Intelligent Information Research Articles

Optimizing Urban Layout: A System Dynamics, Artificial Intelligence, and 3D Urban Information Model Approach for Active Management of City Complexity

Optimizing Urban Layout: A System Dynamics, Artificial Intelligence, and 3D Urban Information Model Approach for Active Management of City Complexity

Онтолого-орiєнтована мультиагентна система для децентралiзованого керування групою БПЛА

Introduction. Today, UAVs are becoming an increasingly important tool for performing complex tasks in various fields of application, both civil (economic) and military, as they are particularly effective in dynamically uncertain environments with hard-to-reach areas. In addition, technological advances such as blockchain, artificial intelligence (AI) and machine learning have enabled the development of updated and improved UAV systems. To create and deploy a swarm of UAVs, coordinate actions, manage, and exchange data, a model of a multi-agent system (MAC) based on an ontological representation of knowledge is proposed. This model enables a swarm of UAVs to effectively make decisions in various situations while performing assigned tasks. This approach enables the safety, reliability, and efficiency of the tasks of the UAV group. The purpose of the paper is to develop further the theoretical and practical foundations of the integration of the multi-agent system (MAS) based on the ontological representation of knowledge with the UAV network. This involves the development of a MAS architecture and a hierarchical set of ontologies of different levels. The goal is to create a common data description lan guage, define data semantics to ensure data uniqueness and consistency, provide support for decision-making during UAV swarm management, and swarm survivability in the event of aircraft failures or loss. It is necessary to develop algorithms and a method of dividing a complex task into sub-tasks in a swarm of UAVs among all MAS agents. This is to ensure reliable exchange of messages (data) between agents during the joint performance of the assigned task, and the possibility of dynamic redistribution of roles between UAV agents as needed. Methods. During the research, the general theory of intelligent information technologies was applied; agent theory methods in particular intelligent BDI agents; methods of analyzing the performance of wireless data exchange networks; theory of combinatorial optimization for dividing tasks into subtasks; methods of ontological analysis and descriptive logic to create an ontological hierarchical model of the subject area; methods of enriching ontological models from external semantically marked information resources. Results. As a result of the performed scientific research, the MAS architecture was proposed and its main functions were determined for the decentralized control of a swarm of UAVs. A set of agents with assigned roles was formed, who jointly (cooperatively) perform tasks, exchanging messages, and information with each other, which ensures the survivability of the system (in case of a failure or loss of the device, its task must be distributed among other drones). Plans and scenarios of MAS actions for various situations and means of coordinating actions between agents have been developed to perform the mission by a swarm of UAVs. A hierarchical ontological model of the subject area related to the work of the UAV swarm has been created. The algorithms and methods were based on the integration of semantic technologies that support the MAS during the execution of the UAV swarm mission, decision-making, assessment of the dynamic environment, and response to its changes. Conclusions. An original approach, algorithms, and method for improving the system of decentralized control of a group of UAVs were proposed. Expanding the functionality of the system for maintaining the interaction of a swarm of unmanned systems based on MAS artificial intelligence was suggested. This system was based on ontological models. The models describe knowledge of the subject area, processes of UAV swarm operation, scenarios of actions in difficult situations, distribution of roles to agents, principles of planning, and coordination. The proposed MAS is integrated with the UAV swarm software platform, which makes it possible to improve the efficiency of the decentralized control system and adapt UAVs to dynamic changes in the environment. The practical result of the work will be a prototype of a software agent system that interacts with ontologies while performing simple tasks. The economic significance of the work consists of focusing on the creation of new intelligent information technologies, which were based on AI and knowledge of the subject area, and this significantly increases the efficiency of the functioning of modern systems. Keywords: multi-agent system, ontology, formalization of knowledge, UAV, drone, decentralized control, task allocation.

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums.

This study aims to develop a digital retrieval system for art museums to solve the problems of inaccurate information and low retrieval efficiency in the digital management of cultural heritage. By introducing an improved Genetic Algorithm (GA), digital management and access efficiency are enhanced, to bring substantial optimization and innovation to the digital management of cultural heritage. Based on the collection of art museums, this study first integrates the collection's images, texts, and metadata with multi-source intelligent information to achieve a more accurate and comprehensive description of digital content. Second, a GA is introduced, and a GA 2 Convolutional Neural Network (GA2CNN) optimization model combining domain knowledge is proposed. Moreover, the convergence speed of traditional GA is improved to adapt to the characteristics of cultural heritage data. Lastly, the Convolutional Neural Network (CNN), GA, and GA2CNN are compared to verify the proposed system's superiority. The results show that in all models, the sample output results' actual value is 2.62, which represents the real data observation results. For sample number 5, compared with the actual value of 2.62, the predicted values of the GA2CNN and GA models are 2.6177 and 2.6313, and their errors are 0.0023 and 0.0113. The CNN model's predicted value is 2.6237, with an error of 0.0037. It can be found that the network fitting accuracy after optimization of the GA2CNN model is high, and the predicted value is very close to the actual value. The digital retrieval system integrated with the GA2CNN model has a good performance in enhancing retrieval efficiency and accuracy. This study provides technical support for the digital organization and display of cultural heritage and offers valuable references for innovative exploration of museum information management in the digital era.

A robust statistical framework for cyber-vulnerability prioritisation under partial information in threat intelligence

Proactive cyber-risk assessment is gaining momentum due to the wide range of sectors that can benefit from the prevention of cyber-incidents by preserving integrity, confidentiality, and the availability of data. The rising attention to cybersecurity also results from the increasing connectivity of cyber–physical systems, which generates multiple sources of uncertainty about emerging cyber-vulnerabilities.This work introduces a robust statistical framework for quantitative and qualitative reasoning under uncertainty about cyber-vulnerabilities and their prioritisation. Specifically, we take advantage of mid-quantile regression to deal with ordinal risk assessments, and we compare it to current alternatives for cyber-risk ranking and graded responses. For this purpose, we identify a novel accuracy measure suited for rank invariance under partial knowledge of the whole set of existing vulnerabilities.The model is tested on both simulated and real data from selected databases that support the evaluation, exploitation, or response to cyber-vulnerabilities in realistic contexts. Such datasets allow us to compare multiple models and accuracy measures, discussing the implications of partial knowledge about cyber-vulnerabilities on threat intelligence and decision-making in operational scenarios.

Application of AI Technology for the Development of Destination Tourism towards an Intelligent Information System

"The pervasive influence of Artificial Intelligence (AI) technology is fundamentally transforming industries and daily life. AI, an emulation of human intelligence in machines, endows systems with the capacity to autonomously learn reason and make decisions. By processing extensive datasets at unprecedented speeds, AI is ushering in a revolution across diverse domains, including healthcare, finance, education and manufacturing. Its applications span from elevating customer experiences through personalized recommendations to propelling advancements in areas like autonomous vehicles and precision agriculture. With the tourism industry in transformative evolution, AI is pivotal in enhancing visitor experiences, streamlining operations, and fostering sustainable destination management. AI-powered solutions personalize and simplify the entire traveller journey, offering tailored recommendations for pre-trip planning, on-site experiences and post-travel engagement. This not only boosts customer satisfaction but also elevates the overall appeal and competitiveness of destinations. Moreover, AI optimizes operational processes by utilizing predictive analytics and machine learning for demand forecasting, resource allocation, and crowd management. This facilitates data-driven decision-making, promoting efficient resource utilization and minimizing environmental impact. In sustainable tourism, AI monitors and manages environmental factors, employing smart sensors and analytics to assess ecological impact. This enables the implementation of proactive measures to preserve natural resources and protect biodiversity, contributing to smart infrastructure development for destination resilience. The integration of AI in destination tourism drives economic growth and enhances safety for travellers. AI-powered systems analyse real-time data to detect and respond to potential threats, ensuring a secure environment. As the tourism industry evolves, embracing AI becomes imperative for destinations to remain competitive, sustainable, and resilient. This abstract provides insights into the diverse applications of AI in destination tourism, underscoring benefits for tourists and destination management stakeholders."

Concept graph construction and applied research of agricultural remote sensing

ABSTRACT Remote sensing technology in the new era gradually need a new scientific paradigm driven by big data and knowledge. However, in the current research, the use of existing knowledge is slightly insufficient. As for the whole research process, there is a lack of guidance based on the field knowledge. In view of the above problems in the field of remote sensing in agriculture, it is of great significance to organize knowledge based on concept graph and develop knowledge-driven big data analysis theory and methods. This paper focuses on the construction and application of agricultural remote sensing concept graph. Firstly, it explores the core contents, research hotspots and development trends of the current agricultural remote sensing field through reference visualization and analysis methods such as keyword frequency statistics, keyword co-occurrence, word detection with stronger citation burst and so on. Then, this paper summarizes the comprehensive ontology of agricultural remote sensing based on the results of keyword clustering. Thus, the schema layer of concept graph is constructed from top to bottom. Under the guidance of the schema layer, the data layer of the concept graph is artificially filled from the bottom up with different triads stored in Neo4j. In terms of application, retrieval, query and recommendation of the proposed concept graph for agricultural remote sensing tasks is a typical application case. Based on the results of the recommendation, it could guide the process of information extraction such as cultivated land parcel delineation and crop type identification at the parcel scale. The study of concept graph helps to summarize and generalize the relevant knowledge in the field of agricultural remote sensing. Relevant research provide effective solutions for automated and intelligent agricultural information extraction and application.

An intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

The travel industry's contribution to sustainable development is noteworthy, since it offers travel experiences that are mutually beneficial to both local populations and tourists. But in order to maximize multiple resources in social tourism, an intelligent information system (IIS) is necessary for giving valuable tourism recommendations. Such systems use cutting-edge technologies like artificial intelligence, machine learning, and data analytics to improve the social tourism sector's overall efficacy and efficiency. The proposed intelligent information recommender system (IIRS) combines a number of different components to handle a number of tourism-related issues, such as impact assessment, community involvement, travel planning, and destination selection with the purpose of promoting sustainable social tourism. Fuzzy C-means clustering (FCM) is used in the proposed system for extraction of features. After extraction of features, the model is trained and tested using ensemble machine learning classifiers such as decision tree (DT) and extreme gradient boosting (XGB). Lastly, the evaluation of the system can be done with distinguished parameters such as accuracy, precision, recall, and F1-score.

The Progress and Trend of UAV Power Line Inspection: A Bibliometrics-based Visualization Analysis

<p> Background: The application of Unmanned Aerial Vehicle (UAV) is a major turning point in the history of power line inspection and is of increasing interest to a growing number of researchers. </p><p> Objective: This study aimed to clarify the research status, hotspots, and evolutionary trends of UAV power line inspection, to help researchers understand the dynamic evolution of research topics and provide guidance for future research directions. </p><p> Methods: 737 high-quality papers were collected from the Web of Science Core Collection (WoSCC) database from 2001 to 2023, and descriptive statistical analysis, cooperation network analysis, keyword co-occurrence analysis, keyword clustering analysis, and keyword citation burst analysis were conducted using VOSviewer and CiteSpace. </p><p> Results: The popularity of research in this field is increasing. Chinese academic institutions and scholars have made outstanding contributions. The level of cooperation between different countries, academic institutions, and scholars is low. The research on UAV power line inspection has a wide range of topics and obvious interdisciplinary characteristics, mainly focusing on fault detection and diagnosis, path planning, and the application of deep learning in intelligent inspection. The research hotspot and trend is the integration of artificial intelligence, deep learning, and modern information technology to achieve UAV autonomous intelligent inspection. Some of the challenges faced in the development of the field are also summarized and possible solutions are proposed. </p><p> Conclusion: This study provides a comprehensive and systematic review, and the results can provide a quick overview of the research status, hotspots, and evolutionary trends.

ПІДГОТОВКА МАЙБУТНІХ ОФІЦЕРІВ РОЗВІДНИКІВ ДО ПРОФЕСІЙНОЇ ДІЯЛЬНОСТІ В УМОВАХ ЗМІШАНОГО НАВЧАННЯ ЗАСОБАМИ ІНФОРМАЦІЙНО-ОСВІТНЬОГО СЕРЕДОВИЩА

The article analyzes scientific works, the regulatory and legal framework and the content of educational and methodological documentation regarding the organization of professional training of future intelligence officers. Modern approaches related to distance learning and digitalization of professional training of future specialists of the national security and defense sector of Ukraine, as well as the European Union, have been identified. The results of previous studies have been summarized. It has been established that the professional training of future intelligence officers in the conditions of pandemics and full-scale war can be carried out in the format of blended learning using the means of a specially created informational and educational environment. It is substantiated that blended learning of future military intelligence officers consists of the educational activity in two forms: in form of training sessions in specialized classrooms under the direct supervision of the scientific and pedagogical workers and in form of independent work with electronic resources of a specially created information and educational environment. Varieties of blended learning models in the conditions of pandemic and full-scale war are distinguished, namely "rotational model", "flexible model", "person-oriented model" and "enriched virtual environment model". Pedagogical components of information and educational environment tools for blended learning of future intelligence officers have been developed, which include: independent study of new educational material, independent study of material using elements of creativity, independent improvement of theoretical knowledge and practical skills in conditions of quasi-professional activity, and independent in-depth study of material. The introduction of innovative forms, methods and means of obtaining intelligence information into the professional training of future intelligence officers in the format of blended learning by means of the information and educational environment is proposed. The experience of conducting military intelligence acquired by the intelligence units of the armies of NATO member countries and the Armed Forces of Ukraine during the de-occupation of the territories of sovereign Ukraine captured by Russian-terrorist forces is considered as part of the blended learning. Standards of individual and collective training of future intelligence officers of the Armed Forces of Ukraine and NATO are applied. Key words: future intelligence officers, blended learning, blended learning models, information and educational environment tools, pedagogical components of information and educational environment tools.

Multiperformance PAM/PVA/CaCO3 Hydrogel for Flexible Sensing and Information Encryption.

Currently, the development of hydrogels with excellent mechanical properties (elasticity, fatigue resistance, etc.) and conductive properties can better meet their needs in the field of flexible sensor device applications. Generally, hydrogels with a denser cross-linking density tend to have better mechanical properties, but the improvement in mechanical properties comes at the expense of reduced electrical conductivity. Directly generating CaCO3 in the hydrogel prepolymer can not only increase the cross-linking density of its network but also introduce additional ions to enhance its internal ionic strength, which is beneficial to improving the conductivity of the hydrogel. It is still a big challenge to directly generate CaCO3 in the static prepolymer solution and ensure its uniform dispersion in the hydrogel. Herein, we adopted an improved preparation method to ensure that the directly generated CaCO3 particles can be evenly dispersed in the static prepolymer solution until the polymerization is completed. Finally, a PAM/PVA/CaCO3 hydrogel with supertensile, compressive, toughness, and fatigue resistance properties was prepared. In addition, the presence of free Na+ and Cl- gives the hydrogel excellent conductivity and sensing performance to monitor daily human activities. On the basis of the application of hydrogels in information communication, we have further deepened this application by combining the characteristics of hydrogels themselves. Combined with ASCII code, the hydrogel can also be applied in information exchange and information encryption and decryption, achieving the antitheft function in smart locks. A variety of excellent performance integrated PAM/PVA/CaCO3 hydrogels have broad application prospects for flexible sensors, highlighting great potential in human-computer interaction and intelligent information protection.

DEVELOPMENT OF THE SMART ECONOMY AS THE MAIN SOURCE OF COMPETITIVENESS AND SUSTAINABLE DEVELOPMENT

The development of the smart economy is a topic of great relevance in the contemporary world, particularly in the context of rapid technological advancement and digital transformation. The smart economy is predicated upon the utilisation of sophisticated technologies, including artificial intelligence, the Internet of Things, data analytics, blockchain, and others, with the objective of optimising processes across a multitude of domains, including production, transportation, healthcare, education, energy, trade, and others. The principal advantages of the advancement of the smart economy are that the utilisation of the most recent technologies permits the automation of processes, the optimisation of resources and the enhancement of production efficiency. Those countries and companies that adopt smart technologies proactively can secure a competitive advantage in the market through innovation supported by digital solutions. The objective of this article is to conceptualise the development of the smart economy as the primary source of competitiveness and sustainability. The successful development of the smart economy requires the right management strategy, investment in innovation and education, and partnership between public authorities, the private sector and the public. The growing role of the smart economy defines it as a key factor of competitiveness and sustainability for countries and companies in the modern world. The research methodology includes the integration of different philosophical methods (axiological, systemic, synergistic) with the Agile methodology, which has made it possible to comprehensively analyse the development of the smart economy as the main source of competitiveness and sustainability. Agile methodology, as a methodology of complex adaptive systems, flexible management and innovative components, has allowed to show the practical levers of using mechanisms to ensure sustainable growth, the role of information technologies in the development of a smart economy to achieve the goals of ESG (environmental, social and corporate management) of sustainable development. Research results. The emergence and development of the smart economy as an economy of knowledge, innovation, science and progress, based on intelligent machines and information networks, platforms and tools, which is an integral part of the innovative economy, is presented; the criteria and advantages of the smart economy are defined. The directions of development of the smart economy, its potential impact on competitiveness and sustainability are identified. Among the directions of development of the smart economy, the symmetric economy is distinguished, which is based on symmetric relations between economic subjects and objects, which are the driving force of economic development. The symmetry of the economic structure is a model for the development of a smart economy, a source of economic development, a subject-centred smart economy. It turns out that the smart economy includes the development of the main trends of the latest technologies, such as artificial intelligence, the Internet of Things (IoT), blockchain and data analytics, to optimise production, manage resources and create effective business models.

Method for testing the stability of an autoregressive model of the vocal tract and adjusting its parameters

Within the framework of the traditional direction of research in the field of acoustic measurements, an autoregressive model of the vocal tract as a key link in the human speech apparatus is considered. The acute problem of ensuring the stability of the autoregressive model in systems with adaptation of its parameters to the observed speech signal of short duration is pointed out. To overcome this problem, the task was set of testing the stability of the autoregressive model and adjusting its parameters based on the results of this testing. The study is based on the author’s method of formant analysis of vowel sounds of speech through the synthesis of a recursive shaping filter in the free oscillation mode. To solve sated task, a method is proposed for testing the stability and adjusting the parameters of the autoregressive model of the vocal tract based on a two-stage algorithm for its transformation. At the first stage of transformation, the stability of the autoregressive model is tested using the impulse response of the shaping filter. At the second stage, if the stability of the autoregressive model is violated, its impulse response is modified by element-by-element multiplication by a variable exponential value that asymptotically converges to zero. A regular algorithm has been developed for recalculating the modified impulse response into an adjusted vector of autoregressive parameters at the second stage of transformation. Based on the results of experimental testing of the proposed method, it was concluded that guaranteed stability of the autoregressive model of the vocal tract has been achieved with minimal distortion in the frequency domain. The results obtained are useful in the development and modernization of automatic speech recognition systems, digital speech communications, artificial intelligence and other information systems that use data compression and speech coding based on an autoregressive model of the vocal tract in automatic speech signal processing.

S3Net: Semi-self-supervised neural network for visibility enhancement of speckled images

The tremendous growth of imaging sensors, which visually acquire the objects of interest, has attracted substantial interest in different engineering applications, such as underwater sonar imaging, synthetic aperture radar (SAR) imaging, and medical ultrasound imaging, etc. However, due to limited imaging environments, the intelligent vision-empowered visual information often suffers from the low usability of imaging data, e.g., Gamma-distributed speckle noise of unknown level in several imaging systems. It is commonly intractable to implement blind despeckling of visual data since the speckle noise is signal-dependent and multiplicative in nature. To improve the imaging quality, we tended to propose a semi-self-supervised neural network (termed S3Net) that enables intelligent blind despeckling in different practical applications. To be specific, our S3Net is mainly composed of two sub-networks, i.e., self-supervised feature extraction network (FExNet) and supervised feature enhancement network (FEnNet). The robustness and generalization abilities of S3Net can be accordingly guaranteed in different imaging scenarios. It is thus capable of extracting meaningful features and enhancing geometrical features under different receptive fields, leading to image quality improvement. The S3Net denoiser can balance the trade-off between noise suppression and detail preservation. Numerous experiments have been implemented on synthetic and realistic speckle-degraded imaging scenarios. Results have demonstrated that S3Net remarkably outperformed other state-of-the-art methods in terms of both qualitative and quantitative evaluations, with significant boosting on metric and visual performance.

Exploration on multimodal data recognition method for Internet of Things based on deep learning

With the rapid development of Internet of Things (IoT) technology, a large amount of sensor data, images, voice, and other data are being widely used, bringing new opportunities for intelligent and cross-domain information fusion. Effective feature extraction and accurate recognition remain urgent issues to be addressed. This article explores the application of deep learning (DL) in multimodal data recognition methods of the IoT and proposes path optimization for multimodal data recognition methods of the IoT under DL. This article also provides in-depth analysis and discussion on the optimization of multimodal data recognition models based on DL, as well as specific measures for optimizing the path of multimodal data recognition based on DL. In this paper, the long short-term memory (LSTM) technology is introduced, and the LSTM technology is used to optimize the multi-modal data recognition method. It can be seen from the comparison that the processing efficiency of data analysis, information fusion, speech recognition, and emotion analysis of the multimodal data recognition method optimized by LSTM technology is 0.29, 0.35, 0.31, and 0.24 higher, respectively, than that of data analysis, information fusion, speech recognition, and emotion analysis before optimization. Introducing DL methods in multimodal data recognition of the IoT can effectively improve the effectiveness of data recognition and fusion and achieve higher levels of recognition for speech recognition and sentiment analysis.

Multi-agent modeling of traffic organization in urban agglomerations

The authors consider the features of multi-agent modeling for traffic optimization in the central areas of cities. While evaluating the unique challenges associated with the high concentration of vehicles, pedestrians and historical buildings, the potential of multi-agent systems to effectively solve the problem of congestion, safety and quality of life in urban areas is investigated. The potential of multi-agent modeling in the context of traffic management in the central areas of the city allows us to identify the key challenges and opportunities. Many scientists address the main aspects of such modeling and use them in the transport and road sectors. A review of current research and development has shown that multi-agent models aim to simulate and optimize the supervision and control of transportation in various traffic scenarios. Modeling traffic organization in the central areas of cities is one of the main elements of urban development planning and management. Due to the growing population of cities and the increasing number of vehicles, the problems of congestion, air pollution, and inefficient use of infrastructure are becoming increasingly relevant. Therefore, it can be noted that multi-agent traffic modeling opens up new prospects for developing effective traffic management strategies, providing a flexible and adaptive solution to these problems. The research analyzes the existing approaches, identifies the system`s key components, and develops a model that demonstrates the interaction between agents and the environment based on a mathematical description. A practical simulation of the model, carried out using the AnyLogic software on the example of Lesia Ukrainka Boulevard in Kyiv, confirms the effectiveness of the multi-agent approach. The results of the study indicate the possibility of applying the developed model to improve intelligent information systems for traffic flow management, which opens up new prospects for improving traffic in the central areas of cities.

Application and Exploration of Artificial Intelligence in Teaching and Learning in Private Colleges and Universities

The rapid advancement of artificial intelligence technology across various sectors has sparked profound transformation in the field of education, particularly in the realms of pedagogy and administration within private higher education institutions. The discourse delves into the specific applications of AI educational aids in both classroom instruction and post-class learning, encompassing intelligent tutoring systems, virtual laboratories, and personalized learning recommendation systems, among others. Furthermore, it addresses the utilization of AI-driven question-answering systems, automated homework grading systems, and speech recognition technology. In terms of educational administration, it investigates the integration of intelligent student information systems, learning trajectory tracking systems, as well as course resource recommendation systems and teaching evaluation systems. The aim of the article is to furnish educators with valuable insights to enhance teaching quality and administrative efficiency, thereby promoting the intelligent development of private higher education institutions.

Voice interactive information metasurface system for simultaneous wireless information transmission and power transfer

Intelligent voice interaction offers a flexible and powerful way to connect individuals with smart devices beyond our expectations. The real-time nature of voice communication enables smart devices to comprehend the user language, execute the corresponding instructions, and facilitate seamless communications, transforming our lives in unprecedented ways. Owing to self-adaptive and reprogrammable functionalities, information metasurface (IMS) opens up a new avenue for smart home and smart cities. To further enhance the intelligence of IMS, we propose an IMS system via intelligent voice interaction and information processing. The voice interaction enables the efficient remote control on the IMS in a flexible, convenient, touchless manner. Leveraging speech recognition, speech synthesis, target detection, and communication technologies, the IMS system achieves automatic beam manipulation capabilities for wireless information transmissions and wireless power transfers. The IMS system is designed to operate in two distinct modes: instruction mode, wherein the user instructs the operations, and autonomous mode, wherein the automatic detections govern the actions, in which seamless mode switching through the voice commands is supported. Users can flexibly achieve precise control over the functions of the intelligent metasurface system through voice interaction at a distance, without the need for close-range manual touch control, which greatly simplifies the operation difficulty and is particularly suitable for remote control and complex application scenarios. A series of experiments, including wireless video transmissions and wireless power transfers are conducted to demonstrate the flexibility and convenience of the IMS system. The incorporation of intelligent voice interaction technology with the IMS presents a novel paradigm for the applications of programmable and information metasurfaces.

The Application of Intelligent Information Systems Driven by 6G Big Data in Product Sales Traceability

The Application of Intelligent Information Systems Driven by 6G Big Data in Product Sales Traceability

IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors

IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors

AI-Enhanced Automotive Navigation: Enriching Driving Experiences through Intelligent Contextual Information Delivery

AI-Enhanced Automotive Navigation: Enriching Driving Experiences through Intelligent Contextual Information Delivery