For voice-enabled car navigation systems that use a multi-purpose cloud speech recognition service (cloud ASR), utterance classification that is robust against speech recognition errors is needed to realize a user-friendly voice interface. The purpose of this study is to improve the accuracy of utterance classification for voice-enabled car navigation systems when inputs to a classifier are error-prone speech recognition results obtained from a cloud ASR. The role of utterance classification is to predict which car navigation function a user wants to execute from a spontaneous utterance. A cloud ASR causes speech recognition errors due to the noises that occur when traveling in a car, and the errors degrade the accuracy of utterance classification. There are many methods for reducing the number of speech recognition errors by modifying the inside of a speech recognizer. However, application developers cannot apply these methods to cloud ASRs because they cannot customize the ASRs. In this paper, we propose a system for improving the accuracy of utterance classification by modifying both speech-signal inputs to a cloud ASR and recognized-sentence outputs from an ASR. First, our system performs speech enhancement on a user's utterance and then sends both enhanced and non-enhanced speech signals to a cloud ASR. Speech recognition results from both speech signals are merged to reduce the number of recognition errors. Second, to reduce that of utterance classification errors, we propose a data augmentation method, which we call “optimal doping,” where not only accurate transcriptions but also error-prone recognized sentences are added to training data. An evaluation with real user utterances spoken to car navigation products showed that our system reduces the number of utterance classification errors by 54% from a baseline condition. Finally, we propose a semi-automatic upgrading approach for classifiers to benefit from the improved performance of cloud ASRs.

This paper proposes a novel environmental monitoring strategy, incremental environmental monitoring, that enables scientists to reveal the ecology of wild animals in the field. We applied this strategy to the habitat of endangered freshwater fish. Specifically, we designed and implemented a network-based system using distributed sensors to continuously monitor and record the habitat of endangered fish. Moreover, we developed a set of analytical tools to exploit a variety of sensor data, including environmental time-series data such as amount of dissolved oxygen, as well as underwater video capturing the interaction of fish and their environment. We also describe the current state of monitoring the behavior and habitat of endangered fish and discuss solutions for making such environmental monitoring more efficient in the field.

After large-scale disasters, information sharing among people becomes more important than usual. This, however, is extremely difficult to achieve in disaster zones due to serious damage to the existing network infrastructure, power outages, and high traffic congestion. For the quick provision of alternative networks to serve heavy communication demands after disasters, establishing local area networks (LANs) consisting of portable servers with data storage has been considered as one of the most promising solutions. Based on the established LAN and a data server in each area, people can share many kinds of disaster-related information such as emergency information and supply/demand information via deployed neighboring servers. However, due to the lack of stable Internet connection, these servers are isolated and cannot be synchronized in real time. To enable and guarantee more efficient information sharing across the whole disaster-hit area, data stored on each server should be synchronized without the Internet. Our solution is to propose an intermittent data synchronization scheme that uses moving vehicles as relays to exchange data between isolated servers after disasters. With the objective of maximizing the total number of synchronized high priority data under the capability constraints of mobile relays, we first propose a data allocation scheme (DAS) from a server to a mobile relay. After that, we propose a trajectory planning scheme for the relays which is formulated as a Mixed Integer Linear Fractional Programming (MILFP) problem, and an algorithm to solve it efficiently. Extensive simulations and comparisons with other methods show the superior performance of our proposals.

The traditional tests are planned and designed at the early stages, but it is possible to execute test cases after implementing source code. Since there is a time difference between design stage and testing stage, by the time a software design error is found it will be too late. To solve this problem, this paper suggests a virtual pre-testing process. While the virtual pre-testing process can find software and testing errors before the developing stage, it can automatically generate and execute test cases with modeling and simulation (M&S) in a virtual environment. The first part of this method is to create test cases with state transition tree based on state diagram, which include state, transition, instruction pair, and all path coverage. The second part is to model and simulate a virtual target, which then pre-test the target with test cases. In other words, these generated test cases are automatically transformed into the event list. This simultaneously executes test cases to the simulated target within a virtual environment. As a result, it is possible to find the design and test error at the early stages of the development cycle and in turn can reduce development time and cost as much as possible.

In data centers, large numbers of computers are run simultaneously. These computers consume an enormous amount of energy. Several challenges related to this issue have been published. An energy-efficient storage management method that cooperates with applications was one effective approach. In this method, data and storage devices are managed using application support and the power consumption of storage devices is significantly decreased. However, existing studies do not take the virtualized environment into account. Recently, many data-intensive applications have been run in a virtualized environment, such as the cloud computing environment. In this paper, we focus on a virtualized environment wherein multiple virtual machines run on a physical computer and a data intensive application runs on each virtual machine. We discuss a method for reducing storage device power consumption using application support. First, we propose two storage management methods using application information. One method optimizes the inter-HDD file layout. This method removes frequently-accessed files from a certain HDD and switches the HDD to power-off mode. To balance loads and reduce seek distances, this method separates a heavily accessed file and consolidates files in a virtual machine with low access frequency. The other method optimizes the intra-HDD file layout, in addition to performing inter-HDD optimization. This method places frequently accessed files near each other. Second, we present our experimental results and demonstrate that the proposed methods can create sufficiently long HDD access intervals that power-off mode can be used, and thereby, reduce the power consumption of storage devices.

With the rapid spread of smart devices, such as smartphones and tablet PCs, user authentication is becoming increasingly important because various kinds of data concerning user privacy are processed within them. At present, in the case of smart devices, password-based authentication is frequently used; however, biometric authentication has attracted more attention as a user authentication technology. A smart device is equipped with various sensors, such as cameras, microphones, and touch panels, many of which enable biometric information to be obtained. While the function of biometric authentication is available in many smart devices, there remain some problems to be addressed for more secure and convenient user authentication. In this paper, we summarize the current problems with user authentication on smart devices and propose a novel user authentication system based on the concept of context awareness to resolve these problems. We also present our evaluation of the performance of the system by using biometric information that was acquired from smart devices. The evaluation demonstrates the effectiveness of our system.

In the case of drive-by download attacks, most malicious web sites identify the software environment of the clients and change their behavior. Then we cannot always obtain sufficient information appropriate to the client organization by automatic dynamic analysis in open services. It is required to prepare for expected incidents caused by re-accessing same malicious web sites from the other client in the organization. To authors' knowledge, there is no study of utilizing analysis results of malicious web sites for digital forensic on the incident and hedging the risk of expected incident in the organization. In this paper, we propose a system for evaluating the impact of accessing malicious web sites by using the results of multi-environment analysis. Furthermore, we report the results of evaluating malicious web sites by the multi-environment analysis system, and show how to utilize analysis results for forensic analysis and risk hedge based on actual cases of analyzing malicious web sites.

We developed a system called DELTA that supports the students' use of backward chaining (BC) to prove the congruence of two triangles. DELTA is designed as an interactive learning environment and supports the use of BC by providing hints and a function to automatically check the proofs inputted by the students. DELTA also has coloring, marking, and highlighting functions to support students' attempts to prove the congruence of two triangles. We evaluated the efficacy of DELTA with 36 students in the second grade of a junior high school in Japan. We found that (1) the mean number of problems, which the experimental group (EG) completely solved, was statistically higher than that of the control group on the post-test; (2) the EG effectively used the BC strategy to solve problems; and (3) the students' attempt to use both the forward chaining strategy and the BC strategy led to solving the problems completely.

Systematic research on electromagnetic compatibility (EMC) in Japan started in 1977 by the establishment of a technical committee on “environmental electromagnetic engineering” named EMCJ, which was founded both in the Institute of Electronics and Communication Engineers or the present IEICE (Institute of Electronics, Information and Communication Engineers) and in the Institute of Electrical Engineers of Japan or the IEEJ. The research activities have been continued as the basic field of interdisciplinary study to harmonize even in the electromagnetic (EM) environment where radio waves provide intolerable EM disturbances to electronic equipment and to that environment itself. The subjects and their outcomes which the EMCJ has dealt with during about 40 years from the EMCJ establishment include the evaluation of EM environment, EMC of electric and electronic equipment, and EMC of biological effects involving bioelectromagnetics and so on. In this paper, the establishment history and structure of the EMCJ are reviewed along with the change in activities, and topics of the technical reports presented at EMCJ meetings from 2006 to 2016 are surveyed. In addition, internationalization and its related campaign are presented in conjunction with the EMCJ research activities, and the status quo of the EMCJ under the IEICE is also discussed along with the prospects.

In this paper, we clarify the issues in a metropolitan environment involving overlying frequency bands with various bandwidths and propose a cell selection scheme that improves the communications quality based on user and network characteristics. Different frequency bands with various signal bandwidths will be overlaid on each other in forthcoming fifth-generation (5G) radio access networks. At the same time, services, applications or features of sets of user equipment (UEs) will become more diversified and the requirements for the quality of communications will become more varied. Moreover, in real environments, roads and buildings have irregular constructions. Especially in an urban or metropolitan environment, the complex architecture present in a metropolis directly affects radio propagation. Under these conditions, the communications quality is degraded because cell radio resources are depleted due to many UE connections and the mismatch between service requirements and cell capabilities. The proposed scheme prevents this degradation in communications quality. The effectiveness of the proposed scheme is evaluated in an ideal regular deployment and in a non-regular metropolitan environment based on computer simulations. Simulation results show that the average of the time for the proposed scheme from the start of transmission to the completion of reception at the UE is improved by approximately 40% compared to an existing cell selection scheme that is based on the Maximum Signal-to-Interference plus Noise power Ratio (SINR).

The software development process is front-loaded when formal specification is deployed and as a consequence more problems are identified and solved at an earlier point of time. This places extra importance on the quality and efficiency of the different formal specification tasks. We use the term “exploratory modeling” to denote the modeling that is conducted during the early stages of software development before the requirements are clearly understood. We believe tools that support not only rigorous but also flexible construction of the specification at the same time are helpful in such exploratory modeling phases. This paper presents a web-based IDE named VDMPad to demonstrate the concept of exploratory modeling. VDMPad has been evaluated by experienced professional VDM engineers from industry. The positive evaluation resulting from such industrial users are presented. It is believed that flexible and rigorous tools for exploratory modeling will help to improve the productivity of the industrial software developments by making the formal specification phase more efficient.

The performance of devices based on two dimensional (2D) materials is significantly affected upon prolonged exposure to atmosphere. We analyzed time based environmental degradation of electrical properties of HfS2 field effect transistors. Atmospheric entities like oxygen and moisture adversely affect the device surface and reduction in drain current is observed over period of 48 hours. Two corrective measures, namely, PMMA passivation and vacuum annealing, have been studied to address the diminution of current by contaminants. PMMA passivation prevents the device from environment and reduces the effect of Coulomb scattering. Improvement in current characteristics signifies the importance of dielectric passivation for 2D materials. On the other hand, vacuum annealing is useful in removing contaminants from the affected surface. In order to figure out optimum process conditions, properties have been studied at various annealing temperatures. The improvement in drain current level was observed upon vacuum annealing within optimum range of annealing temperature.

Recently, mobile networks employing high-speed high-capacity communications have been investigated extensively to satisfy the demand for faster and higher-capacity data communications. In one approach, frequencies between 6 and 100GHz are candidates to utilize relatively wide frequency bandwidths. Accordingly, radio propagation loss in these frequency bands must be characterized. Ray-tracing (RT) is the most common modeling approach to predict propagation loss in site-specific scenarios. The accuracy of RT simulations has been investigated in urban street cell environments based on comparison to measurement results and we observed that the difference between RT simulation and measurement results tends to increase as the frequency increases. In this paper, we focus on the shape of building corners at an intersection because it is this shape that is a dominant contributing factor in the region away from the intersection. In order to correct the error in the conventional RT method, we propose an alternative model that considers the detailed shape of the building corner and surface roughness. The performance of the RT simulation using the proposed method is then investigated based on comparison of two different sets of measurement results. Finally, we extract the optimal size and roughness for the proposed modeling method. Consequently, we confirm that using the proposed method with optimized parameters significantly enhances the accuracy compared to the conventional method.

This paper proposed that a path loss model for outdoor-to-indoor corridor is presented to construct next generation mobile communication systems. The proposed model covers the frequency range of millimeter wave bands up to 40GHz and provides three dimensional incident angle characteristics. Analysis of path loss characteristics is conducted by ray tracing. We clarify that the paths reflected multiple times between the external walls of buildings and then diffracted into one of the buildings are dominant. Moreover, we also clarify how the paths affect the path loss dependence on frequency and three dimensional incident angle. Therefore, by taking these dependencies into consideration, the proposed model decreases the root mean square errors of prediction results to within about 2 to 6dB in bands up to 40GHz.

The development of assistive devices for automated sound recognition is an important field of research and has been receiving increased attention. However, there are still very few methods specifically developed for identifying environmental sounds. The majority of the existing approaches try to adapt speech recognition techniques for the task, usually incurring high computational complexity. This paper proposes a sound recognition method dedicated to environmental sounds, designed with its main focus on embedded applications. The pre-processing stage is loosely based on the human hearing system, while a robust set of binary features permits a simple k-NN classifier to be used. This gives the system the capability of in-field learning, by which new sounds can be simply added to the reference set in real-time, greatly improving its usability. The system was implemented in an FPGA based platform, developed in-house specifically for this application. The design of the proposed method took into consideration several restrictions imposed by the hardware, such as limited computing power and memory, and supports up to 12 reference sounds of around 5.3 s each. Experimental results were performed in a database of 29 sounds. Sensitivity and specificity were evaluated over several random subsets of these signals. The obtained values for sensitivity and specificity, without additional noise, were, respectively, 0.957 and 0.918. With the addition of +6 dB of pink noise, sensitivity and specificity were 0.822 and 0.942, respectively. The in-field learning strategy presented no significant change in sensitivity and a total decrease of 5.4% in specificity when progressively increasing the number of reference sounds from 1 to 9 under noisy conditions. The minimal signal-to-noise ration required by the prototype to correctly recognize sounds was between -8 dB and 3 dB. These results show that the proposed method and implementation have great potential for several real life applications.

A generalized covariance matrix taper (GCMT) model is proposed to enhance the performance of knowledge-aided space-time adaptive processing (KA-STAP) under sea clutter environments. In KA-STAP, improving the accuracy degree of the a priori clutter covariance matrix is a fundamental issue. As a crucial component in the a priori clutter covariance matrix, the taper matrix is employed to describe the internal clutter motion (ICM) or other subspace leakage effects, and commonly constructed by the classical covariance matrix taper (CMT) model. This work extents the CMT model into a generalized CMT (GCMT) model with a greater degree of freedom. Comparing it with the CMT model, the proposed GCMT model is more suitable for sea clutter background applications for its improved flexibility. Simulation results illustrate the efficiency of the GCMT model under different sea clutter environments.

In recent years, multiple-input multiple-output (MIMO) channel models for crowded areas, such as indoor offices, shops, and outdoor hotspot environments, have become a topic of significant interest. In such crowded environments, propagation paths are frequently shadowed by moving objects, such as pedestrians or vehicles. These shadowing effects can cause time variations in the delay and angle-of-arrival (AoA) characteristics of a channel. In this paper, we propose a method for modeling the shadowing effects of pedestrians in a cluster-based channel model. The proposed method uses cluster power variations to model the time-varying channel properties. We also propose a novel method for estimating the cluster power variation properties from measured data. In order to validate our proposed method, channel sounding in the 3GHz band is conducted in a cafeteria during lunchtime. The results for the K parameter, delay spreads, and AoA azimuth spreads are compared for the measured data and the channel data generated using the proposed method. The results indicate that the time-varying delay-AoA characteristics can be effectively modeled using our proposed method.

Network virtualization environments (NVEs) are emerging to meet the increasing diversity of demands by Internet users where a virtual network (VN) can be constructed to accommodate each specific application service. In the future Internet, diverse service providers (SPs) will provide application services on their own VNs running across diverse infrastructure providers (InPs) that provide physical resources in an NVE. To realize both efficient resource utilization and good QoS of each individual service in such environments, SPs should perform adaptive control on network and computational resources in dynamic and competitive resource sharing, instead of explicit and sufficient reservation of physical resources for their VNs. On the other hand, two novel concepts, software-defined networking (SDN) and network function virtualization (NFV), have emerged to facilitate the efficient use of network and computational resources, flexible provisioning, network programmability, unified management, etc., which enable us to implement adaptive resource control. In this paper, therefore, we propose an architectural design of network orchestration for enabling SPs to maintain QoS of their applications aggressively by means of resource control on their VNs efficiently, by introducing virtual network provider (VNP) between InPs and SPs as 3-tier model, and by integrating SDN and NFV functionalities into NVE framework. We define new north-bound interfaces (NBIs) for resource requests, resource upgrades, resource programming, and alert notifications while using the standard OpenFlow interfaces for resource control on users' traffic flows. The feasibility of the proposed architecture is demonstrated through network experiments using a prototype implementation and a sample application service on nation-wide testbed networks, the JGN-X and RISE.

This paper empirically validates battery-less sensor activation via wireless energy transmission to release sensors from wires and batteries. To seamlessly extend the coverage and activate sensor nodes distributed in any indoor environment, we proposed multi-point wireless energy transmission with carrier shift diversity. In this scheme, multiple transmitters are employed to compensate path-loss attenuation and orthogonal frequencies are allocated to the multiple transmitters to avoid the destructive interference that occurs when the same frequency is used by all transmitters. In our previous works, the effectiveness of the proposed scheme was validated theoretically and also empirically by using just a spectrum analyzer to measure the received power. In this paper, we develop low-energy battery-less sensor nodes whose consumed power and required received power for activation are respectively 142µW and 400µW. In addition, we conduct indoor experiments in which the received power and activation of battery-less sensor node are simultaneously observed by using the developed battery-less sensor node and a spectrum analyzer. The results show that the coverage of single-point and multi-point wireless energy transmission without carrier shift diversity are, respectively, 84.4% and 83.7%, while the coverage of the proposed scheme is 100%. It can be concluded that the effectiveness of the proposed scheme can be verified by our experiments using real battery-less sensor nodes.

Change-aware development environments can automatically record fine-grained code changes on a program and allow programmers to replay the recorded changes in chronological order. However, since they do not always need to replay all the code changes to investigate how a particular entity of the program has been changed, they often eliminate several code changes of no interest by manually skipping them in replaying. This skipping action is an obstacle that makes many programmers hesitate when they use existing replaying tools. This paper proposes a slicing mechanism that automatically removes manually skipped code changes from the whole history of past code changes and extracts only those necessary to build a particular class member of a Java program. In this mechanism, fine-grained code changes are represented by edit operations recorded on the source code of a program and dependencies among edit operations are formalized. The paper also presents a running tool that slices the operation history and replays its resulting slices. With this tool, programmers can avoid replaying nonessential edit operations for the construction of class members that they want to understand. Experimental results show that the tool offered improvements over conventional replaying tools with respect to the reduction of the number of edit operations needed to be examined and over history filtering tools with respect to the accuracy of edit operations to be replayed.