This paper proposes out-of-bound signal demapping for lattice reduction-aided iterative linear receivers in overloaded MIMO channels. While lattice reduction aided linear receivers sometimes output hard-decision signals that are not contained in the modulation constellation, the proposed demapping converts those hard-decision signals into binary digits that can be mapped onto the modulation constellation. Even though the proposed demapping can be implemented with almost no additional complexity, the proposed demapping achieves more gain as the linear reception is iterated. Furthermore, we show that the transmission performance depends on bit mapping in modulations such as the Gray mapping and the natural mapping. The transmission performance is confirmed by computer simulation in a 6 × 2 MIMO system, i.e., the overloading ratio of 3. One of the proposed demapping called “modulo demapping” attains a gain of about 2 dB at the packet error rate (PER) of 10-1 when the 64QAM is applied.

Triboelectric generator is attracting much attention as a power source of electronics application. Electromotive force induced by rubbing is a key for triboelectric generator. From dielectric physics point of view, there are two microscopic origins for electromotive force, i.e., electronic charge displacement and dipolar rotation. A new way for evaluating these two origins is an urgent task. We have been developing an optical second-harmonic generation (SHG) technique as a tool for probing charge displacement and dipolar alignment, selectively, by utilizing wavelength dependent response of SHG to the two origins. In this paper, an experimental way that identifies polarity of electronic charge displacement, i.e., positive charge and negative charge, is proposed. Results showed that the use of local oscillator makes it possible to identify the polarity of charges by means of SHG. As an example, positive and negative charge distribution created by rubbing polyimide surface is illustrated.

An optical-layer adaptive restoration scheme is validated by a real-time experiment and numerical analyses. In this paper, it is assumed that this scheme can adaptively optimize the bitrate (up to 600Gb/s) and an optical reach with 100Gb/s granularity to maintain high-capacity optical signal transmission. The practicality of 600-Gb/s/carrier optical signal transmission over 101.6-km field-installed fiber is confirmed prior to the adaptive restoration experiment. After modifying the field setup, a real-time experiment on network recovery is demonstrated with bitrate adaptation for 600-Gb/s to 400-Gb/s signals. The results indicate that this scheme can restore failed connections with recovery times comparable to those of conventional restoration scheme; thus 99.9999% system availability can be easily attained even under double-link failures. Numerical analysis clarifies that adaptive restoration can recover >80% of double-link failures on several realistic topologies and improvement amount against conventional scheme is semi-statistically characterized by restoration path length.

A differential on chip oscillator (OCO) is proposed in this paper for low supply voltage, high frequency accuracy and fast startup. The differential architecture helps the OCO achieve a good power supply rejection ratio (PSRR) without using a regulator so as to make the OCO suitable for a low power supply voltage of 1.38V. A reference voltage generator is also developed to generate two output voltages lower than Vbe for low supply voltage operation. The output frequency is locked to 48MHz by a frequency-locked loop (FLL) and a 3.3-ppm/°C temperature coefficient of frequency is realized by the differential voltage ratio adjusting (differential VRA) technique. The startup time is only 1.47μs because the differential OCO is not necessary to charge a big capacitor for ripple reduction.

In array signal processing, many methods of handling cases of impulsive noise with an alpha-stable distribution have been studied. By introducing correntropy with a robust statistical property, this paper proposes a novel fractional lower order correntropy (FLOCR) method. The FLOCR-based estimator for array outputs is defined and applied with multiple signal classification (MUSIC) to estimate the direction of arrival (DOA) in alpha-stable distributed noise environments. Comprehensive Monte Carlo simulation results demonstrate that FLOCR-MUSIC outperforms existing algorithms in terms of root mean square error (RMSE) and the probability of resolution, especially in the presence of highly impulsive noise.

A new structure of the electro-optic modulator to compensate the third-order intermodulation distortion (IMD3) is introduced. The modulator includes two Mach-Zehnder modulators (MZMs) operating with frequency chirp and the two modulated outputs are combined with an adequate phase difference. We revealed by theoretical analysis and numerical calculations that the IMD3 components in the receiver output could be selectively suppressed when the two MZMs operate with chirp parameters of opposite signs to each other. Spectral power of the IMD3 components in the proposed modulator was more than 15dB lower than that in a normal Mach-Zehnder modulator at modulation index between 0.15π and 0.25π rad. The IMD3 compensation properties of the proposed modulator was experimentally confirmed by using a dual parallel Mach-Zehnder modulator (DPMZM) structure. We designed and fabricated the modulator with the single-chip structure and the single-input operation by integrating with 180° hybrid coupler on the modulator substrate. Modulation signals were applied to each modulation electrode by the 180° hybrid coupler to set the chirp parameters of two MZMs of the DPMZM. The properties of the fabricated modulator were measured by using 10GHz two-tone signals. The performance of the IMD3 compensation agreed with that in the calculation. It was confirmed that the IMD3 compensation could be realized even by the fabricated modulator structure.

This paper describes the effectiveness of the geometric multi-grid method in a current density analysis using a numerical human body model. The scalar potential finite difference (SPFD) method is used as a numerical method for analyzing the current density inside a human body due to contact with charged objects in a low-frequency band, and research related to methods to solve faster large-scale simultaneous equations based on the SPFD method has been conducted. In previous research, the block incomplete Cholesky conjugate gradients (ICCG) method is proposed as an effective method to solve the simultaneous equations faster. However, even though the block ICCG method is used, many iterations are still needed. Therefore, in this study, we focus on the geometric multi-grid method as a method to solve the problem. We develop the geometric-multi-grid method and evaluate performances by comparing it with the block ICCG method in terms of computation time and the number of iterations. The results show that the number of iterations needed for the geometric multi-grid method is much less than that for the block ICCG method. In addition, the computation time is much shorter, depending on the number of threads and the number of coarse grids. Also, by using multi-color ordering, the parallel performance of the geometric multi-grid method can be greatly improved.

Speech enhancement has been proposed to reduce the impulsive noise whose frequency characteristic is wideband. On the other hand, it is challenging to reduce the ringing sound, which is narrowband in impulsive noise. Therefore, we propose the modeling of the ringing sound and its estimation by a linear predictor (LP). However, it is difficult to estimate the ringing sound only in noisy speech due to the auto-correlation property of speech. The proposed system adopts the 4th order moment-based adaptive algorithm by noticing the difference between the 4th order statistics of speech and impulsive noise. The brief analysis and simulation results show that the proposed system has the potential to reduce ringing sound while keeping the quality of enhanced speech.

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.

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.

The spectral efficiency of photonic networks can be enhanced by the use of higher modulation orders and narrower channel bandwidth. Unfortunately, these solutions are precluded by the margins required to offset uncertainties in system performance. Furthermore, as recently highlighted, the disaggregation of optical transport systems increases the required margin. We propose here highly spectrally efficient networks, whose margins are minimized by transmission-quality-aware adaptive modulation-order/channel-bandwidth assignment enabled by optical performance monitoring (OPM). Their effectiveness is confirmed by experiments on 400-Gbps dual-polarization quadrature phase shift keying (DP-QPSK) and 16-ary quadrature amplitude modulation (DP-16QAM) signals with the application of recently developed Q-factor-based OPM. Four-subcarrier 32-Gbaud DP-QPSK signals within 150/162.5/175GHz and two-subcarrier 32-Gbaud DP-16QAM signals within 75/87.5/100GHz are experimentally analyzed. Numerical network simulations in conjunction with the experimental results demonstrate that the proposed scheme can drastically improve network spectral efficiency.

Figure-ground (FG) segregation has been considered as a fundamental step towards object recognition. We explored plausible mechanisms that estimate global figure-ground segregation from local image features by investigating the human visual system. Physiological studies have reported border-ownership (BO) selective neurons in V2 which signal the local direction of figure (DOF) along a border; however, how local BO signals contribute to global FG segregation has not been clarified. The BO and FG processing could be independent, dependent on each other, or inseparable. The investigation on the differences and similarities between the BO and FG judgements is important for exploring plausible mechanisms that enable global FG estimation from local clues. We performed psychophysical experiments that included two different tasks each of which focused on the judgement of either BO or FG. The perceptual judgments showed consistency between the BO and FG determination while a longer distance in gaze movement was observed in FG segregation than BO discrimination. These results suggest the involvement of distinct neural mechanism for local BO determination and global FG segregation.

Recent years have witnessed a rapid increase in cyber-attacks through unauthorized accesses and DDoS attacks. Since packet classification is a fundamental technique to prevent such illegal communications, it has gained considerable attention. Packet classification is achieved with a linear search on a classification rule list that represents the packet classification policy. As such, a large number of rules can result in serious communication latency. To decrease this latency, the problem is formalized as optimal rule ordering (ORO). In most cases, this problem aims to find the order of rules that minimizes latency while satisfying the dependency relation of the rules, where rules ri and rj are dependent if there is a packet that matches both ri and rj and their actions applied to packets are different. However, there is a case in which although the ordering violates the dependency relation, the ordering satisfies the packet classification policy. Since such an ordering can decrease the latency compared to an ordering under the constraint of the dependency relation, we have introduced a new model, called relaxed optimal rule ordering (RORO). In general, it is difficult to determine whether an ordering satisfies the classification policy, even when it violates the dependency relation, because this problem contains unsatisfiability. However, using a zero-suppressed binary decision diagram (ZDD), we can determine it in a reasonable amount of time. In this paper, we present a simulated annealing method for RORO which interchanges rules by determining whether rules ri and rj can be interchanged in terms of policy violation using the ZDD. The experimental results show that our method decreases latency more than other heuristics.

Advanced information-processing services based on cloud computing are in great demand. However, users want to be able to customize cloud services for their own purposes. To provide image-processing services that can be optimized for the purpose of each user, we propose a technique for chaining image-processing functions in a CPU-field programmable gate array (FPGA) coupled server architecture. One of the most important requirements for combining multiple image-processing functions on a network, is low latency in server nodes. However, large delay occurs in the conventional CPU-FPGA architecture due to the overheads of packet reordering for ensuring the correctness of image processing and data transfer between the CPU and FPGA at the application level. This paper presents a CPU-FPGA server architecture with a real-time packet reordering circuit for low-latency image processing. In order to confirm the efficiency of our idea, we evaluated the latency of histogram of oriented gradients (HOG) feature calculation as an offloaded image-processing function. The results show that the latency is about 26 times lower than that of the conventional CPU-FPGA architecture. Moreover, the throughput decreased by less than 3.7% under the worst-case condition where 90 percent of the packets are randomly swapped at a 40-Gbps input rate. Finally, we demonstrated that a real-time video monitoring service can be provided by combining image processing functions using our architecture.

This paper introduces methods to modify a buggy sequential gate-level circuit to conform to the specification. In order to preserve the optimization efforts, the modifications should be as small as possible. Assuming that the locations to be modified are given, our proposed method finds an appropriate set of fan-in signals for the patch function of those locations by iteratively calculating the state correspondence between the specification and the buggy circuit and applying a method for debugging combinational circuits. The experiments are conducted on ITC99 benchmark circuits, and it is shown that our proposed method can work when there are at most 30,000 corresponding reachable state pairs between two circuits. Moreover, a heuristic method using the information of data-path FFs is proposed, which can find a correct set of fan-ins for all the benchmark circuits within practical time.

This paper proposes a new approach to optimize the number of necessary SWAP gates when we perform a quantum circuit on a two-dimensional (2D) NNA. Our new idea is to change the order of quantum gates (if possible) so that each sub-circuit has only gates performing on adjacent qubits. For each sub-circuit, we utilize a SAT solver to find the best qubit placement such that the sub-circuit has only gates on adjacent qubits. Each sub-circuit may have a different qubit placement such that we do not need SWAP gates for the sub-circuit. Thus, we insert SWAP gates between two sub-circuits to change the qubit placement which is desirable for the following sub-circuit. To reduce the number of such SWAP gates between two sub-circuits, we utilize A* algorithm.

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.

Correctly understanding microarchitectural bottlenecks is important to optimize performance and energy of OoO (Out-of-Order) processors. Although CPI (Cycles Per Instruction) stack has been utilized for this purpose, it stacks architectural events heuristically by counting how many times the events occur, and the order of stacking affects the result, which may be misleading. It is because CPI stack does not consider the execution path of dynamic instructions. Critical path analysis (CPA) is a well-known method to identify the critical execution path of dynamic instruction execution on OoO processors. The critical path consists of the sequence of events that determines the execution time of a program on a certain processor. We develop a novel representation of CPCI stack (Cycles Per Critical Instruction stack), which is CPI stack based on CPA. The main challenge in constructing CPCI stack is how to analyze a large number of paths because CPA often results in numerous critical paths. In this paper, we show that there are more than ten to the tenth power critical paths in the execution of only one thousand instructions in 35 benchmarks out of 48 from SPEC CPU2006. Then, we propose a statistical method to analyze all the critical paths and show a case study using the benchmarks.

This paper is concerned with a mixed-integer optimization (MIO) approach to selecting a subset of relevant features from among many candidates. For ordinal classification, a sequential logit model and an ordered logit model are often employed. For feature subset selection in the sequential logit model, Sato et al.[22] recently proposed a mixed-integer linear optimization (MILO) formulation. In their MILO formulation, a univariate nonlinear function contained in the sequential logit model was represented by a tangent-line-based approximation. We extend this MILO formulation toward the ordered logit model, which is more commonly used for ordinal classification than the sequential logit model is. Making use of tangent planes to approximate a bivariate nonlinear function involved in the ordered logit model, we derive an MILO formulation for feature subset selection in the ordered logit model. Our computational results verify that the proposed method is superior to the L1-regularized ordered logit model in terms of solution quality.

Data rate requirements, from consumer application to automotive and aerospace grew rapidly in the last years. This led to the development of a series of communication protocols (i.e. Ethernet, PCI-Express, RapidIO and SpaceFibre), which use more than one communication lane, both to speed up data rate and to increase link reliability. Some of these protocols, such as SpaceFibre, are able to detect real-time changes in the number of active lanes and to adapt the data flow appropriately, providing a flexible solution, robust to lane failures. This results in a real time varying data path in the lower layers of the data handling system. The aim of this paper is to propose the architecture of a hardware block capable of reading a fixed number of words from a host FIFO and shaping them on a real time variable number of words equal to the number of active lanes.