Network Applications Research Articles

Artificial Neural Network Application in Construction and the Built Environment: A Bibliometric Analysis

Over the past decade, there has been a dramatic increase in the use of various technologies in the Architecture, Engineering, and Construction sector. Artificial intelligence has played a significant role throughout the different phases of the design and construction process. A growing body of literature recognizes the importance of artificial neural network applications in numerous areas of the construction industry and the built environment, presenting a need to explore the main research themes, attributes, benefits, and challenges. A three-step extensive research method was utilized by conducting a bibliometric search of English language articles and conducting quantitative and qualitative analyses. The bibliometric analysis aimed to identify the current research directions and gaps forming future research areas. The scientometric analysis of keywords revealed diverse areas within the construction industry linked to ANNs. The qualitative analysis of the selected literature revealed that energy management in buildings and construction cost predictions were the leading research topics in the study area. These findings recommend directions for further research in the field, for example, broadening the application ranges of ANNs in the current Construction 4.0 technologies, such as robotics, 3D printing, digital twins, and VR applications.

Technological aspects and recommendations for applying teledentistry in oral medicine: a scoping review

BackgroundTeledentistry is applied in oral medicine to help dental practitioners and specialists manage complex oral conditions. This scoping review aims to synthesize the available evidence regarding the technical requirements and the provision of security services, as well as the recommendations on standard oral cavity photography methods for using teledentistry in oral medicine.MethodThe present scoping review was conducted in 2022 according to the Joanna Briggs Institute (JBI) manual. Four databases were systematically searched, including ISI Web of Science, PubMed, Scopus, and ProQuest, from January 1999 to December 2021. After finalizing the search strategy, all the original articles, reviews, editorials, letters, comments, and book chapters were included. All the dissertations uploaded in English with a full-text electronic file were also included to achieve a comprehensive picture of the available literature. Two types of analysis were applied: the quantitative descriptive analysis applying Excel 2016 and the qualitative thematic analysis using MAXQDA version 10.ResultsThe included papers were published during 1999–2021. Most of the included articles were from Brazil (n = 9, 21.43%), India (n = 6, 14.28%), the USA (n = 6, 14.28%), and the UK (n = 5, 11.90%). Social media usage as a teledentistry platform increased considerably between 2019 and 2021. Teledentistry platforms have been classified into four categories, including email, telephone, social network applications (WhatsApp, zoom, Google Meeting, Instagram, line Application, Viber, and Messenger), and teledentistry systems (teledentistry systems, which were designed specially to perform a teledentistry project). Teledentistry has been conducted to store and forward (n = 15, 30.71%), real-time (n = 9, 21.43%), or both (n = 12, 35.71%). According to the thematic results, basic technical requirements have been categorized into four, including teledentistry platform, type of interactions, internet connection, and photography tool. Other thematic results demonstrate seven categories of recommendations, including general factors, light, patient position, phone position, setting of a mobile phone camera, photo sending requirements, and photo visualization.ConclusionAccording to the results, over the past 7 years, especially during the COVID-19 pandemic, social network applications such as WhatsApp and Zoom have been used more frequently. Also, photography recommendations with a mobile phone camera depend on several factors such as general factors, light, patient position, phone position, setting of the mobile phone camera, and photo-sending requirements. Given the increased use of mobile cameras for oral photography, these recommendations can help improve the quality of images and subsequently improve the reliability of clinical decision-making in managing patients' conditions.

ВЛИЯНИЕ НЕЙРОСЕТЕЙ НА РАЗВИТИЕ ПРАКТИКИ УПРАВЛЕНИЯ В СОВРЕМЕННЫХ КОМПАНИЯХ

Relevance. In the modern world, neural networks are playing an increasingly significant role in various fields of activity, including management in companies. The use of neural networks in management practice can lead to significant changes in the methods of decision making, data analysis, forecasting and optimization of business processes. Therefore, it is important to explore exactly how neural networks influence the development of management practices and what new opportunities they provide for companies to improve efficiency. Goal. The purpose of this study is to study the influence of neural networks on the development of management practices in modern companies and to identify the main aspects that determine this process. Tasks: - Analysis of current trends and applications of neural networks in business management. - Study of the advantages and disadvantages of using neural networks. - Development of recommendations for the effective use of neural networks in management in modern companies. Research results: The specifics and main current trends in the development of neural networks are analyzed. Examples of their use in different companies are considered. Both the advantages and disadvantages of neural network technologies are identified. Recommendations have been developed for the effective use of neural networks in management in modern companies.

Classification of DDoS attack traffic on SDN network environment using deep learning

Distributed Denial of Service (DDoS) attack is a major threat to the Internet of Things (IoT), Software Defined Networks (SDN), and Cloud Computing Networks. Due to the tremendous applications of IoT networks, the number of DDoS attacks is increasing significantly, and most sophisticated DDoS attacks are generated through IoT botnets. An IoT botnet-based DDoS attack can disrupt the network quickly with a surge of malicious traffic. Especially in an SDN network, it is important to detect the DDoS attack before it occurs to the SDN controller. DDoS attacks on the centralized controller of the SDN can disrupt the whole network. So, identifying DDoS attacks at the earliest is a critical security measure for network experts and practitioners. In this paper, we analyze the DDoS attack on an SDN environment and develop a method to identify the DDoS attack using Deep Learning (DL). The proposed method converts the captured raw network traffic to image data and classifies the malicious data from normal data. The method is evaluated on our test-bed simulated dataset and two other benchmark datasets. The experimental comparison shows that the proposed method performs better on all three datasets, giving more than 99% classification accuracy.

Survey on Neural Networks in Networking: Applications and Advancements

The integration of neural networks into networking has paved the way for substantial advancements in network performance, security, and management. This survey delves into the diverse applications of neural networks within the networking domain, highlighting their impact on traffic prediction, anomaly detection, network optimization, and security enhancements. By leveraging the inherent capabilities of neural networks to model complex and non-linear relationships, significant improvements in efficiency and reliability can be achieved. This survey provides a thorough examination of current methodologies, key advancements, and practical implementations, offering insights into the future potential of neural networks in the field of networking.

Network Slicing Strategy for Real-Time Applications in Large-Scale Satellite Networks With Heterogeneous Transceivers

Network Slicing Strategy for Real-Time Applications in Large-Scale Satellite Networks With Heterogeneous Transceivers

A series‐fed frequency scanning antenna for millimeter wave wireless communication

AbstractIn the realms of 5G and the upcoming 6G communications, the millimeter‐wave bands offer substantially greater bandwidth. However, due to their propagation characteristics, aligning antennas becomes a challenging issue, particularly at the node side in industrial and vehicular network applications. Angle‐scanning antennas can significantly enhance communication reliability or provide new possibilities for spatial multiplexing. Previous studies have predominantly concentrated on frequency‐scanning antennas characterized by either low‐gain individual units or expensive digital arrays. Consequently, there has been a scarcity of solutions that are both cost‐effective and high‐gain. In this study, a method is proposed for achieving frequency scanning by extending the feed lines between elements. Based on this approach, a millimeter‐wave high‐gain serial‐feed frequency‐scanning antenna is developed. This antenna operates within the 60–64 GHz millimeter‐wave ISM (Industrial, Scientific, and Medical) band and is capable of performing a 10 scan in the E‐plane. Moreover, the antenna maintains a width of less than half a wavelength, allowing for the assembly of multiple antennas into a MIMO array.

Unveiling Structure-Enhanced Positional Embedding Module in Transformer-Based 3D Human Pose Estimation

Abstract With the advancement of computational capabilities, the realm of deep learning has expanded from 2D image tasks to encompass 3D video tasks. Human action recognition stands as a typical 3D video task, which emphasizes the accurately capture of local body structures. However, existing methods encounter limitations in precisely capturing these structures, especially in joints within the same body region that exhibits diverse static and dynamic features. To address this complexity, our study innovatively introduces a Structure-Enhanced Positional Embedding Module seamlessly integrated into the PoseFormer model, resulting in the innovative HStruPE-Former model. Experimental validation on the Human3.6M dataset demonstrates a significant performance improvement achieved by the HStruPE-Former model in the task of 3D human pose estimation. Particularly in scenarios involving intricate movements and postures, the module demonstrates improved stability and accuracy. Although the improvements are modest, this technology is expected to facilitate the exploration of untapped potential of structure-enhanced postion embedding modules in diverse neural network applications, thereby promoting further exploration in neural network research. This research outcome not only enhances the performance in current 3D video tasks but also paves the way for novel research directions in the field of deep learning.

Research on polarization compensation for practical satellite-based quantum key distribution

Satellite-based quantum key distribution (QKD) is one of the effective solutions for building a global-scale quantum network. For practical satellite-based polarization-encoding QKD systems, the polarization degradation introduced by the single-mode fiber and the relative rotation between satellites and ground stations will lead to a decrease in the polarization performance and need to be compensated. In this work, we propose two novel on-orbit satellite-to-ground joint polarization measurement and compensation methods, which can ensure high polarization fidelity while effectively saving hardware resources and reducing costs. By establishing a polarization compensation model and performing theoretical analysis, combined with desktop experimental verification, we demonstrated the effectiveness of the satellite-to-ground joint polarization compensation and achieved a low quantum bit error rate below 0.3% after background removal. The presented polarization compensation laid the foundation for the subsequent applications in satellite-based QKD and satellite-based quantum networks.

Software-Defined Wide Area Networks (SD-WANs): A Survey

SD-WANs are an innovative software-defined network (SDN) technology used to reinvent networks, services, and applications in wide area network (WANs). The development of SD-WANs ranges from network optimization in the past to service provision platforms at present and distributed computing systems in the future. The existing surveys on SD-WANs are fragmented, covering specific problems only, and are not comprehensive with detailed research directions. This paper seeks to provide a systematic survey on SD-WANs by introducing major research directions and stating specific problems. Therefore, four major research directions related to traffic engineering, network optimization and systems, service orchestration, and the security issues of SD-WANs are sequentially introduced, along with detailed statements relating to specific problems and the classification of state-of-the-art research. Finally, the trends and challenges regarding SD-WANs are summarized and our future work is described.

The Role of Mathematics in Artificial Intelligence and Machine Learning

Mathematics serves as the foundational backbone of artificial intelligence (AI) and machine learning (ML), providing the essential tools and frameworks for developing sophisticated algorithms and models. the pivotal role of various mathematical disciplines, including linear algebra, calculus, probability theory, and optimization, in advancing AI and ML technologies. We begin by examining how linear algebra facilitates the manipulation and transformation of high-dimensional data, which is crucial for techniques such as principal component analysis (PCA) and singular value decomposition (SVD). Next, we delve into the applications of calculus in training neural networks through gradient-based optimization methods, highlighting the importance of differentiation and integration in backpropagation and loss function minimization. the role of probability theory in handling uncertainty and making predictions, emphasizing its application in Bayesian networks, Markov decision processes, and probabilistic graphical models. Additionally, we discuss optimization techniques, both convex and non-convex, that are fundamental to finding optimal solutions in machine learning tasks, including support vector machines (SVMs) and deep learning architectures.

GTR: GAN-Based Trusted Routing Algorithm for Underwater Wireless Sensor Networks.

The transmission environment of underwater wireless sensor networks is open, and important transmission data can be easily intercepted, interfered with, and tampered with by malicious nodes. Malicious nodes can be mixed in the network and are difficult to distinguish, especially in time-varying underwater environments. To address this issue, this article proposes a GAN-based trusted routing algorithm (GTR). GTR defines the trust feature attributes and trust evaluation matrix of underwater network nodes, constructs the trust evaluation model based on a generative adversarial network (GAN), and achieves malicious node detection by establishing a trust feature profile of a trusted node, which improves the detection performance for malicious nodes in underwater networks under unlabeled and imbalanced training data conditions. GTR combines the trust evaluation algorithm with the adaptive routing algorithm based on Q-Learning to provide an optimal trusted data forwarding route for underwater network applications, improving the security, reliability, and efficiency of data forwarding in underwater networks. GTR relies on the trust feature profile of trusted nodes to distinguish malicious nodes and can adaptively select the forwarding route based on the status of trusted candidate next-hop nodes, which enables GTR to better cope with the changing underwater transmission environment and more accurately detect malicious nodes, especially unknown malicious node intrusions, compared to baseline algorithms. Simulation experiments showed that, compared to baseline algorithms, GTR can provide a better malicious node detection performance and data forwarding performance. Under the condition of 15% malicious nodes and 10% unknown malicious nodes mixed in, the detection rate of malicious nodes by the underwater network configured with GTR increased by 5.4%, the error detection rate decreased by 36.4%, the packet delivery rate increased by 11.0%, the energy tax decreased by 11.4%, and the network throughput increased by 20.4%.

Error bounds for one-dimensional constrained Langevin approximations for nearly density-dependent Markov chains

AbstractContinuous-time Markov chains are frequently used to model the stochastic dynamics of (bio)chemical reaction networks. However, except in very special cases, they cannot be analyzed exactly. Additionally, simulation can be computationally intensive. An approach to address these challenges is to consider a more tractable diffusion approximation. Leite and Williams (Ann. Appl. Prob.29, 2019) proposed a reflected diffusion as an approximation for (bio)chemical reaction networks, which they called the constrained Langevin approximation (CLA) as it extends the usual Langevin approximation beyond the first time some chemical species becomes zero in number. Further explanation and examples of the CLA can be found in Anderson et al. (SIAM Multiscale Modeling Simul.17, 2019).In this paper, we extend the approximation of Leite and Williams to (nearly) density-dependent Markov chains, as a first step to obtaining error estimates for the CLA when the diffusion state space is one-dimensional, and we provide a bound for the error in a strong approximation. We discuss some applications for chemical reaction networks and epidemic models, and illustrate these with examples. Our method of proof is designed to generalize to higher dimensions, provided there is a Lipschitz Skorokhod map defining the reflected diffusion process. The existence of such a Lipschitz map is an open problem in dimensions more than one.

Software-Defined Radio Implementation of a LoRa Transceiver.

The number of applications of low-power wide-area networks (LPWANs) has been growing quite considerably in the past few years and so has the number of protocol stacks. Despite this fact, there is still no fully open LPWAN protocol stack available to the public, which limits the flexibility and ease of integration of the existing ones. The closest to being fully open is LoRa; however, only its medium access control (MAC) layer, known as LoRaWAN, is open and its physical and logical link control layers, also known as LoRa PHY, are still only partially understood. In this paper, the essential missing aspects of LoRa PHY are not only reverse engineered, but also, a new design of the transceiver and its sub-components are proposed and implemented in a modular and flexible way using GNU Radio. Finally, some examples of applications of both the transceiver and its components, which are made to be run in a simple setup by using cheap and widely available off-the-shelf hardware, are given to show how the library can be used and extended.

Higher-Order Networks Representation and Learning: A Survey

Network data has become widespread, larger, and more complex over the years. Traditional network data is dyadic, capturing the relations among pairs of entities. With the need to model interactions among more than two entities, significant research has focused on higher-order networks and ways to represent, analyze, and learn from them. There are two main directions to studying higher-order networks. One direction has focused on capturing higher-order patterns in traditional (dyadic) graphs by changing the basic unit of study from nodes to small frequently observed subgraphs, called motifs. As most existing network data comes in the form of pairwise dyadic relationships, studying higher-order structures within such graphs may uncover new insights. The second direction aims to directly model higher-order interactions using new and more complex representations such as simplicial complexes or hypergraphs. Some of these models have long been proposed, but improvements in computational power and the advent of new computational techniques have increased their popularity. Our goal in this paper is to provide a succinct yet comprehensive summary of the advanced higher-order network analysis techniques. We provide a systematic review of the foundations and algorithms, along with use cases and applications of higher-order networks in various scientific domains.

Highly Efficient Hybrid Reconfigurable Intelligent Surface Approach for Power Loss Reduction and Coverage Area Enhancement in 6G Networks

This paper introduces a novel efficient hybrid reconfigurable intelligent surface (RIS) approach designed to significantly reduce power loss and enhance coverage area in 6G networks. The core innovation of this approach lies in an advanced iterative algorithm introduced as the Hybrid reconfigurable intelligent surface decision-making algorithm (HRIS-DMA) that integrates precise user location data into the RIS configuration process. By dynamically adjusting RIS elements to reflect and direct signals based on real-time user positions, this method minimises signal attenuation and optimises signal propagation. The mechanism driving the performance gains includes precise beamforming and intelligent reflection, continuously refined through iterative updates. This technique ensures robust signal strength and expanded coverage, addressing the challenges of dense and diverse deployment scenarios in 6G networks. The proposed scheme’s application in 6G networks demonstrates substantial improvements in signal quality and network reliability, paving the way for enhanced user experiences and efficient communication infrastructures. This novel approach was tested using MATLAB R2023a , and its performance was evaluated using three downlink scenarios: zero to few, few to moderate, and moderate to many obstacles. The three scenarios show higher coverages than conventional simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and base station (BS) handover. Based on the evaluation metrics, the analysis results of the novel HRIS-DMA show 70% less signal power loss, 0.17 μs less system delay, 25 dB and 12 dB channel gain compared with the conventional STAR-RIS and BS handover, respectively, and 95% improvement in the overall system’s efficiency compared to STAR-RIS and 13% compared to BS-BS handover.

DIDS: A distributed inference framework with dynamic scheduling capability

Distributed inference is a promising solution for deploying Deep Neural Network (DNN) applications in resource-constrained edge environments. However, due to the complexity and variability of edge scenarios, efficiently completing DNN inference remains challenging. While previous works have made significant progress in various aspects such as partition strategy, device diversity, and memory overhead, few consider the impact of environmental dynamics, particularly environmental workloads. This oversight renders existing solutions impractical. In this study, we propose DIDS, a distributed inference framework with dynamic scheduling capability that considers environmental dynamics. We first conduct a formal analysis of distributed inference and design two basic scheduling strategies: Re-Partition and Complete-Push. Then we introduce Dynamic Push, a runtime scheduler based on Complete-Push that aims to mitigate the impact of environmental workloads through runtime scheduling. The evaluation results show that DIDS can achieve up to 3.7x speedup compared to static distributed inference, in addition, DIDS is also able to achieve significant improvement compared to the state-of-the-art method.

Object-centric Learning with Capsule Networks: A Survey

Capsule networks emerged as a promising alternative to convolutional neural networks for learning object-centric representations. The idea is to explicitly model part-whole hierarchies by using groups of neurons called capsules to encode visual entities, then learn the relationships between these entities dynamically from data. However, a major hurdle for capsule network research has been the lack of a reliable point of reference for understanding their foundational ideas and motivations. This survey provides a comprehensive and critical overview of capsule networks, which aims to serve as a main point of reference going forward. To that end, we introduce the fundamental concepts and motivations behind capsule networks, such as equivariant inference . We then cover various technical advances in capsule routing algorithms as well as alternative geometric and generative formulations. We provide a detailed explanation of how capsule networks relate to the attention mechanism in Transformers and uncover non-trivial conceptual similarities between them in the context of object-centric representation learning. We also review the extensive applications of capsule networks in computer vision, video and motion, graph representation learning, natural language processing, medical imaging, and many others. To conclude, we provide an in-depth discussion highlighting promising directions for future work.

Analyzing performance: Error-efficient, low-power recursive inexact multipliers for CNN applications

In present scenario, significant data manipulations can be espied in image processing and Convolutional Neural Networks (CNNs) applications. The quality performance of these applications relies ponderously on arithmetic operations. In this context, Approximate Computing frequently finds its application, where a certain margin of error is deemed acceptable, on prioritizing circuit speed over absolute precision and the common focus lies on optimizing the binary multiplier which is a crucial yet power-intensive component, so as to streamline the complexity of above mentioned applications. Furthermore, Inexact Multipliers (IMs) are developed by using recursive method, thereby purveying enhanced design and error metrics. This paper introduces higher order Recursive-Based Inexact Multipliers (RBIMs), which integrates the suggested 4-bit IMs, and exact multipliers along with the recursive method, aiming to provide a delicate balance between design performance metrics and error tolerance. For evaluating the proposed approach, most of the existing and recently suggested IMs operating at 8-bit and 16-bit, are designed in Verilog, coded in Python, synthesized and simulated using Cadence Register Transfer Language (RTL) Compiler, Google Colabs. Eventually, the simulation results bespeak a notable improvement in power consumption, with the 8-bit and 16-bit RBIMs by outperforming the previous designs up to 36.9 %, and 35.5 % respectively. Further, the efficacies of the RBIMs are validated through its usage in image processing and CNN applications.

A multilevel backbone extraction framework

As networks grow in size and complexity, backbones become an essential network representation. Indeed, they provide a simplified yet informative overview of the underlying organization by retaining the most significant and structurally influential connections within a network. Network heterogeneity often results in complex and intricate structures, making it challenging to identify the backbone. In response, we introduce the Multilevel Backbone Extraction Framework, a novel approach that diverges from conventional backbone methodologies. This generic approach prioritizes the mesoscopic organization of networks. First, it splits the network into homogeneous-density components. Second, it extracts independent backbones for each component using any classical Backbone technique. Finally, the various backbones are combined. This strategy effectively addresses the heterogeneity observed in network groupings. Empirical investigations on real-world networks underscore the efficacy of the Multilevel Backbone approach in preserving essential network structures and properties. Experiments demonstrate its superiority over classical methods in handling network heterogeneity and enhancing network integrity. The framework is adaptable to various types of networks and backbone extraction techniques, making it a versatile tool for network analysis and backbone extraction across diverse network applications.