A key-order preserving structured overlay network is a class of structured overlay network that preserves, in its structure, the order of keys to support efficient range queries. This paper presents a novel key-order preserving structured overlay network “Suzaku”. Similar to the conventional Chord#, Suzaku uses a periodically updated finger table as a routing table, but extends its uni-directional finger table to bi-directional, which achieves ⌈log2 n⌉-1 maximum lookup hops in the converged state. Suzaku introduces active and passive bi-directional finger table update algorithms for node insertion and deletion. This method maintains good lookup performance (lookup hops increase nearly logarithmically against n) even in churn situations. As well as its good performance, the algorithms of Suzaku are simple and easy to implement. This paper describes the principles of Suzaku, followed by simulation evaluations, in which it showed better performance than the conventional networks, Chord# and Skip Graph.

With the advantages of higher blocking voltage, higher operation temperature, fast-switching characteristics, and lower switching losses, the silicon carbide (SiC) MOSFET has attracted more attentions and become an available replacement of traditional silicon (Si) power semiconductor in applications. Despite of all the merits above, electromagnetic interference (EMI) issues will be induced consequently by the ultra-fast switching transitions of the SiC MOSFET. To quickly and precisely assess the switching behaviors of the SiC MOSFET for EMI investigation, an analytical model is proposed. This model has comprehensively considered most of the key factors, including parasitic inductances, non-linearity of the junction capacitors, negative feedback effect of Ls and Cgd shared by the power and the gate stage loops, non-linearity of the trans-conductance, and skin effect during voltage and current ringing stages, which will considerably affect the switching performance of the SiC MOSFET. Additionally, a finite-state machine (FSM) is especially utilized so as to analytically and intuitively describe the switching behaviors of the SiC MOSFET via Stateflow. Based on double pulse test (DPT), the effectiveness and correctness of the proposed model are validated through the comparison between the calculated and the measured waveforms during switching transitions. Besides, the model can appropriately depict the spectrum of the drain-source voltage of the MOSFET and is suitable for EMI investigation in applying of SiC devices.

Traceability links between software artifacts can assist in several software development tasks. There are some automatic traceability recovery methods that help with managing the massive number of software artifacts and their relationships, but they do not work well for software artifacts whose descriptions are different in terms of language or abstraction level. To overcome these weakness, we propose the Connecting Links Method (CLM), which recovers transitive traceability links between two artifacts by intermediating a third artifact. In order to apply CLM for general use without limitation in terms of software artifact type, we have designed a standardized method to calculate the relation score of transitive traceability links using the scores of direct traceability links between three artifacts. Furthermore, we propose an improvement of CLM by considering software version. We evaluated CLM by applying it to three software products and found that it is more effective for software artifacts whose language type or vocabulary are different compared to previous methods using textual similarity.

Image quality depends on the randomness of the k-space signal under-sampling in compressed sensing MRI (CS-MRI), especially for two-dimensional image acquisition. We investigate the feasibility of non-random signal under-sampling CS-MRI to stabilize the quality of reconstructed images and avoid arbitrariness in sampling point selection. Regular signal under-sampling for the phase-encoding direction is adopted, in which sampling points are chosen at equal intervals for the phase-encoding direction while varying the sampling density. Curvelet transform was adopted to remove the aliasing artifacts due to regular signal under-sampling. To increase the incoherence between the measurement matrix and the sparsifying transform function, the scale of the curvelet transform was varied in each iterative image reconstruction step. We evaluated the obtained images by the peak-signal-to-noise ratio and root mean squared error in localized 3×3 pixel regions. Simulation studies and experiments showed that the signal-to-noise ratio and the structural similarity index of reconstructed images were comparable to standard random under-sampling CS. This study demonstrated the feasibility of non-random under-sampling based CS by using the multi-scale curvelet transform as a sparsifying transform function. The technique may help to stabilize the obtained image quality in CS-MRI.

In the obnoxious facility game, we design mechanisms that output a location of an undesirable facility based on the locations of players reported by themselves. The benefit of a player is defined to be the distance between her location and the facility. A player may try to manipulate the output of the mechanism by strategically misreporting her location. We wish to design a λ-group strategy-proof mechanism i.e., for every group of players, at least one player in the group cannot gain strictly more than λ times her primary benefit by having the entire group change their reports simultaneously. In this paper, we design a k-candidate λ-group strategy-proof mechanism for the obnoxious facility game in the metric defined by k half lines with a common endpoint such that each candidate is a point in each of the half-lines at the same distance to the common endpoint as other candidates. Then, we show that the benefit ratio of the mechanism is at most 1+2/(k-1)λ. Finally, we prove that the bound is nearly tight.

Recent research on graph drawing focuses on Right-Angle-Crossing (RAC) drawings of 1-plane graphs, where each edge is drawn as a straight line and two crossing edges only intersect at right angles. We give a transformation from a restricted case of the RAC drawing problem to a problem of finding a straight-line drawing of a maximal plane graph where some angles are required to be acute. For a restricted version of the latter problem, we show necessary and sufficient conditions for such a drawing to exist, and design an O(n2)-time algorithm that given an n-vertex plane graph produces a desired drawing of the graph or reports that none exists.

Kakuro is a popular logic puzzle, in which a player fills in all empty squares with digits from 1 to 9 so that the sum of digits in each (horizontal or vertical) line is equal to a given number, called a clue, and digits in each line are all different. In 2016, Bultel, Dreier, Dumas, and Lafourcade proposed a physical zero-knowledge proof protocol for Kakuro using a deck of cards; their proposed protocol enables a prover to convince a verifier that the prover knows the solution of a Kakuro puzzle without revealing any information about the solution. One possible drawback of their protocol would be that the protocol is not perfectly extractable, implying that a prover who does not know the solution can convince a verifier with a small probability; therefore, one has to repeat the protocol to make such an error become negligible. In this paper, to overcome this, we design zero-knowledge proof protocols for Kakuro having perfect extractability property. Our improvement relies on the ideas behind the copy protocols in the field of card-based cryptography. By executing our protocols with a real deck of physical playing cards, humans can practically perform an efficient zero-knowledge proof of knowledge for Kakuro.

We propose a class-incremental learning framework for human activity recognition based on the Bag-of-Sequencelets model (BoS). The framework updates learned models efficiently without having to relearn them when training data of new classes are added. In this framework, all types of features including hand-crafted features and Convolutional Neural Networks (CNNs) based features and combinations of those features can be used as features for videos. Compared with the original BoS, the new framework can reduce the learning time greatly with little loss of classification accuracy.

The consistent alignment of point clouds obtained from multiple scanning positions is a crucial step for many 3D modeling systems. This is especially true for environment modeling. In order to observe the full scene, a common approach is to rotate the scanning device around a rotation axis using a turntable. The final alignment of each frame data can be computed from the position and orientation of the rotation axis. However, in practice, the precise mounting of scanning devices is impossible. It is hard to locate the vertical support of the turntable and rotation axis on a common line, particularly for lower cost consumer hardware. Therefore the calibration of the rotation axis of the turntable is an important step for the 3D reconstruction. In this paper we propose a novel calibration method for the rotation axis of the turntable. With the proposed rotation axis calibration method, multiple 3D profiles of the target scene can be aligned precisely. In the experiments, three different evaluation approaches are used to evaluate the calibration accuracy of the rotation axis. The experimental results show that the proposed rotation axis calibration method can achieve a high accuracy.

Epidemic cyber incidents are caused by malicious websites using exploit kits. The exploit kit facilitate attackers to perform the drive-by download (DBD) attack. However, it is reported that malicious websites using an exploit kit have similarity in their website structure (WS)-trees. Hence, malicious website identification techniques leveraging WS-trees have been studied, where the WS-trees can be estimated from HTTP traffic data. Nevertheless, the defensive component of the exploit kit prevents us from capturing the WS-tree perfectly. This paper shows, hence, a new WS-tree construction procedure by using the fact that a DBD attack happens in a certain duration. This paper proposes, moreover, a new malicious website identification technique by clustering the WS-tree of the exploit kits. Experiment results assuming the D3M dataset verify that the proposed technique identifies exploit kits with a reasonable accuracy even when HTTP traffic from the malicious sites are partially lost.

As the demand of data reliability becomes more and more larger, most of today's storage systems adopt erasure codes to assure the data could be reconstructed when suffering from physical device failures. In order to fast recover the lost data from a single failure, recovery optimization methods have attracted a lot of attention in recent years. However, most of the existing optimization methods focus on homogeneous devices, ignoring the fact that the storage devices are usually heterogeneous. In this paper, we propose a new recovery optimization method named HSR (Heterogeneous Storage Recovery) method, which uses both loads and speed rate among physical devices as the optimization target, in order to further improve the recovery performance for heterogeneous devices. The experiment results show that, compared to existing popular recovery optimization methods, HSR method gains much higher recovery speed over heterogeneous storage devices.

Today, trust plays a central role in services in distributed environments. Conventionally deployed trust has been based on static framework in which a server responds to a service request under statically determined policies. However, in accordance with evolution of distributed environments empowered with IoT and federated access mechanisms, dynamic behavior must be analyzed and taken into service provision, which conventional trust cannot properly handle. In this paper, we propose an extension of PDP (Policy Decision Point) in which assertions together with service requests are evaluated. Furthermore, the evaluation may be dynamically configured in dynamically evolving trust environment. We propose an elastic trust model in view of dynamic trust environment. This enables intuitionistic modeling of typical concrete elastic distributed services.

A fast cross-validation algorithm for model selection in kernel ridge regression problems is proposed, which is aiming to further reduce the computational cost of the algorithm proposed by An et al. by eigenvalue decomposition of a Gram matrix.

Conventional approaches to statistical parametric speech synthesis use context-dependent hidden Markov models (HMMs) clustered using decision trees to generate speech parameters from linguistic features. However, decision trees are not always appropriate to model complex context dependencies of linguistic features efficiently. An alternative scheme that replaces decision trees with deep neural networks (DNNs) was presented as a possible way to overcome the difficulty. By training the network to represent high-dimensional feedforward dependencies from linguistic features to acoustic features, DNN-based speech synthesis systems convert a text into a speech. To improved the naturalness of the synthesized speech, this paper presents a novel pre-training method for DNN-based statistical parametric speech synthesis systems. In our method, a deep relational model (DRM), which represents a joint probability of two visible variables, is applied to describe the joint distribution of acoustic and linguistic features. As with DNNs, a DRM consists several hidden layers and two visible layers. Although DNNs represent feedforward dependencies from one visible variables (inputs) to other visible variables (outputs), a DRM has an ability to represent the bidirectional dependencies between two visible variables. During the maximum-likelihood (ML) -based training, the model optimizes its parameters (connection weights between two adjacent layers, and biases) of a deep architecture considering the bidirectional conversion between 1) acoustic features given linguistic features, and 2) linguistic features given acoustic features generated from itself. Owing to considering whether the generated acoustic features are recognizable, our method can obtain reasonable parameters for speech synthesis. Experimental results in a speech synthesis task show that pre-trained DNN-based systems using our proposed method outperformed randomly-initialized DNN-based systems, especially when the amount of training data is limited. Additionally, speaker-dependent speech recognition experimental results also show that our method outperformed DNN-based systems, by setting the initial parameters of our method are the same as that in the synthesis experiments.

In this letter, we investigate the physical layer security in multi-user multi-relay networks, where each relay is not merely a traditional helper, but at the same time, can become a potential eavesdropper. We first propose an efficient low-complexity user and relay selection scheme to significantly reduce the amount of channel estimation as well as the amount of potential links for comparison. For the proposed scheme, we derive the closed-form expression for the lower bound of ergodic secrecy rate (ESR) to evaluate the system secrecy performance. Simulation results are provided to verify the validity of our expressions and demonstrate how the ESR scales with the number of users and relays.

Towards furthering the industrial revolution, the concept of a new cellular network began to be drawn up around 2010 as the fifth generation (5G) mobile wireless communication system. One of the main differences between the fourth generation (4G) mobile communication system Long Term Evolution (LTE) and 5G new radio (NR) is the frequency bands utilized. 5G NR assumes higher frequency bands. Effective utilization of the higher frequency bands needs to resolve the technical issue of the larger path-loss. Massive multiple-input multiple-output (Massive MIMO) beamforming (BF) technology contributes to overcome this problem, hence further study of Massive MIMO BF for each frequency band is necessary toward high-performance and easy implementation. In this paper, then, we propose a Massive MIMO method with fully-digital BF based on two-tap precoding for low super high frequency (SHF) band downlink (DL) transmissions (called as Digital FBCP). Additionally, three intersite coordination algorithms for Digital FBCP are presented for multi-site environments and one of the three algorithms is enhanced. It is shown that Digital FBCP achieves better throughput performance than a conventional algorithm with one-tap precoding. Considering performance of intersite coordination as well, it is concluded that Digital FBCP can achieve around 5 Gbps in various practical environments.

Field trials of the fifth-generation (5G) mobile communication system using 28GHz band at which almost 1GHz bandwidth will be available have been performed all over the world. To realize large coverage with such a high frequency band, beamforming by Massive MIMO (Multiple Input Multiple Output) is necessary to compensate the large path loss. Furthermore, beam tracking which adaptively changes beam direction according to user location, is an important function to support user mobility. In previous works, field trials in subway environment at 25GHz band was carried out, but only fixed beam were employed. On the other hand, the field trials result of 28 GHz-band 5G transmission employing beam tracking in the road environment has been reported. Therefore, we conducted 5G field trials in the actual railway environment using 28GHz band experimental equipment employing beam tracking. This paper reveals the downlink performance achieved by using railway cars traveling at 90km/h. In addition, we show how mobile stations position in the railway car affects the performance of 5G transmission.

In fifth-generation mobile communications systems (5G), grant-free non-orthogonal multiple access (NOMA) schemes have been considered as a way to accommodate the many wireless connections required for Internet of Things (IoT) devices. In NOMA schemes, both system capacity enhancement and transmission protocol simplification are achieved, and an overload test of more than one hundred percent of the transmission samples over conducted. Multi-user shared multiple access (MUSA) has been proposed as a representative scheme for NOMA. However, the performance of MUSA has not been fully analyzed nor compared to other NOMA or orthogonal multiple access schemes. Therefore, in this study, we theoretically and numerically analyze the performance of MUSA in uplink fading environments and compare it with orthogonal frequency division multiple access (OFDMA), space division multiple access-based OFDMA, low-density signature, and sparse code multiple access. The characteristics and superiority of MUSA are then clarified.

This paper proposes frequency domain precoding vector switching (PVS) transmit diversity for synchronization signals to achieve fast physical cell identity (PCID) detection for the narrowband (NB)-Internet-of-Things (IoT) radio interface. More specifically, we propose localized and distributed frequency domain PVS transmit diversity schemes for the narrowband primary synchronization signal (NPSS) and narrowband secondary synchronization signal (NSSS), and NPSS and NSSS detection methods including a frequency offset estimation method suitable for frequency domain PVS transmit diversity at the receiver in a set of user equipment (UE). We conduct link-level simulations to compare the detection probabilities of NPSS and NSSS, i.e., PCID using the proposed frequency domain PVS transmit diversity schemes, to those using the conventional time domain PVS transmit diversity scheme. The results show that both the distributed and localized frequency domain PVS transmit diversity schemes achieve a PCID detection probability almost identical to that of the time domain PVS transmit diversity scheme when the effect of the frequency offset due to the frequency error of the UE temperature compensated crystal oscillator (TCXO) is not considered. We also show that for a maximum frequency offset of less than approximately 8 kHz, localized PVS transmit diversity achieves almost the same PCID detection probability. It also achieves a higher PCID detection probability than one-antenna transmission although it is degraded compared to the time domain PVS transmit diversity when the maximum frequency offset is greater than approximately 10 kHz.

Wavelength-division multiplexing multicore fibers can transmit a large amount of information over one fiber, and high-density core allocations enable a large number of fiber lines to be deployed in limited spaces. However, inter-core crosstalk degrades the signal in these systems. This paper describes the design of a frequency interleaving scheme for a 7-core hexagonal multicore fiber. Interleaving schemes shift signal spectra between neighboring cores to reduce the signal degradation caused by inter-core crosstalk. The channel frequency allocation that most efficiently lowers the bit error rate is numerically determined in this study. The results indicate that the optimum frequency interleaving improves the allowable crosstalk ratio by 6.3 dB for QPSK signals, demonstrating its potential for improving wavelength-division multiplexing multicore fiber transmission systems.