Advances In Communication Technologies Research Articles

Anomaly Detection in Smart Grid Application

The smart grid, a modernized electrical grid integrating advanced communication and information technologies, enables bidirectional flow of electricity and information, offering numerous benefits such as enhanced reliability, efficiency, and sustainability. However, the complexity and scale of the smart grid introduce new challenges, including the need for effective anomaly detection to ensure its secure and reliable operation. This research paper presents a comprehensive review of state-of-the-art anomaly detection techniques in smart grid applications. We explore various methodologies, including statistical methods, machine learning algorithms, and hybrid approaches, highlighting their strengths and limitations. Furthermore, we discuss the importance of accurate data preprocessing and feature selection to enhance anomaly detection performance. Through a comparative analysis, we demonstrate the effectiveness of different techniques in detecting anomalies in smart grid datasets. Our findings provide valuable insights for researchers and practitioners in developing robust anomaly detection systems for smart grid applications, ultimately contributing to the advancement of smart grid technology.

The application of artificial intelligence in Unmanned Underwater Vehicle communication systems

Artificial Intelligence (AI) has the potential to significantly enhance decision-making techniques in Unmanned Underwater Vehicle (UUV) communication systems by utilizing real-time sensor feedback and environmental data. To address the challenges posed by high data volume and error rates, we propose an advanced methodology known as the Variational Deep Network (VDN). This method integrates Variational Modal Decomposition (VMD) algorithms with the Deep Convolutional Neural Network (DCNN) to create a realizable AI decision-making system. In conjunction with the DCNN and VMD algorithms, our research focuses on accurately identifying received signals under data constraints. Considering the spectral efficiency and robustness, the Filter-Bank Multicarrier (FBMC) technology have been employed in this paper. Our results demonstrate that the VDN significantly reduces the volume of data needed for neural network training and effectively recognizes FBMC signals. These insights confirm the potential of VDN decision-making systems to drive advancements in UUV communication technologies.

Improving care pathways through BPM and telemedicine: an Italian study

PurposeThe future increase of chronic diseases in the world requires new challenges in the health domain to improve patients' care from the point of view of the organizational processes, clinical pathways and technological solutions of digital health. For this reason, the present paper aims to focus on the study and application of well-known clinical practices and efficient organizational approaches through an innovative model (TALIsMAn) to support new care process redesign and digitalization for chronic patients.Design/methodology/approachIn addition to specific clinical models employed to manage chronic conditions such as the Population Health Management and Chronic Care Model, we introduce a Business Process Management methodology implementation supported by a set of e-health technologies, in order to manage Care Pathways (CPs) digitalization and procedures improvement.FindingsThis study shows that telemedicine services with advanced devices and technologies are not enough to provide significant changes in the healthcare sector if other key aspects such as health processes, organizational systems, interactions between actors and responsibilities are not considered and improved. Therefore, new clinical models and organizational approaches are necessary together with a deep technological change, otherwise, theoretical benefits given by telemedicine services, which often employ advanced Information and Communication Technology (ICT) systems and devices, may not be translated into effective enhancements. They are obtained not only through the implementation of single telemedicine services, but integrating them in a wider digital ecosystem, where clinicians are supported in different clinical steps they have to perform.Originality/valueThe present work defines a novel methodological framework based on organizational, clinical and technological innovation, in order to redesign the territorial care for people with chronic diseases. This innovative ecosystem applied in the Italian research project TALIsMAn is based on the concept of a continuum of care and digitalization of CPs supported by Business Process Management System and telemedicine services. The main goal is to organize the different socio-medical activities in a unique and integrated IT system that should be sustainable, scalable and replicable.

Information and communication technology in smart agriculture: A scientometric review

SummaryAgriculture has been affected by several global trends raising the concern for food security. Agri‐food demands are amplifying due to the ever‐escalating population numbers. Thus, the notion of extending the use of smart innovative technology in managing agricultural practices has emerged rapidly over the last decade. Technological innovations have contributed significantly to shape modern agriculture as smart agriculture. Smart agriculture unfolds various benefits such as increased production, real time data and production insights and remote monitoring. The rising advancements in Information and Communication Technologies (ICT) have paved the way for researchers to use these technologies in managing agricultural practices, leading to greater benefits for farmers and society. These innovations are the key to establishing agriculture as a research discipline. The purpose of this article is to conduct a scientometric analysis to study the structure and evolution of research activities in the field of smart agriculture. The scientometric analysis aims to empirically map the scientific knowledge and identify any possible challenges in the field. This study performs elementary analysis to study publication growth over the years, impact analysis to assess the leading journals, authors, and countries, and articles analysis for findings patterns among the citations over the years and among the keyword‐based clusters. There has been a considerable increase of more than 200% in the number of publications from 2011 to 2022. However, around 60% of authors have contributed with a single publication. The findings of the study reveal the research trends and hot topics for future research fronts. Deep learning, digital agriculture, object detection, blockchain, and semantic segmentation have been identified as trending topics in smart agriculture. Comprehensively, an intellectual view of the agriculture domain is presented as a scientific field in this article.

Low-Cost, Open-Source, Experimental Setup Communication Platform for Emergencies, Based on SD-WAN Technology

The rapid advancement of communication technologies underscores the urgent need for robust and adaptable emergency communication systems (ECSs), particularly crucial during crises and natural disasters. Although network-based ECSs have been extensively studied, integrating open-source technologies, such as software-defined wide area networks (SD-WAN) with private long-term evolution (LTE) base stations, is a relatively unexplored domain. This study endeavors to fill this gap by introducing an experimental ECS platform that utilizes a hybrid network, incorporating a VoIP network to enhance open-source and on-premises communications in targeted areas. Our hypothesis posits that a hybrid network architecture, combining SD-WAN and private LTE, can substantially improve the reliability and efficiency of ECSs. Our findings, supported by the open-source OMNeT++ simulator, illuminate the enhanced communication reliability of the network. Moreover, the proposed platform, characterized by autonomous wireless 4G/LTE base stations and an Asterisk VoIP server, demonstrates improved quality of service (QoS) and quality of experience (QoE), with minimal data loss. This research not only has immediate practical applications but also bears significant implications for the development of cost-effective, open-source communication networks, optimized for emergencies, critical infrastructure, and remote areas.

Joint Metric Learning-Based Class-Specific Representation for Image Set Classification.

With the rapid advances in digital imaging and communication technologies, recently image set classification has attracted significant attention and has been widely used in many real-world scenarios. As an effective technology, the class-specific representation theory-based methods have demonstrated their superior performances. However, this type of methods either only uses one gallery set to measure the gallery-to-probe set distance or ignores the inner connection between different metrics, leading to the learned distance metric lacking robustness, and is sensitive to the size of image sets. In this article, we propose a novel joint metric learning-based class-specific representation framework (JMLC), which can jointly learn the related and unrelated metrics. By iteratively modeling probe set and related or unrelated gallery sets as affine hull, we reconstruct this hull sparsely or collaboratively over another image set. With the obtained representation coefficients, the combined metric between the query set and the gallery set can then be calculated. In addition, we also derive the kernel extension of JMLC and propose two new unrelated set constituting strategies. Specifically, kernelized JMLC (KJMLC) embeds the gallery sets and probe sets into the high-dimensional Hilbert space, and in the kernel space, the data become approximately linear separable. Extensive experiments on seven benchmark databases show the superiority of the proposed methods to the state-of-the-art image set classifiers.

Proactive ransomware prevention in pervasive IoMT via hybrid machine learning

Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT). These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem.

Dielectric, electrical and thermal properties of Sodium Molybdate-hexagonal Boron Nitride composites enabled by cold sintering

Advances in 5G and 6G communication technologies and their applications are in part limited by the capabilities of current electronic packaging materials, as expanded bandwidth is a pressing need for novel dielectric substrates capable of co-firing into packages and devices, characterized by low dielectric loss and enhanced thermal conductivity. This investigation provides further characterization in the dielectric, electrical and thermal conductivity properties over previously reported cold sintered composite of Sodium Molybdate Na2Mo2O7 (NMO) with hexagonal Boron Nitride (hBN), such as relative permittivity (εr) and dielectric loss (tan δ) values at high frequencies of 75–110 GHz, electrical resistivity (ρ) fitting to percolation theory, Weibull statistical analysis of electrical breakdown strength (Eb) and its anisotropic thermal conductivity (κ) influenced by the filler's crystal structure. The cold sintered composites were systematically characterized with respect to filler volume fraction, temperature, and frequency. The findings in this analysis position engineered composites as a promising alternative for microwave substrate materials, with using a densification method that limits interactions and maximizes densification, hence the demonstration with cold sintering.

Application of the full consistency method (FUCOM) - Cosine similarity framework in 5G infrastructure investment planning: An approach for telecommunication quality improvements

In the rapidly evolving telecommunications landscape, the shift towards advanced communication technologies marks a critical milestone. This transition promises to revolutionize connectivity by enabling seamless data downloads, high-quality video streaming, and instant access to applications. However, adapting to these advanced technologies poses significant challenges for infrastructure expansion, requiring innovative investment and deployment strategies. These strategies aim not only to enhance service quality but also to ensure extensive network coverage. To address the need for systematic planning in infrastructure investment, this paper presents a novel methodology that combines the Full Consistency Method (FUCOM) with cosine similarity analysis. This integrated approach effectively prioritizes service areas for the deployment of 5G technology, emphasizing the importance of detailed planning in mobile strategy development. By leveraging FUCOM to determine the weights of various criteria and employing cosine similarity analysis to rank service areas, the methodology facilitates efficient resource allocation and service quality enhancements. Empirical validation using real data from a Turkish telecommunications company confirmed the effectiveness of the proposed algorithm. The results indicate that this integrated approach can significantly advance the telecommunications industry by providing essential insights for companies seeking to improve service quality amidst the transition to 5G and beyond. The successful implementation of the proposed algorithm demonstrates its effectiveness in addressing the challenges faced by telecommunications companies and underscores the importance of a data-driven approach in strategic decision-making and resource allocation. Furthermore, the findings suggest that the integrated FUCOM and cosine similarity analysis approach can offer a valuable tool for telecommunications companies worldwide, offering a systematic method for prioritizing infrastructure investments and enhancing network performance.

K-WISC-V Processing Speed Index Analysis to Verify the Effectiveness of ADHD Symptom Improvement Using Pediatric Digital Content

The most common treatment approach for children diagnosed with attention deficit hyperactivity disorder (ADHD) involves drug therapy; however, persuading parents and motivating children in the early stages of treatment is challenging. Consequently, there is a growing interest among parents of children with ADHD in non-drug therapies. Moreover, recent advancements in information and communication technology have increased the accessibility of digital treatments for ADHD and non-drug therapy content. However, some challenges persist in confirming specific and objective effects. In this retrospective study, we developed game-type digital therapy content for children aged 6–16 years and monitored improvements in ADHD symptoms using the K-WISC-V subtest processing speed index. The analysis revealed that the rate of change in the sum of converted scores on the 14th day was 0.64% lower in the experimental group compared with the control group; however, on the 28th day, the rate of change increased by 6.93%. This suggests that the supplementary use of Neuroworld DTx therapy proved effective for visual enhancement. Furthermore, improvements were observed in visual discrimination, short-term memory, and motor cooperation abilities. Consequently, game-based digital content is an effective adjunctive therapy for children dealing with ADHD.

Service blockage on the downlink in large-scale satellite optical networks: a multi-downlink scheduling for routing selection

Over years of space laser communication technology advances, satellite optical networks (SONs) have emerged as a pivotal component in 6 G networks. Satellite services are transmitted from the global view, undergoing transmission through SONs, and being downloaded to the targeted areas. However, the transmission capacity of satellites passing through the areas where users are concentrated may be insufficient to download services transmitted worldwide. This problem exists in various kinds of satellite networks and may cause a large amount of service congestion. In this paper, we propose a multi-downlink delivery routing selection (MDD-RS) strategy to study the total utilization of transmission capacity of SONs. We construct an integer linear programming (ILP) model to establish an optimal case study for minimal network capacity occupation. Also, we design an online option, MDD-RS heuristic algorithm, dynamically calculating path routes, considering bandwidth allocation and resource constraints. A comparative analysis against the conventional single-downlink scheme reveals superior performance of the MDD-RS heuristic algorithm, with a reduction in blocking probability of 0.129 and an improvement in bandwidth utilization of 0.032.

AI Call Assistant – Extractive Summarization of Call Recordings

In today's fast-paced business landscape, effective communication is a key for success. With the increasing volume of phone calls in various industries, there is a growing need for efficient call management and analysis tools. The AI Call Assistant project aims to address this need by leveraging artificial intelligence (AI) technology to summarize call recordings, enhancing productivity and decision-making processes. The core functionality of this project revolves around its ability to process audio data from call recordings and extract key insights and information. Using advanced natural language processing (NLP) techniques, the system identifies important topics, sentiments, and actions discussed during the call. The call transcripts attained from call recording pose unique challenges that are not adequately addressed by most open-source automatic text summarizers. This project aims to contribute to the field of artificial intelligence by providing efficient and effective methods to recognize call recording and summarize, offering a valuable tool for extracting insights from spoken audio efficiently.Top of Form This research aligns with the broader fields of natural language processing (NLP), machine learning, and artificial intelligence (AI), particularly in the domain of speech recognition and understanding. It also intersects with communication technology and data analytics, focusing on optimizing call management processes and extracting actionable insights from conversational data. Moreover, it aligns with the goal of improving efficiency and decision-making in various industries through the application of AI-driven solutions. Overall, this project offers a novel approach to improving call management processes through AI-driven extractive summarization, contributing to advancements in natural language processing and communication technologies. Key Words: Artificial Intelligence, BERT, Textual Summarization, Transformers, Natural Language Processing, Speech Recognition, Speech-To-Text.

Advances in carbon fiber-based electromagnetic shielding materials: Composition, structure, and application

With the swift advancement of wireless communication technology, there is increasing concern regarding the potential hazards posed by electromagnetic radiation emitted from various electronic devices. Carbon fiber (CF)-based composites have emerged as a vital component in structure function integrated electromagnetic interference (EMI) shielding research, attributable to their exceptional electrical conductivity and mechanical properties. This review summarizes recent research advances in CF-based composites for EMI shielding. Initially, it explores the influence of factors such as the geometric form of CFs on their EMI shielding properties. Since CF is inefficient when used alone for EMI shielding, so additional EMI filler materials are often introduced. Therefore, delves into an in-depth analysis of the reinforcement mechanisms provided by the incorporated second-phase materials, as well as the smart structural designs within the CF-based composites. On this basis, the applications of CF-based EMI shielding materials in the fields of thermal conductivity, flexible wearable technology, and aerospace are reviewed. Additionally, the review offers a perspective on the future development trends and prospects of CF-based composites in the field of EMI shielding based on the current research status.

ANALISIS DAN PERANCANGAN SISTEM PENDAFTARAN PENGAJUAN DANA KERJA LAPANGAN DI YAYASAN CAPPA KEADILAN EKOLOGI BERBASIS WEBSITE

In this era of globalization where advances in information and communication technology (ICT) are growing rapidly and the use of IT in the field of organizations. As with the CAPPA Foundation's Ecological Justice Systems, information systems have. An important role in an institution or organization to help effectiveness and performance. This designed system is intended to process financial data, especially in the field of travel transportation money.The main purpose of creating a financial information system is to process financial data quickly and accurately, because the manual system that is still used causes several problems, such as not producing 100% accurate data and the travel transportation financial data processing system.Manual administration also takes a long time. Based on the problem analysis, the author tries to design a transportation financial information system that is expected to support financial data reporting so that the resulting report is faster and more accurate

Rate splitting with semantics as a generalized multi-access framework for intelligent reflecting surfaces

The rapid advancement of modern communication technologies necessitates the development of generalized multi-access frameworks and the continuous implementation of rate splitting, augmented with semantic awareness. This trend, coupled with the mounting pressure on wireless services, underscores the need for intelligent approaches to radio signal propagation. In response to these challenges, intelligent reflecting surfaces (IRS) have garnered significant attention for their ability to control data transmission systems in a goal-oriented and dynamic manner. This innovation is largely attributed to equitable resource allocation and the dynamic enhancement of network performance. However, the integration of rate-splitting multi-access (RSMA) architecture with semantic considerations imposes stringent requirements on IRS platforms to ensure seamless connectivity and broad coverage for a diverse user base without interference. Semantic communications hinge on a knowledge base-a centralized repository of integrated information related to the transmitted data-which becomes critically important in multi-antenna scenarios. This article proposes a novel set of design strategies for RSMA-IRS systems, enabled by reconfigurable intelligent surface synergizing with semantic communication principles. An experimental analysis is presented, demonstrating the effectiveness of these design guidelines in the context of Beyond 5G/6G communication systems. The RSMA-IRS model, infused with semantic communication, offers a promising solution for future wireless networks. Performance evaluations of the proposed approach reveal that, despite an increase in the number of users, the delay in the RSMA-IRS framework incorporating semantics is 2.94% less than that of a RSMA-IRS system without semantic integration.

Virtual Reality for Spatial Planning and Emergency Situations: Challenges and Solution Directions

The notion of the smart city involves embedding Industry 4.0 technologies to improve the lives of inhabitants in urban environments. Within this context, smart city data layers (SCDLs) concern the integration of extra tiers of information for the purposes of improving communication potential. Under the Industry 4.0 technology grouping, advanced communication technologies, such as virtual reality (VR), further the opportunities to model, recreate, evaluate and communicate scenarios that potentially improve citizens lives at multiple SCDL scales in a smart cities context. The use of added contextual information in SCDLs is of special interest for emergency planning situations at the building scale. In this research, a literature review to understand the current approaches for the use, development and evaluation of VR applications in the context of emergency planning was conducted. The results indicated four main categories of relevant challenges for these types of applications, for which recommendations and a roadmap for VR development are presented. In total, the study identified 10 commonly occurring challenges (e.g., optimization and discomfort) and 19 solution directions (e.g., model construction and spatial directions) in related articles when considering the development of VR for spatial planning and emergency situations.

Digital divide, gender gap, and entrepreneurial orientation: How to foster technology adoption among Pakistani higher education students?

Advances in Information and Communication Technology (ICT) bear the promise of multiple benefits, including individual and entrepreneurial empowerment. The prospect of harnessing ICT as a means of creating growth and development opportunities is particularly appealing in developing countries. Still, several factors, including digital illiteracy and gender inequality, hamper the possibility of effective and universal utilization of these opportunities, in extreme cases leading to digital divide, gender-based exclusion, poverty, and precarity. Using this observation as a springboard, the objective of this paper is to conceptualize and quantify the digital divide-gender-entrepreneurial orientation nexus as it emerges in the perception of Pakistan higher education students. Using a quantitative approach, this study identifies the impact of digital skills (and thus the knowledge inequalities that can determine the potential digital gap) on entrepreneurship-related technology adoption (as measured through Unified Theory of Acceptance and Use of Technology, UTAUT), whereby gender is treated as a moderator. The empirical findings, based on Structure Equation Modelling (SEM) and Multiple Group Analysis (MGA), show that digital skills and perceptions of technology capabilities have a substantial impact on the desire to use ICT in entrepreneurial activities. The influence of technology adoption intent on the development of entrepreneurial orientation is also confirmed. This paper adds to the broader debate on gender, digital divide and entrepreneurship. Importantly, by identifying some of the drivers behind students’ technology adoption in a developing nation such as Pakistan, filled with technological and economic unbalances, the conclusions of this study will have conceptual, managerial, and policymaking applications, notably in terms of technological access, digital literary, and gender-based exclusion.

Can I Trust What You Say about the Smart Speaker? Effects of AI Anxiety and Biased Assimilation on Blog Comment Credibility

ABSTRACT Advancements in Internet and communication technologies have facilitated the proliferation of product reviews and commentary pertaining to smart speakers. This study investigated the impacts of uncivil comments on blog comment credibility. Results show that the civil opinion commentary appears to be more credible than uncivil commentary. The interaction between civil opinion and low AI anxiety generates more blog review credibility than the interaction between uncivil opinion and low AI anxiety. Finally, we identify moderating effects of AI anxiety and provide deeper insights into how the interaction of comment civility and AI anxiety shape the credibility assessments of blog reviews.

Hazardous Chemicals Logistics Internet of Vehicles Based on Encryption Algorithm

With the advancement of information and communication technology, vehicles role in people's lives is not just for transportation but also equipment for the carrier of mobile communication. The new direction is provided by the Internet for the automobiles development and upgrading. Internet of Vehicles (IoVs) includes smart vehicles, Autonomous Vehicles (AVs) as well as roadside units (RSUs) that communicate for providing the enhanced transportation services such as high traffic efficiency and reduced congestion and accidents. In the hazardous chemical logistics sector, the integration of Internet of Vehicles (IoVs) introduces significant security, privacy, and trust challenges. Vulnerabilities to cyberattacks, such as hacking and data manipulation, threaten the integrity of sensitive information regarding hazardous cargo, while concerns about location privacy and data minimization arise due to the tracking of vehicle movements. Ensuring authentication, authorization, and compliance with regulatory standards is essential for building trust within the IoV ecosystem, as any compromise in supply chain integrity could lead to safety hazards or legal liabilities. Robust encryption algorithms like AES and RSA play a crucial role in securing data transmission, but proper implementation and key management practices are necessary to prevent cryptographic weaknesses. Addressing these issues comprehensively is vital for safeguarding the transportation of hazardous chemicals and ensuring the safety of both the environment and the public. IoVs, suffer from security, privacy and trust issues. In order to solve the problem of hazardous chemical logistics of encryption algorithm, the author proposed a research on the information security of the Internet of Vehicles. Firstly, the information characteristics of dangerous chemicals vehicles are analyzed, then analyze the Data Encryption Standard (DES) algorithm. Advanced Encryption Standard (AES) algorithm is analyzed in the symmetric cryptosystem and then RSA algorithm is analyzed in the public key cryptosystem. Finally, the performance of the algorithm and the characteristics of vehicle information are comprehensively analyzed. The security of Internet of Vehicle's data transmission is efficiently improved by the proposed method.

Integrated driving risk surrogate model and car-following behavior for freeway risk assessment

Drivers’ risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.