This paper proposes a dual-polarized filtering antenna with extracted-pole unit (EPU) using LTCC substrate. The EPU realizes the high skirt characteristic of the bandpass filter with transmission zeros (TZs) located near the passband without cross coupling. The filtering antenna with EPU is designed and fabricated in 28GHz band for 5G Band-n257 (26.5-29.5GHz). The measured S11 is less than -10.6dB in Band-n257, and the isolation between two ports for dual polarization is greater than 20.0dB. The measured peak antenna gain is 4.0dBi at 28.8GHz and the gain is larger than 2.5dBi in Band-n257. The frequency characteristics of the measured antenna gain shows the high skirt characteristic out of band, which are in good agreement with electromagnetic (EM)-simulated results.

Recently, “Both-Side Retrodirective System” was proposed, as a beam convergence technique, for microwave high power transmission. To demonstrate the effectiveness of the both-side retrodirective system by experiment, the authors propose a 2-dimensional measurement equipment. Propagation in the parallel plate waveguide was analogized based on free-space propagation, and the theory and characteristics were clarified by simulation. The electric field distribution in the waveguide was measured by electric probe with the proposed equipment. Two types of measurement equipment were developed. One is a 4-element experiment system, which is a small-scale device for principle verification. The other is a 16-element measurement equipment, which is intended to evaluate beam convergence of a both-side retrodirective system in the next step. The measured results were compared with simulation results. As a result, it was confirmed that the beam formed in the waveguide was successfully measured. Thus, the effectiveness of 2-dimensional measurement equipment for evaluation of beam convergence was shown.

In this paper, we applied the circuit synthesis theory of filters to the design of transmission-type metasurface cells and arbitrarily designed the amplitude and phase of the transmission and reflection by adjusting the resonant frequency and coupling coefficient. In addition, we successfully designed the phase of the unit cell by using the frequency conversion of filter theory. Moreover, we designed a refractive transmission-type metasurface plate with a novel cell structure that reacts to both polarizations. The prototype operated at the desired refraction angle, confirming the design theory.

This contribution introduces a novel, dielectric waveguide based, permittivity sensor. Next to the fundamental hybrid mode theory, which predicts exceptional wave propagation behavior, a design concept is presented that realizes a pseudo-transmission measurement approach for attenuating feed-side reflections. Furthermore, a transmission line length independent signal processing is introduced, which fosters the robustness and applicability of the sensor concept. Simulation and measurement results that prove the sensor concept and validate the high measurement accuracy, are presented and discussed in detail.

We propose a novel silicon diplexer integrated with filters for frequency-division multiplexing in the 300-GHz band. The diplexer consists of a directional coupler formed of unclad silicon wires, a photonic bandgap-based low-pass filter, and a high-pass filter based on frequency-dependent bending loss. These integrated filters are capable of suppressing crosstalk and providing >15dB isolation over 40GHz, which is highly beneficial for terahertz-range wireless communications applications. We have used this diplexer in a simultaneous error-free wireless transmission of 300-GHz and 335-GHz channels at the aggregate data rate of 36Gbit/s.

In this paper, we describe a wavelength-division multiplexing visible-light communication (VLC) system using two colored light-emitting diodes (LEDs) with similar emission wavelengths. A multi-input multi-output signal-separation method using a neural network is proposed to cancel the optical cross chatter caused by the spectral overlap of LEDs. The experimental results demonstrate that signal separation using neural networks can be achieved in wavelength-multiplexed VLC systems with a bit error rate of less than 3.8×10-3 (forward error correction limit). Furthermore, the simulation results reveal that the carrier-to-noise ratio (CNR) is improved by 2dB for the successive interference canceller (SIC) compared to the zero-forcing method.

One of cost-effective ways to increase the transmission capacity of current standard wavelength division multiplexing (WDM) transmission systems is to use a wavelength band other than the C-band to transmit in multi-band. We proposed the concept of multi-band system using wavelength conversion, which can simultaneously process signals over a wide wavelength range. All-optical wavelength conversion could be used to convert C-band WDM signals into other bands in a highly nonlinear fiber (HNLF) by four-wave mixing and allow to simultaneously transmit multiple WDM signals including other than the C-band, with only C-band transceivers. Wavelength conversion has been reported for various nonlinear waveguide materials other than HNLF. In such nonlinear materials, we noticed the possibility of wideband transmission by dispersion-tailored silicon-on-insulator (SOI) waveguides. Based on the CMOS process has high accuracy, it is expected that the chromatic dispersion fluctuation could be reduced in mass production. As a first step in the investigation of the broadness of wavelength conversion using SOI-based waveguides, we designed and fabricated dispersion-tailored 12 strip waveguides provided with an edge coupler at both ends. Each of the 12 waveguides having different widths and lengths and is connected to fibers via lensed fibers or by lenses. In order to characterize each waveguide, the pump-probe experimental setup was constructed using a tunable light source as pump and an unmodulated 96-ch C-band WDM test signal. Using this setup, we evaluate insertion loss, input power dependence, conversion bandwidth and conversion efficiency. We confirmed C-band test signal was converted to the S-band and the L-band using the same silicon waveguide with 3dB conversion bandwidth over 100-nm. Furthermore, an increased design tolerance of at least 90nm was confirmed for C-to-S conversion by shortening the waveguide length. It is confirmed that the wavelength converters using the nonlinear waveguide has sufficiently wide conversion bandwidth to enhance the multi-band WDM transmission system.

We propose a novel mode-multiplexed light source using angularly-multiplexed volume holograms. Mode division multiplexing beams can be generated from a simple transmitter that is made of a laser array, single lens, and volume holograms. Hologram media has low recording sensitivity; hence, using holograms in the communication band is difficult. However, a dual wavelength method that uses different wavelengths for recording and reading holograms can realize the volume holograms for the infrared region. The volume holograms for three spatial mode multiplexing are formed using a compact Michelson interferometer type recording setup; simultaneous generations of three modes were demonstrated using a fiber array or vertical cavity surface emitting laser array with the volume holograms. A low loss coupling of three modes to few-mode-fiber can be achieved through the precise design and recording of volume holograms. The simple and low-cost mode-multiplexed light source using the volume holograms has the potential to broaden the application of MDM.

The diffusion model has achieved success in generating and editing high-quality images because of its ability to produce fine details. Its superior generation ability has the potential to facilitate more detailed segmentation. This study presents a novel approach to segmentation tasks using an inverse heat dissipation model, a kind of diffusion-based models. The proposed method involves generating a mask that gradually shrinks to fit the shape of the desired segmentation region. We comprehensively evaluated the proposed method using multiple datasets under varying conditions. The results show that the proposed method outperforms existing methods and provides a more detailed segmentation.

This paper mainly proposes a line segment detection method based on pseudo peak suppression and local Hough transform, which has good noise resistance and can solve the problems of short line segment missing detection, false detection, and oversegmentation. In addition, in response to the phenomenon of uneven development in nuclear emulsion tomographic images, this paper proposes an image preprocessing process that uses the “Difference of Gaussian” method to reduce noise and then uses the standard deviation of the gray value of each pixel to bundle and unify the gray value of each pixel, which can robustly obtain the linear features in these images. The tests on the actual dataset of nuclear emulsion tomographic images and the public YorkUrban dataset show that the proposed method can effectively improve the accuracy of convolutional neural network or vision in transformer-based event classification for alpha-decay events in nuclear emulsion. In particular, the line segment detection method in the proposed method achieves optimal results in both accuracy and processing speed, which also has strong generalization ability in high quality natural images.

Orthogonal frequency division multiplexing (OFDM) is very sensitive to the carrier frequency offset (CFO). The CFO estimation precision heavily makes impacts on the OFDM performance. In this paper, a new Bayesian learning-assisted joint CFO tracking and channel impulse response estimation is proposed. The proposed algorithm is modified from a Bayesian learning-assisted estimation (BLAE) algorithm in the literature. The BLAE is expectation-maximization (EM)-based and displays the estimator mean square error (MSE) lower than the Cramer-Rao bound (CRB) when the CFO value is near zero. However, its MSE value may increase quickly as the CFO value goes away from zero. Hence, the CFO estimator of the BLAE is replaced to solve the problem. Originally, the design criterion of the single-time-sample (STS) CFO estimator in the literature is maximum likelihood (ML)-based. Its MSE performance can reach the CRB. Also, its CFO estimation range can reach the widest range required for a CFO tracking estimator. For a CFO normalized by the sub-carrier spacing, the widest tracking range required is from -0.5 to +0.5. Here, we apply the STS CFO estimator design method to the EM-based Bayesian learning framework. The resultant Bayesian learning-assisted STS algorithm displays the MSE performance lower than the CRB, and its CFO estimation range is between ±0.5. With such a Bayesian learning design criterion, the additional channel noise power and power delay profile must be estimated, as compared with the ML-based design criterion. With the additional channel statistical information, the derived algorithm presents the MSE performance better than the CRB. Two frequency-selective channels are adopted for computer simulations. One has fixed tap weights, and the other is Rayleigh fading. Comparisons with the most related algorithms are also been provided.

The existing literature focuses on the applications of fast filter bank due to its excellent frequency responses with low complexity. However, the topic is not addressed related to the general transfer function expressions of the corresponding subfilters for a specific channel. To do this, in this paper, general closed-form transfer function expressions for fast filter bank are derived. Firstly, the cascaded structure of fast filter bank is modelled by a binary tree, with which the index of the subfilter at each stage within the channel can be determined. Then the transfer functions for the two outputs of a subfilter are expressed in a unified form. Finally, the general closed-form transfer functions for the channel and its corresponding subfilters are obtained by variables replacement if the prototype lowpass filters for the stages are given. Analytical results and simulations verify the general expressions. With such closed-form expressions lend themselves easily to analysis and direct computation of the transfer functions and the frequency responses without the structure graph.

In the cellular system, the Worst Case User (WCU), whose distances to three nearest BSs are the similar, usually achieves the lowest performance. Improving user performance, especially the WCU, is a big problem for both network designers and operators. This paper works on the WCU in terms of coverage probability analysis by the stochastic geometry tool and data rate optimization with the transmission power constraint by the reinforcement learning technique under the Stretched Pathloss Model (SPLM). In analysis, only fast fading from the WCU to the serving Base Stations (BSs) is taken into the analysis to derive the lower bound coverage probability. Furthermore, the paper assumes that the Coordinated Multi-Point (CoMP) technique is only employed for the WCU to enhance its downlink signal and avoid the explosion of Intercell Interference (ICI). Through the analysis and simulation, the paper states that to improve the WCU performance under bad wireless environments, an increase in transmission power can be a possible solution. However, in good environments, the deployment of advanced techniques such as Joint Transmission (JT), Joint Scheduling (JS), and reinforcement learning is an suitable solution.

Hybrid automatic repeat request (HARQ) is an essential technology that efficiently reduces the transmission error rate. However, for ultra-reliable low latency communications (URLLC) in the 5th generation mobile communication systems and beyond, the increase in latency due to retransmission must be minimized in HARQ. In this paper, we propose a highly-efficient low-latency HARQ method built on non-orthogonal multiple access (NOMA) for URLLC while minimizing the performance loss for coexisting services (use cases) such as enhanced mobile broadband (eMBB). The proposed method can be seen as an extension of the conventional link-level non-orthogonal HARQ to the system-level protocol. This mitigates the problems of the conventional link-level non-orthogonal HARQ, which are decoding error under poor channel conditions and an increase in transmission delay due to restrictions in retransmission timing. In the proposed method, delay-sensitive URLLC packets are preferentially multiplexed with best-effort eMBB packets in the same channel using superposition coding to reduce the transmission latency of the URLLC packet while alleviating the throughput loss in eMBB. This is achieved using a weighted channel-aware resource allocator (scheduler). The inter-packet interference multiplexed in the same channel is removed using a successive interference canceller (SIC) at the receiver. Furthermore, the transmission rates for the initial transmission and retransmission are controlled in an appropriate manner for each service in order to deal with decoding errors caused by error in transmission rate control originating from a time varying channel. We show that the proposed method significantly improves the overall performance of a system that simultaneously provides eMBB and URLLC services.

The transfer distance of the wireless power transfer (WPT) system with relay coil is longer, so this technology have a better practical perspective. But the location of the relay coil has a great impact on the transmission efficiency of the WPT system, and it is not easy to analyze. In order to research the influence law of the relay coil location on the transmission efficiency and obtain the optimal location, the paper firstly proposes the concept of relay coil location factor. And based on the location factor, a novel method for studying the influence of the relay coil location on the transmission efficiency is proposed. First, the mathematical model between the transmission efficiency and the location factor is built. Next, considering the transfer distance, coil radius, coil turns and load resistance, a lot of simulations are carried out to analyze the influence of the location factor on the transmission efficiency, respectively. The influence law and the optimal location factor were obtained with different parameters. Finally, a WPT system with relay coil was built for experiments. And the experiment results verify that the theoretical analysis is correct and the proposed method can simplify the analysis progress of the influence of relay coil location on the transmission efficiency. Moreover, the proposed method and the research conclusions can provide guidance for designing the multiple coils structure WPT system.

Various haze removal methods based on the atmospheric scattering model have been presented in recent years. Most methods have targeted strong haze images where light is scattered equally in all color channels. This paper presents a haze removal method using near-infrared (NIR) images for relatively weak haze images. In order to recover the lost edges, the presented method first extracts edges from an appropriately weighted NIR image and fuses it with the color image. By introducing a wavelength-dependent scattering model, our method then estimates the transmission map for each color channel and recovers the color more naturally from the edge-recovered image. Finally, the edge-recovered and the color-recovered images are blended. In this blending process, the regions with high lightness, such as sky and clouds, where unnatural color shifts are likely to occur, are effectively estimated, and the optimal weighting map is obtained. Our qualitative and quantitative evaluations using 59 pairs of color and NIR images demonstrated that our method can recover edges and colors more naturally in weak haze images than conventional methods.

Video-based point cloud compression (V-PCC) utilizes video compression technology to efficiently encode dense point clouds providing state-of-the-art compression performance with a relatively small computation burden. V-PCC converts 3-dimensional point cloud data into three types of 2-dimensional frames, i.e., occupancy, geometry, and attribute frames, and encodes them via video compression. On the other hand, the quality of these frames may be degraded due to video compression. This paper proposes an adaptive neural network-based post-processing filter on attribute frames to alleviate the degradation problem. Furthermore, a novel training method using occupancy frames is studied. The experimental results show average BD-rate gains of 3.0%, 29.3% and 22.2% for Y, U and V respectively.

With the emergence of a large quantity of data in science and industry, it is urgent to improve the prediction accuracy and reduce the high complexity of Gaussian process regression (GPR). However, the traditional global approximation and local approximation have corresponding shortcomings, such as global approximation tends to ignore local features, and local approximation has the problem of over-fitting. In order to solve these problems, a large-scale Gaussian process regression algorithm (RFFLT) combining random Fourier features (RFF) and local approximation is proposed. 1) In order to speed up the training time, we use the random Fourier feature map input data mapped to the random low-dimensional feature space for processing. The main innovation of the algorithm is to design features by using existing fast linear processing methods, so that the inner product of the transformed data is approximately equal to the inner product in the feature space of the shift invariant kernel specified by the user. 2) The generalized robust Bayesian committee machine (GRBCM) based on Tsallis mutual information method is used in local approximation, which enhances the flexibility of the model and generates a sparse representation of the expert weight distribution compared with previous work. The algorithm RFFLT was tested on six real data sets, which greatly shortened the time of regression prediction and improved the prediction accuracy.

Micro-expression recognition (MER) draws intensive research interest as micro-expressions (MEs) can infer genuine emotions. Prior information can guide the model to learn discriminative ME features effectively. However, most works focus on researching the general models with a stronger representation ability to adaptively aggregate ME movement information in a holistic way, which may ignore the prior information and properties of MEs. To solve this issue, driven by the prior information that the category of ME can be inferred by the relationship between the actions of facial different components, this work designs a novel model that can conform to this prior information and learn ME movement features in an interpretable way. Specifically, this paper proposes a Decomposition and Reconstruction-based Graph Representation Learning (DeRe-GRL) model to efectively learn high-level ME features. DeRe-GRL includes two modules: Action Decomposition Module (ADM) and Relation Reconstruction Module (RRM), where ADM learns action features of facial key components and RRM explores the relationship between these action features. Based on facial key components, ADM divides the geometric movement features extracted by the graph model-based backbone into several sub-features, and learns the map matrix to map these sub-features into multiple action features; then, RRM learns weights to weight all action features to build the relationship between action features. The experimental results demonstrate the effectiveness of the proposed modules, and the proposed method achieves competitive performance.

HDR video is possible to display the maximum 1200% luminance, however, it is limited in SDR display. In this study, we expand high luminance area considering with perceptual performance to improve a presentation performance of HDR video in the SDR display. As results of objective experiments, it is recognized that the proposed method can improve the presentation performance maximally 0.8dB in WPSNR.