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Despite increasing digitalization and automation, complex production processes often require human judgment/decision-making adaptability. Humans can abstract and transfer knowledge to new situations. People in production are an irreplaceable resource. This paper presents a new concept for digitizing human expertise and their ability to make knowledge-based decisions in the production area based on ontologies and causal Bayesian networks for further research. Dedicated approaches for the ontology-based creation of Bayesian networks exist in the literature. Therefore, we first comprehensively analyze previous studies and summarize the approaches. We then add the causal perspective, which has often not been an explicit subject of consideration. We see a research gap in the systematic and structured approach to ontology-based generation of causal graphs (CGs). At the current state of knowledge, the semantic understanding of a domain formalized in an ontology can contribute to developing a generic approach to derive a CG. The ontology functions as a knowledge base by formally representing knowledge and experience. Causal inference calculations can mathematically imitate the human decision-making process under uncertainty. Therefore, a systematic ontology-based approach to building a CG can allow digitizing the human ability to make decisions based on experience and knowledge. [ABSTRACT FROM AUTHOR]

The text describes the organization of a Special Issue in the Sensors journal titled "Recent Trends and Advances in Telecommunications and Sensing." The Special Issue was based on papers presented at the 17th International Conference on Telecommunications (ConTEL) held at Graz University of Technology in Austria. The conference covered various topics related to telecommunications, including optical communications, satellite communications, IoT, and security. Nine papers were accepted for publication in the Special Issue, covering subjects such as network experimentation, optical camera communications in healthcare, and anomaly detection in firewall logs. The authors of the text are Erich Leitgeb, Maja Matijašević, Wilfried Gappmair, and Mario Kušek. [Extracted from the article]

Long Short-Term Memory (LSTM)-Based Dog Activity Detection Using Accelerometer and Gyroscope. The third paper, authored by Jeong et al. [[14]], introduced a fall detection system that combines a simple threshold method (STM) and LSTM, creating the STM-LSTM-based fall detection system. With rapid advancements in artificial intelligence (AI), the Internet of Things (IOTs), and communication networks, the future of information and communication engineering (ICE) promises remarkable opportunities and challenges. 10.3390/app12199801 18 Zhang M., Wang C., Zhu W., Shen Y. An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning. [Extracted from the article]

This document is a response to a comment made by Robertas Damaševičius regarding an article on info-autopoiesis and artificial general intelligence (AGI). The response addresses specific points raised in the comment and clarifies the author's position. It emphasizes that info-autopoiesis, the self-creation of information, is fundamental only to humans and not achievable by artificial creations. The article argues for the impossibility of achieving AGI using info-autopoiesis and suggests that resources should be directed towards more achievable goals in AI. The response also discusses the relationship between biological aspects of human intelligence and AI research, highlighting the use of neural networks and considering the architecture of AGI. It concludes by stating that info-autopoiesis sets a limit to human knowledge based on fundamental principles and that all human creations are syntactic and incapable of semantic interpretation. The text discusses the concept of info-autopoiesis and its application to AI research. Info-autopoiesis is a framework that aims to explain how living organisms function, and the paper explores its potential implications for AI and AGI development. The author acknowledges the limitations of info-autopoiesis and emphasizes the need for interdisciplinary collaboration and ethical considerations in AI research. The text concludes by suggesting areas for further research and expressing gratitude for the comments and suggestions provided by Robertas Damaševičius. [Extracted from the article]

Orthogonal polynomials and their moments serve as pivotal elements across various fields. Discrete Krawtchouk polynomials (DKraPs) are considered a versatile family of orthogonal polynomials and are widely used in different fields such as probability theory, signal processing, digital communications, and image processing. Various recurrence algorithms have been proposed so far to address the challenge of numerical instability for large values of orders and signal sizes. The computation of DKraP coefficients was typically computed using sequential algorithms, which are computationally extensive for large order values and polynomial sizes. To this end, this paper introduces a computationally efficient solution that utilizes the parallel processing capabilities of modern central processing units (CPUs), namely the availability of multiple cores and multithreading. The proposed multi-threaded implementations for computing DKraP coefficients divide the computations into multiple independent tasks, which are executed concurrently by different threads distributed among the independent cores. This multi-threaded approach has been evaluated across a range of DKraP sizes and various values of polynomial parameters. The results show that the proposed method achieves a significant reduction in computation time. In addition, the proposed method has the added benefit of applying to larger polynomial sizes and a wider range of Krawtchouk polynomial parameters. Furthermore, an accurate and appropriate selection scheme of the recurrence algorithm is introduced. The proposed approach introduced in this paper makes the DKraP coefficient computation an attractive solution for a variety of applications. [ABSTRACT FROM AUTHOR]

Digital financial inclusion and common prosperity are pivotal elements in promoting the sustainable socioeconomic development of China. This study introduces a novel Multi-Criteria Decision Analysis (MCDA) method to evaluate the Common Prosperity Index (CPI). Using this index, alongside the Digital Financial Inclusion Index (DFII) released by Peking University, it examines the evolution of the coupling coordination relationship between digital financial inclusion and common prosperity within the Yangtze River Delta (YRD) urban agglomeration from 2011 to 2021. By integrating gravity models and social network analysis, in this paper, we thoroughly investigate the spatiotemporal evolution characteristics of the spatial network of this coupling coordination relationship. The results indicate that both the DFII and CPI generally exhibit an upward trend, but the decline in the coupling degree reflects a weakened interaction strength between them. Specifically, Anhui significantly lags behind Jiangsu, Zhejiang, and Shanghai in the development of digital financial inclusion and common prosperity, indicating regional development imbalances. Furthermore, the strength of spatial connections in city coupling coordination has significantly increased, with Nanjing's siphon effect on cities in Anhui becoming markedly stronger, and the number of core cities in the network increasing, which demonstrates a geographical proximity feature in network development. Additionally, the overall network characteristics are transitioning towards higher density and "small-world" properties, suggesting a trend toward network stabilization. The disparity in centrality among cities has decreased, with an overall enhancement in centrality, where the spatial spillover effects from core areas such as Hangzhou-Ningbo, Nanjing-Changzhou, and Shanghai-Suzhou-Wuxi significantly promote the development of peripheral cities. Based on these findings, this paper proposes policy recommendations for the sustainable development of digital financial inclusion and common prosperity in the YRD region. [ABSTRACT FROM AUTHOR]

This paper addresses the scientific production of pharmaceutical communication in Spain around the COVID-19 crisis, in which information overload, amplified by the digital media, evidenced the relevance of communication in the digital society. The research observes the evolution and characteristics of such studies, identifying scientific fields and disciplines related to communication, thematic lines, agents and publics. To this end, it proposes an exploratory review study adjusted to the PRISMA protocol with a search strategy including three databases (Scopus, WOS and Dialnet) and whose filtration produced a final population of 56 publications on Spanish pharmaceutical communication between 2018 and 2022. The results point to a greater production of scientific papers around the year of the pandemic. These papers were published by university institutions in health sciences journals, although differences in authorship by gender were detected. Most of them are empirical papers, with a predominance of mixed content analyses. The field of public relations stands out, but terminological confusion was also detected. This leads to a reflection on its causes and solutions in favour of the transparency and accountability in pharmaceutical communication. [ABSTRACT FROM AUTHOR]

The grid terminal deploys numerous types of communication equipment for the digital construction of the smart grid. Once communication equipment failure occurs, it might jeopardize the safety of the power grid. The massive amount of communication equipment leads to a dramatic increase in fault research and judgment data, making it difficult to locate fault information in equipment maintenance. Therefore, this paper designs a knowledge-graph-driven method for intelligent decision making on power communication equipment faults. The method consists of two parts: power knowledge extraction and user intent multi-feature learning recommendation. The power knowledge extraction model utilizes a multi-layer bidirectional encoder to capture the global features of the sentence and then characterizes the deep local semantics of the sentence through a convolutional pooling layer, which achieves the joint extraction and visual display of the fault entity relations. The user intent multi-feature learning recommendation model uses a graph convolutional neural network to aggregate the higher-order neighborhood information of faulty entities and then the cross-compression matrix to solve the feature interaction degree of the user and graph, which achieves accurate prediction of fault retrieval. The experimental results show that the method is optimal in knowledge extraction compared to classical models such as BERT-CRF, in which the F1 value reaches 81.7%, which can effectively extract fault knowledge. User intent multi-feature learning recommendation works best, with an F1 value of 87%. Compared with the classical models such as CKAN and KGCN, it is improved by 5%~11%, which can effectively solve the problem of insufficient mining of user retrieval intent. This method realizes accurate retrieval and personalized recommendation of fault information of electric power communication equipment. [ABSTRACT FROM AUTHOR]

This paper proposes an all-digital calibration algorithm that utilizes a reference channel to suppress the timing mismatch in the Time-Interleaved Analog-to-Digital Converter (TIADC). The output of the reference channel is aligned with each sub-channel in turn, therefore enabling the simultaneous sampling and conversion of the same input signal. First, the statistical characteristics across the channels are employed for estimating the timing mismatch; then, by comparing the output difference between the reference channel and the sub-channels that are sampled simultaneously, the deviation of the derivator can be calibrated. Finally, combining both calibration results yields an accurate final output. This proposed algorithm provides an effective solution to improve TIADC performance in high-speed data acquisition systems. The proposed architecture is applied to a 12-bit 2.4 GS/s four-channel TIADC model, and then its effectiveness is verified. The simulation results exhibit that the Effective Number Of Bits (ENOB) at an input signal frequency of 984 MHz shows a remarkable improvement from 6.88 bits to 11.92 bits. The effectiveness of this technique is also demonstrated through the off-chip calibration of a commercial 12-bit four-channel 2 GS/s TIADC using a 680 MHz input signal that is based on the actual chip results. [ABSTRACT FROM AUTHOR]

Despite the huge advances in digital communications in the last decade, physical documents are still the most common media for information transfer, especially in the official context. However, the readily available document processing devices and techniques (printers, scanners, etc.) facilitate the illegal manipulation or imitation of original documents by forgers. Therefore, verification of the authenticity and detection of forgery is of paramount importance to all agencies receiving printed documents. We suggest an unsupervised forgery detection framework that can distinguish whether a document is forged based on the spectroscopy of the document's ink. The spectra of the tested documents inks (original and questioned) were obtained using laser-induced breakdown spectroscopy (LIBS) technology. Then, a correlation matrix of the spectra was calculated for both the original and questioned documents together, which were then transformed into an adjacency matrix aiming at converting it into a weighted network under the concept of graph theory. Clustering algorithms were then applied to the network to determine the number of clusters. The proposed approach was tested under a variety of scenarios and different types of printers (e.g., inkjet, laser, and photocopiers) as well as different kinds of papers. The findings show that the proposed approach provided a high rate of accuracy in identifying forged documents and a high detection speed. It also provides a visual output that is easily interpretable to the non-expert, which provides great flexibility for real-world application. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic air-to-ground (A2G) channel model selection method based on machine learning (ML) using digital surface model (DSM) terrain data. In order to verify whether a communication network for a new non-terrestrial user service such as Urban Air Mobility (UAM) satisfies the required performance, it is necessary to perform a simulation reflecting the characteristics of the corresponding terrain environments as accurately as possible. For this simulation, A2G channel models corresponding to various terrain environments and a method of automatically classifying the terrain type of the simulation area must be provided. Many A2G channel models based on actual measurement results exist, but the practical automatic topography classification method still needs to be developed. This paper proposes the first practical automatic topography classification method using a two-step neural network-based classifier utilizing various geographic feature data as input. Since there is no open topography dataset to evaluate the accuracy of the proposed method, we built a new dataset for five topography classes that reflect the characteristics of Korea's topography, which is also a contribution of our study. The simulation results using the new data set show that the proposed ML-based method could increase the selection accuracy compared to the technique for direct classification by humans or the existing cross-correlation-based classification method. Since the proposed method utilizes the DSM data, open to the public, it can easily reflect the different terrain characteristics of each country. Therefore, the proposed method can be effectively used in the realistic performance evaluation of new non-terrestrial communication networks utilizing vast airspace such as UAM or 6G mobile communications. [ABSTRACT FROM AUTHOR]

With the rapid development of the digital new infrastructure industry, the energy demand for communication base stations in smart grid systems is escalating daily. The country is vigorously promoting the communication energy storage industry. However, the energy storage capacity of base stations is limited and widely distributed, making it difficult to effectively participate in power grid auxiliary services by only implementing the centralized control of base stations. Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper. Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling potential of battery clusters in multiple scenarios is explored. Then, based on the time of use electricity price and user fitness indicators, with the maximum transmission signal and minimum operating cost as objective functions, a decentralized control device is used to locally and quickly regulate the communication system. Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a virtual battery management system. Finally, a simulation analysis was conducted on data from different types of base stations in the region, designing two distinct scheduling schemes for four regional categories. The analysis results demonstrate that the proposed model can effectively reduce the power consumption of base stations while mitigating the fluctuation of the power grid load. [ABSTRACT FROM AUTHOR]

A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework's versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments. [ABSTRACT FROM AUTHOR]

Highlights: What are the main findings? First, the combination of the methods of machine learning with psychological methods to predict the user's OCEAN personality model could achieve a higher accuracy; Second, the model proposed in this paper that is a combination of LDA plus BP neural network is generally superior to the combination model of the same type. What is the implication of the main finding? First, through digital footprints and understanding the rules of user behavior, user behavior can be predicted and targeted recommendations made; Second, it was found that predicting the user's OCEAN personality model and then their behavior can provide an effective method for micro-directional recommendations in network communication. In the era of big data, the Internet is enmeshed in people's lives and brings conveniences to their production and lives. The analysis of user preferences and behavioral predictions of user data can provide references for optimizing information structure and improving service accuracy. According to the present research, user's behavior on social networking sites has a great correlation with their personality, and the five characteristics of the OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) personality model can cover all aspects of a user's personality. It is important in identifying a user's OCEAN personality model to analyze their digital footprints left on social networking sites and to extract the rules of users' behavior, and then to make predictions about user behavior. In this paper, the Latent Dirichlet Allocation (LDA) topic model is first used to extract the user's text features. Second, the extracted features are used as sample input for a BP neural network. The results of the user's OCEAN personality model obtained by a questionnaire are used as sample output for a BP neural network. Finally, the neural network is trained. A mapping model between the probability of the user's text topic and their OCEAN personality model is established to predict the latter. The results show that the present approach improves the efficiency and accuracy of such a prediction. [ABSTRACT FROM AUTHOR]

Digital substations, also referred to as modern power grid substations, utilize the IEC 61850 station and process bus in conjunction with IP-based communication. This includes communication with switch yard equipment within the substation as well as the dispatch center. IEC 61850 is a global standard developed to standardize power grid communications, covering multiple communication needs related to modern power grid substations or digital substations. Unlike the legacy communication standards, IEC 60870-5-104 and DNP3, IEC 61850 is specifically designed for IP-based communication. It comprises several communication models and supports real-time communication by introducing the process bus to replace traditional peer-to-peer communication with standard network communication between substation equipment and the switch yard. The process bus, especially Sampled Measured Values (SMV) communication, in modern power grid substations relies on extremely accurate and synchronized time to prevent equipment damage, maintain power grid system balance, and ensure safety. In IEC 61850, time synchronization is provided by the Precision Time Protocol (PTP). This paper discusses the significance and challenges of time synchronization in IEC 61850 substations, particularly those associated with PTP. It presents the results of a controlled experiment that subjects time synchronization and PTP to cyber-attacks and discusses the potential consequences of such attacks. The paper also provides recommendations for potential mitigation strategies. The contribution of this paper is to provide insights and recommendations for enhancing the security of IEC 61850-based substations against cyber-attacks targeting time synchronization. The paper also explores the potential consequences of cyber-attacks and provides recommendations for potential mitigation strategies. [ABSTRACT FROM AUTHOR]

In today's industry, the fourth industrial revolution is underway, characterized by the integration of advanced technologies such as artificial intelligence, the Internet of Things, and big data. One of the key pillars of this revolution is the technology of digital twin, which is rapidly gaining importance in various industries. However, the concept of digital twins is often misunderstood or misused as a buzzword, leading to confusion in its definition and applications. This observation inspired the authors of this paper to create their own demonstration applications that allow the control of both the real and virtual systems through automatic two-way communication and mutual influence in context of digital twins. The paper aims to demonstrate the use of digital twin technology aimed at discrete manufacturing events in two case studies. In order to create the digital twins for these case studies, the authors used technologies as Unity, Game4Automation, Siemens TIA portal, and Fishertechnik models. The first case study involves the creation of a digital twin for a production line model, while the second case study involves the virtual extension of a warehouse stacker using a digital twin. These case studies will form the basis for the creation of pilot courses for Industry 4.0 education and can be further modified for the development of Industry 4.0 educational materials and technical practice. In conclusion, selected technologies are affordable, which makes the presented methodologies and educational studies accessible to a wide range of researchers and solution developers tackling the issue of digital twins, with a focus on discrete manufacturing events. [ABSTRACT FROM AUTHOR]

The paper presents a DSP-based design and control of a three-axis servo motion system that effectively divides control tasks between the master control terminal and four slave drives. The proposed architecture enhances the immediacy of platform positioning and the integration between software and hardware. The paper utilizes a high-accuracy encoder as a reference value for accuracy improvement and integrates firmware via the proposed embedded execution program, thereby reducing the accuracy error of the low-accuracy encoder. The main control core employs a digital signal processor (DSP) for input and output signal reading and writing, digital communication interface processing, and user interface. The slave controller utilizes four digital signal processors to accomplish servo position control in a digital way. The paper employs encoder calibration technology to upgrade the positioning accuracy of the surge axis. Finally, the paper validates the correctness and feasibility of the proposed method through experimental results from a set of adjustable experimental platforms of linear motion stroke and rotary motion stroke. In summary, the paper underscores the integration of hardware and software to attain high-precision and dependable control of the motion system. [ABSTRACT FROM AUTHOR]

To address the fuzzy reconstruction effect on distant objects in unbounded scenes and the difficulty in feature matching caused by the thin structure of power lines in images, this paper proposes a novel image-based method for the reconstruction of power transmission lines (PTLs). The dataset used in this paper comprises PTL progressive motion sequence datasets, constructed by a visual acquisition system carried by a developed Flying–walking Power Line Inspection Robot (FPLIR). This system captures close-distance and continuous images of power lines. The study introduces PL-NeRF, that is, an enhanced method based on the Neural Radiance Fields (NeRF) method for reconstructing PTLs. The highlights of PL-NeRF include (1) compressing the unbounded scene of PTLs by exploiting the spatial compression of normal L ∞ ; (2) encoding the direction and position of the sample points through Integrated Position Encoding (IPE) and Hash Encoding (HE), respectively. Compared to existing methods, the proposed method demonstrates good performance in 3D reconstruction, with fidelity indicators of PSNR = 29, SSIM = 0.871, and LPIPS = 0.087. Experimental results highlight that the combination of PL-NeRF with progressive motion sequence images ensures the integrity and continuity of PTLs, improving the efficiency and accuracy of image-based reconstructions. In the future, this method could be widely applied for efficient and accurate 3D reconstruction and inspection of PTLs, providing a strong foundation for automated monitoring of transmission corridors and digital power engineering. [ABSTRACT FROM AUTHOR]

Today, the use of digital medicine is becoming more and more common in medicine. With the use of digital medicine, health data can be shared, processed, and visualized using computer algorithms. One of the problems currently facing digital medicine is the rapid transmission of large amounts of data and their appropriate visualization, even in 3D. Advances in technology offer the possibility to use new image processing, networking, and visualization solutions for the evaluation of medical samples. Because of the resolution of the samples, it is not uncommon that it takes a long time for them to be analyzed, processed, and shared. This is no different for 3D visualization. In order to be able to display digitalized medical samples in 3D at high resolution, a computer with computing power that is not necessarily available to doctors and researchers is needed. COVID-19 has shown that everyday work must continue even when there is a physical distance between the participants. Real-time network streaming can provide a solution to this, by creating a 3D environment that can be shared between doctors/researchers in which the sample being examined can be visualized. In order for this 3D environment to be available to everyone, it must also be usable on devices that do not have high computing capacity. Our goal was to design a general-purpose solution that would allow users to visualize large amounts of medical imaging data in 3D, regardless of the computational capacity of the device they are using. With the solution presented in this paper, our goal was to create a 3D environment for physicians and researchers to collaboratively evaluate 3D medical samples in an interdisciplinary way. [ABSTRACT FROM AUTHOR]

The concept of Digital Twin is of fundamental importance to meet the main requirements of Industry 4.0. Among the standards currently available to realize Digital Twins there is the Digital Twins Definition Language. Digital Twin requires exchange of data with the real system it models and with other applications that use the digital replica of the system. In the context of Industry 4.0, a reference standard for an interoperable exchange of information between applications, is Open Platform Communications Unified Architecture. The authors believe that interoperability between Digital Twins and Open Platform Communications Unified Architectures communication standard should be enabled. For this reason, the main goal of this paper is to allow a Digital Twin based on the Digital Twins Definition Language to exchange data with any applications compliant to the Open Platform Communications Unified Architecture. A proposal about the mapping from Digital Twins Definition Language to the Open Platform Communications Unified Architecture will be presented. In order to verify the feasibility of the proposal, an implementation has been made by the authors, and its description will be introduced in the paper. Furthermore, the main results of the validation process accomplished on the basis of this implementation will be given. [ABSTRACT FROM AUTHOR]

Modern power grids are undergoing a significant transformation with the massive integration of renewable, decentralized, and electronically interfaced energy sources, alongside new digital and wireless communication technologies. This transition necessitates the widespread adoption of robust online diagnostic and monitoring tools. Sensors, known for their intuitive and smart capabilities, play a crucial role in efficient condition monitoring, aiding in the prediction of power outages and facilitating the digital twinning of power equipment. This review comprehensively analyzes various sensor technologies used for monitoring power transformers, focusing on the critical need for reliable and efficient fault detection. The study explores the application of fiber Bragg grating (FBG) sensors, optical fiber sensors, wireless sensing networks, chemical sensors, ultra-high-frequency (UHF) sensors, and piezoelectric sensors in detecting parameters such as partial discharges, core condition, temperature, and dissolved gases. Through an extensive literature review, the sensitivity, accuracy, and practical implementation challenges of these sensor technologies are evaluated. Significant advances in real-time monitoring capabilities and improved diagnostic precision are highlighted in the review. It also identifies key challenges such as environmental susceptibility and the long-term stability of sensors. By synthesizing the current research and methodologies, this paper provides valuable insights into the integration and optimization of sensor technologies for enhancing transformer condition monitoring and reliability in modern power systems. [ABSTRACT FROM AUTHOR]

The advent of 3D virtual presentation technology for clothing has led to the gradual popularisation of digital virtual clothing in the modern fashion industry. However, there remains a gap between the application of this technology and the integration of cultural attributes in the field of digital communication of traditional cultural clothing. Consequently, the objective of this paper is to propose the establishment of a fusion system integrating archaeological research on traditional culture with emerging virtual presentation technology. This paper draws inspiration from the replicability and easy dissemination of digital products to combine cultural archaeology and digital technology. The aim is to provide ideas for the diversity of dissemination of cultural heritage. The research object is Diplomatic Envoys, a Chinese mural painting of the Tang Dynasty that depicts friendly exchanges between countries. The research is divided into two research stages. A CLO3D software-based digital restoration test was conducted to reproduce the costumes of officials and foreign envoys depicted in the Tang Dynasty mural. The FAHP model was employed to verify the accuracy of the restoration results. The experiment demonstrated that the digitally reconstructed clothing exhibited a high degree of similarity to the unearthed mural figure clothing object. Furthermore, the restoration result passed the credibility verification, resulting in a 'credible' outcome. The application of digital virtual simulation clothing restoration methods offers two key advantages. Firstly, in comparison with traditional clothing restoration methods, digital restoration enables the rapid assessment of the resulting clothing effect, thereby reducing the likelihood of secondary damage to cultural relics due to manual errors. Secondly, the benefits of digital technology facilitate the convenient storage, replication, and dissemination of clothing data information. Data can not only be extended to online exhibition halls but also to game animation, clothing production, and other fields for the purposes of creative redesign and information dissemination. Furthermore, these benefits can penetrate the education industry to disseminate information to the public through all-round display models and explanations. [ABSTRACT FROM AUTHOR]

In the quest for robust and efficient digital communication, this paper introduces cutting-edge key exchange protocols leveraging the computational prowess of tropical semirings and the structural resilience of block matrices. Moving away from the conventional use of finite fields, these protocols deliver markedly faster processing speeds and heightened security. We present two implementations of our concept, each utilizing a different platform for the set of commuting matrices: one employing tropical polynomials of matrices and the other employing Linde–de la Puente matrices. The inherent simplicity of tropical semirings leads to a decrease in operational complexity, while using block matrices enhances our protocols' security profile. The security of these protocols relies on the Matrix Decomposition Problem. In addition, we provide a comparative analysis of our protocols against existing matrix block-based protocols in finite fields. This research marks a significant shift in cryptographic protocol design, is specifically tailored for demanding engineering applications, and sets a new standard in secure and efficient digital communication. [ABSTRACT FROM AUTHOR]

Amidst an unprecedented period of technological progress, incorporating digital platforms into diverse domains of existence has become indispensable, fundamentally altering the operational processes of governments, businesses, and individuals. Nevertheless, the swift process of digitization has concurrently led to the emergence of cybercrime, which takes advantage of weaknesses in interconnected systems. The growing dependence of society on digital communication, commerce, and information sharing has led to the exploitation of these platforms by malicious actors for hacking, identity theft, ransomware, and phishing attacks. With the growing dependence of organizations, businesses, and individuals on digital platforms for information exchange, commerce, and communication, malicious actors have identified the susceptibilities present in these systems and have begun to exploit them. This study examines 28 research papers focusing on intrusion detection systems (IDS), and phishing detection in particular, and how quickly responses and detections in cybersecurity may be made. We investigate various approaches and quantitative measurements to comprehend the link between reaction time and detection time and emphasize the necessity of minimizing both for improved cybersecurity. The research focuses on reducing detection and reaction times, especially for phishing attempts, to improve cybersecurity. In smart grids and automobile control networks, faster attack detection is important, and machine learning can help. It also stresses the necessity to improve protocols to address increasing cyber risks while maintaining scalability, interoperability, and resilience. Although machine-learning-based techniques have the potential for detection precision and reaction speed, obstacles still need to be addressed to attain real-time capabilities and adjust to constantly changing threats. To create effective defensive mechanisms against cyberattacks, future research topics include investigating innovative methodologies, integrating real-time threat intelligence, and encouraging collaboration. [ABSTRACT FROM AUTHOR]

The diverse application vertices of internet-of-things (IoT) including internet of vehicles (IoV), industrial IoT (IIoT) and internet of drones things (IoDT) involve intelligent communication between the massive number of objects around us. This digital transformation strives for seamless data flow, uninterrupted communication capabilities, low latency and ultra-high reliability. The limited capabilities of fifth generation (5G) technology have given way to sixth generation (6G) wireless technology. This paper presents a dynamic cell-free framework for a 6G-enabled IoT network. A number of access points (APs) are distributed over a given geographical area to serve a large number of user nodes. A pilot-based AP selection (PBAS) algorithm is proposed, which offers robust resource control through AP selection based on pilots. Selecting a subset of APs against all APs for each user node results in improved performance. In this paper, the performance of the proposed transmission model is evaluated for the achieved data rate and spectral efficiency using the proposed algorithm. It is shown that the proposed PBAS algorithm improves the spectral efficiency by 22 % at the cell-edge and 1.5 % at the cell-center. A comparison of the different combining techniques used at different user locations is also provided, along with the mathematical formulations. Finally, the proposed model is compared with two other transmission models for performance evaluation. It is observed that the spectral efficiency achieved by an edge node with the proposed scheme is 5.3676 bits/s/Hz, compared to 0.756 bits/s/Hz and 1.0501 bits/s/Hz, attained with transmission schemes 1 and 2, respectively. [ABSTRACT FROM AUTHOR]

Due to increasingly strong and varied performance requirements, cooperative wireless communication systems today occupy a prominent place in both academic research and industrial development. The technological and economic challenges for future sixth-generation (6G) wireless systems are considerable, with the objectives of improving coverage, data rate, latency, reliability, mobile connectivity and energy efficiency. Over the past decade, new technologies have emerged, such as massive multiple-input multiple-output (MIMO) relay systems, intelligent reflecting surfaces (IRS), unmanned aerial vehicular (UAV)-assisted communications, dual-polarized (DP) antenna arrays, three dimensional (3D) polarized channel modeling, and millimeter-wave (mmW) communication. The objective of this paper is to provide an overview of tensor-based MIMO cooperative communication systems. Indeed, during the last two decades, tensors have been the subject of many applications in signal processing, especially for digital communications, and more broadly for big data processing. After a brief reminder of basic tensor operations and decompositions, we present the main characteristics allowing to classify cooperative systems, illustrated by means of different architectures. A review of main codings used for cooperative systems is provided before a didactic and comprehensive presentation of two-hop systems, highlighting different tensor models. In a companion paper currently in preparation, we will show how these tensor models can be exploited to develop semi-blind receivers to jointly estimate transmitted information symbols and communication channels. [ABSTRACT FROM AUTHOR]

The L-band digital aeronautical communication system (LDACS) is one of the candidate technologies for future broadband digital aeronautical communications, utilizing the unused L-band spectrum between distance measuring equipment (DME) channels. However, the higher signal power of DME complicates LDACS implementation. This paper proposes an advanced DME mitigation approach for the LDACS, integrating joint direction of arrival (DOA) estimation with adaptive beamforming techniques. The proposed method begins by exploiting the cyclostationary characteristics of signals, accurately obtaining the preliminary direction of the LDACS signal using the Cyclic-MUSIC method. Subsequent precise steering vectors (SVs) are selected through Capon spectrum search, followed by the reconstruction of the interference plus noise covariance matrix (INCM). Using the obtained SV and INCM, the weight vector is calculated and beamforming is performed. Simulation results validate that the proposed method not only accurately estimates the direction of LDACS signal but also efficiently mitigates DME interference, demonstrating a superior performance and reduced algorithmic complexity, even in scenarios with lower signal-to-noise ratios (SNRs) and the presence of DOA estimation errors. Additionally, the proposed method achieves a low bit error rate (BER), further validating its ability to ensure communication quality and enhance the reliability of LDACS. [ABSTRACT FROM AUTHOR]

Voltage and current measurements in high-voltage substations are fundamental for stable operation. Conventional instrument transformers (ITs) face challenges in gas-insulated switchgears (GISs), such as size, weight, accuracy limitations, and behavioral instability at abnormal voltages and currents. Non-conventional instrument transformers (NCITs) have emerged to address these issues, complying with International Electrotechnical Commission (IEC) standards and providing millivolt-level signals, enabling downsizing of GIS bays. The transition to digital substations, as mandated by IEC 61850-9-2, requires a shift from the conventional 110 V/5 A outputs to levels ranging from millivolts to volts. Electronic instrument transformers (EITs), compliant with the IEC 60044-7 and 8 standards, offer alternatives to conventional ITs with smaller sizes and wider frequency ranges. However, issues remain with EITs, including limited adoption, the necessity of separate power sources, and susceptibility to electromagnetic interference. Recent standards, transitioning to IEC 61869, focus on low-power instrument transformers (LPITs). Low-power voltage transformers (LPVTs) and low-power current transformers (LPCTs), designed with passive components, present potential solutions by directly connecting to merging units (MUs) for digital signal transmission. This review outlines the current status of various IT standards, covering conventional ITs, EITs based on IEC 60044-7 and 8, and LPITs based on IEC 61869-10 and 11. Advancements in sensor technology relevant to these standards are also explored. The paper provides insights into the evolving landscape of instrument transformers, addressing challenges and offering potential pathways for future developments in digital substations. [ABSTRACT FROM AUTHOR]

The hybrid in-band on-channel (IBOC) transmission system, which serves as a digital audio radio scheme, facilitates the simultaneous transmission of analog FM and digital audio. In IBOC systems, broadcasters transmit signals within allocated channel bands, posing a challenge in bandwidth allocation for digital audio transmission. To address this, cooperative game theory, supported by the bankruptcy game and Shapley's value, is proposed as a strategy to optimize bandwidth allocation at each node or station. This approach considers service demand, the number of stations, and radio channel conditions in the FM band. The paper presents a scenario under saturated traffic conditions to evaluate the degree of optimization achievable using the Shapley value under clearly defined traffic and channel conditions. The results indicate that cooperative game theory, with the support of the Shapley value, offers an excellent alternative for optimizing bandwidth in digital broadcasting over IBOC in FM, with potential applicability in other digital radio broadcasting systems. [ABSTRACT FROM AUTHOR]

Quantum technologies promise major advances in different areas. From computation to sensing or telecommunications, quantum implementations could bring significant improvements to these fields, arousing the interest of researchers, companies, and governments. In particular, the deployment of Quantum Key Distribution (QKD) networks, which enable the secure dissemination of cryptographic keys to remote application entities following Quantum Mechanics Principles, appears to be one of the most attractive and relevant use cases. Quantum devices and equipment are still in a development phase, making their availability low and their price high, hindering the deployment of physical QKD networks and, therefore, the research and experimentation activities related to this field. In this context, this paper focuses on providing research stakeholders with an open-access testbed where it is feasible to emulate the deployment of QKD networks, thus enabling the execution of experiments and trials, where even potential network attacks can be analyzed, without the quantum physical equipment requirement, nor compromising the integrity of an already built QKD network. The designed solution allows users to automatically deploy, configure, and run a digital twin environment of a QKD network, offering cost-effectiveness and great flexibility in the study of the integration of quantum communications in the current network infrastructures. This solution is aligned with the European Telecommunications Standard Institute (ETSI) standardized application interface for QKD, and is built upon open-source technologies. The feasibility of this solution has been validated throughout several functional trials carried out in the 5G Telefónica Open Network Innovation Centre (5TONIC), verifying the service performance in terms of speed and discarded qubits when generating the quantum keys. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to the creation and application of confusion matrices for error pattern discovery in spellchecking for the Croatian language. The experimental dataset has been derived from a corpus of mistyped words and user corrections collected since 2008 using the Croatian spellchecker available at ispravi.me. The important role of confusion matrices in enhancing the precision of spellcheckers, particularly within the diverse linguistic context of the Croatian language, is investigated. Common causes of spelling errors, emphasizing the challenges posed by diacritic usage, have been identified and analyzed. This research contributes to the advancement of spellchecking technologies and provides a more comprehensive understanding of linguistic details, particularly in languages with diacritic-rich orthographies, like Croatian. The presented user-data-driven approach demonstrates the potential for custom spellchecking solutions, especially considering the ever-changing dynamics of language use in digital communication. [ABSTRACT FROM AUTHOR]

Theoretically, the spectral efficiency of in-band full-duplex underwater acoustic communications (IBFD-UWAC) is twice that of a half-duplex one. However, the actual achievable spectral efficiency of IBFD-UWAC is determined by the performance of the self-interference cancellation (SIC). In addition, the hostile underwater environment poses a challenge to the tracking performance of the SIC due to its complexity and variability. In this paper, we propose a digital SIC method based on the improved variable step-size least mean square (IVSS-LMS) algorithm where we modify the step-size adjustment criterion in the classical LMS filter and establishes a nonlinear relationship with the Sigmoid function to control the step-size using the instantaneous state error, thus improving the robustness and tracking performance of IVSS-LMS. Hardware-in-loop simulation (HLS) based on Simulink ® platform verifies the real-time implementability and effectiveness of the proposed IVSS-LMS algorithm. Furthermore, the sea trial results show that the digital SIC method based on the proposed algorithm can be implemented in real-time and the convergence speed, and steady-state performance are significantly improved. [ABSTRACT FROM AUTHOR]

The metaverse is emerging as a next-generation internet paradigm that will enhance human interaction and connectivity. Digital twinning, a fundamental strategy used in the metaverse, allows for the virtualization of real-world items, people, actions, and settings. A virtual world called the metaverse is built on a variety of technologies. Wireless communication is an important part of these technologies. In particular, wireless 6G communication can be essential for the growth of the metaverse. In line with the goal of achieving higher rates in the next-generation wireless network for the metaverse, in this paper, a novel conceptualization of intelligent reflecting surface (IRS)-assisted multiple-input single output-based wireless communication in physical world environments is proposed. More specifically, this paper proposes that in the physical world, the IRS-assisted communication between a communication network and users can be reflected in the metaverse through the virtual world (such as digital avatars and the virtual environment). In the simulations, the bit-error rate and spectral efficiency of the receiver terminal were performed and calculated in the metaverse engine for future consideration. [ABSTRACT FROM AUTHOR]

This paper presents findings from ongoing research that explores the ability to use Micro-Electromechanical Systems (MEMS)-based technologies and various digital communication protocols for earthquake early warning (EEW). The paper proposes a step-by-step guide to developing a unique EEW network architecture driven by a Software-Defined Wide Area Network (SD-WAN)-based hole-punching technology consisting of MEMS-based, low-cost accelerometers hosted by the general public. In contrast with most centralised cloud-based approaches, a node-level decentralised data-processing is used to generate warnings with the support of a modified Propagation of Local Undamped Motion (PLUM)-based EEW algorithm. With several hypothetical earthquake scenarios, experiments were conducted to evaluate the system latencies of the proposed decentralised EEW architecture and its performance was compared with traditional centralised EEW architecture. The results from sixty simulations show that the SD-WAN-based hole-punching architecture supported by the Transmission Control Protocol (TCP) creates the optimum alerting conditions. Furthermore, the results provide clear evidence to show that the decentralised EEW system architecture can outperform the centralised EEW architecture and can save valuable seconds when generating EEW, leading to a longer warning time for the end-user. This paper contributes to the EEW literature by proposing a novel EEW network architecture. [ABSTRACT FROM AUTHOR]

Arguably, 5G and next-generation technology with its key features (specifically, supporting high data rates and high mobility platforms) make it valuable for coping with the emerging needs of medical healthcare. A 5G-enabled portable device receives the sensitive detection signals from the head imaging system and transmits them over the 5G network for real-time monitoring, analysis, and storage purposes. In terms of material, graphene-based flexible electronics have become very popular for wearable and healthcare devices due to their exceptional mechanical strength, thermal stability, high electrical conductivity, and biocompatibility. A graphene-based flexible antenna for data communication from wearable head imaging devices over a 5G network was designed and modelled. The antenna operated at the 34.5 GHz range and was designed using an 18 µm thin graphene film for the conductive radiative patch and ground with electric conductivity of 3.5 × 105 S/m. The radiative patch was designed in a fractal fashion to provide sufficient antenna flexibility for wearable uses. The patch was designed over a 1.5 mm thick flexible polyamide substrate that made the design suitable for wearable applications. This paper presented the 3D modelling and analysis of the 5G flexible antenna for communication in a digital care-home model. The analyses were carried out based on the antenna's reflection coefficient, gain, radiation pattern, and power balance. The time-domain signal analysis was carried out between the two antennas to mimic real-time communication in wearable devices. [ABSTRACT FROM AUTHOR]

Automatic modulation recognition is a necessary part of cooperative and noncooperative communication systems and plays an important role in military and civilian fields. Although the constellation diagram (CD) is an essential feature for different digital modulations, it is hard to be extracted under noncooperative complex communication environment. Frequency offset, especially the nonlinear frequency offset is a vital problem of complex communication environment, which greatly affects the extraction of traditional CD and the performance of modulation recognition methods. In the current paper, we propose an antifrequency offset constellation diagram (AFO-CD) extraction method, which combines the constellation diagram with a convolutional neural network (CNN). The proposed method indicates the change of the CD with time and enables us to suppress the influence of frequency offset efficiently. Additionally, a residual units-based classifier is designed for multiscale feature extraction and modulation classification. The experimental results demonstrate that the proposed method can effectively improve the recognition accuracy and has a good application prospect in the complex electromagnetic environment. [ABSTRACT FROM AUTHOR]

This paper discusses the results of simulations relating to the performances of turbo codes, low density parity check (LDPC) codes, and polar codes over an additive white Gaussian noise (AWGN) channel in the presence of inter symbol interference, denoting the disturbances that altered the original signal. To eliminate the negative effects of inter symbol interference (ISI), an equalizer was used at the level of the receiver. Practically, two types of equalizers were used: zero forcing (ZF) and minimum mean square error (MMSE), considering the case of perfect channel estimation and the case of estimation using the least square algorithm. The performance measure used was the modification of the bit error rate compared to a given signal to noise ratio; in this sense, the MMSE equalizer offered a higher performance than the ZF equalizer. The aspect of channel equalization considered here is not novel, but there have been very few works that dealt with equalization in the context of the use of turbo codes, especially LDPC codes and polar codes for channel coding. In this respect, this research can be considered a contribution to the field of digital communications. [ABSTRACT FROM AUTHOR]

The technology that is most likely to change the corporate world in the next ten years is not social networks, Big Data, cloud computing, robots, not even artificial intelligence, but blockchain. 10.3390/sym14102167 5 Huang D.-C., Liu L.-C., Deng Y.-Y., Chen C.-L., Huang D.-C., Liu L.-C., Deng Y.-Y., Chen C.-L. An artwork rental system based on blockchain technology. Using blockchain technology, in an environment where there is no central authority, under the premise of preventing the tampering of artwork-related information, ensures that the detailed information of the art-work information is shared. 10.3390/sym14020237 4 Huang D.-C., Liu L.-C., Deng Y.-Y., Chen C.-L. A Digital Media Subscription Management System Combined with Blockchain and Proxy Re-Encryption Mechanisms. [Extracted from the article]

Featured Application: A deep learning application to improve speech clarity in digital audio affected by environmental noises, showing potential for enhancing real-time streaming communication in noisy settings. This paper presents an approach to enhancing the clarity and intelligibility of speech in digital communications compromised by various background noises. Utilizing deep learning techniques, specifically a Variational Autoencoder (VAE) with 2D convolutional filters, we aim to suppress background noise in audio signals. Our method focuses on four simulated environmental noise scenarios: storms, wind, traffic, and aircraft. The training dataset has been obtained from public sources (TED-LIUM 3 dataset, which includes audio recordings from the popular TED-TALK series) combined with these background noises. The audio signals were transformed into 2D power spectrograms, upon which our VAE model was trained to filter out the noise and reconstruct clean audio. Our results demonstrate that the model outperforms existing state-of-the-art solutions in noise suppression. Although differences in noise types were observed, it was challenging to definitively conclude which background noise most adversely affects speech quality. The results have been assessed with objective (mathematical metrics) and subjective (listening to a set of audios by humans) methods. Notably, wind noise showed the smallest deviation between the noisy and cleaned audio, perceived subjectively as the most improved scenario. Future work should involve refining the phase calculation of the cleaned audio and creating a more balanced dataset to minimize differences in audio quality across scenarios. Additionally, practical applications of the model in real-time streaming audio are envisaged. This research contributes significantly to the field of audio signal processing by offering a deep learning solution tailored to various noise conditions, enhancing digital communication quality. [ABSTRACT FROM AUTHOR]

The simultaneous transmit and receive (STAR) array system provides higher radiation gain and data rate compared to traditional radio system. Because of the various mutual couplings between each pair of transmit and receive elements, it is a great challenge to suppress the incident self-interference power at multiple receive elements, which is usually much higher than the desired signal of interest (SoI) power and causes the saturation of receive links and the distortion of the digital SoI. In this paper, we propose an optimized method for transmit beamforming based on radiation power constraints and transmit power control. Through adaptive transmit beamforming, high isolation between the transmit array and each receive link is achieved, minimizing the self-interference power at each receiving element. This method effectively reduces the self-interference power, avoiding distortion of the SoI digital signal caused by limited-bit analog-to-digital converters (ADCs). Simulation results demonstrate that this optimized transmit beamforming method can achieve more than 100 dB effective isotropic isolation (EII) on a 32-element two-dimensional phased array designed in HFSS, reducing the maximum incident self-interference power at the receive channels by approximately 35 dB, while effectively controlling the attenuation of the transmit gain. We also present the advantages in receive subarray isolation and lower ADCs digits under the transmit ABF method. [ABSTRACT FROM AUTHOR]

A totem-pole bridgeless boost converter is one of the most promising topologies for the power factor correction (PFC) stage in high-power applications due to its high efficiency and small number of components. However, due to the totem-pole structure of the field-effect transistor (FET), very high switching loss occurs via the reverse recovery current of the body diode. To solve these problems, critical mode (CRM) control is a good solution to achieve the valley switching technique. With valley switching of CRM control, the switching loss decreases drastically with decreasing turn-on voltage. But, although the CRM control enables valley switching, it is hard to make an exact valley switching control with general zero-voltage detection circuits. In addition, when a frequency limitation scheme is applied to prevent a very high frequency, the switch can operate with hard switching at the boundary of the frequency limitation. Furthermore, the CRM boost PFC has a low PF and high total harmonic distortion (THD) under light-load conditions due to the large negative current resulting from resonance between the inductor and parasitic capacitance. It becomes worse at near-zero input voltage since the resonance current becomes larger near zero-input voltage. Therefore, in this paper, a totem-pole bridgeless boost PFC converter with high efficiency, high PF, and low THD is developed using TMS320F28377 by Texas Instruments. Based on the basic digital structure of the totem-pole bridgeless converter, the proposed controls help with exact valley switching, PF and THD improvement, and frequency limitation. The prototype converter is verified using 90–264 VAC input voltages and 450 V/3.3 kW output specifications. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) are an emerging industrial technology for monitoring physiological data. These networks employ medical wearable and implanted biomedical sensors aimed at improving quality of life by providing body-oriented services through a variety of industrial sensing gadgets. The sensors collect vital data from the body and forward this information to other nodes for further services using short-range wireless communication technology. In this paper, we provide a multi-aspect review of recent advancements made in this field pertaining to cross-domain security, privacy, and trust issues. The aim is to present an overall review of WBAN research and projects based on applications, devices, and communication architecture. We examine current issues and challenges with WBAN communications and technologies, with the aim of providing insights for a future vision of remote healthcare systems. We specifically address the potential and shortcomings of various Wireless Body Area Network (WBAN) architectures and communication schemes that are proposed to maintain security, privacy, and trust within digital healthcare systems. Although current solutions and schemes aim to provide some level of security, several serious challenges remain that need to be understood and addressed. Our aim is to suggest future research directions for establishing best practices in protecting healthcare data. This includes monitoring, access control, key management, and trust management. The distinguishing feature of this survey is the combination of our review with a critical perspective on the future of WBANs. [ABSTRACT FROM AUTHOR]

In the era of digital communication and social networking, the authenticity and integrity of personal social network profiles have become crucial for establishing trust and ensuring secure interactions. Existing methods often suffer from vulnerabilities like password theft, identity impersonation, and data breaches. To overcome these challenges, the paper introduces a new steganography method as a robust solution, leveraging the concept of hiding information within a seemingly innocent digital cover image. The proposed methodology involves imperceptible authentication and embedding profile information within a profile image or any other uploaded pictures in a profile's timeline. This scheme is developed using a shell matrix, DNA encoding and absolute moment block truncation coding (AMBTC) compression. A shell matrix is used for concealing the private information and AMBTC compression is applied to compress large data files into smaller ones, which can speed up the network transmission of compressed code. By exploiting the redundancy in image data, the authentication data are embedded in a manner that is indistinguishable to human observers. To estimate the effectiveness of the proposed approach, wide experiments were conducted using real-world social network profiles. The results demonstrate the ability of the proposed technique to successfully embed and extract authentication data while maintaining the profile photo's visual appearance. [ABSTRACT FROM AUTHOR]

The modernization of logistics through the use of Wireless Sensor Network (WSN) Internet of Things (IoT) devices promises great efficiencies. Sensor devices can provide real-time or near real-time condition monitoring and location tracking of assets during the shipping process, helping to detect delays, prevent loss, and stop fraud. However, the integration of low-cost WSN/IoT systems into a pre-existing industry should first consider security within the context of the application environment. In the case of logistics, the sensors are mobile, unreachable during the deployment, and accessible in potentially uncontrolled environments. The risks to the sensors include physical damage, either malicious/intentional or unintentional due to accident or the environment, or physical attack on a sensor, or remote communication attack. The easiest attack against any sensor is against its communication. The use of IoT sensors for logistics involves the deployment conditions of mobility, inaccesibility, and uncontrolled environments. Any threat analysis needs to take these factors into consideration. This paper presents a threat model focused on an IoT-enabled asset tracking/monitoring system for smart logistics. A review of the current literature shows that no current IoT threat model highlights logistics-specific IoT security threats for the shipping of critical assets. A general tracking/monitoring system architecture is presented that describes the roles of the components. A logistics-specific threat model that considers the operational challenges of sensors used in logistics, both malicious and non-malicious threats, is then given. The threat model categorizes each threat and suggests a potential countermeasure. [ABSTRACT FROM AUTHOR]

As one of the main candidates for future civil aviation communications systems, the L-band digital aeronautical communication system (L-DACS) is expected to achieve secure and reliable transmission. Due to the broadcasting nature of air–ground wireless links, the L-DACS has the risk of being intercepted by malicious eavesdroppers, which negatively affects aviation communication security. In addition, because the spectrum of the L-DACS overlaps with the aviation distance measuring equipment (DME), the pulse interference caused by the DME signal may lead to the wireless link being more fragile and susceptible to wiretapping. In this paper, with a focus on enhancing wireless transmission security, we propose a comprehensive physical layer security (PLS) method for the L-DACS. The key to the proposed PLS method is restraining the transmission of the eavesdropper by injecting artificial noise into the transmitted signal while improving the transmission of the legitimate receiver through the adoption of pulse interference mitigation. First, to characterize the L-DACS in the secure scene, we derive the signal-to-interference-plus-noise ratio (SINR) of the legitimate receiver and any potential eavesdropper by constructing equivalent noise. Next, from the perspective of the information theory, we derive the closed form of the secrecy capacity of the L-DACS by employing the proposed PLS methods with three kinds of nonlinear interference mitigation: including ideal pulse blanking, peak threshold-based pulse blanking, and peak threshold-based pulse clipping. Finally, we compare and analyze different ways to enhance the secrecy capacity of the proposed PLS method using various interference mitigation methods. [ABSTRACT FROM AUTHOR]

With the advancement of information technology, the security of digital images has become increasingly important. To ensure the integrity of images, a novel color image-encryption algorithm based on extended DNA coding, Zig-Zag transform, and a fractional-order laser system is proposed in this paper. First, the dynamic characteristics of the fractional-order laser chaotic system (FLCS) were analyzed using a phase diagram and Lyapunov exponent spectra. The chaotic sequences generated by the system were used to design image-encryption algorithms. Second, a modified Zig-Zag confusing method was adopted to confuse the image. Finally, in the diffusion link, the DNA encoding scheme was extended to allow for a greater number of DNA encoding rules, increasing the randomness of the matrix and improving the security of the encryption scheme. The performance of the designed encryption algorithm is analyzed using key space, a histogram, information entropy, correlation coefficients, differential attack, and robustness analysis. The experimental results demonstrate that the algorithm can withstand multiple decryption methods and has strong encryption capability. The proposed novel color image-encryption scheme enables secure communication of digital images. [ABSTRACT FROM AUTHOR]

Granting smart device consumers with information, simply and quickly, is what drives quick response (QR) codes and mobile marketing to go hand in hand. It boosts marketing campaigns and objectives and allows one to approach, engage, influence, and transform a wider target audience by connecting from offline to online platforms. However, restricted printing technology and flexibility in surfaces introduce noise while printing QR code images. Moreover, noise is often unavoidable during the gathering and transmission of digital images. Therefore, this paper proposed an automatic and accurate noise detector to identify the type of noise present in QR code images. For this, the paper first generates a new dataset comprising 10,000 original QR code images of varying sizes and later introduces several noises, including salt and pepper, pepper, speckle, Poisson, salt, local var, and Gaussian to form a dataset of 80,000 images. We perform extensive experiments by reshaping the generated images to uniform size for exploiting Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Logistic Regression (LG) to classify the original and noisy images. Later, the analysis is further widened by incorporating histogram density analysis to trace and target highly important features by transforming images of varying sizes to obtain 256 features, followed by SVM, LG, and Artificial Neural Network (ANN) to identify the noise type. Moreover, to understand the impact of symmetry of noises in QR code images, we trained the models with combinations of 3-, 5-, and 7-noise types and analyzed the classification performance. From comparative analyses, it is noted that the Gaussian and Localvar noises possess symmetrical characteristics, as all the classifiers did not perform well to segregate these two noises. The results prove that histogram analysis significantly improves classification accuracy with all exploited models, especially when combined with SVM, it achieved maximum accuracy for 4- and 6-class classification problems. [ABSTRACT FROM AUTHOR]

An important development direction for the future of the automotive industry is connected and cooperative vehicles. Some functionalities in traffic need the cars to communicate with each other. In platooning, multiple cars driving in succession reduce the distances between them to drive in the slipstream of each other to reduce drag, energy consumption, emissions, and the probability of traffic jams. The car in front controls the car behind remotely, so all cars in the platoon can accelerate and decelerate simultaneously. In this paper, a system for vehicle-to-vehicle communication is proposed using modulated taillights for transmission and an off-the-shelf camera with CMOS image sensor for reception. An Undersampled Differential Phase Shift On–Off Keying modulation method is used to transmit data. With a frame sampling rate of 30 FPS and two individually modulated taillights, a raw data transmission rate of up to 60 bits per second is possible. Of course, such a slow communication channel is not applicable for time-sensitive data transmission. However, the big benefit of this system is that the identity of the sender of the message can be verified, because it is visible in the captured camera image. Thus, this channel can be used to establish a secure and fast connection in another channel, e.g., via 5G or 802.11p, by sending a verification key or the fingerprint of a public key. The focus of this paper is to optimize the raw data transmission of the proposed system, to make it applicable in traffic and to reduce the bit error rate. An improved modulation mode with smoother phase shifts is used that can reduce the visible flickering when data is transmitted. By additionally adjusting the pulse width ratio of the modulation signal and by analyzing the impact of synchronization offsets between transmitter and receiver, major improvements of the bit error rate (BER) are possible. In previously published research, such a system without the mentioned adjustments was able to transmit data with a BER of 3.46%. Experiments showed that with those adjustments a BER of 0.48% can be achieved, which means 86% of the bit errors are prevented. [ABSTRACT FROM AUTHOR]

With the rapid development of industrial digitalization and intelligence, there is an urgent need to accurately depict the physical world in digital space, and, in turn, regulate and optimize the behavior of physical entities based on massive data collection and analysis. As a technology that combines virtual space and physical space, digital twin can satisfy all of the above needs, and has attracted widespread attention. Due to the promising application prospects of digital twins, both academia and industry have launched research in this field, and related studies have been conducted from different perspectives. Accordingly, some articles summarizing the existing work have also been published, but they are all from a single perspective, lacking a systematic introduction and summary. Based on this, this paper conducts a comprehensive review of the existing work on digital twins from four perspectives: data, model, network and application, and strives to gain a better understanding of the development of the field from the physical to the virtual and back to the physical. Meanwhile, current research challenges and future directions for the development of digital twins are all discussed. [ABSTRACT FROM AUTHOR]