In elevator-group control, the average number of running cars should be finely adjusted by the dynamically controlling the number of running cars (DCNRC). Traffic demand in an office building varies throughout the day. In this paper, we propose a new energy-saving method for elevator-group control that adjusts the number of running cars according to the traffic demand, simulate the proposed energy-saving method under nearly real traffic demand conditions of an office building, and reduce the daily energy consumption to the target level after several days.

Computing and power resources are often limited in real-time embedded control systems. In this paper, we resolve the trade-off problem between control performance and power consumption in a real-time embedded control system with a dynamic voltage and frequency scaling (DVFS) uniprocessor implementing multiple control tasks. We formulate an optimization problem whose cost function depends on both the control performance and the power consumption. We introduce an adapter into the real-time embedded control system that adaptively assigns deadlines of jobs and clock frequencies according to the plant's stability and schedulability by solving the optimization problem. In numerical simulations, we show that the proposed adapter can reduce the power consumption while maintaining the control performance.

Rainbow is one of signature schemes based on the problem solving a set of multivariate quadratic equations. While its signature generation and verification are fast and the security is presently sufficient under suitable parameter selections, the key size is relatively large. Recently, Quaternion Rainbow — Rainbow over a quaternion ring — was proposed by Yasuda, Sakurai and Takagi (CT-RSA'12) to reduce the key size of Rainbow without impairing the security. However, a new vulnerability emerges from the structure of quaternion ring; in fact, Thomae (SCN'12) found that Quaternion Rainbow is less secure than the same-size original Rainbow. In the present paper, we further study the structure of Quaternion Rainbow and show that Quaternion Rainbow is one of sparse versions of the Rainbow. Its sparse structure causes a vulnerability of Quaternion Rainbow. Especially, we find that Quaternion Rainbow over even characteristic field, whose security level is estimated as about the original Rainbow of at most 3/4 by Thomae's analysis, is almost as secure as the original Rainbow of at most 1/4-size.

For estimating user's location, Global Navigation Satellite System (GNSS) is very useful. Especially, Global Positioning System (GPS) by USA is very popular. A GPS receiver needs multiple satellites (usually 4 and more satellites). Propagation to the satellites needs line-of-sight. However, in urban area, there are many buildings. Received signals tend to become bad quality. Such signals are often called as non-line-of-sight (NLOS) or multipath signals. The problem is that the receiver cannot get line-of-sight signals from adequate number of the satellites coinstantaneously. This case leads to degradation of estimation quality or impossibility of estimation. In this paper, we will introduce a novel estimation algorithm, which can estimate own position with as low number of satellites as possible. The proposal achieves the estimation by only two satellites. The proposal also uses a traveling distance sensor which is often equipped on vehicles. By recorded satellite data, we will confirm our effectiveness.

A monolithic frequency synthesizer with wide tuning range, low phase noise and spurs was realized in 0.13,$mu$m CMOS technology. It consists of an analog PLL, a harmonic-rejection mixer and injection-locked frequency doublers to cover the whole 6--18,GHz frequency range. To achieve a low phase noise performance, a sub-sampling PLL with non-dividers was employed. The synthesizer can achieve phase noise $-$113.7,dBc/Hz@100,kHz in the best case and the reference spur is below $-$60,dBc. The core of the synthesizer consumes about 110,mA*1.2,V.

In recent years, there has been growing interest in systems for sharing resources, which were originally used for personal purposes by individual users, among many unspecified users via a network. An example of such systems is a peer-to-peer (P2P) data storage system that enables users to share a portion of unused space in their own storage devices among themselves. In a recent paper on a P2P data storage system, the user behavior model was defined based on supply and demand functions that depend only on the storage space unit price in a virtual marketplace. However, it was implicitly assumed that other factors, such as unused space of storage devices possessed by users and additional storage space asked by users, did not affect the characteristics of the supply and demand functions. In addition, it was not clear how the values of parameters used in the user behavior model were determined. Therefore, in this paper, we modify the supply and demand functions and determine the values of their parameters by taking the above mentioned factors as well as the price structure of storage devices in a real marketplace into account. Moreover, we provide a numerical example to evaluate the social welfare realized by the P2P data storage system as a typical application of the modified supply and demand functions.

Recently, a malicious user attacks a web browser through a malicious page that exploits the vulnerability of the browser and that executes malicious code. To prevent this attack, some methods have been devised such as DEP (Data Execution Prevention) that prevents data in stack frame or heap region from being executed. However, to evade these defense techniques, return-oriented programming (ROP) is introduced. ROP executes arbitrary code indirectly using gadget, which is group of instructions including ret instruction in a module that doesn't apply ASLR (Address Space Layout Randomization). In this paper, we propose a static approach to detect ROP payload in a network irrespective of the environment of the system under attack. Most studies have tried to detect ROP attacks using dynamic analysis, because ROP has various addresses of gadgets according to loaded modules. These methods have a limitation that must consider the environment of system to operate ROP, such as the version of OS and modules including gadgets. To overcome this limitation, our method detects ROP payload using static analysis without preliminary knowledge about the environment. We extract five characteristics of ROP and then propose a novel algorithm, STROP, to detect ROP in payload without execution. Our idea is as follows: STROP makes stack frame using input payload statically. It extracts addresses suspected as indicating gadgets and makes groups using the addresses. And then, STROP determine whether the payload includes ROP based on static characteristics. We implement a prototype using snort (network-based intrusion system) and evaluate it. Experiments show that our technique can detect ROP payload with a low number of false alarms. False positive (FP) is 1.3% for 2,239 benign files and 0.05-0.51% for 1GB packet dump file. Among 68 ROP payloads, STROP detects 51 payloads. This research can be applied to existing systems that collect malicious codes, such as Honeypot.

A dynamic controller, based on the Stability Transformation Method (STM), has been used to stabilize unknown and unstable periodic orbits (UPOs) in dynamical systems. An advantage of the control method is that it can stabilize unknown UPOs. In this study, we introduce a novel control method, based on STM, to stabilize UPOs in DC-DC switching power converters. The idea of the proposed method is to apply temporal perturbations to the switching time. These perturbations are calculated without information of the locations of the target orbits. The effectiveness of the proposed method is verified by numerical simulations and laboratory measurements.

Energy conservation is one of the hot topics within the domain of traffic problems. It is well known that shortening the distance between vehicles reduces the aerodynamic drag of the lagging (or following) vehicle and leads to energy savings, which benefits the drivers. Recently, systems have been developed in which trucks or vehicles travel in a platoon with reduced headway from the preceding vehicle by using automated driving or driver assistance systems. The objective of the present study is to investigate how human factors, such as driving style, a driver's condition, or a driver's personal characteristics, influence the decision of a driver to close the gap with a preceding vehicle and obtain the benefit of aerodynamic drag reduction. We developed a realistic experimental paradigm for investigating the relationship between distance and several factors including the driver's personal characteristics and the size of preceding vehicle. Our experimental setup made use of real vehicles on a test track, as opposed to a vehicle simulator. We examined behavior of subjects that drove the following vehicle as well as subjects that sat in the passenger seat in the following vehicle. The experimental results demonstrate that all subjects attempted to reduce the distance to the preceding vehicle in order to gain the benefit. Based on the experimental and questionnaire results, we conclude that there are relationships between the category of subjects and subject's following distances.

Although multi-user multiple-input multiple-output (MI-MO) systems provide high data rate transmission, they may suffer from interference. Block diagonalization and eigenbeam-space division multiplexing (E-SDM) can suppress interference. The transmitter needs to determine beamforming weights from channel state information (CSI) to use these techniques. However, MIMO channels change in time-varying environments during the time intervals between when transmission parameters are determined and actual MIMO transmission occurs. The outdated CSI causes interference and seriously degrades the quality of transmission. Channel prediction schemes have been developed to mitigate the effects of outdated CSI. We evaluated the accuracy of prediction of autoregressive (AR)-model-based prediction and Lagrange extrapolation in the presence of channel estimation error. We found that Lagrange extrapolation was easy to implement and that it provided performance comparable to that obtained with the AR-model-based technique.

This paper presents a framework for reducing the energy consumption of embedded real-time systems. We implemented the presented framework as both an optimization toolchain and an energy-aware real-time operating system. The framework consists of the integration of multiple techniques to optimize the energy consumption. The main idea behind our approach is to utilize trade-offs between the energy consumption and the performance of different processor configurations during task checkpoints, and to maintain memory allocation during task context switches. In our framework, a target application is statically analyzed at both intra-task and inter-task levels. Based on these analyzed results, runtime optimization is performed in response to the behavior of the application. A case study shows that our toolchain and real-time operating systems have achieved energy reduction while satisfying the real-time performance. The toolchain has also been successfully applied to a practical application.

This paper represents an illumination modeling method for lighting control which can model the illumination distribution inside office buildings. The algorithm uses data from the illumination sensors to train Radial Basis Function Neural Networks (RBFNN) which can be used to calculate 1) the illuminance contribution from each luminaire to different positions in the office 2) the natural illuminance distribution inside the office. This method can be used to provide detailed illumination contribution from both artificial and natural light sources for lighting control algorithms by using small amount of sensors. Simulations with DIALux are made to prove the feasibility and accuracy of the modeling method.

This article introduces a self-organizing model which builds the topology of a DHT mapping system for ICN. Due to its self-organizing operation and low average degree of maintenance, the management overhead of the system is reduced dramatically, which yields inherent scalability. The proposed model can improve latency by around 10% compared to an existing approach which has a near optimal average distance when the number of nodes and degree are given. In particular, its operation is simple which eases maintenance concerns. Moreover, we analyze the model theoretically to provide a deeper understanding of the proposal.

In anonymous reputation systems, where after an interaction between anonymous users, one of the users evaluates the peer by giving a rating. Ratings for a user are accumulated, which becomes the reputation of the user. By using the reputation, we can know the reliability of an anonymous user. Previously, anonymous reputation systems have been proposed, using an anonymous e-cash scheme. However, in the e-cash-based systems, the bank grasps the accumulated reputations for all users, and the fluctuation of reputations. These are private information for users. Furthermore, the timing attack using the deposit times is possible, which makes the anonymity weak. In this paper, we propose an anonymous reputation system, where the reputations of users are secret for even the reputation manager such as the bank. Our approach is to adopt an anonymous credential certifying the accumulated reputation of a user. Initially a user registers with the reputation manager, and is issued an initial certificate. After each interaction with a rater, the user as the ratee obtains an updated certificate certifying the previous reputation summed up by the current rating. The update protocol is based on the zero-knowledge proofs, and thus the reputations are secret for the reputation manager. On the other hand, due to the certificate, the user cannot maliciously alter his reputation.

To support the processing of spatial window queries efficiently in a non-flat wireless data broadcasting system, we propose a Two-Tier Spatial Index (TTSI) that uses a two tier data space to distinguish hot and regular data items. Unlike an existing index which repeats regular data items located near hot items at the same time as the hot data items during the broadcast cycle, TTSI makes it possible to repeat only hot data items during a cycle. Simulations show that the proposed TTSI outperforms the existing scheme with respect to access time and energy consumption.

The development of high data rate wireless communications systems using Multiple Input — Multiple Output (MIMO) antenna techniques requires detectors with reduced complexity and good Bit Error Rate (BER) performance. In this paper, we present the Semi-fixed Complexity Sphere Decoder (SCSD), which executes the process of detection in MIMO systems with a significantly lower computation cost than the high-performance/reduced-complexity detectors: Sphere Decoder (SD), K-best, Fixed Complexity Sphere Decoder (FSD) and Adaptive Set Partitioning (ASP). Simulation results show that when the Signal-to-Noise Ratio (SNR) is less than 15dB, the SCSD reduces the complexity by up to 90% with respect to SD, up to 60% with respect to K-best or ASP and by up to 90% with respect to FSD. In the proposed algorithm, the BER performance does not show significant degradation and therefore, can be considered as a complexity reduction scheme suitable for implementing in MIMO detectors.

This paper proposes an automatic error correction method for melanosome tracking. Melanosomes in intracellular images are currently tracked manually when investigating diseases, and an automatic tracking method is desirable. We detect all melanosome candidates by SIFT with 2 different parameters. Of course, the SIFT also detects non-melanosomes. Therefore, we use the 4-valued difference image (4-VDimage) to eliminate non-melanosome candidates. After tracking melanosome, we re-track the melanosome with low confidence again from t+1 to t. If the results from t to t+1 and from t+1 to t are different, we judge that initial tracking result is a failure, the melanosome is eliminated as a candidate and re-tracking is carried out. Experiments demonstrate that our method can correct the error and improves the accuracy.

In conventional wireless cellular networks, cell coverage is static and fixed, and each user equipment (UE) is connected to one or a few local base stations (BS). However, the users' distribution in the network area commonly fluctuates during a day. When there are congeries of users in some areas, conventional networks waste idle network resources in sparse areas. To address this issue, we propose a novel approach for cooperative cluster formation to dynamically transfer idle network resources from sparse cells to crowded cells or hotspots. In our proposed scheme, BS coverage is directed to hotspots by dynamically changing the antennas' beam angles, and forming large optimal cooperative clusters around hotspots. In this study, a cluster is a group of BSs that cooperatively perform joint transmission (JT) to several UEs. In this paper, a mathematical framework for calculation of the system rate of a cooperative cluster is developed. Next, the set of BSs for each cluster and the antennas' beam angles of each BS are optimized so that the system rate of the network is maximized. The trend of performance variation versus cluster size is studied and its limitations are determined. Numerical results using 3GPP specifications show that the proposed scheme attains several times higher capacity than conventional systems.

In this paper, we propose a parameter estimation method using Volterra kernels for the nonlinear IIR filters, which are used for the linearization of closed-box loudspeaker systems. The nonlinear IIR filter, which originates from a mirror filter, employs nonlinear parameters of the loudspeaker system. Hence, it is very important to realize an appropriate estimation method for the nonlinear parameters to increase the compensation ability of nonlinear distortions. However, it is difficult to obtain exact nonlinear parameters using the conventional parameter estimation method for nonlinear IIR filter, which uses the displacement characteristic of the diaphragm. The conventional method has two problems. First, it requires the displacement characteristic of the diaphragm but it is difficult to measure such tiny displacements. Moreover, a laser displacement gauge is required as an extra measurement instrument. Second, it has a limitation in the excitation signal used to measure the displacement of the diaphragm. On the other hand, in the proposed estimation method for nonlinear IIR filter, the parameters are updated using simulated annealing (SA) according to the cost function that represents the amount of compensation and these procedures are repeated until a given iteration count. The amount of compensation is calculated through computer simulation in which Volterra kernels of a target loudspeaker system is utilized as the loudspeaker model and then the loudspeaker model is compensated by the nonlinear IIR filter with the present parameters. Hence, the proposed method requires only an ordinary microphone and can utilize any excitation signal to estimate the nonlinear parameters. Some experimental results demonstrate that the proposed method can estimate the parameters more accurately than the conventional estimation method.

Due to the recent network service market trends, network infrastructure providers must make their network infrastructures tolerant of network service complexity and swift at providing new network services. To achieve this, we first make a design decision for the single domain network infrastructure in which we use network virtualization and separate the network service control and management from the network infrastructure and leave the resource connectivity control and management in the network infrastructure so that the infrastructure can maintain simplicity and the network service can become complex and be quickly provided. Along with the decision, we construct an architecture of the network infrastructure and a network management model. The management model defines a slice as being determined by abstracted resource requirements and restructures the roles and planes from the viewpoint of network infrastructure usability so that network service requesters can manage network resources freely and swiftly in an abstract manner within the authorities the network infrastructure operator provides. We give the details of our design and implementation for a network virtualization management system along with the model. We deployed and evaluated our designed and implemented management system on the Japan national R&E testbed (JGN-X) to confirm the feasibility of our management system design and discuss room for improvement in terms of response time and scalability towards practical use. We also investigated certain cases of sophisticated network functions to confirm that the infrastructure can accept these functions without having to be modified.