This study involves proposing a high-speed computer-generated hologram playback by using a digital micromirror device for high-definition spatiotemporal division multiplexing electroholography. Consequently, the results indicated that the study successfully reconstructed a high-definition 3-D movie of 3-D objects that was comprised of approximately 900,000 points at 60 fps when each frame was divided into twelve parts.

Novel roll-to-roll (R2R) deposition and patterning of ITO on ultra-thin glass were developed with no photolithography and applied to flexible organic light emitting diodes (OLEDs). The developed deposition consists of low temperature sputtering and annealing. The developed patterning utilizes an etching paste printed by novel R2R screen printing.

We proposed the simulation method of reconstructed holographic images in considering phase distribution in the small pixels of liquid crystal spatial light modulator (LC-SLM) and clarified zero-order diffraction appeared on the reconstructed images when the phase distribution in a single pixel is non-uniform. These results are useful for design of fine LC-SLM for realizing wide-viewing-angle holographic displays.

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.

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is suitable for data access control on cloud storage systems. In ABE, to revoke users' attributes, it is necessary to make them unable to decrypt ciphertexts. Some CP-ABE schemes for efficient attribute revocation have been proposed. However, they have not been given a formal security proof against a revoked user, that is, whether they satisfy forward secrecy has not been shown or they just do not achieve fine-grained access control of shared data. We propose an attribute revocable attribute-based encryption with the forward secrecy for fine-grained access control of shared data. The proposed scheme can use both “AND” and “OR” policy and is IND-CPA secure under the Decisional Parallel Bilinear Diffie-Hellman Exponent assumption in the standard model.

This paper presents an application of low-coherence interferometry for measurement of mode field diameters (MFDs) of a few-mode fiber and shows its performance compared with another method using a mode multiplexer. We found that the presented method could measure MFDs in a few-mode fiber even without any special mode multiplexers.

A widely-used query on a graph is a regular path query (RPQ) whose answer is a set of tuples of nodes connected by paths corresponding to a given regular expression. Traditionally, evaluating an RPQ on a large graph takes substantial memory spaces and long response time. Recently, several studies have focused on improving response time for evaluating an RPQ by splitting an original RPQ into smaller subqueries, evaluating them in parallel and combining partial answers. In these works, how to choose split labels in an RPQ is one of key points of the performance of RPQ evaluation, and rare labels of a graph can be used as split labels. However there is still a room for improvement, because a rare label cannot guarantee the minimum evaluation cost all the time. In this paper, we propose a novel approach of selecting split labels by estimating evaluation cost of each split subquery with a unit-subquery cost matrix (USCM), which can be obtained from a graph in prior to evaluate an RPQ. USCM presents the evaluation cost of a unit-subquery which is the smallest possible subquery, and we can estimate the evaluation cost of an RPQ by decomposing into a set of unit-subqueries. Experimental results show that our proposed approach outperforms rare label based approaches.

Cayley hash functions are a family of cryptographic hash functions constructed from Cayley graphs, with appealing properties such as a natural parallelism and a security reduction to a clean, well-defined mathematical problem. As this problem involves non-Abelian groups, it is a priori resistant to quantum period finding algorithms and Cayley hash functions may therefore be a good foundation for post-quantum cryptography. Four particular parameter sets for Cayley hash functions have been proposed in the past, and so far dedicated preimage algorithms have been found for all of them. These algorithms do however not seem to extend to generic parameters, and as a result it is still an open problem to determine the security of Cayley hash functions in general. In this paper, we study the case of Chiu's Ramanujan graphs. We design a polynomial time preimage attack against the resulting Cayley hash function, showing that these particular parameters like the previous ones are not suitable for the construction. We extend our attacks on hash functions based on similar Cayley graphs as Chiu's Ramanujan graphs. On the positive side, we then suggest some possible ways to construct the Cayley hashes that may not be affected by this type of attacks. Our results contribute to a better understanding of the hard problems underlying the security of Cayley hash functions.

A non-deterministic finite automaton (NFA)-based parallel string matching scheme is proposed. To parallelize the operations of NFAs, a graphic processing unit (GPU) is adopted. Considering the resource occupancy of threads and size of the shared memory, the optimized resource allocation is performed in the proposed string matching scheme. Therefore, the performance is enhanced significantly in all evaluations.

For subgraph enumeration problems, very efficient algorithms have been proposed whose time complexities are far smaller than the number of subgraphs. Although the number of subgraphs can exponentially increase with the input graph size, these algorithms exploit compressed representations to output and maintain enumerated subgraphs compactly so as to reduce the time and space complexities. However, they are designed for enumerating only some specific types of subgraphs, e.g., paths or trees. In this paper, we propose an algorithm framework, called the frontier-based search, which generalizes these specific algorithms without losing their efficiency. Our frontier-based search will be used to resolve various practical problems that include constrained subgraph enumeration.

This paper deals with a problem to decide whether a given graph structure appears as a pattern in the structure of a given graph. A graph pattern is a triple p=(V,E,H), where (V,E) is a graph and H is a set of variables, which are ordered lists of vertices in V. A variable can be replaced with an arbitrary connected graph by a kind of hyperedge replacements. A substitution is a collection of such replacements. The graph pattern matching problem (GPMP) is the computational problem to decide whether or not a given graph G is obtained from a given graph pattern p by a substitution. In this paper, we show that GPMP for a graph pattern p and a graph G is solvable in polynomial time if the length of every variable in p is 2, p is of bounded treewidth, and G is connected.

In this paper, we propose a novel design method of two channel critically sampled compactly supported biorthogonal graph wavelet filter banks with half-band kernels. First of all, we use the polynomial half-band kernels to construct a class of biorthogonal graph wavelet filter banks, which exactly satisfy the PR (perfect reconstruction) condition. We then present a design method of the polynomial half-band kernels with the specified degree of flatness. The proposed design method utilizes the PBP (Parametric Bernstein Polynomial), which ensures that the half-band kernels have the specified zeros at λ=2. Therefore the constraints of flatness are satisfied at both of λ=0 and λ=2, and then the resulting graph wavelet filters have the flat spectral responses in passband and stopband. Furthermore, we apply the Remez exchange algorithm to minimize the spectral error of lowpass (highpass) filter in the band of interest by using the remaining degree of freedom. Finally, several examples are designed to demonstrate the effectiveness of the proposed design method.

A study on quantification of human stress using low beta waves of electroencephalography (EEG) is presented. For the very first time the importance of low beta waves as a feature for quantification of human stress is highlighted. In this study, there were twenty-eight participants who filled the Perceived Stress Scale (PSS) questionnaire and recorded their EEG in closed eye condition by using a commercially available single channel EEG headset placed at frontal site. On the regression analysis of beta waves extracted from recorded EEG, it has been observed that low beta waves can predict PSS scores with a confidence level of 94%. Consequently, when low beta wave is used as a feature with the Naive Bayes algorithm for classification of stress level, it not only reduces the computational cost by 7 folds but also improves the accuracy to 71.4%.

We propose a non-blind deconvolution algorithm of point cloud attributes inspired by multi-Wiener SURE-LET deconvolution for images. The image reconstructed by the SURE-LET approach is expressed as a linear combination of multiple filtered images where the filters are defined on the frequency domain. The coefficients of the linear combination are calculated so that the estimate of mean squared error between the original and restored images is minimized. Although the approach is very effective, it is only applicable to images. Recently we have to handle signals on irregular grids, e.g., texture data on 3D models, which are often blurred due to diffusion or motions of objects. However, we cannot utilize image processing-based approaches straightforwardly since these high-dimensional signals cannot be transformed into their frequency domain. To overcome the problem, we use graph signal processing (GSP) for deblurring the complex-structured data. That is, the SURE-LET approach is redefined on GSP, where the Wiener-like filtering is followed by the subband decomposition with an analysis graph filter bank, and then thresholding for each subband is performed. In the experiments, the proposed method is applied to blurred textures on 3D models and synthetic sparse data. The experimental results show clearly deblurred signals with SNR improvements.

Biomimetics is a new research field that creates innovation through the collaboration of different existing research fields. However, the collaboration, i.e., the exchange of deep knowledge between different research fields, is difficult for several reasons such as differences in technical terms used in different fields. In order to overcome this problem, we have developed a new retrieval platform, “Biomimetics image retrieval platform,” using a visualization-based image retrieval technique. A biological database contains a large volume of image data, and by taking advantage of these image data, we are able to overcome limitations of text-only information retrieval. By realizing such a retrieval platform that does not depend on technical terms, individual biological databases of various species can be integrated. This will allow not only the use of data for the study of various species by researchers in different biological fields but also access for a wide range of researchers in fields ranging from materials science, mechanical engineering and manufacturing. Therefore, our platform provides a new path bridging different fields and will contribute to the development of biomimetics since it can overcome the limitation of the traditional retrieval platform.

Side match vector quantization (SMVQ) has been originally developed for image compression and is also useful for steganography. SMVQ requires to create its own state codebook for each block in both encoding and decoding phases. Since the conventional method for the state codebook generation is extremely time-consuming, this letter proposes a fast generation method. The proposed method is tens times faster than the conventional one without loss of perceptual visual quality.

Discrete structure manipulation is a fundamental technique for many problems solved by computers. BDDs/ZDDs have attracted a great deal of attention for twenty years, because those data structures are useful to efficiently manipulate basic discrete structures such as logic functions and sets of combinations. Recently, one of the most interesting research topics related to BDDs/ZDDs is Frontier-based search method, a very efficient algorithm for enumerating and indexing the subsets of a graph to satisfy a given constraint. This work is important because many kinds of practical problems can be efficiently solved by some variations of this algorithm. In this article, we present recent research activity related to BDD and ZDD. We first briefly explain the basic techniques for BDD/ZDD manipulation, and then we present several examples of the state-of-the-art algorithms to show the power of enumeration.

An incident response organization such as a CSIRT contributes to preventing the spread of malware infection by analyzing compromised websites and sending abuse reports with detected URLs to webmasters. However, these abuse reports with only URLs are not sufficient to clean up the websites. In addition, it is difficult to analyze malicious websites across different client environments because these websites change behavior depending on a client environment. To expedite compromised website clean-up, it is important to provide fine-grained information such as malicious URL relations, the precise position of compromised web content, and the target range of client environments. In this paper, we propose a new method of constructing a redirection graph with context, such as which web content redirects to malicious websites. The proposed method analyzes a website in a multi-client environment to identify which client environment is exposed to threats. We evaluated our system using crawling datasets of approximately 2,000 compromised websites. The result shows that our system successfully identified malicious URL relations and compromised web content, and the number of URLs and the amount of web content to be analyzed were sufficient for incident responders by 15.0% and 0.8%, respectively. Furthermore, it can also identify the target range of client environments in 30.4% of websites and a vulnerability that has been used in malicious websites by leveraging target information. This fine-grained analysis by our system would contribute to improving the daily work of incident responders.

Detecting signals in a very large multiple-input multiple-output (MIMO) system requires high complexy of implementation. Thus, belief propagation based detection has been studied recently because of its low complexity. When the transmitted signal sequence is encoded using a channel code decodable by a factor-graph-based algorithm, MIMO signal detection and channel decoding can be combined in a single factor graph. In this paper, a low density parity check (LDPC) coded MIMO system is considered, and two types of factor graphs: bipartite and tripartite graphs are compared. The former updates the log-likelihood-ratio (LLR) values at MIMO detection and parity checking simultaneously. On the other hand, the latter performs the updates alternatively. Simulation results show that the tripartite graph achieves faster convergence and slightly better bit error rate performance. In addition, it is confirmed that the LLR damping in LDPC decoding is important for a stable convergence.

Knowledge graph (KG) embedding aims at learning the latent semantic representations for entities and relations. However, most existing approaches can only be applied to KG completion, so cannot identify relations including unseen entities (or Out-of-KG entities). In this paper, motivated by the zero-shot learning, we propose a novel model, namely JointE, jointly learning KG and entity descriptions embedding, to extend KG by adding new relations with Out-of-KG entities. The JointE model is evaluated on entity prediction for zero-shot embedding. Empirical comparisons on benchmark datasets show that the proposed JointE model outperforms state-of-the-art approaches. The source code of JointE is available at https://github.com/yzur/JointE.