Cloud computing is a widely used computing platform in business and academic communities. Performance is an important issue when a user runs an application in the cloud. The user may want to estimate the application-execution time beforehand to guarantee the application performance or to choose the most suitable cloud. Moreover, the cloud system architect and the designer need to understand the application performance characteristics, such as the scalability or the utilization of cloud platforms, to improve performance. However, because the application performance in clouds sometime fluctuates, estimation of the application performance is difficult. In this paper, we discuss the performance fluctuation of Hadoop jobs in both a public cloud and a community cloud for one to three months. The experimental results indicate phenomena that we cannot see without long-term experiments and phenomena inherent in Hadoop. The results suggest better ways to estimate Hadoop application performances in clouds. For example, we should be aware of application characteristics (CPU intensive or communication intensive), datacenter characteristics (busy or not), and time frame (time of day and day of the week) to estimate the performance fluctuation due to workload congestion in cloud platforms. Furthermore, we should be aware of performance degradation due to task re-execution in Hadoop applications.

Verification of a design with respect to its requirement specification is important to prevent errors before constructing an actual implementation. The existing works focus on verifications where the specifications are described using temporal logics or using the same languages as that used to describe the designs. Our work considers cases where the specifications and the designs are described using different languages. To verify such cases, we propose a framework to check if a design conforms to its specification based on their simulation relation. Specifically, we define the semantics of the specifications and the designs commonly as labelled transition systems (LTSs). We appreciate LTSs since they could interpret information about the system and actions that the system may perform as well as the effect of these actions. Then, we check whether a design conforms to its specification based on the simulation relation of their LTS. In this paper, we present our framework for the verification of reactive systems, and we present the case where the specifications and the designs are described in Event-B and Promela/Spin, respectively. We also present two case studies with the results of several experiments to illustrate the applicability of our framework on practical systems.

Recently, the growing concepts that information communication technologies apply to social infrastructures have caused deep interests with wireless sensor networks (WSNs). WSNs can be used for various application areas such as home, health, factory and so on. For the different application areas, there are different technical issues (e.g., security, reliability, real time gathering, long life time, scalability). Efficient information gathering can be potentially obtained if we take a suitable information gathering method with considering the requirements of each WSN application. Thus, we have not persisted all information gathering perfectly and have proposed one of simple information gathering methods in response to the requirements of WSN applications in this paper. In the proposed method, the information is converted to physical-layer parameters of wireless communications, such as frequency and time. Also, simulations are performed to validate the effectiveness of the proposed method in real time gathering and estimating with high precision.

In this paper, the multicell distributed beamforming (MDBF) design problem of suppressing intra-cell interference (InCI) and inter-cell interference (ICI) is studied. To start with, in order to decrease the InCI and ICI caused by a user, we propose a gradient-iteration altruistic algorithm to derive the beamforming vectors. The convergence of the proposed iterative algorithm is proved. Second, a metric function is established to restrict the ICI and maximize cell rate. This function depends on only local channel state information (CSI) and does not need additional CSIs. Moreover, an MDBF algorithm with the metric function is proposed. This proposed algorithm utilizes gradient iteration to maximize the metric function to improve sum rate of the cell. Finally, simulation results demonstrate that the proposed algorithm can achieve higher cell rates while offering more advantages to suppress InCI and ICI than the traditional ones.

A practical model-checking (MC) approach for fault analysis, that is one of the most cost-effective tasks in software development, is proposed. The proposed approach is based on a technique, named “Program-oriented Modeling” (POM) for extracting a model from source code. The framework of model extraction by POM provides configurable abstraction based on user-defined transformation rules, and it supports trial-and-error model extraction. An environment for MC called POM/MC was also built. POM/MC analyzes C source code to extract Promela models used for the SPIN model checker. It was applied to an industrial software system to evaluate the efficiency of the configurable model extraction by POM for fault analysis. Moreover, it was shown that the proposed MC approach can reduce the effort involved in analyzing software faults by MC.

Millimeter-wave (mm-wave) radio is attracting attention as one of the key enabling physical layer technologies for the fifth-generation (5G) mobile access and backhaul. This paper aims at clarifying possible roles of mm-wave radio in the 5G development and performing a comprehensive literature survey on mm-wave radio channel modeling essential for the feasibility study. Emphasis in the literature survey is laid on grasping the typical behavior of mm-wave channels, identifying missing features in the presently available channel models for the design and evaluation of the mm-wave radio links within the 5G context, and exemplifying different channel modeling activities through analyses performed in the authors' group. As a key technological element of the mm-wave radios, reduced complexity beamforming is also addressed. Design criteria of the beamforming are developed based on the spatial multipath characteristics of measured indoor mm-wave channels.

Memory leak occurs when useless objects cannot be released for a long time during program execution. Memory leaked objects may cause memory overflow, system performance degradation and even cause the system to crash when they become serious. This paper presents a dynamic approach for detecting and measuring memory leaked objects in Java programs. First, our approach tracks the program by JDI and records heap information to find out the potentially leaked objects. Second, we present memory leaking confidence to measure the influence of these objects on the program. Finally, we select three open-source programs to evaluate the efficiency of our approach. Furthermore, we choose ten programs from DaCapo 9.12 benchmark suite to reveal the time overhead of our approach. The experimental results show that our approach is able to detect and measure memory leaked objects efficiently.

In this paper, we introduce a promising iterative interference alignment (IA) strategy for multiple-input multiple-output (MIMO) multi-cell downlink networks, which utilizes the channel reciprocity between uplink/downlink channels. We intelligently combine iterative beamforming and downlink IA issues to design an iterative multiuser MIMO IA algorithm. The proposed scheme uses two cascaded beamforming matrices to construct a precoder at each base station (BS), which not only efficiently reduce the effect of inter-cell interference from other-cell BSs, referred to as leakage of interference, but also perfectly eliminate intra-cell interference among spatial streams in the same cell. The transmit and receive beamforming matrices are iteratively updated until convergence. Numerical results indicate that our IA scheme exhibits higher sum-rates than those of the conventional iterative IA schemes. Note that our iterative IA scheme operates with local channel state information, no time/frequency expansion, and even relatively a small number of mobile stations (MSs), unlike opportunistic IA which requires a great number of MSs.

In a wireless communication system, the nonlinearity of the power amplifier (PA) in the transmitter is a limiting factor of the system performance. To achieve high efficiency, the PA input signal is driven into the nonlinear region. Signals with large peak-to-power ratio (PAPR) suffer uneven distortion where large signals receive additional distortion. Orthogonal linear transformations, such as orthogonal frequency division multiplexing (OFDM) modulation, spread the nonlinear distortion evenly to each data symbol, thus improving the system performance. In this paper, we provide theoretical analysis on the benefit of orthogonal linear transform for a memoryless nonlinear communication system. We show that the multicarrier system based on orthogonal linear transform performs better than the single carrier system in the presence of nonlinearity. Simulation results validate the theoretical analysis.

Monopulse is a classical technique for radar angle estimation and still adopted for fast angle estimation in phased array antenna. The classical formula can be applied to a 2-dimentional phased array antenna if two conditions---the unbiasedness and the independence of the azimuth and the elevation estimate---are satisfied. However, if the sum and difference beams are adapted to suppress the interference under jamming condition, they can be severely distorted. Thus the difference beams become highly correlated and violate the conditions. In this paper, we show the numerical implementation of the generalized monopulse estimation using the distorted and correlated beams, especially for a subarray configured antenna. Because we use the data from the measured subarray patterns rather than the mathematical model, this numerical method can be easily implemented for the complex array configuration and gives good performance for the uncertainty of the real system.

Region of interest (ROI) coding is a useful function for many applications. JPEG2000 supports ROI coding and can decode ROIs preferentially regardless of the shape and number of the regions. However, if the number of regions is quite large, the ROI coding performance of JPEG2000 declines because the code-stream includes many useless non-ROI codes. This paper proposes a wavelet-based ROI coding method suited for multiple ROIs. The proposed wavelet transform does not access any non-ROIs when transforming the ROIs. Additionally, the proposed method eliminates the need for unnecessary coding of the bits in the higher bit planes of non-ROI regions by adding an ROI map to the code-stream. The experimental results show that the proposed method achieves a higher peak signal-to-noise ratio than the ROI coding of JPEG2000. The proposed method can be applied to both max-shift and scaling-based ROI coding.

A rotating shaft with attached sensors is wrapped in a two-dimensional waveguide sheet through which the data and power are wirelessly transmitted. A retrodirective transponder array affixed to the sheet beamforms power to the moving sensor to eliminate the need for a battery. A universal on-sheet reference scheme is proposed for calibrating the transponder circuit delay variation and eliminating a crystal oscillator from the sensor. A base signal transmitted from the on-sheet reference device is used for generating the pilot signal transmitted from the sensor and the power signal transmitted from the transponder. A 0.18-µm CMOS transponder chip and the sheet with couplers were fabricated. The coupler has three resonant frequencies used for the proposed system. The measured propagation gain of the electric field changes to less than ±1.5dB within a 2.0-mm distance between the coupler and the sheet. The measured power transmission efficiency with beamforming is 23 times higher than that without it. Each transponder outputs 1W or less for providing 3mW to the sensor.

We analyze the error performance of coding for ordered random variables with side information at the decoder, where encoder input as well as side information are deliberately extracted from the output of correlated sources. A tight upper bound on error probability is derived for linear encoding, and minimum distance decoding for a binary source and Gaussian distributed side information. Some features caused by the signal extraction are discussed.

This paper describes an architectural design and related services of a new Japanese academic backbone network, called SINET5, which will be launched in April 2016. The network will cover all 47 prefectures with 100-Gigabit Ethernet technology and connect each pair of prefectures with a minimized latency. This will enable users to leverage evolving cloud-computing powers as well as draw on a high-performance platform for data-intensive applications. The transmission layer will form a fully meshed, SDN-friendly, and reliable network. The services will evolve to be more dynamic and cloud-oriented in response to user demands. Cyber-security measures for the backbone network and tools for performance acceleration and visualization are also discussed.

This paper deals with a problem where a robot identifies an object that a human asks it to bring by voice when there is a set of objects that the human and the robot can see. When the robot knows the requested object, it must identify the object and when it does not know the object, it must say it does not. This paper presents a new method for discriminating unknown objects from known objects using object images and human speech. It uses a confidence measure that integrates image recognition confidences and speech recognition confidences based on logistic regression.

In this paper, we explore possibilities and difficulties to prove super-quadratic formula size lower bounds from the following aspects. First, we consider recursive Boolean functions and prove their general formula size upper bounds. We also discuss recursive Boolean functions based on exact 2-bit functions. We show that their formula complexity are at least Ω(n2). Hence they can be candidate Boolean functions to prove super-quadratic formula size lower bounds. Next, we consider the reason of the difficulty of resolving the formula complexity of the majority function in contrast with the parity function. In particular, we discuss the structure of an optimal protocol partition for the Karchmer-Wigderson communication game.

In order to verify the channel sum-rate improvement by multi-user multiple-input multiple-output (MU-MIMO) transmission in distributed antenna systems (DASs), we investigate and compare the characteristics of channel sum-rates in both centralized antenna systems (CASs) and DASs under the effects of path loss, spatially correlated shadowing, correlated multi-path fading, and inter-cell interference. In this paper, we introduce two different types of functions to model the shadowing, auto-correlation and cross-correlation, and a typical exponential decay function to model the multi-path fading correlation. Thus, we obtain the distribution of the channel sum-rate and investigate its characteristics. Computer simulation results indicate that DAS can improve the performance of the channel sum-rate compared to CAS, even in the case under consideration. However, this improvement decreases as interference power increases. Moreover, the decrease in the channel sum-rate due to the increase in the interference power becomes slow under the effect of shadowing correlation. In addition, some other analyses on the shadowing correlation that occurs on both the transmit and receiver sides are provided. These analysis results show that the average channel sum-rate in a DAS without inter-cell interference considerably decreases because of the shadowing correlation. In contrast, there appears to be no change in the CAS. Furthermore, there are two different types of sum-rate changes in a DAS because of the difference in shadowing auto-correlation and cross-correlation.

This paper studies the problem of power allocation for a two-way orthogonal frequency division multiplexing (OFDM) based cognitive radio (CR) network to maximize the ergodic capacities of the secondary users (SUs). Under the assumption that the SUs know the channel state information (CSI) of the interference links between the SUs and the primary user (PU) perfectly, the optimal power allocation algorithm under the interference and transmit power constraints is derived. In addition, we further assume that the SUs only know the channel distribution information (CDI) of the interference links and propose a heuristic algorithm under the interference outage and transmit power constraints based on Gaussian approximation. It is shown that the ergodic capacity with CDI is not degraded compared to that with perfect CSI under tight transmit power constraint. Moreover, it is shown that the interference outage constraint is satisfied even for very small number of subcarriers if an interference power margin is adopted for Gaussian approximation.

In this letter, we propose a new no-reference blur estimation method in the frequency domain. It is based on computing the cumulative distribution function (CDF) of the Fourier transform spectrum of the blurred image and analyzing the relationship between its shape and the blur strength. From the analysis, we propose and evaluate six curve-shaped analytic metrics for estimating blur strength. Also, we employ an SVM-based learning scheme to improve the accuracy and robustness of the proposed metrics. In our experiments on Gaussian blurred images, one of the six metrics outperformed the others and the standard deviation values between 0 and 6 could be estimated with an estimation error of 0.31 on average.

Detecting traffic anomalies is an indispensable component of overall security architecture. As Internet and traffic data with more sophisticated attacks grow exponentially, preserving security with signature-based traffic analyzers or analyzers that do not support massive traffic are not sufficient. In this paper, we propose a novel method based on combined sketch technique and S-transform analysis for detecting anomalies in massive traffic streams. The method does not require any prior knowledge such as attack patterns and models representing normal traffic behavior. To detect anomalies, we summarize the entropy of traffic data over time and maintain the summarized data in sketches. The entropy fluctuation of the traffic data aggregated to the same bucket is observed by S-transform to detect spectral changes referred to as anomalies in this work. We evaluated the performance of the method with real-world backbone traffic collected at the United States and Japan transit link in terms of both accuracy and false positive rates. We also explored the method parameters' influence on detection performance. Furthermore, we compared the performance of our method to S-transform-based and Wavelet-based methods. The results demonstrated that our method was capable of detecting anomalies and overcame both methods. We also found that our method was not sensitive to its parameter settings.