Many selected mapping (SLM) schemes have been proposed to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signal sequences. In this paper, an efficient selection (ES) method of the OFDM signal sequence with minimum PAPR among many alternative OFDM signal sequences is proposed; it supports various SLM schemes. Utilizing the fact that OFDM signal components can be sequentially generated in many SLM schemes, the generation and PAPR observation of the OFDM signal sequence are processed concurrently. While the u-th alternative OFDM signal components are being generated, by applying the proposed ES method, the generation of that alternative OFDM signal components can be interrupted (or stopped) according to the selection criteria of the best OFDM signal sequence in the considered SLM scheme. Such interruption substantially reduces the average computational complexity of SLM schemes without degradation of PAPR reduction performance, which is confirmed by analytical and numerical results. Note that the proposed method is not an isolated SLM scheme but a subsidiary method which can be easily adopted in many SLM schemes in order to further reduce the computational complexity of considered SLM schemes.

A connector model expressed as an inductance is proposed for use in a previously reported common-mode antenna model. The common-mode antenna model is an equivalent model for estimating only common-mode radiation from a printed circuit board (PCB) more quickly and with less computational resources than a calculation method that fully divides the entire structure of the PCB into elemental cells, such as narrow signal traces and thin dielectric layers. Although the common-mode antenna model can estimate the amount of radiation on the basis of the pin configuration of the connector between two PCBs, the calculation results do not show the peak frequency shift in the radiation spectrum when there is a change in the pin configuration. A previous study suggested that the frequency shift depends on the total inductance of the connector, which led to the development of the connector model reported here, which takes into account the effective inductance of the connector. The common-mode antenna model with the developed connector model accurately simulates the peak frequency shift caused by a change in the connector pin configuration. The results agree well with measured spectra (error of 3 dB).

Glial cells include several types of cells such as astrocytes, and oligodendrocytes apart from the neurons in the brain. In particular, astrocytes are known to be important in higher brain function and are therefore sometimes simply called glial cells. An astrocyte can transmit signals to other astrocytes and neurons using ion concentrations. Thus, we expect that the functions of an astrocyte can be applied to an artificial neural network. In this study, we propose a multi-layer perceptron (MLP) with a pulse glial chain. The proposed MLP contains glia (astrocytes) in a hidden layer. The glia are connected to neurons and are excited by the outputs of the neurons. The excited glia generate pulses that affect the excitation thresholds of the neurons and their neighboring glia. The glial network provides a type of positional relationship between the neurons in the hidden layer, which can enhance the performance of MLP learning. We confirm through computer simulations that the proposed MLP has better learning performance than a conventional MLP.

In everyday life, people use past events and their own knowledge in predicting probable unfolding of events. To obtain the necessary knowledge for such predictions, newspapers and the Internet provide a general source of information. Newspapers contain various expressions describing past events, but also current and future events, and opinions. In our research we focused on automatically obtaining sentences that make reference to the future. Such sentences can contain expressions that not only explicitly refer to future events, but could also refer to past or current events. For example, if people read a news article that states “In the near future, there will be an upward trend in the price of gasoline,” they may be likely to buy gasoline now. However, if the article says “The cost of gasoline has just risen 10 yen per liter,” people will not rush to buy gasoline, because they accept this as reality and may expect the cost to decrease in the future. In the following study we firstly investigate future reference sentences in newspapers and Web news. Next, we propose a method for automatic extraction of such sentences by using semantic role labels, without typical approaches (temporal expressions, etc.). In a series of experiments, we extract semantic role patterns from future reference sentences and examine the validity of the extracted patterns in classification of future reference sentences.

The development of image acquisition technology and display technology provide the base for popularization of high-resolution images. On the other hand, the available bandwidth is not always enough to data stream such high-resolution images. Down- and up-sampling, which decreases the data volume of images and increases back to high-resolution images, is a solution for the transmission of high-resolution images. In this paper, motivated by the observation that the high-frequency DCT components are sparse in the spatial domain, we propose a scheme combined with Discrete Cosine Transform (DCT) and Compressed Sensing (CS) to achieve arbitrary-ratio down-sampling. Our proposed scheme makes use of two properties: First, the energy of a image concentrates on the low-frequency DCT components. Second, the high-frequency DCT components are sparse in the spatial domain. The scheme is able to preserve the most information and avoid absolutely blindly estimating the high-frequency components. Experimental results show that the proposed down- and up-sampling scheme produces better performance compared with some state-of-the-art schemes in terms of peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM) and processing time.

This paper proposes a novel technology to detect the orientation of an image relying on its contour which is noised to varying degrees. For the image orientation detection, most methods regard to the landscape image and the image taken of a single object. In these cases, the contours of these images are supposed to be immune to the noise. This paper focuses on the the contour noised after image segmentation. A polar orientation descriptor Orientation Context is viewed as a feature to describe the coarse distribution of the contour points. This descriptor is verified to be independent of translation, isotropic scaling, and rotation transformation by theory and experiment. The relative orientation depends on the minimum distance Roulette Distance between the descriptor of a template image and that of a test image. The proposed method is capable of detecting the direction on the interval from 0 to 359 degrees which is wider than the former contour-based means (Distance Phase [1], from 0 to 179 degrees). What's more, the results of experiments show that not only the normal binary image (Noise-0, Accuracy-1: 84.8%) (defined later) achieves more accurate orientation but also the binary image with slight contour noise (Noise-1, Accuracy-1: 73.5%) could obtain more precise orientation compared to Distance Phase (Noise-0, Accuracy-1: 56.3%; Noise-1, Accuracy-1: 27.5%). Although the proposed method (O(op2)) takes more time to detect the orientation than Distance Phase (O(st)), it could be realized including the preprocessing in real time test with a frame rate of 30.

Cognitive radio (CR) is an important technology to provide high-efficiency data communication for the IoT (Internet of Things) era. Signal detection is a key technology of CR to detect communication opportunities. Energy detection (ED) is a signal detection method that does not have high computational complexity. It, however, can only estimate the presence or absence of signal(s) in the observed band. Cyclostationarity detection (CS) is an alternative signal detection method. This method detects some signal features like periodicity. It can estimate the symbol rate of a signal if present. It, however, incurs high computational complexity. In addition, it cannot estimate the symbol rate precisely in the case of single carrier signal with a low Roll-Off factor (ROF). This paper proposes a method to estimate coarsely a signal's bandwidth and carrier frequency from its power spectrum with lower computational complexity than the CS. The proposed method can estimate the bandwidth and carrier frequency of even a low ROF signal. This paper evaluates the proposed method's performance by numerical simulations. The numerical results show that in all cases the proposed coarse bandwidth and carrier frequency estimation is almost comparable to the performance of CS with lower computational complexity and even outperforms in the case of single carrier signal with a low ROF. The proposed method is generally effective for unidentified classification of the signal i.e. single carrier, OFDM etc.

Wi-Fi P2P has been deployed extensively in mobile devices. However, Wi-Fi P2P is not efficient because it requires an IP layer connection for transmitting even short messages to nearby devices, especially in high density or highly mobile environments owing to the fact that a user on the move has difficulty selecting service-available devices, and a user device has to frequently connect to and be released from nearby devices. This paper proposes a new messaging framework that enables application-level messages to be exchanged between nearby devices with no IP layer connectivity over Wi-Fi P2P. The pre-association messaging framework (PAMF) supports both broadcast and unicast transmission to maximize the delivery success rate, considering the number of peers and messages. Evaluations of PAMF conducted under real scenarios show that application-level messages can be exchanged within a few seconds, with high success rate. PAMF provides high portability and extensibility because it does not breach the Wi-Fi P2P standard. Moreover, the demonstrations show that PAMF is practical for new proximity services such as local marketing and urgent messaging.

Pseudorandom binary sequences balanced and with optimal autocorrelation have many applications in the stream cipher, communication, coding theory, etc. Constructing a binary sequences with three-level autocorrelation is equivalent to finding the corresponding characteristic set of the sequences that should be an almost difference set. In the work of T.W. Cusick, C. Ding, and A. Renvall in 1998, the authors gave the necessary and sufficient conditions by which a set of octic residues modulo an odd prime forms an almost difference set. In this paper we show that no integers verify those conditions by the theory of generalized Pell equations. In addition, by relaxing the definition of almost difference set given by the same authors, we could construct two classes of modified almost difference sets and two ones of difference sets from the set of octic residues.

Cache memories are the major application of high-speed SRAMs, and they are frequently installed in high performance logic VLSIs including microprocessors. This paper presents a 4-way set-associative, SOI cache-tag memory. To obtain higher operating speed with less power dissipation, we devised an I/O-separated memory cell with a dual-rail wordline, which is used to transmit complementary selection signals. The address decoding delay was shortened using CMOS dual-rail logic. To enhance the maximum operating frequency, bitline's recovery operations after writing data were eliminated using a memory array configuration without half-selected cells. Moreover, conventional, sensitive but slow differential amplifiers were successfully removed from the data I/O circuitry with a hierarchical bitline scheme. As regards the stored data management, we devised a new hardware-oriented LRU-data replacement algorithm on the basis of 6-bit directed graph. With the experimental results obtained with a test chip fabricated with a 0.25-µm CMOS/SIMOX process, the core of the cache-tag memory with a 1024-set configuration can achieve a 1.5-ns address access time under typical conditions of a 2-V power supply and 25°C. The power dissipation during standby was less than 14 µW, and that at the 500-MHz operation was 13-83 mW, depending on the bit-stream data pattern.

Noise and area consumption has been a trade-off in circuit design. Especially for switched-capacitor filters (SCF), kT/C noise gives a limitation to the minimum value of unit capacitance. In case of SCFs with a large capacitance spread, this limitation will result in a large area consumption due to large capacitors. This paper introduces a technique to reduce capacitance spread using charge scaling. It will be shown that this technique can reduce total capacitance of SCFs without deteriorating their noise performances. A design method to reduce the output noise of SC low-pass filters (LPF) based on the combination of cut-set scaling, charge scaling and adaptive configuration is proposed. The proposed technique can reduce the output noise voltage by 30% for small input signals.

Patch clamp measurement technique is one of the most important techniques in the field of electrophysiology. The elucidation of the channels, nerve cells, and brain activities as well as contribution of the treatment of neurological disorders is expected from the measurement of ion current. A current-to-voltage converter, which is the front end circuit of the patch clamp measurement system is fabricated using 0.18µm CMOS technology. The current-to-voltage converter requires a resistance as high as 50MΩ as a feedback resistor in order to ensure a high signal-to-noise ratio for very small signals. However, the circuit becomes unstable due to the large parasitic capacitance between the poly layer and the substrate of the on-chip feedback resistor and the instability causes the peaking at lower frequency. The instability of a current-to-voltage converter with a high-resistance as a feedback resistor is analyzed theoretically. A compensation circuit to stabilize the amplifier by driving the N-well under poly resistor to suppress the effect of parasitic capacitance using buffer circuits is proposed. The performance of the proposed circuit is confirmed by both simulation and measurement of fabricated chip. The peaking in frequency characteristic is suppressed properly by the proposed method. Furthermore, the bandwidth of the amplifier is expanded up to 11.3kHz, which is desirable for a patch clamp measurement. In addition, the input referred rms noise with the range of 10Hz ∼ 10kHz is 2.09 Arms and is sufficiently reach the requirement for measure of both whole-cell and a part of single-channel recordings.

We review recent progress in integrated photonics devices and their applications for datacom. In addition to current technology used in 100-Gigabit Ethernet (100GbE) with a compact form-factor of the transceiver, the next generation of technology for 400GbE seeks a larger number of wavelengths with a more sophisticated modulation format and higher bit rate per wavelength. For wavelength scalability and functionality, planar lightwave circuits (PLCs), such as arrayed waveguide gratings (AWGs), will be important, as well higher-order-modulation to ramp up the total bit rate per wavelength. We introduce integration technology for a 100GbE optical sub-assembly that has a 4λ x 25-Gb/s non-return-to-zero (NRZ) modulation format. For beyond 100GbE, we also discuss applications of 100GbE sub-assemblies that provide 400-Gb/s throughput with 16λ x 25-Gb/s NRZ and bidirectional 8λ x 50-Gb/s four-level pulse amplitude modulation (PAM4) using PLC cyclic AWGs.

Traffic is a key aspect of everyday life. Its study, as it happens with other complex phenomena, has found in simulation a basic tool. However, the use of simulations faces important limitations. Building them requires considering different aspects of traffic (e.g. urbanism, car features, and individual drivers) with their specific theories, that must be integrated to provide a coherent model. There is also a variety of simulation platforms with different requirements. Many of these problems demand multi-disciplinary teams, where the different backgrounds can hinder the communication and validation of simulations. The Model-Driven Engineering (MDE) of simulations has been proposed in other fields to address these issues. Such approaches develop graphical Modelling Languages (MLs) that researchers use to model their problems, and then semi-automatically generate simulations from those models. Working in this way promotes communication, platform independence, incremental development, and reutilisation. This paper presents the first steps for a MDE framework for traffic simulations. It introduces a tailored extensible ML for domain experts. The ML is focused on human actions, so it adopts an Agent-Based Modelling perspective. Regarding traffic aspects, it includes concepts commonly found in related literature following the Driver-Vehicle-Environment model. The language is also suitable to accommodate additional theories using its extension mechanisms. The approach is supported by an infrastructure developed using Eclipse MDE projects: the ML is specified with Ecore, and a model editor and a code generator tools are provided. A case study illustrates how to develop a simulation based on a driver's behaviour theory for a specific target platform using these elements.

In this paper, we propose an efficient regularized zero-forcing (RZF) precoding method that has lower hardware resource requirements and produces a shorter delay to the first transmitted symbol compared with truncated polynomial expansion (TPE) that is based on Neumann series in massive multiple-input multiple-output (MIMO) systems. The proposed precoding scheme, named matrix decomposition-polynomial expansion (MDPE), essentially applies a matrix decomposition algorithm based on polynomial expansion to significantly reduce full matrix multiplication computational complexity. Accordingly, it is suitable for real-time hardware implementations and high-mobility scenarios. Furthermore, the proposed method provides a simple expression that links the optimization coefficients to the ratio of BS/UTs antennas (β). This approach can speed-up the convergence to the matrix inverse by a matrix polynomial with small terms and further reduce computation costs. Simulation results show that the MDPE scheme can rapidly approximate the performance of the full precision RZF and optimal TPE algorithm, while adaptively selecting matrix polynomial terms in accordance with the different β and SNR situations. It thereby obtains a high average achievable rate of the UTs under power allocation.

Conversion between multi-level modulation formats is one of key processing functions for flexible networking aimed at high spectral efficiency (SE) in optical fiber transmission. The authors previously proposed an all-optical format conversion system from binary phase-shift keying (BPSK) to quadrature PSK (QPSK) and reported an experimental demonstration. In this paper, we consider its reversed conversion, that is, from QPSK to BPSK. The proposed system consists of a highly nonlinear fiber used to generate complex conjugate signal, and a 3-dB directional coupler used to produce converted signals by interfering the incident signal with the complex conjugate signal. The incident QPSK stream is converted into two BPSK tributaries without any loss of transmitting data. We show the system performances such as bit-error-rate and optical signal-to-noise ratio penalty evaluated by numerical simulation.

In a public-key encryption scheme, if a sender is not concerned about the security of a message and is unwilling to generate costly randomness, the security of the encrypted message can be compromised. In this work, we characterize such lazy parties, who are regarded as honest parties, but are unwilling to perform a costly task when they are not concerned about the security. Specifically, we consider a rather simple setting in which the costly task is to generate randomness used in algorithms, and parties can choose either perfect randomness or a fixed string. We model lazy parties as rational players who behave rationally to maximize their utilities, and define a security game between the parties and an adversary. Since a standard secure encryption scheme does not work in this setting, we provide constructions of secure encryption schemes in various settings.

Virtualization is no longer an emerging research area since the virtual processor and memory operate as efficiently as the physical ones. However, I/O performance is still restricted by the virtualization overhead caused by the costly and complex I/O virtualization mechanism, in particular by massive exits occurring on the guest-host switch and redundant processing of the I/O stacks at both guest and host. A para-virtual device driver may reduce the number of exits to the hypervisor, whereas the network stacks in the guest OS are still duplicated. Previous work proposed a socket-outsourcing technique that bypasses the redundant guest network stack by delivering the network request directly to the host. However, even by bypassing the redundant network paths in the guest OS, the obtained performance was still below 60% of the native device, since notifications of completion still depended on the hypervisor. In this paper, we propose vCanal, a novel network virtualization framework, to improve the performance of network access in the virtual machine toward that of the native machine. Implementation of vCanal reached 96% of the native TCP throughput, increasing the UDP latency by only 4% compared to the native latency.

Data deduplication is a technology that eliminates redundant data to save storage space. Most previous studies on data deduplication target backup storage, where the deduplication ratio and throughput are important. However, data deduplication on primary storage has recently been receiving attention; in this case, I/O latency should be considered equally with the deduplication ratio. Unfortunately, data deduplication causes high sequential-read-latency problems. When a file is created, the file system allocates physically contiguous blocks to support low sequential-read latency. However, the data deduplication process rearranges the block mapping information to eliminate duplicate blocks. Because of this rearrangement, the physical sequentiality of blocks in a file is broken. This makes a sequential-read request slower because it operates like a random-read operation. In this paper, we propose a selective data deduplication scheme for primary storage systems. A selective scheme can achieve a high deduplication ratio and a low I/O latency by applying different data-chunking methods to the files, according to their file access characteristics. In the proposed system, file accesses are characterized by recent access time and the access frequency of each file. No chunking is applied to update-intensive files since they are meaningless in terms of data deduplication. For sequential-read-intensive files, we apply big chunking to preserve their sequentiality on the media. For random-read-intensive files, small chunking is used to increase the deduplication ratio. Experimental evaluation showed that the proposed method achieves a maximum of 86% of an ideal deduplication ratio and 97% of the sequential-read performance of a native file system.

Random forest regressor has recently been proposed as a local landmark estimator in the face alignment problem. It has been shown that random forest regressor can achieve accurate, fast, and robust performance when coupled with a global face-shape regularizer. In this paper, we extend this approach and propose a new Local Forest Classification and Regression (LFCR) framework in order to handle face images with large yaw angles. Specifically, the LFCR has an additional classification step prior to the regression step. Our experiment results show that this additional classification step is useful in rejecting outliers prior to the regression step, thus improving the face alignment results. We also analyze each system component through detailed experiments. In addition to the selection of feature descriptors and several important tuning parameters of the random forest regressor, we examine different initialization and shape regularization processes. We compare our best outcomes to the state-of-the-art system and show that our method outperforms other parametric shape-fitting approaches.