Color transfer is a simple process to change a color tone in one image (source) to look like another image (target). In transferring colors between images, there are several issues needed to be considered including partial color transfer, trial-and-error, and multiple target color transfer. Our approach enables users to transfer colors partially and locally by letting users select their regions of interest from image segmentation. Since there are many ways that we can transfer colors from a set of target regions to a set of source regions, we introduce the region exploration and navigation approach where users can choose their preferred color tones to transfer one region at a time and gradually customize towards their desired results. The preferred color tones sometimes can come from more than one image; therefore our method is extended to allow users to select their preferred color tones from multiple images. Our experimental results have shown the flexibility of our approach to generate reasonable segmented regions of interest and to enable users to explore the possible results more conveniently.

We propose a novel method, built upon the hierarchical Dirichlet process hidden semi-Markov model, to reveal the content structures of unstructured domain-specific texts. The content structures of texts consisting of sequential local contexts are useful for tasks, such as text retrieval, classification, and text mining. The prominent feature of our model is the use of the recursive uniform partitioning, a stochastic process taking a view different from existing HSMMs in modeling state duration. We show that the recursive uniform partitioning plays an important role in avoiding the rapid switching between hidden states. Remarkably, our method greatly outperforms others in terms of ranking performance in our text retrieval experiments, and provides more accurate features for SVM to achieve higher F1 scores in our text classification experiments. These experiment results suggest that our method can yield improved representations of domain-specific texts. Furthermore, we present a method of automatically discovering the local contexts that serve to account for why a text is classified as a positive instance, in the supervised learning settings.

This paper presents a novel speaking aid system to help laryngectomees produce more naturally sounding electrolaryngeal (EL) speech. An electrolarynx is an external device to generate excitation signals, instead of vibration of the vocal folds. Although the conventional EL speech is quite intelligible, its naturalness suffers from the unnatural fundamental frequency (F0) patterns of the mechanically generated excitation signals. To improve the naturalness of EL speech, we have proposed EL speech enhancement methods using statistical F0 pattern prediction. In these methods, the original EL speech recorded by a microphone is presented from a loudspeaker after performing the speech enhancement. These methods are effective for some situation, such as telecommunication, but it is not suitable for face-to-face conversation because not only the enhanced EL speech but also the original EL speech is presented to listeners. In this paper, to develop an EL speech enhancement also effective for face-to-face conversation, we propose a method for directly controlling F0 patterns of the excitation signals to be generated from the electrolarynx using the statistical F0 prediction. To get an "actual feel” of the proposed system, we also implement a prototype system. By using the prototype system, we find latency issues caused by a real-time processing. To address these latency issues, we furthermore propose segmental continuous F0 pattern modeling and forthcoming F0 pattern modeling. With evaluations through simulation, we demonstrate that our proposed system is capable of effectively addressing the issues of latency and those of electrolarynx in term of the naturalness.

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.

Since the sensor nodes are subject to faults due to the highly-constrained resources and hostile deployment environments, fault management in wireless sensor networks (WSNs) is essential to guarantee the proper operation of networks, especially routing. In contrast to existing fault management methods which mainly aim to be tolerant to faults without considering the fault type, we propose a novel efficient fault-aware routing method where faults are classified and dealt with accordingly. More specifically, we first identify each fault and then try to set up the new routing path according to the fault type. Our proposed method can be easily integrated with any kind of existing routing method. We show that our proposed method outperforms AODV, REAR, and GPSR, which are the representative works of single-path routing, multipath routing and location based routing, in terms of energy efficiency and data delivery ratio.

As a with-carry analog (based on modular arithmetic) of the usual Walsh-Hadamard transform (WHT), arithmetic Walsh transform (AWT) has been used to obtain analogs of some properties of Boolean functions which are important in the design and analysis of cryptosystems. The existence of nonzero linear structure of Boolean functions is an important criterion to measure the weakness of these functions in their cryptographic applications. In this paper, we find more analogs of linear structures of Boolean functions from AWT. For some classes of n-variable Boolean functions f, we find necessary and sufficient conditions for the existence of an invariant linear structure and a complementary linear structure 1n of f. We abstract out a sectionally linear relationship between AWT and WHT of n-variable balanced Boolean functions f with linear structure 1n. This result show that AWT can characterize cryptographic properties of these functions as long as WHT can. In addition, for a diagonal Boolean function f, a recent result by Carlet and Klapper says that the AWT of f can be expressed in terms of the AWT of a diagonal Boolean function of algebraic degree at most 3 in a larger number of variables. We provide for the result a complete and more modular proof which works for both even and odd weights (of the parameter c in the Corollary 19 by Carlet and Klapper (DCC 73(2): 299-318, 2014).

Spectral compressive sensing is a novel approach that enables extraction of spectral information from a spectral-sparse signal, exclusively from its compressed measurements. Thus, the approach has received considerable attention from various fields. However, standard compressive sensing algorithms always require a sparse signal to be on the grid, whose spacing is the standard resolution limit. Thus, these algorithms severely degenerate while handling spectral compressive sensing, owing to the off-the-grid issue. Some off-the-grid algorithms were recently proposed to solve this problem, but they are either inaccurate or computationally expensive. In this paper, we propose a novel algorithm named parameterized ℓ1-minimization (PL1), which can efficiently solves the off-the-grid spectral estimation problem with relatively low computational complexity.

The history of optical fiber and optical transmission technologies has been described in many publications. However, the history of other technologies designed to support the physical layer of optical transmission has not been described in much detail. I would like to highlight those technologies in addition to optical fibers. Therefore, this paper describes the history of the development of optical fiber related technologies such as fusion splicers, optical fiber connectors, ribbon fiber, and passive components based on changes in optical fibers and optical fiber cables. Moreover, I describe technologies designed to support multi-core fibers such as fan-in/fan-out devices.

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.

The Euler number of a binary image is an important topological property for pattern recognition, image analysis, and computer vision. A famous method for computing the Euler number of a binary image is by counting certain patterns of bit-quads in the image, which has been improved by scanning three rows once to process two bit-quads simultaneously. This paper studies the bit-quad-based Euler number computing problem. We show that for a bit-quad-based Euler number computing algorithm, with the increase of the number of bit-quads being processed simultaneously, on the one hand, the average number of pixels to be checked for processing a bit-quad will decrease in theory, and on the other hand, the length of the codes for implementing the algorithm will increase, which will make the algorithm less efficient in practice. Experimental results on various types of images demonstrated that scanning five rows once and processing four bit-quads simultaneously is the optimal tradeoff, and that the optimal bit-quad-based Euler number computing algorithm is more efficient than other Euler number computing algorithms.

Train-borne video captured from the camera installed in the front or back of the train has been used for railway environment surveillance, including missing communication units and bolts on the track, broken fences, unpredictable objects falling into the rail area or hanging on wires on the top of rails. Moreover, the track condition can be perceived visually from the video by observing and analyzing the train-swaying arising from the track irregularity. However, it's a time-consuming and labor-intensive work to examine the whole large scale video up to dozens of hours frequently. In this paper, we propose a simple and effective method to detect the train-swaying quickly and automatically. We first generate the long rail track panorama (RTP) by stitching the stripes cut from the video frames, and then extract track profile to perform the unevenness detection algorithm on the RTP. The experimental results show that RTP, the compact video representation, can fast examine the visual train-swaying information for track condition perceiving, on which we detect the irregular spots with 92.86% recall and 82.98% precision in only 2 minutes computation from the video close to 1 hour.

At the present time, as downsizing of connectors causes thin gold plated layer and low contact load, serious problem of degradation of contact resistance property is induced. For these contacts, corrosion of the contacts surface under environment and high temperature as soldering and reflow process should be existed. Oxidation of base metal atoms which are diffused from under layer and additives occurs. Contact resistance increases for both surface contamination and low contact load. In order to resolve these problems and wear of surface, application of contact lubricants is useful and effective. However, degradation of the lubricants under such reflow process as high temperature possibly occurs. Therefore, in this study, from view point of change of lubricant quality as viscosity, weight loss, polymerization, oxidation and molecular orientation were clarified. For increase in contact resistance, orientation of lubricant molecular acts as important factor was found. The other factors of the lubricant hardly does not effect on contact resistance.

While conventional studies on real-time systems have mostly considered the real-time constraint of real-time systems only, recent research initiatives are trying to incorporate a security constraint into real-time scheduling due to the recognition that the violation of either of two constrains can cause catastrophic losses for humans, the system, and even environment. The focus of most studies, however, is the single-criticality systems, while the security of mixed-criticality systems has received scant attention, even though security is also a critical issue for the design of mixed-criticality systems. In this paper, we address the problem of the information leakage that arises from the shared resources that are used by tasks with different security-levels of mixed-criticality systems. We define a new concept of the security constraint employing a pre-flushing mechanism to cleanse the state of shared resources whenever there is a possibility of the information leakage regarding it. Then, we propose a new non-preemptive real-time scheduling algorithm and a schedulability analysis, which incorporate the security constraint for mixed-criticality systems. Our evaluation demonstrated that a large number of real-time tasks can be scheduled without a significant performance loss under a new security constraint.

Telecommunication networks have evolved from telephony networks to the Internet, and they sustainably support the development of a secured, safe, and comfortable society. The so-called “switching technology” including the evolved “network system technology” is one of the main infrastructure technologies used for realizing information communication services. On the occasion of completion of 100 years since the establishment of the IEICE, we summarize the history of network system technologies and present their future direction for the next generation. We mainly focus on a series of technologies that evolved through the discussions of the IEICE technical committees on switching engineering, launched 50 years ago, switching systems engineering, and network systems in action.

Entity descriptions have been exponentially growing in community-generated knowledge databases, such as DBpedia. However, many of those descriptions are not useful for identifying the underlying characteristics of their corresponding entities because semantically redundant facts or triples are included in the descriptions that represent the connections between entities without any semantic properties. Entity summarization is applied to filter out such non-informative triples and meaning-redundant triples and rank the remaining informative facts within the size of the triples for summarization. This study proposes an entity summarization approach based on pre-grouping the entities that share a set of attributes that can be used to characterize the entities we want to summarize. Entities are first grouped according to projected multilingual categories that provide the multi-angled semantics of each entity into a single entity space. Key facts about the entity are then determined through in-group-based rankings. As a result, our proposed approach produced summary information of significantly better quality (p-value =1.52×10-3 and 2.01×10-3 for the top-10 and -5 summaries, respectively) than the state-of-the-art method that requires additional external resources.

Authors previously studied the degradation of electrical contacts under the condition of various external micro-oscillations. They also developed a micro-sliding mechanism (MSM2), which causes micro-sliding and is driven by a piezoelectric actuator and elastic hinges. Using the mechanism, experimental results were obtained on the minimal sliding amplitude (MSA) required to make the electrical resistance fluctuate under various conditions. In this paper, to develop a more realistic model of input waveform than the previous one, Ts/2 is set as the rising or falling time, Tc as the flat time, and τ/2 as the duration in a sliding period T (0.25 s) of the input waveform. Using the Duhamel's integral method and an optimization method, the physical parameters of natural angular frequency ω0 (12000 s-1), damping ratio ζ (0.05), and rising and falling time Ts (1.3 or 1.2 ms) are obtained. Using the parameters and the MSA, the total acceleration of the input TA (=f(t)) and the displacement of the output x(t) are also obtained using the Fourier series expansion method. The waveforms x(t) and the experimental results are similar to each other. If the effective mass m, which is defined as that of the movable parts in the MSM2, is 0.1 kg, each total force TF (=2mTA) is estimated from TA and m. By the TF, the cases for 0.3 N/pin as frictional force or in impulsive as input waveform are more serious than the others. It is essential for the safety and the confidence of electrical contacts to evaluate the input waveform and the frictional force. The ringing waveforms of the output displacements x(t) are calculated at smaller values of Ts (1.0, 0.5, and 0.0 ms) than the above values (1.3 or 1.2 ms). When Ts is slightly changed from 1.3 or 1.2 ms to 1.0 ms, the ringing amplitude is doubled. For the degradation of electrical contacts, it is essential that Ts is reduced in a rectangular and impulsive input. Finally, a very simple wear model comprising three stages (I, II, and III) is introduced in this paper. Because Ts is much shorter in a rectangular or impulsive input than in a sinusoidal input, it is considered that the former more easily causes wear than the latter owing to a larger frictional force. Taking the adhesive wear in Stages I and III into consideration, the wear is expected to be more severe in the case of small damped oscillations owing to the ringing phenomenon.

Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments.

Autonomous Underwater Vehicle (AUV) can be utilized to directly measure the geomagnetic map in deep sea. The traditional map interpolation algorithms based on sampling continuation above the sea level yield low resolution and accuracy, which restricts the applications such as the deep sea geomagnetic positioning, navigation, searching and surveillance, etc. In this letter, we propose a Three-Dimensional (3D) Compressive Sensing (CS) algorithm in terms of the real trajectory of AUV which can be optimized with the required accuracy. The geomagnetic map recovered with the CS algorithm shows high precision compared with traditional interpolation schemes, by which the magnetic positioning accuracy can be greatly improved.

Existing moving object's trajectory prediction algorithms suffer from the data sparsity problem, which affects the accuracy of the trajectory prediction. Aiming to the problem, we present an Entropy-based Sparse Trajectories Prediction method enhanced by Matrix Factorization (ESTP-MF). Firstly, we do trajectory synthesis based on trajectory entropy and put synthesized trajectories into the trajectory space. It can resolve the sparse problem of trajectory data and make the new trajectory space more reliable. Secondly, under the new trajectory space, we introduce matrix factorization into Markov models to improve the sparse trajectory prediction. It uses matrix factorization to infer transition probabilities of the missing regions in terms of corresponding existing elements in the transition probability matrix. It aims to further solve the problem of data sparsity. Experiments with a real trajectory dataset show that ESTP-MF generally improves prediction accuracy by as much as 6% and 4% compared to the SubSyn algorithm and STP-EE algorithm respectively.

Blind deconvolution (BD) is the problem of restoring sharp images from blurry images when convolution kernels are unknown. While it has a wide range of applications and has been extensively studied, traditional shift-invariant (SI) BD focuses on uniform blur caused by kernels that do not spatially vary. However, real blur caused by factors such as motion and defocus is often nonuniform and thus beyond the ability of SI BD. Although specialized methods exist for nonuniform blur, they can only handle specific blur types. Consequently, the applicability of BD for general blur remains limited. This paper proposes a shift-variant (SV) BD method that models nonuniform blur using a field of kernels that assigns a local kernel to each pixel, thereby allowing pixelwise variation. This concept is realized as a Bayesian model that involves SV convolution with the field of kernels and smoothing of the field for regularization. A variational-Bayesian inference algorithm is derived to jointly estimate a sharp latent image and a field of kernels from a blurry observed image. Owing to the flexibility of the field-of-kernels model, the proposed method can deal with a wider range of blur than previous approaches. Experiments using images with nonuniform blur demonstrate the effectiveness of the proposed SV BD method in comparison with previous SI and SV approaches.