City University of Macau

Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal propagation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communications to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work.

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for four different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. Finally, some important yet under-investigated open issues are discussed.

This review presents a critical combined energy analysis of demand in developed/developing countries, including the load requirements of the various business sectors. It summarizes on-demand time-series, energy supply, overall trade in gas, oil, electricity, coal, and renewable energy (e.g., wind, solar, geothermal, tidal, etc.) as well as global carbon dioxide (CO2) emissions. The duration of the review is selected between the energy supply and demand forecast from 1990 to 2040. Multi-energy approaches include primary energy generation, consumption, gross domestic product (GDP) energy intensity, the total trade balance of crude oil production, production of natural gas, trade balance and use of natural gas, production of lignite and coal, demand for trade in lignite and coal, generation and use of electricity, the share of renewables in power generation, total percentage of solar energy. Geographic coverage covered the global energy demand of the Organization for Economic Co-operation and Development (OECD), the group of seven (G7), Brazil, Russia, India, China, and South Africa (BRICS), European Union, Europe, North America, Commonwealth of Independent States (CIS), Asia, Latin America, Pacific, Middle-East and Africa. Market individuals and cooperative policymakers communicate in a variety of ways: our review and its impact on energy trade, social development, economic and climate change, which is then presented in a deeper way, in the future energy outlook. The findings of the review make it clear that there is a great deal of future global energy demand until 2040 in different situations: new aspects of policymaking, the requirement is about 15% lower in the 450-scenario, and 10% higher in the current energy policy scenario.

Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better reconstruction, we propose a novel Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement. Extensive experiments show the effectiveness of the proposed modules, and we further scale up the model to demonstrate that the performance of this task can be greatly improved. Our overall method significantly outperforms the state-of-the-art methods by more than 1dB.

Strain-based non-intrusive approaches for measuring the pressure of pipes have attracted widespread attention due to their great convenience and ability to avoid destroying the integrity of structures. However, the mentioned method usually measures the dynamic pressure based only on the static strain-pressure sensitivity coefficients (SSSCs) instead of the dynamic strain-pressure sensitivity coefficients (DSSCs) due to its complicated calibration, which will inevitably affect the accuracy significantly. To address this issue, a model-driven scheme with dual stages is proposed in the present study to compensate the dynamic pressure measurement. The DSSCs are analytically derived for the first time from the axial governing equations of the pipe, considering the general boundary conditions for the thin-wall pipe and thick-wall pipe simultaneously. In the first stage, the physical parameters involved in the DSSCs are calibrated by minimizing the residual of the experimental results and the theoretical counterparts. In the second stage, the DSSCs calculated from the calibrated analytical model are utilized to compensate the dynamic pressure based on the relationship between the DSSCs and the SSSCs. The proposed method is applied to an industrial hydraulic pipe system, and the experimental results show that the relative error is reduced greatly after the compensation is implemented, demonstrating the validity of the proposed compensation method.

This paper provides an overview of ChatGPT, a natural language processing (NLP) system developed by Open AI. It discusses the features of ChatGPT, its benefits, and its challenges. The paper also provides an analysis of the potential applications of ChatGPT and its limitations. The paper concludes that ChatGPT is a powerful NLP system that can generate human-like conversations, but it has some challenges that must be addressed.

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, a modified PDS scheme is presented to trace the channel dynamic and adjust the beamforming policy against channel uncertainty accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.

Tumor heterogeneity is one of the hallmarks of cancer and a challenge in the field of oncology. Tumor heterogeneity is the main cause of drug resistance, leading to therapeutic failure. Mechanically, tumor heterogeneity either directly affects therapeutic targets or shapes the tumor microenvironment (TME) by defining transcriptomic and phenotypic profiles to influence drug resistance. Tumor heterogeneity evolves spatially and temporally during tumor development, leading to the constant reprogramming of the TME. Advances in molecular profiling technologies and precision oncology platforms have allowed us to uncover the impact of tumor heterogeneity on drug resistance in the context of the TME. In this review, we focus on the processes during which genomic mutations drive tumor heterogeneity and the mechanisms through which tumor heterogeneity reprograms the TME to affect drug resistance and patient prognosis.

Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data. However, the key challenge in federated learning is that the clients have significant statistical heterogeneity among their local data distributions, which would cause inconsistent optimized local models on the clientside. To address this fundamental dilemma, we propose a novel federated learning algorithm with local drift decoupling and correction (FedDC). Our FedDC only introduces lightweight modifications in the local training phase, in which each client utilizes an auxiliary local drift variable to track the gap between the local model parameter and the global model parameters. The key idea of FedDC is to utilize this learned local drift variable to bridge the gap, i.e., conducting consistency in parameter-level. The experiment results and analysis demonstrate that FedDC yields expediting convergence and better performance on various image classification tasks, robust in partial participation settings, non-iid data, and heterogeneous clients.

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) has attracted growing research interests in the context of sixth-generation (6G) wireless networks, in which UAVs will be exploited as aerial wireless platforms to provide better coverage and enhanced sensing and communication (S&C) services. However, due to the size, weight, and power (SWAP) constraints of UAVs, their controllable mobility, and the line-of-sight (LoS) air-ground channels, UAV-enabled ISAC introduces new opportunities and challenges. This article provides an overview of UAV-enabled ISAC and proposes various solutions for optimizing the S&C performance. In particular, we first introduce UAV-enabled joint S&C and discuss UAV motion control, wireless resource allocation, and interference management for ISAC systems employing single and multiple UAVs. Then, we present two application scenarios for exploiting the synergy between S&C, namely sensing-assisted UAV communication and communication-assisted UAV sensing. Finally, we highlight several interesting research directions to guide and motivate future work.

The sustainability of rural development, both economic and environmental, has been increasingly linking to local food, which plays an indispensable role by preserving traditional culture, attracting tourists, and supporting the regional economy. However, the authenticity and quality of local food have not been fully convinced as competitive advantages by most practitioners. Little is known about how authenticity affects quality attributes, tourist satisfaction, and tourist loyalty. Thus, this study examines the role of authenticity in the quality–satisfaction–loyalty framework. The field research was performed in Shunde County, Guangdong Province, China. The results challenge the traditional view of quality attributes by highlighting that authenticity is a key antecedent to the quality–satisfaction–loyalty framework of food tourism. In contrast, the relationships among quality attributes, tourist satisfaction, and tourist loyalty are contingent on the extent to which food tourists perceive the authenticity of rural local food.

Purpose Given the increasing number of travel restrictions, the COVID-19 outbreak has dealt a crippling blow to the hotel industry, and the crisis management practices supporting the industry needs are changing as the pandemic continues. This study aims to compare how the hotel industry has responded to this crisis at the initial stage and the pandemic stage. Design/methodology/approach Data were collected from hotel managers in Macau in two occasions, namely, early February and early April 2020. Importance-usage-performance analysis was conducted to classify six categories of practices (pricing, marketing, maintenance, human resources, government assistance and epidemic prevention) into four executable crisis management strategies (priority, maintain, low priority and possible overkill) for each stage. Follow-up in-person interviews were conducted to validate the results of the study. Findings In the initial stage, priority strategies should be applied in all epidemic prevention, pricing and maintenance practices and in two governmental assistance and human resources practices. In the pandemic stage, all epidemic prevention practices remain at the priority quadrant, but two pricing practices are downgraded. Hotels tended to force labour into unpaid vacations (furlough) and postpone office and system maintenance. Governmental assistance should be at a low priority. Originality/value This study contributes to the knowledge of contingency planning for crisis management across crisis periods. It also demonstrates the processes of importance-usage-performance analysis for researchers to undertake further studies in tourism crisis management. Timely recommendations for governments and hotel industry stakeholders are provided to cope with this crisis.

The topics of visual and audio quality assessment (QA) have been widely researched for decades, yet nearly all of this prior work has focused only on single-mode visual or audio signals. However, visual signals rarely are presented without accompanying audio, including heavy-bandwidth video streaming applications. Moreover, the distortions that may separately (or conjointly) afflict the visual and audio signals collectively shape user-perceived quality of experience (QoE). This motivated us to conduct a subjective study of audio and video (A/V) quality, which we then used to compare and develop A/V quality measurement models and algorithms. The new LIVE-SJTU Audio and Video Quality Assessment (A/V-QA) Database includes 336 A/V sequences that were generated from 14 original source contents by applying 24 different A/V distortion combinations on them. We then conducted a subjective A/V quality perception study on the database towards attaining a better understanding of how humans perceive the overall combined quality of A/V signals. We also designed four different families of objective A/V quality prediction models, using a multimodal fusion strategy. The different types of A/V quality models differ in both the unimodal audio and video quality prediction models comprising the direct signal measurements and in the way that the two perceptual signal modes are combined. The objective models are built using both existing state-of-the-art audio and video quality prediction models and some new prediction models, as well as quality-predictive features delivered by a deep neural network. The methods of fusing audio and video quality predictions that are considered include simple product combinations as well as learned mappings. Using the new subjective A/V database as a tool, we validated and tested all of the objective A/V quality prediction models. We will make the database publicly available to facilitate further research.

Intelligent reflecting surface (IRS) has drawn a lot of attention recently as a promising new solution to achieve high spectral and energy efficiency for future wireless networks. By utilizing massive low-cost passive reflecting elements, the wireless propagation environment becomes controllable and thus can be made favorable for improving the communication performance. Prior works on IRS mainly rely on the instantaneous channel state information (I-CSI), which, however, is practically difficult to obtain for IRS-associated links due to its passive operation and large number of reflecting elements. To overcome this difficulty, we propose in this paper a new two-timescale (TTS) transmission protocol to maximize the achievable average sum-rate for an IRS-aided multiuser system under the general correlated Rician channel model. Specifically, the passive IRS phase shifts are first optimized based on the statistical CSI (S-CSI) of all links, which varies much slowly as compared to their I-CSI; while the transmit beamforming/precoding vectors at the access point (AP) are then designed to cater to the I-CSI of the users' effective fading channels with the optimized IRS phase shifts, thus significantly reducing the channel training overhead and passive beamforming design complexity over the existing schemes based on the I-CSI of all channels. Besides, for ease of practical implementation, we consider discrete phase shifts at each reflecting element of the IRS. For the single-user case, an efficient penalty dual decomposition (PDD)-based algorithm is proposed, where the IRS phase shifts are updated in parallel to reduce the computational time. For the multiuser case, we propose a general TTS stochastic successive convex approximation (SSCA) algorithm by constructing a quadratic surrogate of the objective function, which cannot be explicitly expressed in closed-form. Simulation results are presented to validate the effectiveness of our proposed algorithms and evaluate the impact of S-CSI and channel correlation on the system performance.

Reconfigurable intelligent surface (RIS) is a promising technology for 6 G networks owing to its superior ability to enhance the capacity and coverage of wireless communications by smartly creating a favorable propagation environment. In this paper, we investigate the potential of employing RIS in dual-functional radar-communication (DFRC) systems for improving both radar sensing and communication functionalities. In particular, we consider a RIS-assisted DFRC system in which the multi-antenna base station (BS) simultaneously performs both multi-input multi-output (MIMO) radar sensing and multi-user multi-input single-output (MU-MISO) communications using the same hardware platform. We aim to jointly design the dual-functional transmit waveform and the passive beamforming of RIS to maximize the radar output signal-to-interference-plus-noise ratio (SINR) achieved by space-time adaptive processing (STAP), while satisfying the communication quality-of-service (QoS) requirement under one of three metrics, the constant-modulus constraint on the transmit waveform, and the unit-modulus constraint of RIS reflecting coefficients. An efficient algorithm framework based on the alternative direction method of multipliers (ADMM) and majorization-minimization (MM) methods is developed to solve the complicated non-convex optimization problem. Simulation results verify the advancement of the proposed RIS-assisted DRFC scheme and the effectiveness of the developed ADMM-MM-based joint transmit waveform and passive beamforming design algorithm.

Nowadays, driven by the rapid development of smart mobile equipments and 5G network technologies, the application scenarios of Internet of Things (IoT) technology are becoming increasingly widespread. The integration of IoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of resources, such as the computation unit and battery capacity in the IIoT equipments (IIEs), computation-intensive tasks need to be executed in the mobile edge computing (MEC) server. However, the dynamics and continuity of task generation lead to a severe challenge to the management of limited resources in IIoT. In this article, we investigate the dynamic resource management problem of joint power control and computing resource allocation for MEC in IIoT. In order to minimize the long-term average delay of the tasks, the original problem is transformed into a Markov decision process (MDP). Considering the dynamics and continuity of task generation, we propose a deep reinforcement learning-based dynamic resource management (DDRM) algorithm to solve the formulated MDP problem. Our DDRM algorithm exploits the deep deterministic policy gradient and can deal with the high-dimensional continuity of the action and state spaces. Extensive simulation results demonstrate that the DDRM can reduce the long-term average delay of the tasks effectively.

With the development of communication and information technologies, smart tourism is gradually changing the tourism industry. Internet of Things (IoT) plays an important role in smart tourism. However, it is a challenge to apply IoT for smart tourism because of the need for dealing with a vast amount of data and low-latency communication. To this end, in this article, we outline 5G and AI-empowered IoT systems for smart tourism. Efficient data transmission based on 5G technology and smart data processing based on AI technology are significant to unlocking IoT based smart tourism applications. To demonstrate the superior performance of our proposed method, we perform a case study on POI recommendation. The experiment results demonstrate the efficiency and effectiveness of our proposed method.

By interacting, synchronizing, and cooperating with its physical counterpart in real time, digital twin (DT) is promised to promote an intelligent, predictive, and optimized modern city. Via interconnecting massive physical entities and their virtual twins with inter-twin and intra-twin communications, the Internet of DTs (IoDT) enables free data exchange, dynamic mission cooperation, and efficient information aggregation for composite insights across vast physical/virtual entities. However, as IoDT incorporates various cutting-edge technologies to spawn the new ecology, severe known/unknown security flaws, and privacy invasions of IoDT hinder its wide deployment. Besides, the intrinsic characteristics of IoDT, such as decentralized structure, information-centric routing, and semantic communications, entail critical challenges for security service provisioning in IoDT. To this end, this article presents an in-depth review of the IoDT with respect to system architecture, enabling technologies, and security/privacy issues. Specifically, we first explore a novel distributed IoDT architecture with cyber–physical interactions and discuss its key characteristics and communication modes. Afterward, we investigate the taxonomy of security and privacy threats in IoDT, discuss the key research challenges, and review the state-of-the-art defense approaches. Finally, we point out the new trends and open research directions related to IoDT.

Virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR) are examples of immersive technologies that have the potential to improve medical practice and education. As a result, they have recently sparked much research interest. However, there are few reviews related to the use of immersive technologies (including VR, AR, MR, and XR) in medical practice and education. Remarkably, six research questions related to the trends, application areas, recipients, teaching contents, evaluation methods, and performance remain unanswered. To this end, this study conducts a systematic review to analyse 128 articles from 2012 source papers, all of which are indexed in the Web of Science. The review results indicate that immersive technology is currently used primarily on surgery and anatomy-related subjects for doctors, medical students and interns. Furthermore, group experiments are the most commonly used data collection method. The results provide insights into the current research trends related to immersive technology applications for medical practice and education. They also serve as an essential reference for scholars in the medical practice and education contexts.