Various types of synchronization phenomena have been reported in coupled chaotic systems. In recent years, the applications of these phenomena have been advancing for utilization in sensor network systems, secure communication systems, and biomedical systems. Specifically, chaos-chaos intermittency (CCI) synchronization is a characterized synchronization phenomenon. Previously, we proposed a new chaos control method, termed as the “reduced region of orbit (RRO) method,” to achieve CCI synchronization using external feedback signals. This method has been gathering research attention because of its ability to induce CCI synchronization; this can be achieved even if internal system parameters cannot be adjusted by external factors. Further, additive stochastic noise is known to have a similar effect. The objective of this study was to compare the performance of the RRO method and the method that applies stochastic noise, both of which are capable of inducing CCI synchronization. The results showed that even though CCI synchronization can be realized using both control methods under the induced attractor merging condition, the RRO method possesses higher adoptability and accomplishes a higher degree of CCI synchronization compared to additive stochastic noise. This advantage might facilitate the application of synchronization in coupled chaotic systems.

In the statistic en-route filtering, each report generation node must collect a certain number of endorsements from its neighboring nodes. However, at some point, a node may fail to collect an insufficient number of endorsements since some of its neighboring nodes may have dead batteries. This letter presents a report generation method that can enhance the generation process of sensing reports under such a situation. Simulation results show the effectiveness of the proposed method.

Green cognitive radio (CR) plays an important role in offering secondary users (SUs) with more spectrum with smaller energy expenditure. However, the energy efficiency (EE) issues associated with green CR for fading channels have not been fully studied. In this paper, we investigate the average EE maximization problem for spectrum-sharing CR in fading channels. Unlike previous studies that considered either the peak or the average transmission power constraints, herein, we considered both of these constraints. Our aim is to maximize the average EE of SU by optimizing the transmission power under the joint peak and average transmit power constraints, the rate constraint of SU and the quality of service (QoS) constraint of primary user (PU). Specifically, the QoS for PU is guaranteed based on either the average interference power constraint or the PU outage constraint. To address the non-convex optimization problem, an iterative optimal power allocation algorithm that can tackle the problem efficiently is proposed. The optimal transmission powers are identified under both of perfect and imperfect channel side information (CSI). Simulations show that our proposed scheme can achieve higher EE over the existing scheme and the EE achieved under perfect CSI is better than that under imperfect CSI.

Rehabilitation and evaluation of motor function are important for motor disabled patients. In stride length estimation using an IMU attached to the foot, it is necessary to detect the time of the movement state, in which acceleration should be integrated. In our previous study, acceleration thresholds were used to determine the integration section, so it was necessary to adjust the threshold values for each subject. The purpose of this study was to develop a method for estimating stride length automatically using an artificial neural network (ANN). In this paper, a 4-layer ANN with feature extraction layers trained by autoencoder was tested. In addition, the methods of searching for the local minimum of acceleration or ANN output after detecting the movement state section by ANN were examined. The proposed method estimated the stride length for healthy subjects with error of -1.88 ± 2.36%, which was almost the same as the previous threshold based method (-0.97 ± 2.68%). The correlation coefficients between the estimated stride length and the reference value were 0.981 and 0.976 for the proposed and previous methods, respectively. The error ranges excluding outliers were between -7.03% and 3.23%, between -7.13% and 5.09% for the proposed and previous methods, respectively. The proposed method would be effective because the error range was smaller than the conventional method and no threshold adjustment was required.

This paper proposes a technique that increases the lifetime of large scale integration (LSI) devices. As semiconductor technology improves at miniaturizing transistors, aging effects due to bias temperature instability (BTI) seriously affects their lifetime. BTI increases the threshold voltage of transistors thereby also increasing the delay of an electronics device, resulting in failures due to timing violations. To compensate for aging-induced timing violations, we exploit configurable approximate computing. Assuming that target circuits have exact and approximate modes, they are configured for the approximate mode if an aging sensor predicts violations. Experiments using an example circuit revealed an increase in its lifetime to >10 years.

A limited number of types of sound event occur in an acoustic scene and some sound events tend to co-occur in the scene; for example, the sound events “dishes” and “glass jingling” are likely to co-occur in the acoustic scene “cooking.” In this paper, we propose a method of sound event detection using graph Laplacian regularization with sound event co-occurrence taken into account. In the proposed method, the occurrences of sound events are expressed as a graph whose nodes indicate the frequencies of event occurrence and whose edges indicate the sound event co-occurrences. This graph representation is then utilized for the model training of sound event detection, which is optimized under an objective function with a regularization term considering the graph structure of sound event occurrence and co-occurrence. Evaluation experiments using the TUT Sound Events 2016 and 2017 detasets, and the TUT Acoustic Scenes 2016 dataset show that the proposed method improves the performance of sound event detection by 7.9 percentage points compared with the conventional CNN-BiGRU-based detection method in terms of the segment-based F1 score. In particular, the experimental results indicate that the proposed method enables the detection of co-occurring sound events more accurately than the conventional method.

In this paper, we propose a secure computation of sparse coding and its application to Encryption-then-Compression (EtC) systems. The proposed scheme introduces secure sparse coding that allows computation of an Orthogonal Matching Pursuit (OMP) algorithm in an encrypted domain. We prove theoretically that the proposed method estimates exactly the same sparse representations that the OMP algorithm for non-encrypted computation does. This means that there is no degradation of the sparse representation performance. Furthermore, the proposed method can control the sparsity without decoding the encrypted signals. Next, we propose an EtC system based on the secure sparse coding. The proposed secure EtC system can protect the private information of the original image contents while performing image compression. It provides the same rate-distortion performance as that of sparse coding without encryption, as demonstrated on both synthetic data and natural images.

We propose a new framework named ROICF based on reinforcement learning orienting reliable compilation optimization sequence generation. On the foundation of the LLVM standard compilation optimization passes, we can obtain specific effective phase ordering for different programs to improve program reliability.

The choreography realization problem is a design challenge for systems based on service-oriented architecture. In our previous studies, we studied the problem on a case where choreography was given by one or two scenarios and was expressed by an acyclic relation of events; we introduced the notion of re-constructibility as a property of acyclic relations to be satisfied. However, when choreography is defined by multiple scenarios, the resulting behavior cannot be expressed by an acyclic relation. An event structure is composed of an acyclic relation and a conflict relation. Because event structures are a generalization of acyclic relations, a wider class of systems can be expressed by event structures. In this paper, we propose the use of event structures to express choreography, introduce the re-constructibility of event structures, and show a necessary condition for an event structure to be re-constructible.

We propose a deep learning-based model for classifying pathological voices using a convolutional neural network and a feedforward neural network. The model uses combinations of heterogeneous parameters, including mel-frequency cepstral coefficients, linear predictive cepstral coefficients and higher-order statistics. We validate the accuracy of this model using the Massachusetts Eye and Ear Infirmary (MEEI) voice disorder database and the Saarbruecken Voice Database (SVD). Our model achieved an accuracy of 99.3% for MEEI and 75.18% for SVD. This model achieved an accuracy that is 7.18% higher than that of competitive models in previous studies.

5G network will serve billions of people worldwide in the near future and protecting human privacy from being violated is one of its most important goals. In this paper, we carefully studied the 5G authentication protocols (namely 5G AKA and EAP-AKA') and a location sniffing attack exploiting 5G authentication protocols vulnerability is found. The attack can be implemented by an attacker through inexpensive devices. To cover this vulnerability, a fix scheme based on the existing PKI mechanism of 5G is proposed to enhance the authentication protocols. The proposed scheme is successfully verified with formal methods and automatic verification tool TAMARIN. Finally, the communication overhead, computational cost and storage overhead of the scheme are analyzed. The results show that the security of the fixed authentication protocol is greatly improved by just adding a little calculation and communication overhead.

Link prediction, the computational problem of determining whether there is a link between two objects, is important in machine learning and data mining. Feature-based link prediction, in which the feature vectors of the two objects are given, is of particular interest because it can also be used for various identification-related problems. Although the factorization machine and the higher-order factorization machine (HOFM) are widely used for feature-based link prediction, they use feature combinations not only across the two objects but also from the same object. Feature combinations from the same object are irrelevant to major link prediction problems such as predicting identity because using them increases computational cost and degrades accuracy. In this paper, we present novel models that use higher-order feature combinations only across the two objects. Since there were no algorithms for efficiently computing higher-order feature combinations only across two objects, we derive one by leveraging reported and newly obtained results of calculating the ANOVA kernel. We present an efficient coordinate descent algorithm for proposed models. We also improve the effectiveness of the existing one for the HOFM. Furthermore, we extend proposed models to a deep neural network. Experimental results demonstrated the effectiveness of our proposed models.

This paper introduces a new multi-satellite multi-beam system with single frequency reuse; it uses the MIMO (Multi Input Multi Output) technique to improve the frequency efficiency as the satellite communication band is limited. MIMO is the one of the most important approaches to improve the spectral efficiency in support of broadband communications. Since it is difficult to achieve high spectral efficiency by simply combining conventional MIMO satellite techniques, i.e. combining a multi-beam system with single frequency reuse with a multiple satellite system, this paper proposes transmitter pre-coding and receiver equalization techniques to enhance the channel capacity even under time/frequency asynchronous conditions. A channel capacity comparison shows that the proposed system is superior to conventional alternatives.

Finding the correspondence between two images of the same object or scene is an active research field in computer vision. This paper develops a rapid and effective Content-based Superpixel Image matching and Stitching (CSIS) scheme, which utilizes the content of superpixel through multi-features fusion technique. Unlike popular keypoint-based matching method, our approach proposes a superpixel internal feature-based scheme to implement image matching. In the beginning, we make use of a novel superpixel generation algorithm based on content-based feature representation, named Content-based Superpixel Segmentation (CSS) algorithm. Superpixels are generated in terms of a new distance metric using color, spatial, and gradient feature information. It is developed to balance the compactness and the boundary adherence of resulted superpixels. Then, we calculate the entropy of each superpixel for separating some superpixels with significant characteristics. Next, for each selected superpixel, its multi-features descriptor is generated by extracting and fusing local features of the selected superpixel itself. Finally, we compare the matching features of candidate superpixels and their own neighborhoods to estimate the correspondence between two images. We evaluated superpixel matching and image stitching on complex and deformable surfaces using our superpixel region descriptors, and the results show that new method is effective in matching accuracy and execution speed.

While existing inference engines solved real world problems using probabilistic knowledge representation, one challenging task is to efficiently utilize the representation under a situation of uncertainty during conflict resolution. This paper presents a new approach to straightforwardly combine a rule-based system (RB) with a probabilistic graphical inference framework, i.e., naïve Bayesian network (BN), towards probabilistic argumentation via a so-called probabilistic assumption-based argumentation (PABA) framework. A rule-based system (RB) formalizes its rules into defeasible logic under the assumption-based argumentation (ABA) framework while the Bayesian network (BN) provides probabilistic reasoning. By knowledge integration, while the former provides a solid testbed for inference, the latter helps the former to solve persistent conflicts by setting an acceptance threshold. By experiments, effectiveness of this approach on conflict resolution is shown via an example of liver disorder diagnosis.

This paper presents a novel spectrum sensing technique based on selection diversity combining in cognitive radio networks. In general, a selection diversity combining scheme requires a period to select an optimal element, and spectrum sensing requires a period to detect a target signal. We consider that both these periods are required for the spectrum sensing based on selection diversity combining. However, conventional techniques do not consider both the periods. Furthermore, spending a large amount of time in selection and signal detection increases their accuracy. Because the required period for spectrum sensing based on selection diversity combining is the summation of both the periods, their lengths should be considered while developing selection diversity combining based spectrum sensing for a constant period. In reference to this, we discuss the spectrum sensing technique based on selection diversity combining. Numerical examples are shown to validate the effectiveness of the presented design techniques.

Femtocell structures can offer better voice and data exchange in cellular networks. However, interference in such networks poses a major challenge in the practical development of cellular communication. To tackle this issue, an advanced interference mitigation scheme for Line-Of-Sight (LOS) femtocell networks in indoor environments is proposed in this paper. Using a femtocell management system (FMS) that controls all femtocells in a service area, the aggressor femtocells are identified and then the transmitted beam patterns are adjusted using the linear array antenna equipped in each femtocell to mitigate the interference contribution to the neighbouring femtocells. Prior to that, the affected users are switched to the femtocells that provide better throughput levels to avoid increasing the outage probability. This paper considers different femtocell deployment indexes to verify and justifies the feasibility of the findings in different density areas. Relative to fixed and adaptive power control schemes, the proposed scheme achieves approximately 5% spectral efficiency (SE) improvement, about 10% outage probability reduction, and about 7% Mbps average user throughput improvement.

This paper proposes a dual-polarized metasurface that utilizes multi-layer ceramic capacitors (MLCCs) for radar cross-section (RCS) reduction in the 28GHz band of the quasi-millimeter band. MLCCs are very small in size; therefore, miniaturization of the unit cell structure of the metamaterial can be expected, and the MLCCs can be periodically loaded onto a narrow object. First, the MLCC structure was modeled as a basic structure, and the effective permeability of the MLCC was determined to investigate the influence of the arrangement direction on MLCC interaction. Next, the unit cell structure of the dual-polarized metasurface was designed for an MLCC set on a dielectric substrate. By analyzing the infinite periodic structure and finite structure, the monostatic reduction characteristics, oblique incidence characteristics, and dual-polarization characteristics of the proposed metasurface were evaluated. In the case of the MLCCs arranged in the same direction, the monostatic RCS reduction was approximately 30dB at 29.8GHz, and decreased when the MLCCs were arranged in a checkerboard pattern. The monostatic RCS reductions for the 5 × 5, 10 × 10, and 20 × 20 divisions were roughly the same, i.e., 10.8, 9.9, and 10.3dB, respectively. Additionally, to validate the simulated results, the proposed dual-polarized metasurface was fabricated and measured. The measured results were found to approximately agree with the simulated results, confirming that the RCS can be reduced for dual-polarization operation.

This paper presents an analysis of the relationship between noise and bandwidth in visible light communication (VLC) systems. In the past few years, pre-emphasis and post-equalization techniques were proposed to extend the bandwidth of VLC systems. However, these bandwidth extension techniques also influence noise and sensitivity of the VLC systems. In this paper, first, we build a system model of VLC transceivers and circuit models of pre-emphasis and post-equalization. Next, we theoretically compare the bandwidth and noise of three different transceiver structures comprising a single pre-emphasis circuit, a single post-equalization circuit and a combination of pre-emphasis and post-equalization circuits. Finally, we validate the presented theoretical analysis using experimental results. The result shows that for the same resonant frequency, and for high signal-to-noise ratio (S/N), VLC systems employing post-equalization or pre-emphasis have the same bandwidth extension ability. Therefore, a transceiver employing both the pre-emphasis and post-equalization techniques has a bandwidth √2 times the bandwidth of the systems employing only the pre-emphasis or post-equalization. Based on the theoretical analysis of noise, the VLC system with only active pre-emphasis shows the lowest noise, which is a good choice for low-noise systems. The result of this paper may provide a new perspective of noise and sensitivity of the bandwidth extension techniques in VLC systems.

We address the security issue of RSA with implicitly related keys in this paper. Informally, we investigate under what condition is it possible to efficiently factorize RSA moduli in polynomial time given implicit relation of the related private keys that certain portions of bit pattern are the same. We formulate concrete attack scenarios and propose lattice-based cryptanalysis by using lattice reduction algorithms. A subtle lattice technique is adapted to represent an unknown private key with the help of known implicit relation. We analyze a simple case when given two RSA instances with the known amount of shared most significant bits (MSBs) and least significant bits (LSBs) of the private keys. We further extend to a generic lattice-based attack for given more RSA instances with implicitly related keys. Our theoretical results indicate that RSA with implicitly related keys is more insecure and better asymptotic results can be achieved as the number of RSA instances increases. Furthermore, we conduct numerical experiments to verify the validity of the proposed attacks.