Recently connected car called Vehicle-to-Everything (V2X) has been attracted for smart automotive mobility. Among V2X technologies, cellular V2X (C-V2X) discussed and specified in 3rd generation partnership project (3GPP) is generally regarded as possibly utilized one. In 3GPP, the fourth generation mobile communication system (4G) and the fifth generation (5G) including new radio (NR) provide C-V2X standards specifications. In this paper, we will introduce C-V2X standards and share our views on future C-V2X.

Autonomous driving technology is currently attracting a lot of attention as a technology that will play a role in the next generation of mobility. For autonomous driving in urban areas, it is necessary to recognize various information. Especially, the recognition of traffic lights is important in crossing intersections. In this paper, traffic light recognition technology developed by the authors was evaluated using onboard sensor data during autonomous driving in the Tokyo waterfront area as an example of traffic light recognition technology. Based on the results, it was found that traffic lights could be recognized with an accuracy of approximately 99% to carry out the decision making for intersection approaching. However, from the evaluation results, it was also confirmed that traffic light recognition became difficult under situations involving occlusion by other object, background assimilation, nighttime conditions, and backlight by sunlight. It was also confirmed that these effects are mostly temporary, and do not significantly affect decision-making to enter intersections as a result of utilizing information from multiple traffic lights installed at an intersection. On the other hand, it is expected that recognition with current onboard cameras will become technically difficult during situations in which not all traffic lights are visually recognizable due to the effects of back or front light by sunlight when stopped at the stop line of an intersection. This paper summarizes these results and presents the necessity of appropriate traffic light installation on the assumption of recognition by onboard cameras.

Energy efficiency is one of the critical issues for Wireless Sensor Networks (WSN). IEEE 802.15.4 beacon-enabled MAC protocol achieves low energy consumption by having periodical inactive portions, where nodes run in low power. However, IEEE 802.15.4 beacon-enabled protocol cannot respond to dynamic changes in the number of sensor nodes and data rates in WSN because its duty cycle is fixed and immutable. In this paper, we propose a dynamic superframe duration adaptation scheme based on the Markov chain-based analysis methods for IEEE 802.15.4 beacon-enabled protocol. The proposed methods are flexible enough to accommodate changes in the number of sensor nodes and differences in data rates in WSNs while maintaining low latency and low energy consumption despite slight degradation in packet delivery ratio.

This paper evaluates the bluetooth low energy (BLE) positioning systems using the sparse-training data through the comparison experiments. The sparse-training data is extracted from the database including enough data for realizing the highly accurate and precise positioning. First, we define the sparse-training data, i.e., the data collection time and the number of smartphones, directions, beacons, and reference points, on BLE positioning systems. Next, the positioning performance evaluation experiments are conducted in two indoor environments, that is, an indoor corridor as a one-dimensionally spread environment and a hall as a twodimensionally spread environment. The algorithms for comparison are the conventional fingerprint algorithm and the hybrid algorithm (the authors already proposed, and combined the proximity algorithm and the fingerprint algorithm). Based on the results, we confirm that the hybrid algorithm performs well in many cases even when using sparse-training data. Consequently, the robustness of the hybrid algorithm, that the authors already proposed for the sparse-training data, is shown.

The unit commitment problem (UCP) is the problem of deciding up/down and generation-level patterns of energy production units. Due to the expansion of distributed energy resources and the liberalization of energy trading in recent years, solving the distributed UCP (DUCP) is attracting the attention of researchers. Once an up/down pattern is determined, the generation-level pattern can be decided distributively using the alternating direction method of multipliers (ADMM). However, ADMM does not guarantee convergence when deciding both up/down and generation-level patterns. In this paper, we propose a method to solve the DUCP using ADMM and constraint optimization programming. Numerical experiments show the efficacy of the proposed method.

We consider a reliable decentralized supervisory control problem for discrete event systems in the inference-based framework. This problem requires us to synthesize local supervisors such that the controlled system achieves the specification and is nonblocking, even if local control decisions of some local supervisors are not available for making the global control decision. In the case of single-level inference, we introduce a notion of reliable 1-inference-observability and show that reliable 1-inference-observability together with controllability and L_m(G)-closedness is a necessary and sufficient condition for the existence of a solution to the reliable decentralized supervisory control problem.

This paper presents a novel method for optimal control of timed Petri nets, introducing a novel temporal logic based constraint called a generalized mutual exclusion temporal constraint (GMETC). The GMETC is described by a metric temporal logic (MTL) formula where each atomic proposition represents a generalized mutual exclusion constraint (GMEC). We formulate an optimal control problem of the timed Petri nets under a given GMETC and solve the problem by transforming it into an integer linear programming problem where the MTL formula is encoded by linear inequalities. We show the effectiveness of the proposed approach by a numerical simulation.

In this paper, event-triggered control over a sensor network is studied as one of the control methods of cyber-physical systems. Event-triggered control is a method that communications occur only when the measured value is widely changed. In the proposed method, by solving an LMI (Linear Matrix Inequality) feasibility problem, an event-triggered output feedback controller such that the closed-loop system is asymptotically stable is derived. First, the problem formulation is given. Next, the control problem is reduced to an LMI feasibility problem. Finally, the proposed method is demonstrated by a numerical example.

Because processes run concurrently in multitask systems, the size of the state space grows exponentially. Therefore, it is not straightforward to formally verify that such systems enjoy desired properties. Real-time constrains make the formal verification more challenging. In this paper, we propose the following to address the challenge: (1) a way to model multitask real-time systems as observational transition systems (OTSs), a kind of state transition systems, (2) a way to describe their specifications in CafeOBJ, an algebraic specification language, and (3) a way to verify that such systems enjoy desired properties based on such formal specifications by writing proof scores, proof plans, in CafeOBJ. As a case study, we model Fischer's protocol, a well-known real-time mutual exclusion protocol, as an OTS, describe its specification in CafeOBJ, and verify that the protocol enjoys the mutual exclusion property when an arbitrary number of processes participates in the protocol^*.

In response to fast charging systems, Silicon Carbide (SiC) power semiconductor devices are of great interest of the automotive power electronics applications as the next generation of fast charging systems require high voltage batteries. For high voltage battery EVs (Electric Vehicles) over 800V, SiC power semiconductor devices are suitable for 3-phase inverters, battery chargers, and isolated DC-DC converters due to their high voltage rating and high efficiency performance. However, SiC-MOSFETs have two characteristics that interfere with high-speed switching and high efficiency performance operations for SiC MOS-FET applications in automotive power electronics systems. One characteristic is the low voltage rating of the gate-source terminal, and the other is the large internal gate-resistance of SiC MOS-FET. The purpose of this work was to evaluate a proposed hybrid gate drive circuit that could ignore the internal gate-resistance and maintain the gate-source terminal stability of the SiC-MOSFET applications. It has been found that the proposed hybrid gate drive circuit can achieve faster and lower loss switching performance than conventional gate drive circuits by using the current source gate drive characteristics. In addition, the proposed gate drive circuit can use the voltage source gate drive characteristics to protect the gate-source terminals despite the low voltage rating of the SiC MOS-FET gate-source terminals.

With the spread of the 5th generation mobile phone, the increase of the output power of PA (power amplifier) has become important, and in recent years, differential amplifiers that can increase the output voltage amplitude for the power supply voltage have been examined from the viewpoint of power synthesis. In the case of a differential PA, in addition to the advantage of voltage amplitude, the load impedance can be set 4 times as much as that of a single-ended PA, which makes it possible to reduce the impact of parasitic resistance. With the study of the differential PA, many transformer matching circuits have been studied in addition to the LC matching circuits that have been widely used in the past. The transformer matching circuit can easily realize the differential-single conversion, and the transformer matching circuit is an indispensable technology in the differential PA. As with the LC matching circuit, widening the bandwidth of the transformer matching circuit is at issue. In this paper, characteristics of basic transformer matching circuits are analyzed by adding input/output shunt capacitance to transformers and the conditions of bandwidth improvement are clarified. In addition, by comparing the FBW (fractional bandwidth) with the LC 2-stage matching circuit, it is shown that the FBW can be competitive.

We show that for any convex body Q in the plane, the average distance from the Fermat-Weber center of Q to the points in Q is at least Δ(Q)/6, where Δ(Q) denotes the diameter of Q. Our proof is simple and straightforward, since it needs only elementary calculations. This simplifies a previously known proof that is based on Steiner symmetrizations.

Pairing computations on elliptic curves with odd prime degrees are rarely studied as low efficiency. Recently, Clarisse, Duquesne and Sanders proposed two new curves with odd prime embedding degrees: BW13-P310 and BW19-P286, which are suitable for some special cryptographic schemes. In this paper, we propose efficient methods to compute the optimal ate pairing on this types of curves, instantiated by the BW13-P310 curve. We first extend the technique of lazy reduction into the finite field arithmetic. Then, we present a new method to execute Miller's algorithm. Compared with the standard Miller iteration formulas, the new ones provide a more efficient software implementation of pairing computations. At last, we also give a fast formula to perform the final exponentiation. Our implementation results indicate that it can be computed efficiently, while it is slower than that over the (BLS12-P446) curve at the same security level.

As the mainstream approach in the field of machine translation, neural machine translation (NMT) has achieved great improvements on many rich-source languages, but performance of NMT for low-resource languages ae not very good yet. This paper uses data enhancement technology to construct Mongolian-Chinese pseudo parallel corpus, so as to improve the translation ability of Mongolian-Chinese translation model. Experiments show that the above methods can improve the translation ability of the translation model. Finally, a translation model trained with large-scale pseudo parallel corpus and integrated with soft context data enhancement technology is obtained, and its BLEU value is 39.3.

Nonnegative Matrix Factorization (NMF) is a promising data-driven matrix decomposition method, and is becoming very active and attractive in machine learning and blind source separation areas. So far NMF algorithm has been widely used in diverse applications, including image processing, anti-collision for Radio Frequency Identification (RFID) systems and audio signal analysis, and so on. However the typical NMF algorithms cannot work well in underdetermined mixture, i.e., the number of observed signals is less than that of source signals. In practical applications, adding suitable constraints fused into NMF algorithm can achieve remarkable decomposition results. As a motivation, this paper proposes to add the minimum volume and minimum correlation constrains (MCV) to the NMF algorithm, which makes the new algorithm named MCV-NMF algorithm suitable for underdetermined scenarios where the source signals satisfy mutual independent assumption. Experimental simulation results validate that the MCV-NMF algorithm has a better performance improvement in solving RFID tag anti-collision problem than that of using the nearest typical NMF method.

Syndrome is a crucial principle of Traditional Chinese Medicine. Formula classification is an effective approach to discover herb combinations for the clinical treatment of syndromes. In this study, a local search based firefly algorithm (LSFA) for parameter optimization and feature selection of support vector machines (SVMs) for formula classification is proposed. Parameters C and γ of SVMs are optimized by LSFA. Meanwhile, the effectiveness of herbs in formula classification is adopted as a feature. LSFA searches for well-performing subsets of features to maximize classification accuracy. In LSFA, a local search of fireflies is developed to improve FA. Simulations demonstrate that the proposed LSFA-SVM algorithm outperforms other classification algorithms on different datasets. Parameters C and γ and the features are optimized by LSFA to obtain better classification performance. The performance of FA is enhanced by the proposed local search mechanism.

In 2017, Tang et al. provided a complete characterization of generalized bent functions from ℤ₂ⁿ to ℤ_q(q = 2^m) in terms of their component functions (IEEE Trans. Inf. Theory. vol.63, no.7, pp.4668-4674). In this letter, for a general even q, we aim to provide some characterizations and more constructions of generalized bent functions with flexible coefficients. Firstly, we present some sufficient conditions for a generalized Boolean function with at most three terms to be gbent. Based on these results, we give a positive answer to a remaining question proposed by Hodžić in 2015. We also prove that the sufficient conditions are also necessary in some special cases. However, these sufficient conditions whether they are also necessary, in general, is left as an open problem. Secondly, from a uniform point of view, we provide a secondary construction of gbent function, which includes several known constructions as special cases.

In this letter, the almost binary sequence (sequence with a single zero element) is considered as a special class of binary sequence. Four new bounds on the cross-correlation of balanced (almost) binary sequences with period Q ≡ 1(mod 4) under the precondition of out-of-phase autocorrelation values {-1} or {1, -3} are firstly presented. Then, seven new pairs of balanced (almost) binary sequences of period Q with ideal or optimal autocorrelation values and meeting the lower cross-correlation bounds are proposed by using cyclotomic classes of order 4. These new bounds of (almost) binary sequences with period Q achieve smaller maximum out-of-phase autocorrelation values and cross-correlation values.

In this letter, we analyze the error rate performance of M-ary coherent free-space optical (FSO) communications under strong atmospheric turbulence. Specifically, we derive the exact error rates for M-ary phase shift keying (MPSK) and M-ary quadrature amplitude modulation (MQAM) based on moment-generating function (MGF) with negative exponential distributed turbulence, where maximum ratio combining (MRC) receiver is adopted to mitigate the turbulence effects. Additionally, by evaluating the asymptotic error rate in high signal-to-noise ratio (SNR) regime, it is possible to effectively investigate and predict the error rate performance for various system configurations. The accuracy and the effectiveness of our theoretical analyses are verified via numerical results.

AlphaSeq is a new paradigm to design sequencess with desired properties based on deep reinforcement learning (DRL). In this work, we propose a new metric function and a new reward function, to design an improved version of AlphaSeq. We show analytically and also through numerical simulations that the proposed algorithm can discover sequence sets with preferable properties faster than that of the previous algorithm.