Focusing on the TLP (transmission line pulse) method used for ESD (electrostatic discharge) saturation measurement described in IEC 62228-5 Annex E, we investigated the validity of its measurement conditions. Since there is no provision for the cable length between a TLP test system and a device under test, we investigated the influence of the cable length on the ESD saturation measurement. The result shows that different voltage and current waveforms in the measurement time window, i.e., different I/V characteristics, can be obtained due to the cable propagation delay. This paper proposes a method to correct the influence of the cable length by using the rising edge of the reflected wave observed at the TLP test system as a trigger for the measurement time window.

Internet-of-things systems using mobile networks, such as automobiles, drones, and robots, will become more sophisticated through remote control and cooperative operation. In networked control systems (NCSs), co-design of network control and application control is necessary to achieve stable operation at the application level as well as ultra-low latency and high reliability at the network level. We previously proposed a bandwidth-reserved transmission rate control method for application-aware network control in NCSs with a shared communication channel and evaluated it through numerical simulations. For this study, we demonstrated the effectiveness of our method using real-world NCSs. The experimental results indicate that our method achieves stable remote control of multiple electric motors with different quality-of-performance requirements, even under heavy network loads.

This paper describes a dual-band low-profile bidirectional antenna fed by a triangular monopole antenna (TMA) with a U-shaped metal cover. In addition to the higher band resonance of the TMA, another lower band resonance is generated by the coupling between the TMA and the metal cover so that the proposed antenna has dual operation bands. The proposed antenna has bidirectional radiation patterns for both bands, and the peak actual gain are 2.2dBi and 4.7dBi at the lower band and the higher band, respectively. The measured and analyzed results of the proposed antenna were in good agreement.

In recent years, the use of millimeter-wave bands has been considered for radio communication systems between train and trackside. Millimeter-wave band radio waves are known to have strong linearity and high attenuation on Non-Line-of-Sight (NLoS). On the other hand, it has been reported that they propagate to NLoS by the effects of rails and side walls in sections with side walls and in tunnel sections. Therefore, we simulate to evaluate the influence of side walls in curved sections for the 40GHz band, which is being considered for use in future mobile communication systems and train radio systems. The simulation results shows that the influence of the side wall tends to be larger, and the path loss tends to be smaller for smaller curve radiuses.

Polar codes can achieve arbitrary coding rates by varying the number of frozen bits. However, the large propagation delay in satellite laser communications complicates the adaptive setting of the optimal polar coding rate using bidirectional control signaling. In addition, channel estimation in fading channels requires pilot symbols, resulting in a reduction in rate efficiency. To address these simultaneously, we propose a novel one-way adaptive polar-code transmission scheme that performs both channel and code rate estimations using only frozen bits. Numerical results demonstrate that the proposed scheme produces a higher throughput than existing single-rate transmission schemes.

This paper proposes a simple soft-decision generation method for phase/frequency modulation with a binary convolutional code and symbol-wise Viterbi decoding. Taking symbol mapping of the phase/frequency modulation into account, the proposed method uses the in-phase and quadrature components of a received symbol in the frequency domain as likelihood information for the candidate symbols. Simulation results show that the proposed method improves the decoding performance to good effect, and that the phase/frequency modulation using the proposed method is a promising alternative to M-FSK.

An essential issue in image sensor communication (ISC) is reliable and high-speed data transmission. In order to increase the data rate, this study uses pulse-width modulation (PWM). However, the symbol decision for PWM will become challenging with increasing numbers of luminance levels because of pixel saturation. In this study, we mitigate the pixel saturation problem by achieving high dynamic range (HDR) using two high-speed image sensors with varied exposure settings. Furthermore, we proposed HDR combining for signal demodulation. The field trial experiment results show that a throughput of higher than 87.5 kbps within the communication distance of 50 m can be achieved.

Research and development of wireless emulation technology have been conducted for large-scale evaluation and verification of wireless communication systems in a virtual space. Emulation for various scenarios requires accurate and fast modeling techniques for radio propagation characteristics. The authors have proposed modeling methods using machine learning. However, when the amount of measurement data is slight, such as when new frequencies are implemented, the modeling accuracy is an important issue due to insufficient learning. This paper clarifies the relationship between the amount of data and the modeling accuracy. Moreover, we propose a fine-tuning method for modeling the propagation characteristics in a new frequency by pre-training in a frequency with large training data. Finally, through the evaluation using the measurement data in various areas, we demonstrate the effectiveness of the proposed method.

Recently, contactless input methods have been attracting attention. To meet such a demand, we focus on handwritten digits recognition. We install contactless hand tracking sensors on the lower and right sides of the subject’s fingers and measure data from two directions for each subject’s handwritten digit. We analyze the three types of datasets composed of the data acquired by each sensor and the integrated data by using two types of machine learning models. Based on the results, we select combinations with high accuracy and construct an ensemble learning model. The classification accuracy achieves a maximum of 92.7%, applying the ensemble learning model with the integrated data.

Wireless sensor networks are networks formed by a collection of tiny battery-powered sensors. Clustering has been proven to be one of the most efficient methods to lengthen the lifetime of wireless sensor networks. However, there is room for improvement in how the existing techniques select the cluster heads. In this study, we propose a Linear Programming based Cluster Head Selection (LPCHS) method that aims to optimize the energy consumption in wireless sensor networks. LPCHS selects cluster heads based on a novel linear programming model that determines which node and how many should serve as cluster heads.

A meander metamaterial line (metaline) antenna, which consists of P- and C-type meta-atoms, is investigated to form a circularly polarized (CP) broadside beam across two frequency bands. The antenna has an extremely low-profile flat structure of approximately 0.01 wavelength and a small antenna area of less than 0.5 wavelength × 0.5 wavelength at the low design frequency. It is found that the proposed antenna operates across dual wider frequency bands compared with those published in previous papers.

Traffic prediction is an important technique for network link capacity planning. Supervised Variational Auto-Encoder (SVAE), a deep learning technique, is a suitable approach for the network link capacity planning. The problem with the SVAE is that the mean absolute value error (MAPE) decreases when the correlation between the feature vector and the traffic probability distribution function (PDF) is low. In this paper, we propose an approach to increase the correlation by analytically obtaining values that correlate with the traffic PDF. Simulations are performed under the traffic conditions with the above problem to demonstrate the improvement in correlation and MAPE.

The IEEE 802.11be includes multi-link operation (MLO) as a crucial feature. MLO allows a multi-link device (MLD) with multiple wireless interfaces to use them simultaneously and send packets concurrently. However, the performance of the MLD may degrade due to interference or insufficient chances to access the wireless medium. In this study, we assessed the throughput and the latency of an emulated MLD configured by bundling multiple single-link access points. We also evaluated the effect of the link selection in the presence of interfering links. The results showed that selecting appropriate links according to interference enables improving the latency.

Recently, with the spread of Internet of Things (IoT) technology, the requirements for antennas mounted on these electrical devices have increased. These conditions require a small size, thin profile, and unaffected metal. A metasurface-inspired antenna chip developed by KIT EOE Laboratory (MACKEY) is proposed as an antenna that satisfies the conditions. Among IoT devices, smartphones and smartwatches use circularly polarized waves to accommodate polarization at various angles. Additionally, MACKEY, which radiates circularly polarized waves, has been proposed. Furthermore, we propose a novel model with improved axial ratio characteristics using sequential array technology.

A miniaturized, multi-band and circularly polarized (CP) antenna with high Front-to-Back-Ratio (FTBR) in the L1 band for global navigation satellite systems (GNSS) is developed in this paper. The proposed CP antenna consists of four dielectric substrates, each of which forms a folded monopole antenna and a parasitic inverted-L antenna for four frequency bands (L1/L2/L5/L6). To improve a FTBR, a microstrip resonator is situated in the multi-layer substrate beneath the antenna elements. The FTBR is improved by 4.2 dB in the L1 band when compared to the FTBR of the antenna without the microstrip resonator. The measured boresight gain is more than 1.5 dBic over the four bands.

When free-space optics (FSO) is applied to moving objects, it is necessary for the transmitter and receiver to estimate each other’s positions and continue to adjust their beam directions so that they face each other correctly. For some objects such as drones without a scheduled trajectory, we propose a method to acquire the operator’s control information and use it for tracking. In this method, the transmission distance measurement is also required for accurate adjustment of beam directions. If there is an estimation error in the transmission distance, the beam direction deviates from the ideal one. To address this problem, we also propose directivity control against the error on the transmission distance and evaluate the effect on the improvement of available communication time. By expanding the beam divergence angle, the communicable time is increased by 60%.

In fields such as mobile communications, video, and finance, the Precision Time Protocol (PTP) is an effective method for achieving highly accurate time synchronization. However, PTP profiles differ depending on the field in which PTP is used, and time synchronization is not possible between different profiles. Therefore, when telecommunication carriers distribute time to each field, networks are required for each profile, resulting in high equipment costs. In this paper, we propose a method to convert the profile of the upper equipment to match the profile of the lower equipment. Among the items that need to be converted in the profiles, the conversion of the transmission delay measurement method is examined, and actual equipment verification shows that the time accuracy meets the generally required standard (1000 ns or less).

Radio propagation prediction is important for improving the stability and reliability of wireless communication systems. However, propagation loss prediction models by multiple regression based on measured data have problems in setting appropriate parameters and function types. This paper evaluates a radio propagation model that applies machine learning for indoor environments. The proposed neural network has the input parameters of the environmental characteristics such as floor area, ceiling height, and materials, and a dropout layer that disables the network units randomly is inserted to improve generalization performance. The RMSE (root mean squared error) by the proposed model is less than 5 dB for test data and the average accuracy of the proposed model is better than the conventional model.