Japan encounters an urgent issue of “Carbon Neutrality” as a member of the international world and is required to make the action plans to accomplish this issue, i.e., the zero emission of CO2 by 2050. Our world must change the industries to adapt to the electrification based on the renewable powers. Microwave chemistry is a candidate of electrification of industries for the carbon neutrality on the conditions of usage of renewable energy power generation. This invited paper shows an example of “Microwave Pidgeon process” for smelting magnesium in which heating with burning fossil coals can be replaced with microwave energy for discussing how microwave technology should be developed for that purpose from both the academic and industrial sides.

Nonradiative dielectric waveguide is a transmission medium for millimeter-wave integrated circuits, invented in Japan. This transmission line is characterized by low transmission loss and non-radiating nature in bends and discontinuities. It has been actively researched from 1980 to 2000, primarily at Tohoku University. This paper explains the fundamental characteristics, including passive and active circuits, and provides an overview of millimeter-wave systems such as gigabit-class ultra-high-speed data transmission applications and various radar applications. Furthermore, the performance in the THz frequency band, where future applications are anticipated, is also discussed.

Low signal power and susceptibility to interference cause difficulties for traditional global navigation satellite system (GNSS) receivers in tracking weak signals. Extending coherent integration time is a common approach for enhancing signal gain. However, coherent integration time cannot be indefinitely increased owing to navigation bit sign transition, receiver dynamics, and clock noises. This study proposes a cross-correlation phase combining (CPC) algorithm suitable for distributed multi-antenna receivers to improve carrier-tracking performance in weak GNSS signal conditions. This algorithm cross-correlates each antenna’s intermediate frequency (IF) signal and local carrier to detect the phase differences. Subsequently, the IF signals are weighted to achieve phase alignment and coherently combined. The carrier-to-noise ratio (CNR) and carrier phase equation of the combined signal were derived for the CPC algorithm. Global positioning system (GPS) signals received by distributed antenna array with six elements were used to validate the performance of the algorithm. The results demonstrated that the CPC algorithm could effectively achieve signal phase alignment at 32 dB-Hz, resulting in a combined-signal CNR enhancement of 6 dB. The phase-tracking error variance was reduced by 72% at 30 dB-Hz compared with that of a single-antenna signal. The algorithm exhibited low phased array calibration requirements, overcoming the limitations associated with coherent integration time and effectively enhancing tracking performance in weak-signal environments.

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.

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.

Recently, trace representation of a class of balanced quaternary sequences of period p from the classical cyclotomic classes was given by Yang et al. (Cryptogr. Commun.,15 (2023): 921-940). In this letter, based on the generalized cyclotomic classes, we define a class of balanced quaternary sequences of period pn, where p = ef + 1 is an odd prime number and satisfies e ≡ 0 (mod 4). Furthermore, we calculate the defining polynomial of these sequences and obtain the formula for determining their trace representations over ℤ4, by which the linear complexity of these sequences over ℤ4 can be determined.

To fully exploit the attribute information in graphs and dynamically fuse the features from different modalities, this letter proposes the Attributed Graph Clustering Network with Adaptive Feature Fusion (AGC-AFF) for graph clustering, where an Attribute Reconstruction Graph Autoencoder (ARGAE) with masking operation learns to reconstruct the node attributes and adjacency matrix simultaneously, and an Adaptive Feature Fusion (AFF) mechanism dynamically fuses the features from different modules based on node attention. Extensive experiments on various benchmark datasets demonstrate the effectiveness of the proposed method.

To address the challenge of target signals being completely submerged by ionospheric clutter during typhoon passages, this letter proposes a chaotic detection method for target signals in the background of ionospheric noise under typhoon excitation. Experimental results demonstrate the effectiveness of the proposed method in detecting target signals with harmonic characteristics from strong ionospheric clutter during typhoon passages.

With the emphasis on personal information privacy protection in wireless communications, the new dimension low-interception covert transmission technology represented by the vortex wave with Orbital Angular Momentum (OAM) has received attention from both academia and industry. However, the current OAM low-interception transmission techniques all assume that the eavesdropper can only receive plane wave signals, which is a very ideal situation. Once the eavesdropper is configured with an OAM sensor, the so-called mode covert channel will be completely exposed. To solve this problem, this paper proposes a vortex microwave photon low-interception transmission method. The proposed method utilizes the differential operation between plane and vortex microwave photons signals to construct the covert differential channel, which can hide the user data in the mode domain. Compared with the traditional spread spectrum transmission, our proposed covert differential channel schemes need less transmitted power to achieve reliable transmission, which means less possibility of being intercepted by the eavesdropper.

Constructing new families of APN functions is an important and challenging topic. Up to now, only six infinite families of APN monomials have been found on finite fields of even characteristic. To study APN functions, partially APN functions have attracted plenty of researchers’ particular interests recently. In this paper, we propose several new infinite classes of 0-APN power functions over 𝔽2n by using the multivariate method and resultant elimination. Furthermore, we use Magma soft to show that these 0-APN power functions are CCZ-inequivalent to the known 0-APN power functions.

This letter introduces an innovation for the heterogeneous storage architecture of AI chips, specifically focusing on the integration of six transistors(6T) and eight transistors(8T) hybrid SRAM. Traditional approaches to reducing SRAM power consumption typically involve lowering the operating voltage, a method that often substantially diminishes the recognition rate of neural networks. However, the innovative design detailed in this letter amalgamates the strengths of both SRAM types. It operates at a voltage lower than conventional SRAM, thereby significantly reducing the power consumption in neural networks without compromising performance.

Detecting key frames in videos has garnered substantial attention in recent years, it is a point-level task and has deep research value and application prospect in daily life. For instances, video surveillance system, video cover generation and highlight moment flashback all demands the technique of key frame detection. However, the task is beset by challenges such as the sparsity of key frame instances, imbalances between target frames and background frames, and the absence of post-processing method. In response to these problems, we introduce a novel and effective Temporal Interval Guided (TIG) framework to precisely localize specific frames. The framework is incorporated with a proposed Point-Level-Soft non-maximum suppression (PLS-NMS) post-processing algorithm which is suitable for point-level task, facilitated by the well-designed confidence score decay function. Furthermore, we propose a TIG-loss, exhibiting sensitivity to temporal interval from target frame, to optimize the two-stage framework. The proposed method can be broadly applied to key frame detection in video understanding, including action start detection and static video summarization. Extensive experimentation validates the efficacy of our approach on action start detection benchmark datasets: THUMOS’14 and Activitynet v1.3, and we have reached state-of-the-art performance. Competitive results are also demonstrated on SumMe and TVSum datasets for deep learning based static video summarization.

In this paper, the Towards High Performance Voxel-based 3D Object Detection (Voxel-RCNN) three-dimensional (3D) point cloud object detection model is used as the benchmark network. Aiming at the problems existing in the current mainstream 3D point cloud voxelization methods, such as the backbone and the lack of feature expression ability under the bird’s-eye view (BEV), a high-performance voxel-based 3D object detection network (Reinforced Voxel-RCNN) is proposed. Firstly, a 3D feature extraction module based on the integration of inverted residual convolutional network and weight normalization is designed on the 3D backbone. This module can not only well retain more point cloud feature information, enhance the information interaction between convolutional layers, but also improve the feature extraction ability of the backbone network. Secondly, a spatial feature-semantic fusion module based on spatial and channel attention is proposed from a BEV perspective. The mixed use of channel features and semantic features further improves the network’s ability to express point cloud features. In the comparison of experimental results on the public dataset KITTI, the experimental results of this paper are better than many voxel-based methods. Compared with the baseline network, the 3D average accuracy and BEV average accuracy on the three categories of Car, Cyclist, and Pedestrians are improved. Among them, in the 3D average accuracy, the improvement rate of Car category is 0.23%, Cyclist is 0.78%, and Pedestrians is 2.08%. In the context of BEV average accuracy, enhancements are observed: 0.32% for the Car category, 0.99% for Cyclist, and 2.38% for Pedestrians. The findings demonstrate that the algorithm enhancement introduced in this study effectively enhances the accuracy of target category detection.

In the realm of Single Image Super-Resolution (SISR), the meticulously crafted Nonlocal Sparse Attention-based block demonstrates its efficacy in noise reduction and computational cost reduction for nonlocal (global) features. However, it neglect the traditional Convolutional-based block, which proficient in handling local features. Thus, merging both the Nonlocal Sparse Attention-based block and the Convolutional-based block to concurrently manage local and nonlocal features poses a significant challenge. To tackle the aforementioned issues, this paper introduces the Channel Contrastive Attention-based Local-Nonlocal Mutual block (CCLN) for Super-Resolution (SR). (1) We introduce the CCLN block, encompassing the Local Sparse Convolutional-based block for local features and the Nonlocal Sparse Attention-based network block for nonlocal features. (2) We introduce Channel Contrastive Attention (CCA) blocks, incorporating Sparse Aggregation into Convolutional-based blocks. Additionally, we introduce a robust framework to fuse these two blocks, ensuring that each branch operates according to its respective strengths. (3) The CCLN block can seamlessly integrate into established network backbones like the Enhanced Deep Super-Resolution network (EDSR), achieving in the Channel Attention based Local-Nonlocal Mutual Network (CCLNN). Experimental results show that our CCLNN effectively leverages both local and nonlocal features, outperforming other state-of-the-art algorithms.

For flight simulators, it is crucial to create three-dimensional terrain using clear remote sensing images. However, due to haze and other contributing variables, the obtained remote sensing images typically have low contrast and blurry features. In order to build a flight simulator visual system, we propose a deep learning-based dehaze model for remote sensing images dehazing. An encoder-decoder architecture is proposed that consists of a multiscale fusion module and a gated large kernel convolutional attention module. This architecture can fuse multi-resolution global and local semantic features and can adaptively extract image features under complex terrain. The experimental results demonstrate that, with good generality and application, the model outperforms existing comparison techniques and achieves high-confidence dehazing in remote sensing images with a variety of haze concentrations, multi-complex terrains, and multi-spatial resolutions.

Automatic detection and recognition systems have numerous applications in smart city implementation. Despite the accuracy and widespread use of device-based and optical methods, several issues remain. These include device limitations, environmental limitations, and privacy concerns. The FMWC sensor can overcome these issues to detect and track moving people accurately in commercial environments. However, single-chip mmWave sensor solutions might struggle to recognize standing and sitting people due to the necessary static removal module. To address these issues, we propose a real-time indoor people detection and tracking fusion system using mmWave radar and cameras. The proposed fusion system approaches an overall detection accuracy of 93.8% with a median position error of 1.7 m in a commercial environment. Compared to our single-chip mmWave radar solution addressing an overall accuracy of 83.5% for walking people, it performs better in detecting individual stillness, which may feed the security needs in retail. This system visualizes customer information, including trajectories and the number of people. It helps commercial environments prevent crowds during the COVID-19 pandemic and analyze customer visiting patterns for efficient management and marketing. Powered by an IoT platform, the system can be deployed in the cloud for easy large-scale implementation.

Entity alignment (EA) is a crucial task for integrating cross-lingual and cross-domain knowledge graphs (KGs), which aims to discover entities referring to the same real-world object from different KGs. Most existing embedding-based methods generate aligning entity representation by mining the relevance of triple elements, paying little attention to triple indivisibility and entity role diversity. In this paper, a novel framework named TTEA - Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment is proposed to overcome the above shortcomings from the perspective of ensemble triple representation considering triple specificity and diversity features of entity role. Specifically, the ensemble triple representation is derived by regarding relation as information carrier between semantic and type spaces, and hence the noise influence during spatial transformation and information propagation can be smoothly controlled via specificity-aware triple attention. Moreover, the role diversity of triple elements is modeled via triple-aware entity enhancement in TTEA for EA-oriented entity representation. Extensive experiments on three real-world cross-lingual datasets demonstrate that our framework makes comparative results.

Aiming at the problem that large-scale traffic data lack labels and take too long for feature extraction in network intrusion detection, an unsupervised intrusion detection method ACOPOD based on Adam asymmetric autoencoder and COPOD (Copula-Based Outlier Detection) algorithm is proposed. This method uses the Adam asymmetric autoencoder with a reduced structure to extract features from the network data and reduce the data dimension. Then, based on the Copula function, the joint probability distribution of all features is represented by the edge probability of each feature, and then the outliers are detected. Experiments on the published NSL-KDD dataset with six other traditional unsupervised anomaly detection methods show that ACOPOD achieves higher precision and has obvious advantages in running speed. Experiments on the real civil aviation air traffic management network dataset further prove that the method can effectively detect intrusion behavior in the real network environment, and the results are interpretable and helpful for attack source tracing.

Watermarking methods require robustness against various attacks. Conventional watermarking methods use error-correcting codes or spread spectrum to correct watermarking errors. Errors can also be reduced by embedding the watermark into the frequency domain and by using SIFT feature points. If the type and strength of the attack can be estimated, the errors can be further reduced. There are several types of attacks, such as scaling, rotation, and cropping, and it is necessary to aim for robustness against all of them. Focusing on the scaling tolerance of watermarks, we propose a watermarking method using SIFT feature points and DFT, and introduce a pilot signal. The proposed method estimates the scaling rate using the pilot signal in the form of a grid. When a stego-image is scaled, the grid interval of the pilot signal also changes, and the scaling rate can be estimated from the amount of change. The accuracy of estimating the scaling rate by the proposed method was evaluated in terms of the relative error of the scaling rate. The results show that the proposed method could reduce errors in the watermark by using the estimated scaling rate.

In this paper, we propose two algorithms, B-N2N and B-N2S, that solve the node-to-node and node-to-set disjoint paths problems in the bicube, respectively. We prove their correctness and that the time complexities of the B-N2N and B-N2S algorithms are O(n2) and O(n2 log n), respectively, if they are applied in an n-dimensional bicube with n ≥ 5. Also, we prove that the maximum lengths of the paths generated by B-N2N and B-N2S are both n + 2. Furthermore, we have shown that the algorithms can be applied in the locally twisted cube, too, with the same performance.