For a service-oriented architecture-based system, the problem of synthesizing a concrete model (i.e., behavioral model) for each peer configuring the system from an abstract specification-which is referred to as choreography-is known as the choreography realization problem. A flow of interaction of peers is called a scenario. In our previous study, we showed conditions and an algorithm to synthesize concrete models when choreography is given by one scenario. In this paper, we extend the study for choreography given by two scenarios. We show necessary and sufficient conditions on the realizability of choreography under both cases where there exist conflicts between scenarios and no conflicts exist.

A real-time road-direction point detection model is developed based on convolutional neural network architecture which can adapt to complex environment. Firstly, the concept of road-direction point is defined for either single road or crossroad. For single road, the predicted road-direction point can serve as a guiding point for a self-driving vehicle to go ahead. In the situation of crossroad, multiple road-direction points can also be detected which will help this vehicle to make a choice from possible directions. Meanwhile, different types of road surface can be classified by this model for both paved roads and unpaved roads. This information will be beneficial for a self-driving vehicle to speed up or slow down according to various road conditions. Finally, the performance of this model is evaluated on different platforms including Jetson TX1. The processing speed can reach 12 FPS on this portable embedded system so that it provides an effective and economic solution of road-direction estimation in the applications of autonomous navigation.

This paper proposes a consensus-based distributed Particle Swarm Optimization (PSO) algorithm with event-triggered communications for a non-convex and non-differentiable optimization problem. We consider a multi-agent system whose local communications among agents are represented by a fixed and connected graph. Each agent has multiple particles as estimated solutions of global optima and updates positions of particles by an average consensus dynamics on an auxiliary variable that accumulates the past information of the own objective function. In contrast to the existing time-triggered approach, the local communications are carried out only when the difference between the current auxiliary variable and the variable at the last communication exceeds a threshold. We show that the global best can be estimated in a distributed way by the proposed event-triggered PSO algorithm under a diminishing condition of the threshold for the trigger condition.

Tracking-by-detection methods consider tracking task as a continuous detection problem applied over video frames. Modern tracking-by-detection trackers have online learning ability; the update stage is essential because it determines how to modify the classifier inherent in a tracker. However, most trackers search for the target within a fixed region centered at the previous object position; thus, they lack spatiotemporal consistency. This becomes a problem when the tracker detects an incorrect object during short-term occlusion. In addition, the scale of the bounding box that contains the target object is usually assumed not to change. This assumption is unrealistic for long-term tracking, where the scale of the target varies as the distance between the target and the camera changes. The accumulation of errors resulting from these shortcomings results in the drift problem, i.e. drifting away from the target object. To resolve this problem, we present a drift-free, online learning-based tracking-by-detection method using a single static camera. We improve the latent structured support vector machine (SVM) tracker by designing a more robust tracker update step by incorporating two Kalman filter modules: the first is used to predict an adaptive search region in consideration of the object motion; the second is used to adjust the scale of the bounding box by accounting for the background model. We propose a hierarchical search strategy that combines Bhattacharyya coefficient similarity analysis and Kalman predictors. This strategy facilitates overcoming occlusion and increases tracking efficiency. We evaluate this work using publicly available videos thoroughly. Experimental results show that the proposed method outperforms the state-of-the-art trackers.

In order to utilize higher frequency bands above 6GHz, which is an important technical challenge in fifth generation mobile systems, radio propagation channel properties in a large variety of deployment scenarios should be thoroughly investigated. The authors' group has been involved in a fundamental research project aimed at investigating multiple-input-multiple-output (MIMO) transmission performance and propagation channel properties at microwave frequency above 10GHz from 2009 to 2013, and since then they have been conducting measurement and modeling for high frequency bands. This paper aims at providing comprehensive tutorial of a whole procedure of channel modeling; multi-dimensional channel sounding, propagation channel measurement, analysis, and modeling, by introducing the developed MIMO channel sounders at high frequency bands of 11 and 60GHz and presenting some measurement results in a microcell environment at 11GHz. Furthermore, this paper identifies challenges in radio propagation measurements, and discusses current/future channel modeling issues as future works.

An efficient handover measurement technique is proposed for millimeter-wave (mm-wave) cellular systems with directional antenna beams. As the beam synchronization signal (BSS) carries the cell ID and the beam ID in a hierarchal manner, handover events (interbeam handover and intercell handover) are distinguished at the physical layer. The proposed signal metrics are shown to be effective in detecting the beam boundaries and cell boundaries in mm-wave cellular systems, which allows to distinguish interbeam handover from intercell handover. The proposed handover measurement technique is shown to reduce the processing time significantly using the proposed signal metrics produced by the BSS.

Predicting the receiving sensitivity of an offset Gregorian reflector system antenna requires an accurate prediction of the antenna noise temperature. Calculating the antenna noise temperature is computationally intensive especially for the electrically larger reflector systems. Using the main reflector masking technique, which removes the main reflector from the calculation domain, considerably reduces the computation cost. For an electrically smaller reflector system, diffraction effects affect the accuracy of this technique. Recently an improvement to the technique was proposed that introduces diffraction compensation correction factors. In this paper we introduce new compensation factor and interpolation techniques that improve the accuracy of the approximated antenna noise temperature calculation. The techniques are applied to several offset Gregorian reflector systems similar to those considered for the Square Kilometre Array, with various feeds and the accuracy in terms of receiving sensitivity is evaluated. The techniques can reduce the prediction error of the receiving sensitivity for frequency-invariant feeds to fractions of a percent, while maintaining a significant speed-up over direct calculations.

In this paper, we present a small, wide-band, Inverted-L Antenna (ILA) with non-Foster matching. The antenna's size is 9.5×19.5mm2 and it is integrated on a Printed Circuit Board (PCB) of 90×35mm2. A design procedure is presented and sensitivity and stability analysis are performed. Experiments show that the non-Foster matched antenna has (S11 < -10dB) impedance bandwidth of 92.2% at a central frequency of 1.5GHz, whereas the passive antenna (without the non-Foster matching) has an impedance bandwidth of 12.6% at 2.46GHz.

A method is proposed for improving the accuracy of the characteristic basis function method (CBFM) using the multilevel approach. With this technique, CBFs taking into account multiple scattering calculated for each block (IP-CBFs; improved primary CBFs) are applied to CBFM using a multilevel approach. By using IP-CBFs, the interaction between blocks is taken into account, and thus it is possible to reduce the number of CBFs while maintaining accuracy, even if the multilevel approach is used. The radar cross section (RCS) of a cube, a cavity, and a dielectric sphere were analyzed using the proposed CBFs, and as a result it was found that accuracy is improved over the conventional method, despite no major change in the number of CBFs.

This paper presents the Received-Signal-Strength-Indicator (RSSI) based living-body radar, which uses only a single RF front-end and a few parasitic antennas. This radar measures the RSSI variation at the single active antenna while varying the terminations of the parasitic antennas. The propagation channel is estimated from just the temporal transition of RSSI; our proposal reconstructs the phase information of the signal. In this paper, we aim to estimate the direction of living-body. Experiments are carried out and it is found that most angular errors are within the limit of the angular width of the living-body.

An optical transport network is composed of optical transport systems deployed in thousands of office-buildings. As a common infrastructure to accommodate diversified communication services with drastic traffic growth, it is necessary not only to continuously convey the growing traffic but also to achieve high end-to-end communication quality and availability and provide flexible controllability in cooperation with service layer networks. To achieve high-speed and large-capacity transport systems cost-effectively, system configuration, applied devices, and the manufacturing process have recently begun to change, and the cause of failure or performance degradation has become more complex and diversified. The drastic traffic growth and pattern change of service networks increase the frequency and scale of transport-capacity increase and transport-network reconfiguration in cooperation with service networks. Therefore, drastic traffic growth affects both optical-transport-system configuration and its operational cycles. In this paper, we give an overview of the operational problems emerging in current nationwide optical transport networks, and based on trends analysis for system configuration and network-control schemes, we propose a vision of the future nationwide optical-transport-network architecture expressed using five target features.

In this paper, we consider a two-hop communication system with an amplify-and-forward (AF) relay under channel estimation errors. According to the channel quality of the link between the base station (BS) and the relay, we investigate two typical relay scenarios. We study the capacity performance for both In-Band Full-Duplex (IBFD) and Half-Duplex (HD) transmission modes. Moreover, we consider two operation modes of the user equipment (UE) for each scenario. Closed-form expressions of ergodic capacities with channel estimation errors are obtained for scenario-1. And we derive accurate approximations of ergodic capacities for scenario-2. Numerical experiments are conducted to verify the analytical results and show that our theoretical derivations are perfectly matched with the simulations. We show that with practical signal-to-noise ratio values and effective interference cancellation techniques, IBFD transmission is preferable in terms of capacity.

Designing shaped offset Gregorian reflector systems to operate with several interchangeable feed horns, over frequency bandwidths of more than a decade, with multiple, often conflicting, performance figures of merit such as aperture efficiency, receiving sensitivity, sidelobe levels, and cross polarization isolation is a difficult optimization problem. An additional complication may be that the radiation patterns of all the feeds to be used in the system are not known at the time of the dish designs, as upgrades to the feeds may happen throughout the lifetime of large reflector systems. This paper presents a systematic parametric study to quantify the effects of the main causes of performance degradation in such a system, i.e. reflector diffraction and feed pattern variations. First, ideal Gaussian feed patterns are used in order to isolate the diffraction effects, and then the ideal patterns are varied to model the effect of using wideband feeds exhibiting radiation pattern variations over frequency. It is shown that the peak position in the shaping parameter space of the receiving sensitivity is not strongly influenced by diffraction - although the peak value is, as expected, reduced at lower frequencies. This allows similar feed patterns to be used in different frequency bands to still produce systems operating near the maximum sensitivity. When using non-ideal feed patterns it is shown that, for most performance metrics, diffraction effects dominate the feed variation performance degradation in smaller dishes. This allows possibly relaxed requirements on the radiation patterns of feeds used to illuminate electrically small reflector systems.

A component-oriented FPGA design platform is proposed for robot system integration. FPGAs are known to be a power-efficient hardware platform, but the development cost of FPGA-based systems is currently too high to integrate them into robot systems. To solve this problem, we propose an FPGA component that allows FPGA devices to be easily integrated into robot systems based on the Robot Operating System (ROS). ROS-compliant FPGA components offer a seamless interface between the FPGA hardware and software running on the CPU. Two experiments were conducted using the proposed components. For the first experiment, the results show that the execution time of an FPGA component for image processing was 1.7 times faster than that of the original software-based component and was 2.51 times more power efficient than an ordinary PC processor, despite substantial communication overhead. The second experiment showed that an FPGA component for sensor fusion was able to process multiple sensor inputs efficiently and with very low latency via parallel processing.

Advanced driver assistance system (ADAS) can recognize traffic signals, vehicles, pedestrians, and so on all over the vehicle. However, because the ADAS is based on images taken in an outdoor environment, it is susceptible to ambient weather such as fog. So, preprocessing such as de-fog and de-hazing techniques is required to prevent degradation of object recognition performance due to decreased visibility. But, if such a fog removal technique is applied in an environment where there is little or no fog, the visual quality may be deteriorated due to excessive contrast improvement. And in foggy road environments, typical fog removal algorithms suffer from color distortion. In this paper, we propose a temporal filter-based fog detection algorithm to selectively apply de-fogging method only in the presence of fog. We also propose a method to avoid color distortion by detecting the sky region and applying different methods to the sky region and the non-sky region. Experimental results show that in the actual images, the proposed algorithm shows an average of more than 97% fog detection accuracy, and improves subjective image quality of existing de-fogging algorithms. In addition, the proposed algorithm shows very fast computation time of less than 0.1ms per frame.

Considering that different people are different in their linguistic preference and in order to determine the consensus state when using Computing with Words (CWW) for supporting consensus decision making, this paper first proposes an interval composite scale based 2-tuple linguistic model, which realizes the process of translation from word to interval numerical and the process of retranslation from interval numerical to word. Second, this paper proposes an interval composite scale based personalized individual semantics model (ICS-PISM), which can provide different linguistic representation models for different decision-makers. Finally, this paper proposes a consensus decision making model with ICS-PISM, which includes a semantic translation and retranslation phase during decision process and determines the consensus state of the whole decision process. These models proposed take into full consideration that human language contains vague expressions and usually real-world preferences are uncertain, and provide efficient computation models to support consensus decision making.

In this work, we address the stationary target localization problem by using Doppler frequency shift (DFS) measurements. Based on the measurement model, the maximum likelihood estimation (MLE) of the target position is reformulated as a constrained weighted least squares (CWLS) problem. However, due to its non-convex nature, it is difficult to solve the problem directly. Thus, in order to yield a semidefinite programming (SDP) problem, we perform a semidefinite relaxation (SDR) technique to relax the CWLS problem. Although the SDP is a relaxation of the original MLE, it can facilitate an accurate estimate without post processing. Simulations are provided to confirm the promising performance of the proposed method.

In this paper, the problem of target detection and tracking utilizing the single frequency network (SFN) is addressed. Specifically, by exploiting the characteristics of the signal in SFN, a novel likelihood model which avoids the measurement origin uncertain problem in the point measurement model is proposed. The particle filter based track-before-detect (PF-TBD) algorithm is adopted for the proposed SFN likelihood to detect and track the possibly existed target. The advantage of using TBD algorithm is that it is suitable for the condition of low SNR, and specially, in SFN, it can avoid the data association between the measurement and the transmitters. The performance of the adopted algorithm is examined via simulations.

This letter presents numerical results of lossy source coding for non-uniformly distributed binary source with trellis codes. The results show how the performance of trellis codes approaches the rate-distortion function in terms of the number of states.

SUMPLE, one of important signal combining approaches, its combining loss increases when a sensor in an array fails. A novel failure detection circuit for SUMPLE is proposed by using variability index. This circuit can effectively judge whether a sensor fails or not. Simulation results validate its effectiveness with respect to the existing algorithms.