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.

A multi-band wireless local area network (WLAN) enables flexible use of multiple frequency bands. To efficiently monitor radio resources in multi-band WLANs, a distributed-sensing system that employs a number of stations (STAs) is considered to alleviate sensing constraints at access points (APs). This paper examines the distributed sensing that expands the sensing coverage area and monitors multiple object channels by employing STA-based sensing. To avoid issuing unnecessary reports, each STA autonomously judges whether it should make a report by comparing the importance of its own sensing result and that of the overheard report. We address how to efficiently collect the necessary sensing information from a large number of STAs. We propose a reactive reporting scheme that is highly scalable by the number of STAs to collect such sensing results as the channel occupancy ratio. Evaluation results show that the proposed scheme keeps the number of reports low even if the number of STAs increases. Our proposed sensing scheme provides large sensing coverage.

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.

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.

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.

In automotive control systems, the potential risks of software defects have been increasing due to growing software complexity driven by advances in electric-electronic control. Some kind of defects such as race conditions can rarely be detected by testing or simulations because these defects manifest themselves only in some rare executions. Model checking, which employs an exhaustive state-space exploration, is effective for detecting such defects. This paper reports our approach to applying model checking techniques to real-world automotive control programs. It is impossible to directly model check such programs because of their large size and high complexity; thus, it is necessary to derive, from the program under verification, a model that is amenable to model checking. Our approach uses the SPIN model checker as well as in-house tools that facilitate this process. One of the key features implemented in these tools is boundary-adjustable program slicing, which allows the user to specify and extract part of the source code that is relevant to the verification problem of interest. The conversion from extracted code into Promela, SPIN's input language, is performed using one of the tools in a semi-automatic manner. This approach has been used for several years in practice and found to be useful even when the code size of the software exceeds 400 KLOC.

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.

Reference current used in sense amplifiers is a crucial factor in a single-end read manner for emerging memories. Dummy cell average read scheme uses multiple pairs of dummy cells inside the array to generate an accurate reference current for data sensing. The previous research adopts current mirror sense amplifier (CMSA) which is compatible with the dummy cell average read scheme. However, clamped bit-line sense amplifier (CBLSA) has higher sensing speed and lower power consumption compared with CMSA. Therefore, applying CBLSA to dummy cell average read scheme is expected to enhance the performance. This paper reveals that direct combination of CBLSA and dummy cell average read scheme leads to sense margin degradation. In order to solve this problem, a new array design is proposed to make CBLSA compatible with dummy cell average read scheme. Current mirror structure is employed to prevent CBLSA from being short-circuited directly. The simulation result shows that the minimum sensible tunnel magnetoresistance ratio (TMRR) can be extended from 14.3% down to 1%. The access speed of the proposed sensing scheme is less than 2 ns when TMRR is 70% or larger, which is about twice higher than the previous research. And this circuit design just consumes half of the energy in one read cycle compared with the previous research. In the proposed array architecture, all the dummy cells can be always short-circuited in totally isolated area by low-resistance metal wiring instead of using controlling transistors. This structure is able to contribute to increasing the dummy cell averaging effect. Besides, the array-level simulation validates that the array design is accessible to every data cell. This design is generally applicable to any kinds of resistance-variable emerging memories including STT-MRAM.

Network functions virtualization (NFV) enables telecommunications service providers to realize various network services by flexibly combining multiple virtual network functions (VNFs). To provide such services, an NFV control method should optimally allocate such VNFs into physical networks and servers by taking account of the combination(s) of objective functions and constraints for each metric defined for each VNF type, e.g., VNF placements and routes between the VNFs. The NFV control method should also be extendable for adding new metrics or changing the combination of metrics. One approach for NFV control to optimize allocations is to construct an algorithm that simultaneously solves the combined optimization problem. However, this approach is not extendable because the problem needs to be reformulated every time a new metric is added or a combination of metrics is changed. Another approach involves using an extendable network-control architecture that coordinates multiple control algorithms specified for individual metrics. However, to the best of our knowledge, no method has been developed that can optimize allocations through this kind of coordination. In this paper, we propose an extendable NFV-integrated control method by coordinating multiple control algorithms. We also propose an efficient coordination algorithm based on reinforcement learning. Finally, we evaluate the effectiveness of the proposed method through simulations.

This paper presents a novel statistic computation technique for energy detection-based spectrum sensing with multiple antennas. The presented technique computes the statistic for signal detection after combining all the signals. Because the computation of the statistic for all the received signals is not required, the presented technique reduces the computational complexity. Furthermore, the absolute value of all the received signals are combined to prevent the attenuation of the combined signals. Because the statistic computations are not required for all the received signals, the reduction of the computational complexity for signal detection can be expected. Furthermore, the presented technique does not need to choose anything, such as the binary phase rotator in the conventional technique, and therefore, the performance degradation due to wrong choices can be avoided. Numerical examples indicate that the spectrum sensing performances of the presented technique are almost the same as those of conventional techniques despite the complexity of the presented technique being less than that of the conventional techniques.

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.

In this paper, we propose an interactive system for controlling the pressure while cutting paper with a knife. The purpose is to improve the cutting skill of novices learning the art of paper-cutting. Our system supports skill improvement for novices by measuring and evaluating their cutting pressure in real-time. In this study, we use a knife with a blade attached to a stylus with a pressure sensor, which can measure the pressure, coordinates, and cutting time. We have developed a similar support system using a stylus and a tablet device. This system allows the user to experience the pressure of experts through tracing. Paper-cutting is created by cutting paper with a knife. The practice system in this paper provides practice in an environment more akin to the production of paper cutting. In the first experiment, we observed differences in cutting ability by comparing cutting pressures between novices and experts. As a result, we confirmed that novices cut paper at a higher pressure than experts. We developed a practice system that guides the novices on controlling the pressure by providing information on the cutting pressure values of experts. This system shows the difference in pressure between novices and experts using a synchronous display of color and sound. Using these functions, novices learn to adjust their cutting pressure according to that of experts. Determining the right cutting pressure is a critical skill in the art of paper-cutting, and we aim to improve the same with our system. In the second experiment, we tested the effect of the practice system on the knife device. We compared the changes in cutting pressure with and without our system, the practice methods used in the workshop, and the previously developed stylus-based support system. As a result, we confirmed that practicing with the knife device had a better effect on the novice's skill in controlling cutting pressure than other practice methods.

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.

Silica-LiNbO3 (LN) hybrid modulators have a hybrid configuration of versatile passive silica-based planar lightwave circuits (PLCs) and simple LN phase modulators arrays. By combining the advantages the two components, these hybrid modulators offer large-scale, highly-functionality modulators with low losses for advanced modulation formats. However, the reliability evaluation necessary to implement them in real transmissions has not been reported yet. In terms of reliability characteristics, there are issues originating from the difference in thermal expansion coefficients between silica PLC and LN. To resolve these issues, we propose design guidelines for hybrid modulators to mitigate the degradation induced by the thermal expansion difference. We fabricated several tens of silica-LN dual polarization quadrature phase shift keying (DP-QPSK) modulators based on the design guidelines and evaluated their reliability. The experiment results show that the modules have no degradation after a reliability test based on GR-468, which confirms the validity of the design guidelines for highly reliable silica-LN hybrid modulators. We can apply the guidelines for hybrid modules that realize heterogeneous device integration using materials with different coefficients of thermal expansion.

Blockchain is a distributed ledger system composed of a P2P network and is used for a wide range of applications, such as international remittance, inter-individual transactions, and asset conservation. In Blockchain systems, tamper resistance is enhanced by the property of transaction that cannot be changed or deleted by everyone including the creator of the transaction. However, this property also becomes a problem that unintended transaction created by miss operation or secret key theft cannot be corrected later. Due to this problem, once an illegal transaction such as theft occurs, the damage will expand. To suppress the damage, we need countermeasures, such as detecting illegal transaction at high speed and correcting the transaction before approval. However, anomaly detection in the Blockchain at high speed is computationally heavy, because we need to repeat the detection process using various feature quantities and the feature extractions become overhead. In this paper, to accelerate anomaly detection, we propose to cache transaction information necessary for extracting feature in GPU device memory and perform both feature extraction and anomaly detection in the GPU. We also propose a conditional feature extraction method to reduce computation cost of anomaly detection. We employ anomaly detection using K-means algorithm based on the conditional features. When the number of users is one million and the number of transactions is 100 millions, our proposed method achieves 8.6 times faster than CPU processing method and 2.6 times faster than GPU processing method that does not perform feature extraction on the GPU. In addition, the conditional feature extraction method achieves 1.7 times faster than the unconditional method when the number of users satisfying a given condition is 200 thousands out of one million.

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.

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.

Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.

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.