Accurately simulating physical systems is essential in various fields. In recent years, deep learning has been used to automatically build models of such systems by learning from data. One such method is the neural ordinary differential equation (neural ODE), which treats the output of a neural network as the time derivative of the system states. However, while this and related methods have shown promise, their training strategies still require further development. Inspired by error analysis techniques in numerical analysis while replacing numerical errors with modeling errors, we propose the error-analytic strategy to address this issue. Therefore, our strategy can capture long-term errors and thus improve the accuracy of long-term predictions.

With the development of the Semantic Web, an increasing number of researchers are utilizing ontology technology to construct domain ontology. Since there is no unified construction standard, ontology heterogeneity occurs. The ontology matching method can fuse heterogeneous ontologies, which realizes the interoperability between knowledge and associates to more relevant semantic information. In the case of differences between ontologies, how to reduce false matching and unsuccessful matching is a critical problem to be solved. Moreover, as the number of ontologies increases, the semantic relationship between ontologies becomes increasingly complex. Nevertheless, the current methods that solely find the similarity of names between concepts are no longer sufficient. Consequently, this paper proposes an ontology matching method based on semantic association. Accurate matching pairs are discovered by existing semantic knowledge, and then the potential semantic associations between concepts are mined according to the characteristics of the contextual structure. The matching method can better carry out matching work based on reliable knowledge. In addition, this paper introduces a probabilistic logic repair method, which can detect and repair the conflict of matching results, to enhance the availability and reliability of matching results. The experimental results show that the proposed method effectively improves the quality of matching between ontologies and saves time on repairing incorrect matching pairs. Besides, compared with the existing ontology matching systems, the proposed method has better stability.

Multi-focus image fusion involves combining partially focused images of the same scene to create an all-in-focus image. Aiming at the problems of existing multi-focus image fusion algorithms that the benchmark image is difficult to obtain and the convolutional neural network focuses too much on the local region, a fusion algorithm that combines local and global feature encoding is proposed. Initially, we devise two self-supervised image reconstruction tasks and train an encoder-decoder network through multi-task learning. Subsequently, within the encoder, we merge the dense connection module with the PS-ViT module, enabling the network to utilize local and global information during feature extraction. Finally, to enhance the overall efficiency of the model, distinct loss functions are applied to each task. To preserve the more robust features from the original images, spatial frequency is employed during the fusion stage to obtain the feature map of the fused image. Experimental results demonstrate that, in comparison to twelve other prominent algorithms, our method exhibits good fusion performance in objective evaluation. Ten of the selected twelve evaluation metrics show an improvement of more than 0.28%. Additionally, it presents superior visual effects subjectively.

This article focuses on improving the BiSeNet v2 bilateral branch image segmentation network structure, enhancing its learning ability for spatial details and overall image segmentation accuracy. A modified network called “BiconvNet” is proposed. Firstly, to extract shallow spatial details more effectively, a parallel concatenated strip and dilated (PCSD) convolution module is proposed and used to extract local features and surrounding contextual features in the detail branch. Continuing on, the semantic branch is reconstructed using the lightweight capability of depth separable convolution and high performance of ConvNet, in order to enable more efficient learning of deep advanced semantic features. Finally, fine-tuning is performed on the bilateral guidance aggregation layer of BiSeNet v2, enabling better fusion of the feature maps output by the detail branch and semantic branch. The experimental part discusses the contribution of stripe convolution and different sizes of empty convolution to image segmentation accuracy, and compares them with common convolutions such as Conv2d convolution, CG convolution and CCA convolution. The experiment proves that the PCSD convolution module proposed in this paper has the highest segmentation accuracy in all categories of the Cityscapes dataset compared with common convolutions. BiConvNet achieved a 9.39% accuracy improvement over the BiSeNet v2 network, with only a slight increase of 1.18M in model parameters. A mIoU accuracy of 68.75% was achieved on the validation set. Furthermore, through comparative experiments with commonly used autonomous driving image segmentation algorithms in recent years, BiConvNet demonstrates strong competitive advantages in segmentation accuracy on the Cityscapes and BDD100K datasets.

In this paper, a precise design method of high-order bandpass filters (BPFs) with complicated coupling topologies is proposed, and is demonstrated through the design of an 11-pole BPF using TM010 mode dielectric resonators (DRs). A novel Z-shaped coupling structure is proposed which avoids the mixed use of TM010 and TM01δ modes and enables the tuning and assembling of the filter much easier. The coupling topology of the BPF includes three cascade triplets (CTs) of DRs, and both the capacitive and inductive couplings in the CTs are designed independently tunable, which produce consequently three controllable transmission zeros on both sides of the passband of filter. A procedure of mapping the coupling matrix of BPF to its physical dimensions is developed, and an iterative optimization of these physical dimensions is implemented to achieve best performance. The design of the 11-pole BPF is shown highly precise by the excellent agreement between the electromagnetic simulated response of the filter and the desired target specifications.

In order to calculate photonic devices with slowly varying waveguide structure along propagation direction, we develop finite element beam propagation method (FE-BPM) with coordinate transformation. In this approach, converting a longitudinally varying waveguide into the equivalent straight waveguide, cumbersome processes in FE-BPM, such as mesh updating and field interpolation processes at each propagation step, can be avoided. We employ this simulation technique in shape optimization of photonic devices and show design examples of mode converter. To show the validity of this approach, the calculated results of designed devices are compared with the finite element method (FEM) or the standard FE-BPM.

We develop new 3D full vectorial finite element bidirectional beam propagation method (3DFV-BiBPM) in order to handle the nonradiative dielectric waveguide (NRD guide) components where waveguide profile varies in the direction perpendicular to the parallel metal plates. The BiBPM is one of the transfer-matrix-based methods where only transverse cross sections have to be discretized using the finite difference or the finite element scheme, and it can treat backward and multiple reflections as opposed to the standard BPM. An NRD guide with air-gap and a filter with a sapphire resonator are numerically analyze considering dielectric losses to investigate the validity of our approach.

We report on a method for reconstructing the spectrum of incident light from a single image captured by a snapshot multispectral camera. The camera has a dielectric multilayer multispectral filter array (MSFA) integrated onto a CMOS image sensor. Sparse estimation algorithm was applied to reconstruct the spectrum. Using Gaussian functions with various bandwidths and central wavelengths as the bases matrix, the algorithm has been shown to be highly accurate for estimating the spectra of both narrowband monochromatic and broadband fluorescent light emitting diodes (LEDs), regardless of the wavelength band.

In this paper, a novel Enhanced Spatial Modulation-based Orthogonal Time Frequency Space (ESM-OTFS) is proposed to maximize the benefits of enhanced spatial modulation (ESM) and orthogonal time frequency space (OTFS) transmission. The primary objective of this novel modulation is to enhance transmission reliability, meeting the demanding requirements of high transmission rates and rapid data transfer in future wireless communication systems. The paper initially outlines the system model and specific signal processing techniques employed in ESM-OTFS. Furthermore, a novel detector based on sparse signal estimation is presented specifically for ESM-OTFS. The sparse signal estimation is performed using a fully factorized posterior approximation using Variational Bayesian Inference that leads to a low complexity solution without any matrix inversions. Simulation results indicate that ESM-OTFS surpasses traditional spatial modulation-based OTFS, and the newly introduced detection algorithm outperforms other linear detection methods.

Analytical expression of transmission for the orbital angular momentum (OAM) communication using loop antenna arrays and paraboloids is derived to achieve a communication distance of 100 m. With the field distribution of the single “transformed OAM mode” radiated by a loop antenna, the collimated field by the transmitting paraboloid and its diffracted field are analytically derived. Effects of frequencies, sizes of paraboloids, and shifts of transmitting and receiving arrays from the focal planes are included. With the diffracted field distribution on the focal plane of the receiving paraboloid, transmission between the transmitting and receiving loop antennas is analytically estimated. It is shown that the transmission between the antennas with different OAM modes is null, but the transmission between the antennas with the same mode can be reduced. To clarify the mechanism of the reduction, factors of the reduction are quantitatively defined, and the explicit formulae are derived. Based on the analytical results, numerical estimation for a communication distance of 100 m is demonstrated, where the frequency, the focal length, and the size of the paraboloid are 150 GHz, 50 cm and 100 cm, respectively. Where both arrays are located on each focal plane, the transmission for the signal is more than -7.78 dB for eight kinds of OAM modes. The transmission is the least for the highest-order mode. The transmission loss is shown to be mitigated by optimizing the shifts of transmitting and receiving arrays from their focal planes. The loss is made almost even by exploiting the tradeoff of the improvement for the mode orders. The transmission is improved by 5.98 dB, to be more than -1.80 dB, by optimizing the shifts of the arrays.

In this study, the most recent topics related to the precise global navigation satellite system (GNSS) positioning technology are discussed. Precise positioning here means that the position can be estimated with centimeter-level accuracy. Technologies supporting precise GNSS positioning include an increase in the number of positioning satellites and the availability of correction data. Smartphones are now capable of centimeter-level positioning. For correction data, real-time kinematic positioning (RTK)-GNSS, which has primarily been used in surveying, and the new precise point positioning-real-time kinematic (PPP-RTK) and PPP, are garnering attention. The Japanese Quasi-Zenith Satellite System was among the first to broadcast PPP-RTK and PPP correction data free of charge. RTKLIB has long been popular for both real-time and post-processing precise positioning. Here, I briefly present a method for improving this software. Precise positioning technology remains crucial as the use of GNSS in highly reliable applications, such as advanced driver-assistance systems, autonomous drones, and robots, is increasing. To ensure precise positioning, improving multipath mitigation techniques is essential; therefore, key factors related to these techniques are discussed. I also introduce my efforts to develop software GNSS receivers for young researchers and engineers as a basis for this purpose. This study is aimed at introducing these technologies in light of the most recent trends.

In this paper, we propose a novel communication scheme that combines reconfigurable intelligent surface with transmitted adaptive space shift keying (RIS-TASSK), where the number of active antennas is not fixed. In each time slot, the desired candidate antenna or antenna combination will be selected from all available antenna combinations for conveying information bits. Besides, an antenna selection method based on channel gains is proposed for RIS-TASSK to improve the bit error rate (BER) performance and decrease the complexity, respectively. By comparing with the RIS-aided transmitted space shift keying and RIS-aided transmitted generalized space shift keying schemes, the simulation and theoretical results show that the proposed scheme has better BER performance and appropriate complexity.

A state-space approach for adaptive second-order IIR notch digital filters is explored. A simplified iterative algorithm is derived from the gradient-descent method to minimize the mean-squared output of an adaptive notch digital filter. The stability and parameter-estimation bias are then analyzed by employing a first-order linear dynamical system. As a consequence, it is clarified that the resulting parameter estimate is unbiased. Finally, a numerical example is presented to demonstrate the validity and effectiveness of the adaptive state-space notch digital filter and bias analysis of parameter estimation.

In this paper, we investigate a method for clustering user equipment (UE)-specific transmission access points (APs) in downlink cell-free multiple-input multiple-output (MIMO) assuming that the APs distributed over the system coverage know only part of the instantaneous channel state information (CSI). As a beamforming (BF) method based on partial CSI, we use a layered partially non-orthogonal zero-forcing (ZF) method based on channel matrix muting, which is applicable to the case where different transmitting AP groups are selected for each UE under partial CSI conditions. We propose two AP clustering methods. Both proposed methods first tentatively determine the transmitting APs independently for each UE and then iteratively update the transmitting APs for each UE based on the estimated throughput considering the interference among the UEs. One of the two proposed methods introduces a UE cluster for each UE into the iterative updates of the transmitting APs to balance throughput performance and scalability. Computer simulations show that the proposed methods achieve higher geometric-mean and worst user throughput than those for the conventional methods.

We propose a novel peak-to-average power ratio (PAPR) reduction method based on a peak cancellation (PC) signal vector that considers the variance in the average signal power among transmitter antennas for massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) signals using the null space in a MIMO channel. First, we discuss the conditions under which the PC signal vector achieves a sufficient PAPR reduction effect after its projection onto the null space of the MIMO channel. The discussion reveals that the magnitude of the correlation between the PC signal vector before projection and the transmission signal vector should be as low as possible. Based on this observation and the fact that to reduce the PAPR it is helpful to suppress the variation in the transmission signal power among antennas, which may be enhanced by beamforming (BF), we propose a novel method for generating a PC signal vector. The proposed PC signal vector is designed so that the signal power levels of all the transmitter antennas are limited to be between the maximum and minimum power threshold levels at the target timing. The newly introduced feature in the proposed method, i.e., increasing the signal power to be above the minimum power threshold, contributes to suppressing the transmission signal power variance among antennas and to improving the PAPR reduction capability after projecting the PC signal onto the null space in the MIMO channel. This is because the proposed method decreases the magnitude of the correlation between the PC signal vectors before its projection and the transmission signal vectors. Based on computer simulation results, we show that the PAPR reduction performance of the proposed method is improved compared to that for the conventional method and the proposed method reduces the computational complexity compared to that for the conventional method for achieving the same target PAPR.

In this paper, we delve into wireless communications in the 300 GHz band, focusing in particular on the continuous bandwidth of 44 GHz from 252 GHz to 296 GHz, positioning it as a pivotal element in the trajectory toward 6G communications. While terahertz communications have traditionally been praised for the high speeds they can achieve using their wide bandwidth, focusing the beam has also shown the potential to achieve high energy efficiency and support numerous simultaneous connectivity. To this end, new performance metrics, EIRPλ and EINFλ, are introduced as important benchmarks for transmitter and receiver performance, and their consistency is discussed. We then show that, assuming conventional bandwidth and communication capacity, the communication distance is independent of carrier frequency. Located between radio waves and light in the electromagnetic spectrum, terahertz waves promise to usher in a new era of wireless communications characterized not only by high-speed communication, but also by convenience and efficiency. Improvements in antenna gain, beam focusing, and precise beam steering are essential to its realization. As these technologies advance, the paradigm of wireless communications is expected to be transformed. The synergistic effects of antenna gain enhancement, beam focusing, and steering will not only push high-speed communications to unprecedented levels, but also lay the foundation for a wireless communications landscape defined by unparalleled convenience and efficiency. This paper will discuss a future in which terahertz communications will reshape the contours of wireless communications as the realization of such technological breakthroughs draws near.

Recent years have seen a general resurgence of interest in analog signal processing and computing architectures. In addition, extensive theoretical and experimental literature on chaos and analog chaotic oscillators exists. One peculiarity of these circuits is the ability to generate, despite their structural simplicity, complex spatiotemporal patterns when several of them are brought towards synchronization via coupling mechanisms. While by no means a systematic survey, this paper provides a personal perspective on this area. After briefly covering design aspects and the synchronization phenomena that can arise, a selection of results exemplifying potential applications is presented, including in robot control, distributed sensing, reservoir computing, and data augmentation. Despite their interesting properties, the industrial applications of these circuits remain largely to be realized, seemingly due to a variety of technical and organizational factors including a paucity of design and optimization techniques. Some reflections are given regarding this situation, the potential relevance to discontinuous innovation in analog circuit design of chaotic oscillators taken both individually and as synchronized networks, and the factors holding back the transition to higher levels of technology readiness.

Reliability is an important figure of merit of the system and it must be satisfied in safety-critical applications. This paper considers parallel applications on heterogeneous embedded systems and proposes a two-phase algorithm framework to minimize energy consumption for satisfying applications’ reliability requirement. The first phase is for initial assignment and the second phase is for either satisfying the reliability requirement or improving energy efficiency. Specifically, when the application’s reliability requirement cannot be achieved via the initial assignment, an algorithm for enhancing the reliability of tasks is designed to satisfy the application’s reliability requirement. Considering that the reliability of initial assignment may exceed the application’s reliability requirement, an algorithm for reducing the execution frequency of tasks is designed to improve energy efficiency. The proposed algorithms are compared with existing algorithms by using real parallel applications. Experimental results demonstrate that the proposed algorithms consume less energy while satisfying the application’s reliability requirements.

Due to the revitalization of the semiconductor industry and efforts to reduce labor and unmanned operations in the retail and food manufacturing industries, objects to be recognized at production sites are increasingly diversified in color and design. Depending on the target objects, it may be more reliable to process only color information, while intensity information may be better, or a combination of color and intensity information may be better. However, there are not many conventional method for optimizing the color and intensity information to be used, and deep learning is too costly for production sites. In this paper, we optimize the combination of the color and intensity information of a small number of pixels used for matching in the framework of template matching, on the basis of the mutual relationship between the target object and surrounding objects. We propose a fast and reliable matching method using these few pixels. Pixels with a low pixel pattern frequency are selected from color and grayscale images of the target object, and pixels that are highly discriminative from surrounding objects are carefully selected from these pixels. The use of color and intensity information makes the method highly versatile for object design. The use of a small number of pixels that are not shared by the target and surrounding objects provides high robustness to the surrounding objects and enables fast matching. Experiments using real images have confirmed that when 14 pixels are used for matching, the processing time is 6.3 msec and the recognition success rate is 99.7%. The proposed method also showed better positional accuracy than the comparison method, and the optimized pixels had a higher recognition success rate than the non-optimized pixels.

This letter proposes a scheme for the backward transmission of the propagation-delay based three-user X channel, which is reciprocal to the forward transmission. The given scheme successfully delivers 10 expected messages in 6 time-slots by cyclic interference alignment without loss of degrees of freedom, which supports efficient bidirectional transmission between the two ends of the three-user X channel.