A periodic array of InSb spheres on a substrate is numerically analyzed at terahertz frequencies. The incident field is shown to be coupled to the substrate due to the guided-mode resonance. The effect of the background refractive index on the transmission characteristics is investigated for sensor applications.

Volume integral equations combined with orthogonality of guided mode and non-guided field are proposed for the TE incidence of two-dimensional optical slab waveguide. The slab waveguide is assumed to satisfy the single mode condition. The formulation of the integral equations are described in detail. The matrix equation obtained by applying the method of moments to the integral equations is shown. Numerical results for step, gap, and grating waveguides are given. They are compared to published papers to validate the proposed method.

To achieve smooth beam injection in operation of synchrotron radiation facilities, pulsed multipole magnet beam injectors are developed. It is found that the developed beam injector causes serious disturbance in the circulating storage beam in the Aichi synchrotron radiation center, and that such the unexpected disturbance of the storage beam may be caused by eddy current induced on thin titanium coating inside a beam duct. In this work, the induced eddy current on the titanium layer is evaluated quantitatively by numerical simulations and improvement for the developed beam injector is discussed based on the numerical simulation.

Bitcoin is currently a hot issue worldwide, and it is expected to become a new legal tender that replaces the current currency started with El Salvador. Due to the nature of cryptocurrency, however, difficulties in tracking led to the arising of misuses and abuses. Consequently, the pain of innocent victims by exploiting these bitcoins abuse is also increasing. We propose a way to detect new signatures by applying two-fold NLP-based clustering techniques to text data of Bitcoin abuse reports received from actual victims. By clustering the reports of text data, we were able to cluster the message templates as the same campaigns. The new approach using the abuse massage template representing clustering as a signature for identifying abusers is much efficacious.

A triple loss based framework for generalized zero-shot learning is presented in this letter. The approach learns a shared latent space for image features and attributes by using aligned variational autoencoders and variants of triplet loss. Then we train a classifier in the latent space. The experimental results demonstrate that the proposed framework achieves great improvement.

The spectral envelope parameter is a significant speech parameter in the vocoder's quality. Recently, the Vector Quantized Variational AutoEncoder (VQ-VAE) is a state-of-the-art end-to-end quantization method based on the deep learning model. This paper proposed a new technique for improving the embedding space learning of VQ-VAE with the Generative Adversarial Network for quantizing the spectral envelope parameter, called VQ-VAE-EMGAN. In experiments, we designed the quantizer for the spectral envelope parameters of the WORLD vocoder extracted from the 16kHz speech waveform. As the results shown, the proposed technique reduced the Log Spectral Distortion (LSD) around 0.5dB and increased the PESQ by around 0.17 on average for four target bit operations compared to the conventional VQ-VAE.

In a wireless ad hoc network (MANET), nodes can form a centerless, self-organizing, multi-hop dynamic network without any centralized control function, while hidden and exposed terminals seriously affect the network performance. Meanwhile, the wireless network node is evolving from single communication function to jointly providing a self precise positioning function, especially in indoor environments where GPS cannot work well. However, the existing medium access control (MAC) protocols only deal with collision control for data transmission without positioning function. In this paper, we propose a MAC protocol based on 802.11 standard to enable a node with self-positioning function, which is further used to solve hidden and exposed terminal problems. The location of a network node is obtained through exchanging of MAC frames jointly using a time of arrival (TOA) algorithm. Then, nodes use the location information to calculate the interference range, and judge whether they can transmit concurrently. Simulation shows that the positioning function of the proposed MAC protocol works well, and the corresponding MAC protocol can achieve higher throughput, lower average end-to-end delay and lower packet loss rate than that without self-localization function.

In this paper, an automatic retransmission request (ARQ) scheme for IEEE 802.11ac is presented, which can solve the problem of severe packet loss and greatly improve the performance in error-prone environments. The proposed solution only requires to be deployed on the sender and is compatible with the 802.11 protocol. The algorithm utilizes the basic strategy of sliding retransmission and then adds the method of copying frames. The media access control (MAC) protocol data unit (MPDU) lost in the transmission and the newly added data frame brought by the sliding window change are replicated. The scheme retransmits the duplicated aggregated packet and further improves the throughput by increasing the probability of successful transmission of sub-frames. Besides, we also establish a mathematical model to analyze the performance of the proposed scheme. We introduce the concept of average aggregated sub-frames and express the sliding retransmission strategy as the aggregated transmission of average aggregated sub-frames, thereby simplifying the model and effectively analyzing the theoretical throughput of the proposed algorithm. The simulation results of Network simulator 3 (NS-3) simulation results demonstrate that the performance of the proposed algorithm is better than the traditional sliding retransmission ARQ algorithm in error-prone channels with a higher physical layer rate.

In this paper, the scattering far-field from a circular electric conducting cylinder has been analyzed by physical optics (PO) approximation for both H and E polarizations. The evaluation of radiation integrations due to the PO current is conducted numerically and analytically. While non-uniform and uniform asymptotic solutions have been derived by the saddle point method, a separate approximation has been made for forward scattering direction. Comparisons among our approximation, direct numerical integration and exact solution results yield a good agreement for electrically large cylinders.

The electromagnetic environment is increasingly complex and changeable, and radar needs to meet the execution requirements of various tasks. Modern radars should improve their intelligence level and have the ability to learn independently in dynamic countermeasures. It can make the radar countermeasure strategy change from the traditional fixed anti-interference strategy to dynamically and independently implementing an efficient anti-interference strategy. Aiming at the performance optimization of target tracking in the scene where multiple signals coexist, we propose a countermeasure method of cognitive radar based on a deep Q-learning network. In this paper, we analyze the tracking performance of this method and the Markov Decision Process under the triangular frequency sweeping interference, respectively. The simulation results show that reinforcement learning has substantial autonomy and adaptability for solving such problems.

This paper addresses single image super-resolution (SR) based on convolutional neural networks (CNNs). It is known that recovery of high-frequency components in output SR images of CNNs learned by the least square errors or least absolute errors is insufficient. To generate realistic high-frequency components, SR methods using generative adversarial networks (GANs), composed of one generator and one discriminator, are developed. However, when the generator tries to induce the discriminator's misjudgment, not only realistic high-frequency components but also some artifacts are generated, and objective indices such as PSNR decrease. To reduce the artifacts in the GAN-based SR methods, we consider the set of all SR images whose square errors between downscaling results and the input image are within a certain range, and propose to apply the metric projection onto this consistent set in the output layers of the generators. The proposed technique guarantees the consistency between output SR images and input images, and the generators with the proposed projection can generate high-frequency components with few artifacts while keeping low-frequency ones as appropriate for the known noise level. Numerical experiments show that the proposed technique reduces artifacts included in the original SR images of a GAN-based SR method while generating realistic high-frequency components with better PSNR values in both noise-free and noisy situations. Since the proposed technique can be integrated into various generators if the downscaling process is known, we can give the consistency to existing methods with the input images without degrading other SR performance.

A novel jig structure for S11 calibration with short/open conditions and one reference material (referred to here as SOM) in dielectric measurement of liquids using a coaxial feed type stepped cut-off circular waveguide and a formula for exact calculation of S11 for the analytical model of the structure using the method of moments (MoM) was proposed. The accuracy and validity of S11 values calculated using the relevant formula was then verified for frequencies of 0.50, 1.5 and 3.0 GHz, and S11 measurement accuracy with each termination condition was verified after calibration with SOM by combining the jig of the proposed structure with the study's electromagnetic (EM) analysis method. The relative complex permittivity was then estimated from S11 values measured with various liquids in the jig after calibration, and differences in results obtained with the proposed method and the conventional jig, the analytical model and the EM analysis method were examined. The validity of the proposed dielectric measurement method based on a combination of the above jig structure, numerical S11 calculation and the calibration method was thus confirmed.

Near-field beamforming has played an important role in many scenarios such as radar imaging and acoustic detection. In this paper, the near-field beamforming is implemented in the time modulated array with the harmonic. The beam pattern with a low sidelobe level in precise position is achieved by controlling the switching sequence in time modulated cross array. Numerical results verify the correctness of the proposed method.

High Frequency Surface Wave Radar (HFSWR) can achieve over-the-horizon detection, which can effectively detect and track the ships and ultra-low altitude aircrafts, as well as the acquisition of sea state information such as icebergs and ocean currents and so on. However, HFSWR is seriously affected by the clutters, especially sea clutter and ionospheric clutter. In this paper, we propose a deep learning image semantic segmentation method based on optimized Deeplabv3+ network to achieve the automatic detection of sea clutter and ionospheric clutter using the measured R-D spectrum images of HFSWR during the typhoon as experimental data, which avoids the disadvantage of traditional detection methods that require a large amount of a priori knowledge and provides a basis for subsequent the clutter suppression or the clutter characteristics research.

The energy efficiency of intelligent reflecting surface (IRS) enabled internet of things (IoT) networks is studied in this letter. The energy efficiency is mathematically expressed, respectively, as the number of reflecting elements and the spectral efficiency of the network and is shown to scale in the logarithm of the reflecting elements number in the high regime of transmit power from source node. Furthermore, it is revealed that the energy efficiency scales linearly over the spectral efficiency in the high regime of transmit power, in contrast to conventional studies on energy and spectral efficiency trade-offs in the non-IRS wireless IoT networks. Numerical simulations are carried out to verify the derived results for the IRS enabled IoT networks.

In recent years, compared with the traditional face super-resolution (SR) algorithm, the face SR based on deep neural network has shown strong performance. Among these methods, attention mechanism has been widely used in face SR because of its strong feature expression ability. However, the existing attention-based face SR methods can not fully mine the missing pixel information of low-resolution (LR) face images (structural prior). And they only consider a single attention mechanism to take advantage of the structure of the face. The use of multi-attention could help to enhance feature representation. In order to solve this problem, we first propose a new pixel attention mechanism, which can recover the structural details of lost pixels. Then, we design an attention fusion module to better integrate the different characteristics of triple attention. Experimental results on FFHQ data sets show that this method is superior to the existing face SR methods based on deep neural network.

In this paper, a novel method of resource abstraction and an abstracted-resource model for dynamic resource control in optical access networks are proposed. Based on this proposal, an implementation assuming application to 5G mobile fronthaul and backhaul is presented. Finally, an evaluation of the processing time for resource allocation using this method is performed using a software prototype of the control function. From the results of the evaluation, it is confirmed that the proposed method offers better characteristics than former approaches, and is suitable for dynamic resource control in 5G applications.

For recent datacenter networks, RDMA (Remote Direct Memory Access) can ease the overhead of the TCP/IP protocol suite. The RoCEv2 (RDMA over Converged Ethernet version 2) standard enables RDMA on widely deployed Ethernet technology. RoCEv2 leverages priority-based flow control (PFC) for realizing the lossless environment required by RDMA. However, PFC is well-known to have the technical weakness of head-of-line blocking. Congestion control for RDMA is a very hot research topic for datacenter networks. In this paper, we propose a novel congestion control algorithm for RoCEv2, TIDD (Timer-based Increase and Delay-based Decrease). TIDD basically combines the timer-based increase of DCQCN and delay-based decrease of TIMELY. Extensive simulation results show that TIDD satisfies the high throughput and low latency required for datacenter networks.

In our previous paper, we have proposed a new numerical technique for transient scattering problem of periodically arrayed dispersion media by using a combination of the fast inversion Laplace transform (FILT) method and Fourier series expansion method (FSEM), and analyzed the pulse response for several widths of the dispersion media or rectangular cavities. From the numerical results, we examined the influence of a periodically arrayed dispersion media with a rectangular cavity on the pulse response. In this paper, we analyzed the transient scattering problem for the case of dispersion media with slanted air regions by utilizing a combination of the FILT, FSEM, and multilayer division method (MDM), and investigated an influence for the slanted angle of an air region. In addition, we verified the computational accuracy for term of the MDM and truncation mode number of the electromagnetic fields.

Nowadays there is no way to automatically obtain the function points when using function point analyze (FPA) method, especially for the requirement documents written in Chinese language. Considering the characteristics of Chinese grammar in words segmentation, it is necessary to divide words accurately Chinese words, so that the subsequent entity recognition and disambiguation can be carried out in a smaller range, which lays a solid foundation for the efficient automatic extraction of the function points. Therefore, this paper proposed a method of K-Means clustering based on TF-IDF, and conducts experiments with 24 software requirement documents written in Chinese language. The results show that the best clustering effect is achieved when the extracted information is retained by 55% to 75% and the number of clusters takes the middle value of the total number of clusters. Not only for Chinese, this method and conclusion of this paper, but provides an important reference for automatic extraction of function points from software requirements documents written in other Oriental languages, and also fills the gaps of data preprocessing in the early stage of automatic calculation function points.