In a world of continuously expanding amounts of data, retrieving interesting information from enormous data sets becomes more complex every day. Solutions for precomputing views on these big data sets mostly follow either an offline approach, which is slow but can take into account the entire data set, or a streaming approach, which is fast but only relies on the latest data entries. A hybrid solution was introduced through the Lambda architecture concept. It combines both offline and streaming approaches by analyzing data in a fast speed layer first, and in a slower batch layer later. However, this introduces a new synchronization challenge: once the data is analyzed by the batch layer, the corresponding information needs to be removed in the speed layer without introducing redundancy or loss of data. In this paper we propose a new approach to implement the Lambda architecture concept independent of the technologies used for offline and stream computing. A universal solution is provided to manage the complex synchronization introduced by the Lambda architecture and techniques to provide fault tolerance. The proposed solution is evaluated by means of detailed experimental results.

Flexible resource utilization in terms of adaptive use of optical bandwidth with agile reconfigurability is key for future metro networks. To address this issue, we focus on optical subwavelength switched network architectures that leverage high-speed optical switching technologies and can accommodate dynamic traffic cost-effectively. Although optical subwavelength switched networks have been attracting attention, most conventional studies apply static (pre-planned) protection scenarios in the networks of limited sizes. In this paper, we discuss optical switch requirements, the use of transceivers, and protection schemes to cost-effectively create large-scale reliable metro networks. We also propose a cost-effective adaptive protection scheme appropriate for optical subwavelength switched networks using our fast time-slot allocation algorithm. The proposed scheme periodically re-optimizes the bandwidth of both working and protection paths to prevent bandwidth resources from being wasted. The numerical examples verify the feasibility of our proposed scheme and the impact on network resources.

When an access point transmits multi-view video over a wireless network with subcarriers, bit errors occur in the low quality subcarriers. The errors cause a significant degradation of video quality. The present paper proposes Significance based Multi-view Video Streaming with Subcarrier Allocation (SMVS/SA) for the maintenance of high video quality. SMVS/SA transmits a significant video frame over a high quality subcarrier to minimize the effect of the errors. SMVS/SA has two contributions. The first contribution is subcarrier-gain based multi-view rate distortion to predict each frame's significance based on the quality of subcarriers. The second contribution is heuristic algorithms to decide the sub-optimal allocation between video frames and subcarriers. The heuristic algorithms exploit the feature of multi-view video coding, which is a video frame is encoded using the previous time or camera video frame, and decides the sub-optimal allocation with low computation. To evaluate the performance of SMVS/SA in a real wireless network, we measure the quality of subcarriers using a software radio. Evaluations using MERL's benchmark test sequences and the measured subcarrier quality reveal that SMVS/SA achieves low traffic and communication delay with a slight degradation of video quality. For example, SMVS/SA improves video quality by up to 2.7 [dB] compared to the multi-view video transmission scheme without subcarrier allocation.

It is known that in the selected mapping (SLM) scheme for orthogonal frequency division multiplexing (OFDM), correlation (CORR) metric outperforms the peak-to-average power ratio (PAPR) metric in terms of bit error rate (BER) performance. It is also well known that four times oversampling is used for estimating the PAPR performance of continuous OFDM signal. In this paper, the oversampling effect of OFDM signal is analyzed when CORR metric is used for the SLM scheme in the presence of nonlinear high power amplifier. An analysis based on the correlation coefficients of the oversampled OFDM signals shows that CORR metric of two times oversampling in the SLM scheme is good enough to achieve the same BER performance as four times and 16 times oversampling cases. Simulation results confirm that for the SLM scheme using CORR metric, the BER performance for two times oversampling case is almost the same as that for four and 16 times oversampling cases.

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.

This paper proposes a novel image integral equation of the first type (IIE-1) for a TE plane wave scattering from periodic rough surfaces with perfect conductivity by means of the method of image Green's function. Since such an IIE-1 is valid for any incident wavenumbers including the critical wavenumbers, the analytical properties of the scattered wavefield can be generally and rigorously discussed. This paper firstly points out that the branch point singularity of the bare propagator inevitably appears on the incident wavenumber characteristics of the scattered wavefield and its related quantities just at the critical wavenumbers. By applying a quadrature method, the IIE-1 becomes a matrix equation to be numerically solved. For a periodic rough surface, several properties of the scattering are shown in figures as functions of the incident wavenumbers. It is then confirmed that the branch point singularity clearly appears in the numerical solution. Moreover, it is shown that the proposed IIE-1 gives a numerical solution satisfying sufficiently the optical theorem even for the critical wavenumbers.

We experimentally investigate the performance of a distributed Raman amplifier (DRA)-based pulse compressor for a phase modulated signal. A 10 Gb/s return-to-zero (RZ)-differential phase shift keying (DPSK) signal is compressed to picosecond range after transmission. Pulsewidth is continuously compressed in a wide range from 20 to 3.2 ps by changing the pump power of the DRA while the compressed waveforms are well-matched with sech2 function. Error-free operations at bit-error-rate (BER) of 10-9 are achieved for the compressed signals of various pulsewidths with low power penalties within 2.3 dB compared to the back-to-back. After the compression, the 10 Gb/s signal is used to generate a 40 Gb/s RZ-DPSK optical time division multiplexing (OTDM) signal. This 40 Gb/s OTDM signal is then successfully demultiplexed to 10 Gb/s DPSK signal by using an optical gate based on four-wave mixing (FWM) in a highly nonlinear fiber (HNLF).

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.

In this paper, we propose an example-based single image super resolution (SR) method by l2 approximation with self-sampled image patches. Example-based super resolution methods can reconstruct high resolution image patches by a linear combination of atoms in an overcomplete dictionary. This reconstruction requires a pair of two dictionaries created by tremendous low and high resolution image pairs from the prepared image databases. In our method, we introduce the dictionary by random sampling patches from just an input image and eliminate its training process. This dictionary exploits the self-similarity of images and it will no more depend on external image sets, which consern the storage space or the accuracy of referred image sets. In addition, we modified the approximation of input image to an l2-norm minimization problem, instead of commonly used sparse approximation such as l1-norm regularization. The l2 approximation has an advantage of computational cost by only solving an inverse problem. Through some experiments, the proposed method drastically reduces the computational time for the SR, and it provides a comparable performance to the conventional example-based SR methods with an l1 approximation and dictionary training.

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.

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.

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.

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.

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.

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.

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.

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.

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.