We present Ksformer, utilizing Multi-scale Key-select Routing Attention (MKRA) for intelligent selection of key areas through multi-channel, multi-scale windows with a top-k operator, and Lightweight Frequency Processing Module (LFPM) to enhance high-frequency features, outperforming other dehazing methods in tests.

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.

We proposed a procedure to pre-process data used in a vector autoregressive (VAR) modeling of a temporal point process by using kernel density estimation. Vector autoregressive modeling of point-process data, for example, is being used for causality inference. The VAR model discretizes the timeline into small windows, and creates a time series by the presence of events in each window, and then models the presence of an event at the next time step by its history. The problem is that to get a longer history with high temporal resolution required a large number of windows, and thus, model parameters. We proposed the local density estimation procedure, which, instead of using the binary presence as the input to the model, performed kernel density estimation of the event history, and discretized the estimation to be used as the input. This allowed us to reduce the number of model parameters, especially in sparse data. Our experiment on a sparse Poisson process showed that this procedure vastly increases model prediction performance.

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.

1 millisecond (1-ms) vision systems are gaining increasing attention in diverse fields like factory automation and robotics, as the ultra-low delay ensures seamless and timely responses. Superpixel segmentation is a pivotal preprocessing to reduce the number of image primitives for subsequent processing. Recently, there has been a growing emphasis on leveraging deep network-based algorithms to pursue superior performance and better integration into other deep network tasks. Superpixel Sampling Network (SSN) employs a deep network for feature generation and employs differentiable SLIC for superpixel generation. SSN achieves high performance with a small number of parameters. However, implementing SSN on FPGAs for ultra-low delay faces challenges due to the final layer’s aggregation of intermediate results. To address this limitation, this paper proposes an aggregated to pipelined structure for FPGA implementation. The final layer is decomposed into individual final layers for each intermediate result. This architectural adjustment eliminates the need for memory to store intermediate results. Concurrently, the proposed structure leverages decomposed layers to facilitate a pipelined structure with pixel streaming input to achieve ultra-low latency. To cooperate with the pipelined structure, layer-partitioned memory architecture is proposed. Each final layer has dedicated memory for storing superpixel center information, allowing values to be read and calculated from memory without conflicts. Calculation results of each final layer are accumulated, and the result of each pixel is obtained as the stream reaches the last layer. Evaluation results demonstrate that boundary recall and under-segmentation error remain comparable to SSN, with an average label consistency improvement of 0.035 over SSN. From a hardware performance perspective, the proposed system processes 1000 FPS images with a delay of 0.947 ms/frame.

Renal Denervation (RDN) has been developed as a potential treatment for hypertension that is resistant to traditional antihypertensive medication. This technique involves the ablation of nerve fibers around the renal artery from inside the blood vessel, which is intended to suppress sympathetic nerve activity and result in an antihypertensive effect. Currently, clinical investigation is underway to evaluate the effectiveness of RDN in treating treatment-resistant hypertension. Although radio frequency (RF) ablation catheters are commonly used, their heating capacity is limited. Microwave catheters are being considered as another option for RDN. We aim to solve the technical challenges of applying microwave catheters to RDN. In this paper, we designed a catheter with a helix structure and a microwave (2.45 GHz) antenna. The antenna is a coaxial slot antenna, the dimensions of which were determined by optimizing the reflection coefficient through simulation. The measured catheter reflection coefficient is -23.6 dB using egg white and -32 dB in the renal artery. The prototype catheter was evaluated by in vitro experiments to validate the simulation. The procedure performed successfully with in vivo experiments involving the ablation of porcine renal arteries. The pathological evaluation confirmed that a large area of the perivascular tissue was ablated (> 5 mm) in a single quadrant without significant damage to the renal artery. Our proposed device allows for control of the ablation position and produces deep nerve ablation without overheating the intima or surrounding blood, suggesting a highly capable new denervation catheter.

Numerical dispersion property is investigated for the finite-difference time-domain (FDTD) method based on the iterated Crank-Nicolson (ICN) scheme. The numerical dispersion relation is newly derived from the amplification matrix and its property is discussed with attention to the eigenvalue of the matrix. It is shown that the ICN-FDTD method is conditionally stable but slightly dissipative.

In this paper, we propose the transient analysis technique to analyze the multilayered dispersive media by using a combination of fast inversion Laplace transform (FILT) and the continued fraction expanded methods. Numerical results are given by the reflection response, inside-time response waveforms, and electric field distributions of the reflection component. Further, we verify the calculation accuracy of FILT method for the two types using a convergence test.

This paper introduces a computational approach for transient analysis of extensive scattering problems. This novel method is based on the combination of physical optics (PO) and the fast inverse Laplace transform (FILT). PO is a technique for analyzing electromagnetic scattering from large-scale objects. We modify PO for application in the complex frequency domain, where the scattered fields are evaluated. The complex frequency function is efficiently transformed into the time domain using FILT. The effectiveness of this combination is demonstrated through large-scale analysis and transient response for a short pulse incidence. The accuracy is investigated and validated by comparison with reference solutions.

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.

A domain decomposition method is widely utilized for analyzing large-scale electromagnetic problems. The method decomposes the target model into small independent subdomains. An electromagnetic analysis has inherently suffers from late convergence analyzed with iterative algorithms such as Krylov subspace algorithms. The DDM remedies this issue by decomposing the total system into subdomain problems and gathering the local results as an interface problem to adjust to achieve the total solution. In this paper we report the convergence properties of the domain decomposition method while modifying the size of local domain and the region shape on several mesh sizes. As experimental results show, the convergence speed depends on the number of interface problem variables and the selection of the local region shapes. In addition to that the convergence property differs according to the target frequencies. In general it is demonstrated that the convergence speed can be accelerated with large cubic subdomain shape. We propose the subdomain selection strategies based on the analysis of the condition numbers of the governing equation.

In modern radar systems, the Generalized compound distribution model is more suitable for describing the amplitude distribution characteristics of radar sea clutter. Accurately and efficiently simulating sea clutter has important practical significance for radar signal processing and sea surface target detection. However, in traditional zero memory nonlinearity (ZMNL) method, the correlated Generalized compound distribution model cannot deal with non-integral or non-semi-integral parameter. In order to overcome this shortcoming, a new method of generating correlated Generalized compound distributed clutter is proposed, which changes the generation method of Generalized Gamma distributed random sequences in traditional Generalized compound distribution models. Firstly, by combining with the Gamma distribution and using the additivity of the Gamma distribution, the Probability Density Function (PDF) of Gamma function is transformed into a second-order nonlinear ordinary differential equation, and the Gamma distributed sequence under arbitrary parameter is solved. Then the Generalized Gamma distributed sequence with arbitrary parameter can be obtained through the nonlinear transformation relationship between the Generalized Gamma distribution and the Gamma distribution, so that the shape parameters of the Generalized compound distributed sea clutter are extended to general real numbers. Simulation results show that the proposed method is not only suitable for clutter simulation with non-integral or non-semi-integral shape parameter values, but also further improves the fitting degree.

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.

Phased-array technology is primarily employed in atmospheric and wind profiling radars for meteorological remote sensing. As a novel avenue of advancement in phased-array technology, the Multiple-Input Multiple-Output (MIMO) technique, originally developed for communication systems, has been applied to radar systems. A MIMO radar system can be used to create a virtual receive antenna aperture plane with transmission freedom. The MIMO technique requires orthogonal waveforms on each transmitter to identify the transmit signals using multiple receivers; various methods have been developed to realize the orthogonality. In this study, we focus on the Doppler Division Multiple Access (DDMA) MIMO technique by using slightly different frequencies for the transmit waveforms, which can be separated by different receivers in the Doppler frequency domain. The Middle and Upper atmosphere (MU) radar is a VHF-band phased array atmospheric radar with multi-channel receivers. Additional configurations are necessary, requiring the inclusion of multi-channel transmitters to enable its operation as a MIMO radar. In this study, a comparison between the brightness distribution of the beamformer, utilizing echoes reflected from the moon, and the antenna pattern obtained through calculations revealed a high degree of consistency, which means that the MU radar functions effectively as a MIMO radar. Furthermore, it is demonstrated that the simultaneous application of MIMO and Capon techniques has a mutually enhancing effect.

This paper proposes an anomaly-detection method using the Fast xFlow Proxy, which enables fine-grained measurement of communication traffic. When a fault occurs in services or networks, communication traffic changes from its normal behavior. Therefore, anomalies can be detected by analyzing their autocorrelations. However, in large-scale carrier networks, packets are generally encapsulated and observed as aggregate values, making it difficult to detect minute changes in individual communication flows. Therefore, we developed the Fast xFlow Proxy, which analyzes encapsulated packets in real time and enables flows to be measured at an arbitrary granularity. In this paper, we propose an algorithm that utilizes the Fast xFlow Proxy to detect not only the anomaly occurrence but also its cause, that is, the location of the fault at the end-to-end. The idea is not only to analyze the autocorrelation of a specific flow but also to apply spatial analysis to estimate the fault location by comparing the behavior of multiple flows. Through extensive simulations, we demonstrate that base station, network, and service faults can be detected without any false negative detections.

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.

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.

This paper delves into the utilisation of the subgradient method with geometrically decaying stepsize for Bilinear Form Identification. We introduce the iterative Wiener Filter, an l2 regression method, and highlight its limitations when confronted with noise, particularly heavy-tailed noise. To address these challenges, the paper suggests employing the l1 regression method with a subgradient method utilizing a geometrically decaying step size. The effectiveness of this approach is compared to existing methods, including the ALS algorithem. The study demonstrates that the l1 algorithm, especially when paired with the proposed subgradient method, excels in stability and accuracy under conditions of heavy-tailed noise. Additionally, the paper introduces the standard rounding procedure and the S-outlier bound as relaxations of traditional assumptions. Numerical experiments provide support and validation for the presented results.

High altitude platform (HAP), known as line-of-sight dominated communications, effectively enhance the spectral efficiency of wireless networks. However, the line-of-sight links, particularly in urban areas, may be severely deteriorated due to the complex communication environment. The reconfigurable intelligent surface (RIS) is employed to establish the cascaded-link and improve the quality of communication service by smartly reflecting the signals received from HAP to users without direct-link. Motivated by this, the joint precoding scheme for a novel RIS-aided beamspace HAP with non-orthogonal multiple access (HAP-NOMA) system is investigated to maximize the minimum user signal-to-leakage-plus-noise ratio (SLNR) by considering user fairness. Specifically, the SLNR is utilized as metric to design the joint precoding algorithm for a lower complexity, because the isolation between the precoding obtainment and power allocation can make the two parts be attained iteratively. To deal with the formulated non-convex problem, we first derive the statistical upper bound on SLNR based on the random matrix theory in large scale antenna array. Then, the closed-form expressions of power matrix and passive precoding matrix are given by introducing auxiliary variables based on the derived upper bound on SLNR. The proposed joint precoding only depends on the statistical channel state information (SCSI) instead of instantaneous channel state information (ICSI). NOMA serves multi-users simultaneously in the same group to compensate for the loss of spectral efficiency resulted from the beamspace HAP. Numerical results show the effectiveness of the derived statistical upper bound on SLNR and the performance enhancement of the proposed joint precoding algorithm.