF, the proposed AIGIF achieves 87.5% reduction of feature dimension (from 256 bytes to 32 bytes); compared with the state-of-the-art ORB descriptor which has the same feature dimension with AIGIF, AIGIF achieves higher accuracy and robustness. In summary, the AIGIF combines the advantages of gradient feature and intensity feature, and achieves relatively high accuracy and robustness with low feature dimension.

High-dynamic-range imaging (HDRI) technologies aim to extend the dynamic range of luminance against the limitation of camera sensors. Irradiance information of a scene can be reconstructed by fusing multiple low-dynamic-range (LDR) images with different exposures. The key issue is removing ghost artifacts caused by motion of moving objects and handheld cameras. This paper proposes a robust ghost-free HDRI algorithm by visual salience based bilateral motion detection and stack extension based exposure fusion. For ghost areas detection, visual salience is introduced to measure the differences between multiple images; bilateral motion detection is employed to improve the accuracy of labeling motion areas. For exposure fusion, the proposed algorithm reduces the discontinuity of brightness by stack extension and rejects the information of ghost areas to avoid artifacts via fusion masks. Experiment results show that the proposed algorithm can remove ghost artifacts accurately for both static and handheld cameras, remain robust to scenes with complex motion and keep low complexity over recent advances including rank minimization based method and patch based method by 63.6% and 20.4% time savings averagely.

The ball state tracking and detection technology plays a significant role in volleyball game analysis, whose performance is limited due to the challenges include: 1) the inaccurate ball trajectory; 2) multiple numbers of the ball event category; 3) the large intra-class difference of one event. With the goal of broadcasting supporting for volleyball games which requires a real time system, this paper proposes a ball state based parallel ball tracking and event detection method based on a sequential estimation method such as particle filter. This method employs a parallel process of the 3D ball tracking and the event detection so that it is friendly for real time system implementation. The 3D ball tracking process uses the same models with the past work [8]. For event detection process, a ball event change estimation based multiple system model, a past trajectory referred hit point likelihood and a court-line distance feature based event type detection are proposed. First, the multiple system model transits the ball event state, which consists the event starting time and the event type, through three models dealing with different ball motion situations in the volleyball game, such as the motion keeping and changing. The mixture of these models is decided by estimation of the ball event change estimation. Secondly, the past trajectory referred hit point likelihood avoids the processing time delay between the ball tracking and the event detection process by evaluating the probability of the ball being hit at certain time without using future ball trajectories. Third, the feature of the distance between the ball and the specific court line are extracted to detect the ball event type. Experimental results based on multi-view HDTV video sequences (2014 Inter High School Men's Volleyball Games, Japan), which contains 606 events in total, show that the detection rate reaches 88.61% while the success rate of 3D ball tracking keeps more than 99%.

High-resolution image and video communication in home networks is highly expected to proliferate with the spread of Wi-Fi devices and the introduction of multiple-input multiple-output (MIMO) systems. This paper proposes a joint transmission and coding scheme for broadcasting high-resolution video streams over multiuser MIMO systems with an eigenbeam-space division multiplexing (E-SDM) technique. Scalable video coding makes it possible to produce the code stream comprised of multiple layers having unequal contribution to image quality. The proposed scheme jointly assigns the data of scalable code streams to subcarriers and spatial streams based on their signal-to-noise ratio (SNR) values in order to transmit visually important data with high reliability. Simulation results show that the proposed scheme surpasses the conventional unequal power allocation (UPA) approach in terms of both peak signal-to-noise ratio (PSNR) of received images and correct decoding probability. PSNR performance of the proposed scheme exceeds 35dB with the probability of over 95% when received SNR is higher than 6dB. The improvement in average PSNR by the proposed scheme compared to the conventional UPA comes up to approx. 20dB at received SNR of 6dB. Furthermore, correct decoding probability reaches 95% when received SNR is greater than 4dB.

Real-time and reliable radio communication is essential for wireless control systems (WCS). In WCS, preambles create significant overhead and affect the real-time capability since payloads are typically small. To shorten the preamble transmission time in OFDM systems, previous works have considered adopting either time-direction extrapolation (TDE) or frequency-direction interpolation (FDI) for channel estimation which however result in poor performance in fast fading channels and frequency-selective fading channels, respectively. In this work, we propose a subcarrier-selectable short preamble (SSSP) by introducing selectability to subcarrier sampling patterns of a preamble such that it can provide full sampling coverage of all subcarriers with several preamble transmissions. In addition, we introduce adaptability to a channel estimation algorithm for the SSSP so that it conforms to both fast and frequency-selective channels. Simulation results validate the feasibility of the proposed method in terms of the reliability and real-time capability. In particular, the SSSP scheme shows its advantage in flexibility as it can provide a low error rate and short communication time in various channel conditions.

Distributed channel selection schemes are proposed in this paper to mitigate the flow-in-the-middle (FIM) starvation in dense wireless local area networks (WLANs). The FIM starvation occurs when the middle transmitter is within the carrier sense range of two exterior transmitters, while the two exterior transmitters are not within the carrier sense range of each other. Since an exterior transmitter sends a frame regardless of the other, the middle transmitter has a high probability of detecting the channel being occupied. Under heavy traffic conditions, the middle transmitter suffers from extremely low transmission opportunities, i.e., throughput starvation. The basic idea of the proposed schemes is to let each access point (AP) select the channel which has less three-node-chain topologies within its two-hop neighborhood. The proposed schemes are formulated in strategic form games. Payoff functions are designed so that they are proved to be potential games. Therefore, the convergence is guaranteed when the proposed schemes are conducted in a distributed manner by using unilateral improvement dynamics. Moreover, we conduct evaluations through graph-based simulations and the ns-3 simulator. Simulations confirm that the FIM starvation has been mitigated since the number of three-node-chain topologies has been significantly reduced. The 5th percentile throughput has been improved.

Rational proofs, introduced by Azar and Micali (STOC 2012), are a variant of interactive proofs in which the prover is rational, and may deviate from the protocol for increasing his reward. Guo et al. (ITCS 2014) demonstrated that rational proofs are relevant to delegation of computation. By restricting the prover to be computationally bounded, they presented a one-round delegation scheme with sublinear verification for functions computable by log-space uniform circuits with logarithmic depth. In this work, we study rational proofs in which the verifier is also rational, and may deviate from the protocol for decreasing the prover's reward. We construct a three-message delegation scheme with sublinear verification for functions computable by log-space uniform circuits with polylogarithmic depth in the random oracle model.

In this paper, we study Wi-Fi mesh networks (WMNs) as a promising candidate for wireless networking infrastructure that interconnects a variety of access networks. The main performance bottleneck of a WMN is their limited capacity due to the packet collision from the contention-based IEEE 802.11s MAC. To mitigate this problem, we present the distributed link-activation (DLA) protocol which activates a set of collision-free links for a fixed amount of time by exchanging a few control packets between neighboring MRs. Through the rigorous proof, it is shown that the upper bound of the DLA rounds is O(Smax), where Smax is the maximum number of (simultaneous) interference-free links in a WMN topology. Based on the DLA, we also design the distributed throughput-maximal scheduling (D-TMS) scheme which overlays the DLA protocol on a new frame architecture based on the IEEE 802.11 power saving mode. To mitigate its high latency, we propose the D-TMS adaptive data-period control (D-TMS-ADPC) that adjusts the data period depending on the traffic load of a WMN. Numerical results show that the D-TMS-ADPC scheme achieves much higher throughput performance than the IEEE 802.11s MAC.

This introduces a method which uses LIDAR to identify humans and track their positions, body orientation, and movement trajectories in any public space to read their various types of behavioral responses to surroundings. We use a network of LIDAR poles, installed at the shoulder level of typical adults to reduce potential occlusion between persons and/or objects even in large-scale social environments. With this arrangement, a simple but effective human tracking method is proposed that works by combining multiple sensors' data so that large-scale areas can be covered. The effectiveness of this method is evaluated in an art gallery of a real museum. The result revealed good tracking performance and provided valuable behavioral information related to the art gallery.

This letter proposes an extended image-source model to simulate the room transfer function for a rotatable complex source in a three-dimensional reverberant room. The proposed model uses spherical harmonic decomposition to describe the exterior sound field from the complex source. Based on “axis flip” concept, the mirroring relations between the source and images are summarized by a unified mirroring operator that occurs on the soundfield coefficients. The rotation movement of the source is taken into account by exploiting the rotation property of spherical harmonics. The accuracy of our proposed model is verified through the appropriate simulation examples.

Network traffic forecasts, as it is well known, can be useful for network resource optimization. In order to minimize the forecast error by maximizing information utilization with low complexity, this paper concerns the difference of traffic trends at large time scales and fits a dual-related model to predict it. First, by analyzing traffic trends based on user behavior, we find both hour-to-hour and day-to-day patterns, which means that models based on either of the single trends are unable to offer precise predictions. Then, a prediction method with the consideration of both daily and hourly traffic patterns, called the dual-related forecasting method, is proposed. Finally, the correlation for traffic data is analyzed based on model parameters. Simulation results demonstrate the proposed model is more effective in reducing forecasting error than other models.