In this paper, a novel trench MOS barrier Schottky contact super barrier rectifier (TMB-SSBR) is proposed by combining the advantages of vertical SSBR and conventional TMBS. The operation mechanism and simulation verification are presented. TMB-SSBR consists of MOS trenches with a vertical SSBR grid which replaces the Schottky diode in the mesa of a TMBS. Due to the presence of top p-n junction in the proposed TMB-SSBR, the image force barrier lowering effect is eliminated, the pinching off electric field effect by MOS trenches is weakened, so that the mesa surface electric field is much larger than that in conventional TMBS. Therefore, the mesa width is enlarged and the n-drift concentration is slightly increased, which results in a low specific on-resistance and a good tradeoff between reverse leakage currents and forward voltages. Compared to a conventional TMBS, simulation results show that, with the same breakdown voltage of 124V and the same reverse leakage current at room temperature, TMB-SSBR increases the figure of merit (FOM, equates to VB2/Ron, sp) by 25.5%, and decreases the reverse leakage by 33.3% at the temperature of 423K. Just like the development from SBD to TMBS, from TMBS to TMB-SSBR also brings obvious improvement of performance.

In this paper, we investigate optimal design of millimeter-wave (mmWave) multiuser line-of-sight multiple-input-multiple-output (LOS MIMO) systems using hybrid arrays of subarrays based on hybrid block diagonalization (BD) precoding and combining scheme. By introducing a general 3D geometric channel model, the optimal subarray separation products of the transmitter and receiver for maximizing sum-rate is designed in terms of two regular configurations of adjacent subarrays and interleaved subarrays for different users, respectively. We analyze the sensitivity of the optimal design parameters on performance in terms of a deviation factor, and derive expressions for the eigenvalues of the multiuser equivalent LOS MIMO channel matrix, which are also valid for non-optimal design. Simulation results show that the interleaved subarrays can support longer distance communication than the adjacent subarrays given the appropriate fixed subarray deployment.

The multi-agent surveillance problem is to find optimal trajectories of multiple agents that patrol a given area as evenly as possible. In this paper, we consider the multi-agent surveillance problem based on travel cost minimization. The surveillance area is given by an undirected graph. The penalty for each agent is introduced to evaluate the surveillance performance. Through a mixed logical dynamical system model, the multi-agent surveillance problem is reduced to a mixed integer linear programming (MILP) problem. In model predictive control, trajectories of agents are generated by solving the MILP problem at each discrete time. Furthermore, a condition that the MILP problem is always feasible is derived based on the Chinese postman problem. Finally, the proposed method is demonstrated by a numerical example.

In a multi-agent system, it is important to consider a design method of cooperative actions in order to achieve a common goal. In this paper, we propose two novel multi-agent reinforcement learning methods, where the control specification is described by linear temporal logic formulas, which represent a common goal. First, we propose a simple solution method, which is directly extended from the single-agent case. In this method, there are some technical issues caused by the increase in the number of agents. Next, to overcome these technical issues, we propose a new method in which an aggregator is introduced. Finally, these two methods are compared by numerical simulations, with a surveillance problem as an example.

Linear codes are widely studied due to their important applications in secret sharing schemes, authentication codes, association schemes and strongly regular graphs, etc. In this paper, firstly, a class of three-weight linear codes is constructed by selecting a new defining set, whose weight distributions are determined by exponential sums. Results show that almost all the constructed codes are minimal and thus can be used to construct secret sharing schemes with sound access structures. Particularly, a class of projective two-weight linear codes is obtained and based on which a strongly regular graph with new parameters is designed.

Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable performance in detection and classification tasks. Nevertheless, their feature extraction cannot consider both local and global information, so the detection and classification performance can be further improved. In addition, more and more deep learning networks are designed as more and more complex, and the amount of computation and storage space required is also significantly increased. This paper proposes a combination of CNN and transformer, and designs a local feature enhancement module and global context modeling module to enhance the cascade network. While the local feature enhancement module increases the range of feature extraction, the global context modeling is used to capture the feature maps' global information. To decrease the model complexity, a shared sublayer is designed to realize the sharing of weight parameters between the adjacent convolutional layers or cross convolutional layers, thereby reducing the number of convolutional weight parameters. Moreover, to effectively improve the detection performance of neural networks without increasing network parameters, the optimal transport assignment approach is proposed to resolve the problem of label assignment. The classification loss and regression loss are the summations of the cost between the demander and supplier. The experiment results demonstrate that the proposed Combination of CNN and Transformer with Shared Sublayer (CCTSS) performs better than the state-of-the-art methods in various datasets and applications.

We evaluated the transmission performance, including received power and transmission throughput characteristics, in 4×4 single-user multiple-input multiple-output (SU-MIMO) transmission for synchronous time division duplex (TDD) and downlink data channels in comparison with single-input single-output (SISO) transmission in an environment where a local 5G wireless base station was installed on the roof of a research building at our university. Accordingly, for the received power characteristics, the difference between the simulation value, which was based on the ray tracing method, and the experimental value at 32 points in the area was within a maximum difference of approximately 10 dB, and sufficient compliance was obtained. Regarding the transmission throughput versus received power characteristics, after showing a simulation method for evaluating throughput characteristics in MIMO, we compared the results with experimental results. The cumulative distribution function (CDF) of the transmission throughput shows that, at a CDF of 50%, in SISO transmission, the simulated value is approximately 115Mbps, and the experimental value is 105Mbps, within a difference of approximately 10Mbps. By contrast, in MIMO transmission, the simulation value is 380Mbps, and the experimental value is approximately 420Mbps, which is a difference of approximately 40Mbps. It was shown that the received power and transmission throughput characteristics can be predicted with sufficient accuracy by obtaining the delay profile and the system model at each reception point using the both ray tracing and MIMO simulation methods in actual environments.

The fifth-generation (5G) new radio (NR) standard employs ultra-reliable and low-latency communication (URLLC) to provide real-time wireless interactive capability for the internet of things (IoT) applications. To satisfy the stringent latency and reliability demands of URLLC services, grant-free (GF) transmissions with the K-repetition transmission (K-Rep) have been introduced. However, fading fluctuations can negatively impact signal quality at the base station (BS), leading to an increase in the number of repetitions and raising concerns about interference and energy consumption for IoT user equipment (UE). To overcome these challenges, this paper proposes novel adaptive K-Rep control schemes that employ site diversity reception to enhance signal quality and reduce energy consumption. The performance evaluation demonstrates that the proposed adaptive K-Rep control schemes significantly improve communication reliability and reduce transmission energy consumption compared with the conventional K-Rep scheme, and then satisfy the URLLC requirements while reducing energy consumption.

In this survey we summarize properties of pseudorandomness and non-randomness of some number-theoretic sequences and present results on their behaviour under the following measures of pseudorandomness: balance, linear complexity, correlation measure of order k, expansion complexity and 2-adic complexity. The number-theoretic sequences are the Legendre sequence and the two-prime generator, the Thue-Morse sequence and its sub-sequence along squares, and the prime omega sequences for integers and polynomials.

The calculation of cross-correlation between a sequence with good autocorrelation and its decimated sequence is an interesting problem in the field of sequence design. In this letter, we consider a class of ternary sequences with perfect autocorrelation, proposed by Shedd and Sarwate (IEEE Trans. Inf. Theory, 1979, DOI: 10.1109/TIT.1979.1055998), which is generated based on the cross-correlation between m-sequence and its d-decimation sequence. We calculate the cross-correlation distribution between a certain pair of such ternary perfect sequences and show that the cross-correlation takes three different values.

We have developed an all-optical fiber link antenna measurement system for a millimeter wave 5th generation mobile communication frequency band around 28 GHz. Our developed system consists of an optical fiber link an electrical signal transmission system, an antenna-coupled-electrode electric-field (EO) sensor system for 28GHz-band as an electrical signal receiving system, and a 6-axis vertically articulated robot with an arm length of 1m. Our developed optical fiber link electrical signal transmission system can transmit the electrical signal of more than 40GHz with more than -30dBm output level. Our developed EO sensor can receive the electrical signal from 27GHz to 30GHz. In addition, we have estimated a far field antenna factor of the EO sensor system for the 28GHz-band using an amplitude center modified antenna factor estimation equation. The estimated far field antenna factor of the sensor system is 83.2dB/m at 28GHz.

In this paper, a concept of non-contact PIM evaluation method using balanced transmission lines is proposed for impedance-matched PIM measurement systems. In order to evaluate the PIM characteristics of a MSL by using its image model, measurement system using balanced transmission line is introduced. In non-contact PIM measurement, to reduce undesirable PIM generation by metallic contact and the PIM-degradation in repeated measurements, a non-contact connector which is applicable without any design changes in DUT is introduce. The three-dimensional balun composed of U-balun and balanced transmission line is also proposed so that it can be applicable to conventional unbalanced PIM measurement systems. In order to validate the concept of the proposed system, a sample using nickel producing high PIM is introduced. In order to avoid the effect of the non-contact connection part on observed PIM, a sample-configuration that PIM-source exists outside of the non-contact connection part is introduced. It is also shown using a sample using copper that, nickel-sample can be clearly differentiated in PIM characteristics while it is equivalent to low-PIM sample in scattering-parameter characteristics. Finally, by introducing the TRL-calibration and by extracting inherent DUT-characteristics from whole-system characteristics, a method to estimate the PIM characteristics of DUT which cannot be taken directly in measurement is proposed.

Measurements of contact discharge current waveforms from an ESD generator with a test voltage of 4kV are conducted with the IEC specified arrangement of a 2m long return current cable in different three calibration environments that all comply with the IEC calibration standard to identify the occurrence source of damped oscillations (ringing), which has remained unclear since contact discharge testing was first adopted in 1989 IEC publication 801-2. Their frequency spectra are analyzed comparing with the spectrum calculated from the ideal contact discharge current waveform without ringing (IEC specified waveform) offered in IEC 61000-4-2 and the spectra derived from a simplified equivalent circuit based on the IEC standard in combination with the measured input impedances of one-ended grounding return current cable with the same arrangement in the same calibration environment as those for the current measurements. The results show that the measured contact discharge waveforms have ringing around the IEC specified waveform after the falling edge of the peak, causing their spectra from 20MHz to 200MHz, but the spectra from 40MHz to 200MHz significantly differ depending on the calibration environments even for the same cable arrangement, which do not almost affect the spectra from 20MHz to 40MHz and over 200MHz. In the calibration environment under the cable arrangement close to the reference ground, the spectral shapes of the measured contact discharge currents and their frequencies of the multiple peaks and dips roughly correspond to the spectral distributions calculated from the simplified equivalent circuit using the measured cable input impedances. These findings reveal that the root cause of ringing is mainly due to the resonances of the return current cable, and calibration environment under the cable arrangement away from the reference ground tends to mitigate the cable resonances.

In this paper, we apply two methods in machine learning, dropout and semi-supervised learning, to a recently proposed method called CSQ-SDL which uses deep neural networks for evaluating shift quality from time-series measurement data. When developing a new Automatic Transmission (AT), calibration takes place where many parameters of the AT are adjusted to realize pleasant driving experience in all situations that occur on all roads around the world. Calibration requires an expert to visually assess the shift quality from the time-series measurement data of the experiments each time the parameters are changed, which is iterative and time-consuming. The CSQ-SDL was developed to shorten time consumed by the visual assessment, and its effectiveness depends on acquiring a sufficient number of data points. In practice, however, data amounts are often insufficient. The methods proposed here can handle such cases. For the cases wherein only a small number of labeled data points is available, we propose a method that uses dropout. For those cases wherein the number of labeled data points is small but the number of unlabeled data is sufficient, we propose a method that uses semi-supervised learning. Experiments show that while the former gives moderate improvement, the latter offers a significant performance improvement.

The image-to-image translation aims to learn a mapping between the source and target domains. For improving visual quality, the majority of previous works adopt multi-stage techniques to refine coarse results in a progressive manner. In this work, we present a novel approach for generating plausible details by only introducing a group of intermediate supervisions without cascading multiple stages. Specifically, we propose a Laplacian Pyramid Transformation Generative Adversarial Network (LapTransGAN) to simultaneously transform components in different frequencies from the source domain to the target domain within only one stage. Hierarchical perceptual and gradient penalization are utilized for learning consistent semantic structures and details at each pyramid level. The proposed model is evaluated based on various metrics, including the similarity in feature maps, reconstruction quality, segmentation accuracy, similarity in details, and qualitative appearances. Our experiments show that LapTransGAN can achieve a much better quantitative performance than both the supervised pix2pix model and the unsupervised CycleGAN model. Comprehensive ablation experiments are conducted to study the contribution of each component.

Person authentication using biometric information has recently become popular among researchers. User management based on biometrics is more reliable than that using conventional methods. To secure private information, it is necessary to build continuous authentication-based user management systems. Brain waves are suitable biometric modalities for continuous authentication. This study is based on biometric authentication using brain waves evoked by invisible visual stimuli. Invisible visual stimulation is considered over visual stimulation to overcome the obstacles faced by a user when using a system. Invisible stimuli are confirmed by changing the intensity of the image and presenting high-speed stimulation. To ensure invisibility, stimuli of different intensities were tested, and the stimuli with an intensity of 5% was confirmed to be invisible. To improve the verification performance, a continuous wavelet transform was introduced over the Fourier transform because it extracts both time and frequency information from the brain wave. The scalogram obtained by the wavelet transform was used as an individual feature and for synchronizing the template and test data. Furthermore, to improve the synchronization performance, the waveband was split based on the power distribution of the scalogram. A performance evaluation using 20 subjects showed an equal error rate of 3.8%.

In 2014, the above paper entitled ‘Quasi-Linear Support Vector Machine for Nonlinear Classification’ was published by Zhou, et al. [1]. They proposed a quasi-linear kernel function for support vector machine (SVM). However, in this letter, we point out that this proposed kernel function is a part of multiple kernel functions generated by well-known multiple kernel learning which is proposed by Bach, et al. [2] in 2004. Since then, there have been a lot of related papers on multiple kernel learning with several applications [3]. This letter verifies that the main kernel function proposed by Zhou, et al. [1] can be derived using multiple kernel learning algorithms [3]. In the kernel construction, Zhou, et al. [1] used Gaussian kernels, but the multiple kernel learning had already discussed the locality of additive Gaussian kernels or other kernels in the framework [4], [5]. Especially additive Gaussian or other kernels were discussed in tutorial at major international conference ECCV2012 [6]. The authors did not discuss these matters.

We thank Kamata et al. (2023) [1] for their interest in our work [2], and for providing an explanation of the quasi-linear kernel from a viewpoint of multiple kernel learning. In this letter, we first give a summary of the quasi-linear SVM. Then we provide a discussion on the novelty of quasi-linear kernels against multiple kernel learning. Finally, we explain the contributions of our work [2].

This paper summarizes recent reports on the internet's energy consumption and the internet's benefits on climate actions. It discusses energy-efficiency and the need for a common standard for evaluating the climate impact of future communication technologies and suggests a model that can be adapted to different internet applications such as streaming, online reading and downloading. The two main approaches today are based on how much data is transmitted or how much time the data is under way. The paper concludes that there is a need for a standardized method to estimate energy consumption and CO2 emission related to internet services. This standard should include a method for energy-optimizing future networks, where every Wh will be scrutinized.

A metasurface absorber capable of monitoring two-dimensional (2-d) electric field distributions has been developed, where a matrix of lumped resistors between surface patches formed on a mushroom-type structure works as a 2-d array of short dipole sensors. In this paper absorption and reflection of a spherical wave incident on the metasurface absorber are analyzed by numerical computation by the plane-wave spectrum (PWS) technique using 2-d Fourier analysis. The electromagnetic field of the spherical wave incident on the absorber surface is expanded into a large number of plane waves, for each of which the TE and TM reflection and absorption coefficients are applied. Then by synthesizing all the plane wave fields we obtain the spatial distributions of reflected and absorbed fields. The detailed formulation of the computation is described, and the computed field distributions are compared with those obtained by simulation and actual measurement when the spherical wave from a dipole is illuminated onto a metasurface absorber. It is demonstrated that the PWS technique is effective and efficient in obtaining the accurate field distributions of the spherical wave on and around the absorber. This is useful for evaluating the performance of the metasurface absorber to absorb and measure the spherical wave field distributions around an EM source.