Chinese spelling correction is a foundational task in natural language processing that aims to detect and correct spelling errors in text. Most spelling corrections in Chinese used multimodal information to model the relationship between incorrect and correct characters. However, feature information mismatch occured during fusion result from the different sources of features, causing the importance relationships between different modalities to be ignored, which in turn restricted the model from learning in an efficient manner. To this end, this paper proposes a multimodal language model-based Chinese spelling corrector, named as MISpeller. The method, based on ChineseBERT as the basic model, allows the comprehensive capture and fusion of character semantic information, phonetic information and graphic information in a single model without the need to construct additional neural networks, and realises the phenomenon of unequal fusion of multi-feature information. In addition, in order to solve the overcorrection issues, the replication mechanism is further introduced, and the replication factor is used as the dynamic weight to efficiently fuse the multimodal information. The model is able to control the proportion of original characters and predicted characters according to different input texts, and it can learn more specifically where errors occur. Experiments conducted on the SIGHAN benchmark show that the proposed model achieves the state-of-the-art performance of the F1 score at the correction level by an average of 4.36%, which validates the effectiveness of the model.

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.

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.

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.

An unmanned aerial vehicle (UAV) can be used for wireless communication and localization, among many other things. When terrestrial networks are either damaged or non-existent, and the area is GPS-denied, the UAV can be quickly deployed to provide communication and localization services to ground terminals in a specific target area. In this study, we propose an UAV operation model for unified communication and localization using reinforcement learning (UCL-RL) in a suburban environment which has no cellular communication and GPS connectivity. First, the UAV flies to the target area, moves in a circular fashion with a constant turning radius and sends navigation signals from different positions to the ground terminals. This provides a dynamic environment that includes the turning radius, the navigation signal transmission points, and the height of the unmanned aerial vehicle as well as the location of the ground terminals. The proposed model applies a reinforcement learning algorithm where the UAV continuously interacts with the environment and learns the optimal height that provides the best communication and localization services to the ground terminals. To evaluate the terminal position accuracy, position dilution of precision (PDOP) is measured, whereas the maximum allowable path loss (MAPL) is measured to evaluate the communication service. The simulation result shows that the proposed model improves the localization of the ground terminals while guaranteeing the communication service.

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.

Emergency Medical Services (EMS) play a crucial role in healthcare systems, managing pre-hospital or out-of-hospital emergencies from the onset of an emergency call to the patient’s arrival at a healthcare facility. The design of an efficient ambulance location model is pivotal in enhancing survival rates, controlling morbidity, and preventing disability. Key factors in the classical models typically include travel time, demand zones, and the number of stations. While urban EMS systems have received extensive examination due to their centralized populations, rural areas pose distinct challenges. These include lower population density and longer response distances, contributing to a higher fatality rate due to sparse population distribution, limited EMS stations, and extended travel times. To address these challenges, we introduce a novel mathematical model that aims to optimize coverage and equity. A distinctive feature of our model is the integration of equity within the objective function, coupled with a focus on practical response time that includes the period required for personal protective equipment procedures, ensuring the model’s applicability and realism in emergency response scenarios. We tackle the proposed problem using a tailored genetic algorithm and propose a greedy algorithm for solution construction. The implementation of our tailored Genetic Algorithm promises efficient and effective EMS solutions, potentially enhancing emergency care and health outcomes in rural communities.

This paper introduces a computer-aided low-power design method for tapered buffers that address given load capacitances, output transition times, and source impedances. Cross-voltage-domain tapered buffers involving a low-voltage domain in the frontier stages and a high-voltage domain in the posterior stages are further discussed which breaks the trade-off between the energy dissipation and the driving capability in conventional designs. As an essential circuit block, a dedicated analytical model for the level-shifter is proposed. The energy-optimized tapered buffer design is verified for different source and load conditions in a 180-nm CMOS process. The single-VDD buffer model achieves an average inaccuracy of 8.65% on the transition loss compared with Spice simulation results. Cross-voltage tapered buffers can be optimized to further remarkably reduce the energy consumption. The study finds wide applications in energy-efficient switching-mode analog applications.

Smart cities aim to improve the quality of life of citizens and efficiency of city operations through utilization of 5G communication technology. Based on various technologies such as IoT, cloud computing, artificial intelligence, and big data, they provide smart services in terms of urban planning, development, and management for solving problems such as fine dust, traffic congestion and safety, energy efficiency, water shortage, and an aging population. However, as smart city has an open network structure, an adversary can easily try to gain illegal access and perform denial of service and sniffing attacks that can threaten the safety and privacy of citizens. In smart cities, the global mobility network (GLOMONET) supports mobile services between heterogeneous networks of mobile devices such as autonomous vehicles and drones. Recently, Chen et al. proposed a user authentication scheme for GLOMONET in smart cities. Nevertheless, we found some weaknesses in the scheme proposed by them. In this study, we propose a secure lightweight authentication for roaming services in a smart city, called SLARS, to enhance security. We proved that SLARS is more secure and efficient than the related authentication scheme for GLOMONET through security and performance analysis. Our analysis results show that SLARS satisfies all security requirements in GLOMONET and saves 72.7% of computation time compared to that of Chen et al.’s scheme.

Radar emitter identification (REI) is a crucial function of electronic radar warfare support systems. The challenge emphasizes identifying and locating unique transmitters, avoiding potential threats, and preparing countermeasures. Due to the remarkable effectiveness of deep learning (DL) in uncovering latent features within data and performing classifications, deep neural networks (DNNs) have seen widespread application in radar emitter identification (REI). In many real-world scenarios, obtaining a large number of annotated radar transmitter samples for training identification models is essential yet challenging. Given the issues of insufficient labeled datasets and abundant unlabeled training datasets, we propose a novel REI method based on a semi-supervised learning (SSL) framework with virtual adversarial training (VAT). Specifically, two objective functions are designed to extract the semantic features of radar signals: computing cross-entropy loss for labeled samples and virtual adversarial training loss for all samples. Additionally, a pseudo-labeling approach is employed for unlabeled samples. The proposed VAT-based SS-REI method is evaluated on a radar dataset. Simulation results indicate that the proposed VAT-based SS-REI method outperforms the latest SS-REI method in recognition performance.

Makeup transfer is the process of applying the makeup style from one picture (reference) to another (source), allowing for the modification of characters’ makeup styles. To meet the diverse makeup needs of individuals or samples, the makeup transfer framework should accurately handle various makeup degrees, ranging from subtle to bold, and exhibit intelligence in adapting to the source makeup. This paper introduces a “3-level” adaptive makeup transfer framework, addressing facial makeup through two sub-tasks: 1. Makeup adaptation, utilizing feature descriptors and eyelid curve algorithms to classify 135 organ-level face shapes; 2. Makeup transfer, achieved by learning the reference picture from three branches (color, highlight, pattern) and applying it to the source picture. The proposed framework, termed “Face Shape Adaptive Makeup Transfer” (FSAMT), demonstrates superior results in makeup transfer output quality, as confirmed by experimental results.

Data sparsity has always been a problem in document classification, for which semi-supervised learning and few-shot learning are studied. An even more extreme scenario is to classify documents without any annotated data, but using only category names. In this paper, we introduce a nearest neighbor search-based method Con2Class to tackle this tough task. We intend to produce embeddings for predefined categories and predict category embeddings for all the unlabeled documents in a unified embedding space, such that categories can be easily assigned by searching the nearest predefined category in the embedding space. To achieve this, we propose confidence-driven contrastive learning, in which prompt-based templates are designed and MLM-maintained contrastive loss is newly proposed to finetune a pretrained language model for embedding production. To deal with the issue that no annotated data is available to validate the classification model, we introduce confidence factor to estimate the classification ability by evaluating the prediction confidence. The language model having the highest confidence factor is used to produce embeddings for similarity evaluation. Pseudo labels are then assigned by searching the semantically closest category name, which are further used to train a separate classifier following a progressive self-training strategy for final prediction. Our experiments on five representative datasets demonstrate the superiority of our proposed method over the existing approaches.

This paper shows that sum-of-product expression (SOP) minimization produces the generalization ability. We show this in three steps. First, various classes of SOPs are generated. Second, minterms of SOP are randomly selected to generate partially defined functions. And, third, from the partially defined functions, original functions are reconstructed by SOP minimization. We consider Achilles heel functions, majority functions, monotone increasing cascade functions, functions generated from random SOPs, monotone increasing random SOPs, circle functions, and globe functions. As for the generalization ability, the presented method is compared with Naive Bayes, multi-level perceptron, support vector machine, JRIP, J48, and random forest. For these functions, in many cases, only 10% of the input combinations are sufficient to reconstruct more than 90% of the truth tables of the original functions.

We achieved 10-Gbit/s data transmission using a cutting-edge 120-GHz-band high-speed contactless communication technology, which allows seamless connection to a local area network (LAN) by simply placing devices on a desk. We propose a glass substrate-integrated rectangular waveguide that can control the permeability of the top surface to 120-GHz signals by contacting a dielectric substrate with the substrate. The top surface of the rectangular waveguide was replaced with a glass substrate on which split-ring resonators (SRRs) were integrated. The transmission loss of the waveguide with a glass substrate was 2.5 dB at 125 GHz. When a dielectric sheet with a line pattern formed on the contact surface was in contact with a glass substrate, the transmission loss from the waveguide to the dielectric sheet was 19.2 dB at 125 GHz. We achieved 10-Gbit/s data transmission by contacting a dielectric sheet to the SRR-integrated glass substrate.

Integrated Sensing and Communication at terahertz band (ISAC-THz) has been considered as one of the promising technologies for the future 6G. However, in the phase-shifters (PSs) based massive multiple-input-multiple-output (MIMO) hybrid precoding system, due to the ultra-large bandwidth of the terahertz frequency band, the subcarrier channels with different frequencies have different equivalent spatial directions. Therefore, the hybrid beamforming at the transmitter will cause serious beam split problems. In this letter, we propose a dual-function radar communication (DFRC) precoding method by considering recently proposed delay-phase precoding structure for THz massive MIMO. By adding delay phase components between the radio frequency chain and the frequency-independent PSs, the beam is aligned with the target physical direction over the entire bandwidth to reduce the loss caused by beam splitting effect. Furthermore, we employ a hardware structure by using true-time-delayers (TTDs) to realize the concept of frequency-dependent phase shifts. Theoretical analysis and simulation results have shown that it can increase communication performance and make up for the performance loss caused by the dual-function trade-off of communication radar to a certain extent.

In this letter, we study the dynamic antenna grouping and the hybrid beamforming for high altitude platform (HAP) massive multiple-input multiple-output (MIMO) systems. We first exploit the fact that the ergodic sum rate is only related to statistical channel state information (SCSI) in the large-scale array regime, and then we utilize it to perform the dynamic antenna grouping and design the RF beamformer. By applying the Gershgorin Circle Theorem, the dynamic antenna grouping is realized based on the novel statistical distance metric instead of the value of the instantaneous channels. The RF beamformer is designed according to the singular value decomposition of the statistical correlation matrix according to the obtained dynamic antenna group. Dynamic subarrays mean each RF chain is linked with a dynamic antenna sub-set. The baseband beamformer is derived by utilizing the zero forcing (ZF). Numerical results demonstrate the performance enhancement of our proposed dynamic hybrid precoding (DHP) algorithm.

Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) is envisioned as a key enabling technology of 6G wireless communication. In UM-MIMO systems, downlink channel state information (CSI) has to be fed to the base station for beamforming. However, the feedback overhead becomes unacceptable because of the large antenna array. In this letter, the characteristic of CSI is explored from the perspective of data distribution. Based on this characteristic, a novel network named Attention-GRU Net (AGNet) is proposed for CSI feedback. Simulation results show that the proposed AGNet outperforms other advanced methods in the quality of CSI feedback in UM-MIMO systems.

To improve the direction-of-arrival estimation performance of the small-aperture array, we propose a source localization method inspired by the Ormia fly’s coupled ears and MUSIC-like algorithm. The Ormia can local its host cricket’s sound precisely despite the tremendous incompatibility between the spacing of its ear and the sound wavelength. In this paper, we first implement a biologically inspired coupled system based on the coupled model of the Ormia’s ears and solve its responses by the modal decomposition method. Then, we analyze the effect of the system on the received signals of the array. Research shows that the system amplifies the amplitude ratio and phase difference between the signals, equivalent to creating a virtual array with a larger aperture. Finally, we apply the MUSIC-like algorithm for DOA estimation to suppress the colored noise caused by the system. Numerical results demonstrate that the proposed method can improve the localization precision and resolution of the array.

Diagnosis at an early stage is clinically important for the cure of skin cancer. However, since some skin cancers have similar intuitive characteristics, and dermatologists rely on subjective experience to distinguish skin cancer types, the accuracy is often suboptimal. Recently, the introduction of computer methods in the medical field has better assisted physicians to improve the recognition rate but some challenges still exist. In the face of massive dermoscopic image data, residual network (ResNet) is more suitable for learning feature relationships inside big data because of its deeper network depth. Aiming at the deficiency of ResNet, this paper proposes a multi-region feature extraction and raising dimension matching method, which further improves the utilization rate of medical image features. This method firstly extracted rich and diverse features from multiple regions of the feature map, avoiding the deficiency of traditional residual modules repeatedly extracting features in a few fixed regions. Then, the fused features are strengthened by up-dimensioning the branch path information and stacking it with the main path, which solves the problem that the information of two paths is not ideal after fusion due to different dimensionality. The proposed method is experimented on the International Skin Imaging Collaboration (ISIC) Archive dataset, which contains more than 40,000 images. The results of this work on this dataset and other datasets are evaluated to be improved over networks containing traditional residual modules and some popular networks.