This paper investigates a signal area (SA) estimation method for wideband and long time duration spectrum measurements for dynamic spectrum access. SA denotes the area (in time/frequency domain) occupied by the primary user's signal. The traditional approach, which utilizes only Fourier transform (FT) and energy detector (ED) for SA estimation, can achieve low complexity, but its estimation performance is not very high. Against this issue, we apply post-processing to improve the performance of the FT-based ED. Our proposed method, simple SA (S-SA) estimation, exploits the correlation of the spectrum states among the neighboring tiles and the fact that SA typically has a rectangular shape to estimate SA with high accuracy and relatively low complexity compared to a conventional method, contour tracing SA (CT-SA) estimation. Numerical results will show that the S-SA estimation method can achieve better detection performance. The SA estimation and processing can reduce the number of bits needed to store/transmit the observed information compared to the FT-based ED. Thus, in addition to improved detection performance it also compresses the data.

A novel circularly and linearly polarized loop antenna is presented. A simple loop configuration, twisted like a cross shape, has achieved radiating wide beam circular polarization simultaneously with linear polarization in two close bands. This cross configuration brings good circular polarization to a loop antenna because it uses the transmission line mode of a folded dipole antenna. For these reasons, the antenna is named the Cross Spiral Antenna (CSA). In this paper, a basic structure and the principle of the CSA radiating circular polarization with one port feeding is explained. The prototype CSA, which is tuned to around 1.57GHz and 1.6GHz, is tested for verifying the effectiveness of the suggested antenna configuration.

Recently, notable improvements in voice activity detection (VAD) problem have been achieved by adopting several machine learning techniques. Among them, the deep neural network (DNN) which learns the mapping between the noisy speech features and the corresponding voice activity status with its deep hidden structure has been one of the most popular techniques. In this letter, we propose a novel approach which enhances the robustness of DNN in mismatched noise conditions with multi-task learning (MTL) framework. In the proposed algorithm, a feature enhancement task for speech features is jointly trained with the conventional VAD task. The experimental results show that the DNN with the proposed framework outperforms the conventional DNN-based VAD algorithm.

3GPP Long Term Evolution (LTE) is one of the most advanced technologies in the wireless and mobility field because it provides high speed data and sophisticated applications. LTE was originally deployed by service providers on various platforms using separate dedicated hardware in Access radio layer and the Evolved Packet Core network layer (EPC), thereby limiting the system's flexibility and capacity provisioning. Thus, the concept of virtualization was introduced in the EPC hardware to solve the dedicated hardware platform limitations. It was also introduced in the IP Multimedia Subsystem (IMS) and Machine to Machine applications (M2M) for the same reason. This paper provides a simulation model of a virtualized EPC and virtualized M2M transport application server connected via an external IP network, which has significant importance in the future of mobile networks. This model studies the virtualized server connectivity problem, where two separate virtual machines communicate via the existing external legacy IP network. The simulation results show moderate performance, indicating that the selection of IP technology is much more critical than before. The paper also models MPLS technology as a replacement for the external IP routing mechanism to provide traffic engineering and achieve more efficient network performance. Furthermore, to provide a real network environment, Poisson Pareto Burst Process (PPBP) traffic source is carried over the UDP transport layer which matches the statistical properties of real-life M2M traffic. Furthermore, the paper proves End-to-End interoperability of LTE and MPLS running GTP and MPLS Label Forwarding information Base (LFIB) and MPLS traffic engineering respectively. Finally, it looks at the simulation of several scenarios using Network Simulator 3 (NS-3) to evaluate the performance improvement over the traditional LTE IP architecture under M2M traffic load.

We numerically demonstrated the improvement of single-electron (SE) digital logic gates by utilizing SE input discretizers (IDs). The parameters of the IDs were adjusted to achieve SE tunneling at the threshold voltage designed for switching. An SE four-junction inverter (FJI) with an ID (ID-FJI) had steep switching characteristics between the high and low output voltage levels. The limiting temperature and the critical parameter margins were evaluated. An SE NAND gate with IDs also achieved abrupt switching characteristics between output logic levels.

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.

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.

This paper presents a novel MEMD interval thresholding denoising, where relevant modes are selected by the similarity measure between the probability density functions of the input and that of each mode. Simulation and measured EEG data processing results show that the proposed scheme achieves better performance than other traditional denoisings.

In this paper, we formulate an optimal stabilization problem of quantitative discrete event systems (DESs) under partial observation. A DES under partial observation is a system where its behaviors cannot be completely observed by a supervisor. In our framework, the supervisor observes not only masked events but also masked states. Our problem is then to synthesize a supervisor that drives the DES to a given target state with the minimum cost based on the detected sequences of masked events and states. We propose an algorithm for deciding the existence of an optimal stabilizing supervisor, and compute it if it exists.

To overcome the privacy limitations of conventional PKI (Public Key Infrastructure) systems, combinatorial certificate schemes assign each certificate to multiple users so that users can perform anonymous authentication. From a certificate pool of N certificates, each user is given n certificates. If a misbehaving user revokes a certificate, all the other users who share the revoked certificate will also not be able to use it. When an honest user shares a certificate with a misbehaving user and the certificate is revoked by the misbehaving user, the certificate of the honest user is said to be covered. To date, only the analysis for the worst scenario has been conducted; the probability that all n certificates of an honest user are covered when m misbehaving users revoke their certificates is known. The subject of this article is the following question: how many certificates (among n certificates) of an honest user are covered on average when m misbehaving users revoke their certificates? We present the first average-case analysis of the cover probability in combinatorial certificate schemes.

Computer networks require sophisticated control mechanisms to realize fair resource allocation among users in conjunction with efficient resource usage. To successfully realize fair resource allocation in a network, someone should control the behavior of each user by considering fairness. To provide efficient resource utilization, someone should control the behavior of all users by considering efficiency. To realize both control goals with different granularities at the same time, a hierarchical network control mechanism that combines microscopic control (i.e., fairness control) and macroscopic control (i.e., efficiency control) is required. In previous works, Aida proposed the concept of chaos-based hierarchical network control. Next, as an application of the chaos-based concept, Aida designed a fundamental framework of hierarchical transmission rate control based on the chaos of coupled relaxation oscillators. To clarify the realization of the chaos-based concept, one should specify the chaos-based hierarchical transmission rate control in enough detail to work in an actual network, and confirm that it works as intended. In this study, we implement the chaos-based hierarchical transmission rate control in a popular network simulator, ns-2, and confirm its operation through our experimentation. Results verify that the chaos-based concept can be successfully realized in TCP/IP networks.

Workflow nets (WF-nets for short) are a mathematical model of real world workflows. A WF-net is often updated in accordance with the change of real world. This may cause places that are redundant from the viewpoint of the behavior. Such places are called implicit. We first proposed a necessary and sufficient condition to find implicit places. Then we proved that removing of implicit places is a reduction operation which forms branching bisimilarity. We also constructed an algorithm for the reduction. Next, we applied the proposed reduction operation to WF-net refactoring. Then we showed the usefulness of the proposed refactoring with two examples.

This paper proposes a method based on modulation transfer function (MTF) to restore the power envelope of noisy reverberant speech by using a Kalman filter with linear prediction (LP). Its advantage is that it can simultaneously suppress the effects of noise and reverberation by restoring the smeared MTF without measuring room impulse responses. This scheme has two processes: power envelope subtraction and power envelope inverse filtering. In the subtraction process, the statistical properties of observation noise and driving noise for power envelope are investigated for the criteria of the Kalman filter which requires noise to be white and Gaussian. Furthermore, LP coefficients drastically affect the Kalman filter performance, and a method is developed for deriving LP coefficients from noisy reverberant speech. In the dereverberation process, an inverse filtering method is applied to remove the effects of reverberation. Objective experiments were conducted under various noisy reverberant conditions to evaluate how well the proposed Kalman filtering method based on MTF improves the signal-to-error ratio (SER) and correlation between restored power envelopes compared with conventional methods. Results showed that the proposed Kalman filtering method based on MTF can improve SER and correlation more than conventional methods.

Recent development in network technology can realize the control of a remote plant by a digital controller. However, there is a delay caused by data transmission of control inputs and outputs. The delay degrades the control performance without taking it into consideration. In general, it is a difficult problem to identify the delay beforehand. We also assume that the plant's parameters have uncertainty. To solve the problem, we use reinforcement learning to achieve optimal digital control. First, we consider state feedback control. Next, we consider the case where the plant's outputs are observed, and apply reinforcement learning to output feedback control. Finally, we demonstrate by simulation that the proposed control method can search for the optimal gain and that it can adapt to the change of the delay.

Vanishing point estimation is an important issue for vision based road detection, especially in unstructured roads. However, most of the existing methods suffer from the long calculating time. This paper focuses on improving the efficiency of vanishing point estimation by using a heuristic voting method based on particle swarm optimization (PSO). Experiments prove that with our proposed method, the efficiency of vanishing point estimation is significantly improved with almost no loss in accuracy. Moreover, for sequenced images, this method is further improved and can get even better performance, by making full use of inter-frame information to optimize the performance of PSO.

High structural perfection, wafer uniformity, and reproducibility are key parameters for high-volume, low cost manufacture of resonant tunnelling diode (RTD) terahertz (THz) devices. Low-cost, rapid, and non-destructive techniques are required for the development of such devices. In this paper, we report photoluminescence (PL) spectroscopy as a non-destructive characterisation technique for high current densityInGaAs/AlAs/InP RTD structures grown by metal-organic vapour phase epitaxy (MOVPE) for THz applications. By using a PL line scanning technique across the edge of the sample, we identify characteristic luminescence from the quantum well (QW) and the undoped/n+ InGaAs layers. By using the Moss-Burstein effect, we are able to measure the free-electron concentration of the emitter/collector and contact layers in the RTD structure. Whilst the n+ InGaAs luminescence provides information on the doping concentration, information on the alloy composition and compositional variation is extracted from the InGaAs buffer layer. The QW luminescence provides information on the average well width and provides a monitor of the structural perfection with regard to interface and alloy disorder.

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

One of the patterns that the design of parallel file systems has to solve stems from the difficulty of handling the metadata-intensive I/O generated by parallel applications accessing a single large directory. We demonstrate a middleware design called SFS to support existing parallel file systems for distributed and scalable directory service. SFS distributes directory entries over data servers instead of metadata servers to offer increased scalability and performance. Firstly, SFS exploits an adaptive directory partitioning based on extendible hashing to support concurrent and unsynchronized partition splitting. Secondly, SFS describes an optimization based on recursive split-ordering that emphasizes speeding up the splitting process. Thirdly, SFS applies a write-optimized index structure to convert slow, small, random metadata updates into fast, large, sequential writes. Finally, SFS gracefully tolerates stale mapping at the clients while maintaining the correctness and consistency of the system. Our performance results on a cluster of 32-servers show our implementation can deliver more than 250,000 file creations per second on average.

Quantum computer simulators play an important role when we evaluate quantum algorithms. Quantum computation can be regarded as parallel computation in some sense, and thus, it is suitable to implement a simulator on hardware that can process a lot of operations in parallel. In this paper, we propose a hardware quantum computer simulator. The proposed simulator is based on the register reordering method that shifts and swaps registers containing probability amplitudes so that the probability amplitudes of target basis states can be quickly selected. This reduces the number of large multiplexers and improves clock frequency. We implement the simulator on an FPGA. Experiments show that the proposed simulator has scalability in terms of the number of quantum bits, and can simulate quantum algorithms faster than software simulators.

In this paper, we propose a workload assignment policy for reducing power consumption by air conditioners in data centers. In the proposed policy, to reduce the air conditioner power consumption by raising the temperature set points of the air conditioners, the temperatures of all server back-planes are equalized by moving workload from the servers with the highest temperatures to the servers with the lowest temperatures. To evaluate the proposed policy, we use a computational fluid dynamics simulator for obtaining airflow and air temperature in data centers, and an air conditioner model based on experimental results from actual data center. Through evaluation, we show that the air conditioners' power consumption is reduced by 10.4% in a conventional data center. In addition, in a tandem data center proposed in our research group, the air conditioners' power consumption is reduced by 53%, and the total power consumption of the whole data center is exhibited to be reduced by 23% by reusing the exhaust heat from the servers.