Computational Network Research Articles

Multi-Domain Resource Management for Space–Air–Ground Integrated Sensing, Communication, and Computation Networks

Multi-Domain Resource Management for Space–Air–Ground Integrated Sensing, Communication, and Computation Networks

Minimization of Task Completion Time in Wireless Powered Mobile Edge–Cloud Computing Networks

Minimization of Task Completion Time in Wireless Powered Mobile Edge–Cloud Computing Networks

Joint edge caching and computation offloading for heterogeneous tasks in MEC-enabled vehicular networks

Edge caching is an effective paradigm that can significantly reduce the computation task offloading latency for mobile edge computing (MEC) in vehicular networks, while also alleviating the backhaul transmission pressure for retrieving content data from the cloud server. However, most existing works fail to address how to handle heterogeneous tasks generated by vehicle terminals (VTs), especially in complex scenarios where both computation and content tasks are generated simultaneously. In this paper, we consider a mobility-aware vehicular network model where VTs simultaneously generate heterogeneous task requests, i.e., a computation task and a content task, and investigate joint optimization of caching for heterogeneous tasks data, computation offloading, and computing resource allocation. In order to optimize the latency for processing the heterogeneous tasks, an average execution latency minimization problem with sojourn time and caching capacity constraints is formulated. We decompose this problem into two tractable subproblems, i.e., caching optimization subproblem, and computation offloading and resource allocation optimization subproblem. We first develop a dynamic programming (DP) algorithm to obtain the optimal caching strategies for heterogeneous tasks data. We compare the obtained content retrieval latency with the local computing latency, and derive the optimal computation offloading and edge computing resource allocation solutions. On this basis, we propose a joint computation offloading and resource allocation (JCORA) algorithm to determine the computing resources allocated to each VT and corresponding computation offloading strategy. Numerical results indicate that the proposed algorithm, which integrates DP algorithm and JCORA algorithm, can achieve lower execution latency for heterogeneous tasks compared to the benchmark schemes. Additionally, for task loss scenarios where the sojourn time constraint cannot be met, the impact of VT mobility on the task loss probability is also revealed.

Quantum social network analysis: Methodology, implementation, challenges, and future directions

Quantum social network analysis (QSNA) is a recent advancement in the interdisciplinary field of quantum computing and social network analysis. This manuscript comprehensively reviews QSNA, emphasizing its methodologies, implementation strategies, challenges, and potential applications. It explores the conceptual foundation of key social network analysis research problems, including link prediction, influence maximization, and community detection. The research examines how quantum algorithms can revolutionize such social network tasks by leveraging principles from quantum mechanics and information theory and highlights the advantages of quantum algorithms in handling complex social network structures. The implementation section delves into the practical aspects of QSNA, such as frameworks, experimental setups, and evaluation methods. We assess the capabilities of existing quantum programming language tools and platforms. Various case studies illustrate the potential of quantum computing to enhance the performance of social network analysis. Additionally, we identify several crucial challenges and future research directions for QSNA, including the complexity of developing quantum algorithms, the need for interdisciplinary knowledge, and the challenges of integrating quantum and classical computing resources. This paper aims to serve as a foundational resource for researchers and practitioners, providing insights into the transformative potential of quantum computing in advancing the analysis of social networks and outlining future research directions in this emerging field.

Artificial neural network model for convectively heated Casson fluid with the appliance of solar energy

This study is to illustrate the thermometric exchange of Casson fluid’s double diffusive nonlinear radiative heat flux over vertical plate associated with convective boundary conditions, incorporating artificial intelligence (AI)-based neural network computation technique. The AI analysis provides enhanced and optimized results with predictive modeling. The analysis utilizes a dataset generated through Mathematica environment and then embedded the filtered matrix dataset in MATLAB employs AI analysis using the Neural Auto Regressive Exogenous (NARX) method for optimized solutions. Levenberg–Marquard algorithm is used to train neural network. The model’s importance and applications span nuclear reactors, aerodynamics, hydrodynamics, transportation and radiating processes of energy transfer. The physical problem is governed by PDEs that is transformed into a set of ODEs by using similarity transformation. The flow is analyzed against the variation in significant influencing parameter like Prandtl number (Pr), velocity ratio ([Formula: see text]), convective coefficient ([Formula: see text]), Rayleigh number (Ra), buoyancy ratio parameter ([Formula: see text]), radiation parameter ([Formula: see text]) and Casson fluid parameter ([Formula: see text]). Findings of this paper are significant for various industrial, engineering and research-based activities.

A Survey of Artificial Neural Network Computing Systems

A Survey of Artificial Neural Network Computing Systems

Output feedback control of LTI systems using quantized periodic event-triggering and 1-bit data transmission

This paper presents a novel framework for periodic event-triggered control (PETC) coupled with dynamic quantization for linear systems. Unlike traditional time-driven control methods, our approach leverages event-based mechanisms to judiciously update control actions, thus minimizing computational load and network traffic. We introduce a two-level dynamic quantizer for encoding feedback information with a single bit, thereby enhancing resource efficiency. The proposed PETC mechanism decides the transmission instants based on the quantized output samples. The resulting system is modeled as a hybrid dynamical system to capture both continuous and discrete dynamics. Sufficient conditions for ensuring the stability of the closed-loop system are presented in the form of a linear matrix inequality. Through numerical simulations, we demonstrate that our approach captures the initial output within a finite time and significantly reduces data transmissions compared to traditional methods. This paper makes key contributions in the integration of dynamic quantization with PETC, leading to resource-efficient and stable control systems.

Research on the Influencing Factors of High-Quality Development of Service Industry Under Digital Network Computing

Faced with the growing demand for personalized services and the challenge of high service quality standards in the market, the traditional service industry model is no longer able to meet current needs, and transformation and upgrading are urgently needed. Therefore, it is particularly crucial to deeply analyze how digital network computing can effectively empower and accelerate the high-quality development process of the service industry, while identifying and analyzing the core influencing factors in this process. Digital network computing relies on vast digital knowledge and information resources, with highly interconnected network infrastructure as the cornerstone, integrating advanced information and communication technologies to achieve real-time transmission, processing, and analysis of data, thereby driving the continuous emergence of intelligent decision-making and innovative applications. Its distinctive characteristics include high network interconnectivity, deep data mining and utilization, intelligent decision support, and continuous technological innovation drive. The purpose of this study is to deepen the understanding of the internal mechanism of the integration and development of digital network computing and the service industry, and to reveal the multiple paths and key factors that promote high-quality development of the service industry. Through system analysis, we hope to provide scientific decision-making basis for policy makers, valuable strategic suggestions for service enterprises on the road of digital transformation, and jointly promote the service industry to move toward a more prosperous, efficient, and sustainable new stage of development.

The Impact of Drawing Systems Based on Embedded Neural Network Computing on Student Mental Health

With the rapid development of artificial intelligence technology, embedded neural networks, as their core components, have shown enormous potential for application in multiple fields due to their powerful data processing and pattern recognition capabilities. In the field of painting education, the application of this technology can not only achieve customized teaching plans, but also intelligently analyze the characteristics of students’ behavioral works. This study focuses on the application of embedded neural network computing in painting teaching. Especially how to dynamically adjust the difficulty, style preferences, and interactive methods of teaching content to stimulate students’ creativity and learning interest. By intelligently analyzing students’ emotional reactions and psychological states, timely adjusting teaching strategies, such as providing emotional support, adjusting learning pace, or introducing psychological counseling content, can alleviate students’ learning pressure, and enhance their self-efficacy and sense of belonging. In addition, this technology can promote effective communication between teachers and students, establish more positive and harmonious teaching relationships, and create a more inclusive and encouraging learning environment for students. The research results indicate that compared to traditional painting teaching models, the teaching method based on embedded neural networks significantly improves the mental health level of students, which is manifested in the reduction of anxiety and depression, enhancement of self-confidence, and improvement of social skills.

Research on Decoration of Tourist Homestays and Hotels Based on Digital Image Network Computing

In today’s digital age, the tourism and homestay industry is facing unprecedented opportunities and challenges. With the continuous expansion of the tourism market, tourists’ demands for accommodation experience are becoming increasingly diverse and personalized. Traditional design methods often struggle to quickly respond to these changes, fully showcase regional characteristics, and achieve efficient personalized customization. At the same time, how to integrate modern technology while retaining traditional charm to enhance the attractiveness and comfort of homestays has become an urgent problem to be solved. This paper aims to explore in depth how to use this technological framework to optimize the decoration design of tourist homestays, in order to create living spaces that retain traditional charm and are rich in modern technology. By utilizing digital image network computing technology, it is possible to conduct 3D modeling and virtual simulation of the indoor space of homestays. By simulating different furniture placement and spatial separation schemes, combined with tourist behavior analysis data, the optimal spatial layout scheme is automatically recommended to achieve maximum space utilization and a comfortable living experience. This technology can analyze a large number of color matching cases, combined with the cultural characteristics of the area where the homestay is located, and apply the principles of color psychology to provide personalized color schemes for homestay design. Meanwhile, through style recognition and recommendation algorithms, designers can quickly locate and integrate traditional and modern elements to create unique decorative styles.

Fabric defect detection method based on multi-scale fusion attention mechanisms

Abstract Fabric defect detection is extremely important for the development of the textile industry, but the existing traditional image processing algorithms are not good enough to detect fabric defects, and the detection efficiency and accuracy of the classical deep learning model is not satisfactory, so this paper proposes an improved fabric defect detection method based on multi-scale fusion of attention mechanism YOLOv7-PCBS. Based on the YOLOv7 network structure, some of the standard convolutions of the backbone network are replaced with Partial Convolution modules, which reduces the amount of network computation and improves the network detection speed; add coordinate attention to enhance the ability of extracting the positional features of tiny defects in fabrics; reconfiguration of the SPPCSPC module to improve small target detection; optimization of Bidirectional Feature Pyramid Network (BiFPN) and design of Tiny- BiFPN for simple and fast multi-scale feature fusion; finally, a novel loss function SIoU with angular loss is introduced to facilitate the fitting of the true and predicted frames and enhance the accuracy of defect prediction. The results show that the algorithm achieves a mAP value of 94.4% on the detection of defects in solid-colored fabrics of six denim materials, which is an improvement of 15.1% compared to the original YOLOv7 algorithm, while the model achieves a frame rate of 59.5 per second. Compared with other traditional deep learning algorithms SSD and Faster-RCNN, the detection accuracies are improved by 21.6% and 15.2%, and the FPS values are improved by 78.1% and 101.0%, respectively. Therefore, the YOLOv7-PCBS fabric defect detection algorithm proposed in this paper makes the fabric defect detection results more accurate while realizing lightweight, which provides an important technical reference for the subsequent improvement of textile quality.

Exploring Ground Reflection Effects on Received Signal Strength Indicator and Path Loss in Far-Field Air-to-Air for Unmanned Aerial Vehicle-Enabled Wireless Communication

Unmanned aerial vehicle (UAV)-enabled wireless communications are becoming increasingly important in applications such as maritime and forest rescue operations. UAV systems often depend on wireless networking and mobile edge computing (MEC) devices for effective deployment, particularly in swarm UAV-enabled MEC configurations focusing on channel modeling and path loss characteristics for air-to-air (A2A) communications. This paper examines path loss characteristics in far-field (FF) ground reflection scenarios, specifically comparing two environments: FF1 (forest floor) and FF2 (seawater floor). LoRa modules operating at 868 MHz were deployed for communication between a transmitting UAV (Tx-UAV) and a receiving UAV (Rx-UAV) to conduct this study. We investigated the received signal strength indicator (RSSI) and path loss characteristics across channel bandwidths of 125 kHz and 250 kHz and spread factors (SF) of 7, 9, and 12. Experimental results show that ground reflection has minimal impact in the FF1 scenario, whereas, in the FF2 scenario, ground reflection significantly influences communication. Therefore, in the seawater environment, a UAV-enabled LoRa MEC configuration using a 250 kHz bandwidth and an SF of 7 is recommended to minimize the effects of ground reflection.

Conductive Islands Assisted Resistive Switching in Biomimetic Artificial Synapse for Associative Learning and Image Recognition

AbstractSolution‐processed memristor devices hold significant potential for advancing synaptic applications, offering scalable, cost‐effective, and efficient solutions for next‐generation neuromorphic systems. These devices replicate the behavior of biological synapses with their gradual and continuous changes in resistance, making them promising for neuromorphic computing and artificial neural networks. However, understanding the temporal characteristics and cumulative effect of successive pulses on the devices is essential for emulating the dynamics of biological synapses. This study proposes a hybrid materials‐based conductive islands‐assisted resistive switching (CIARS) in a solution‐processed active layer consisting of thermally exfoliated graphitic carbon nitride (g‐C3N4 abbreviated as CN) nanosheets embedded with silver nanoparticles (Ag NPs). The fabricated devices demonstrate repeatable and bipolar memory features with a lower operating voltage (≤0.5 V), emulating the frequency and amplitude‐dependent synaptic plasticities. The threshold switching occurs due to the formation of conduction filaments (CFs) of silver ion (Ag+), whereas the analog behavior results from the voltage pulse‐dependent modulation of Schottky barrier height combined with metallic CFs formation. The experimental findings demonstrate the efficacy of the CIARS mechanism within the material platform, highlighting its potential for applications in associative learning, Morse code detection, and image recognition.

SD-Meta: The Software-Defined Network of Human-Centric Metaverse for Multi-Lead or Multi-Media Data in Spread Spectrum Communications

In this paper, the novel adjacent two-end link or network multi-end link concept of software-defined network is presented to support the data transmission of electroencephalogram, audio and video streaming in spread spectrum communications. Instead of solving the problem of software-defined prioritization scheme in networking calculation and communication, the southbound-northbound structure of existing neural or information network is found in control and perceptual interactions. The proposed framework of multi-lead and multi-media structures allow the human-centric Metaverse to execute different operations for local and global planning schemes with the high-speed lead or media data transmission of spread spectrum streaming, depending on the kind of brain-computer interaction, and similar to the system of multi-modal perception in the routing or switching. Firstly, this research designed the filed programmable gate array for conventional routing nodes in the computation of different neural networks. Secondly, this study demonstrates its enhanced applicability with software core for multiple routers in the computing power network of the Internet of brains in different brain-computer smart terminals. Experiment results also show the benefits of proposed model and structure, and exposing the different running indicators in the simulation.

Employing Evolutionary Computing and Hybrid Artificial Neural Networks to Improve Drug Discovery

An ultimate relevant translational scientific endeavour that contributes to human vulnerability and happiness might be the creation and advancement of medications. Fast drug discovery procedures necessitate using contemporary computational approaches to tackle pharma data's complexities and high complexity. By combining Evolutionary Computing with Hybrid Artificial Neural Networks (EC-HANNs), this research introduces a novel strategy for optimizing and speeding up the drug development process. To handle various drug-target relations, predict the efficacy of compounds, and discover more accurate potential medications, the proposed model employs EC to evolve the computational construction and hyperparameters of HANNs in real-time. This innovation makes the model a flexible framework. Through repeated refinement of EC- HANN designs, the EC Module uses Particle Swarm Optimization (PSO) to choose the best configuration for individual drug discovery tasks. The model's distinctive feature is that it dynamically adjusts the network configuration to match the details of each drug discovery activity by using PSO to optimize the HANN's architecture and hyperparameters. Regarding sequential biological data, the HANN Module employs Convolutional Neural Networks (CNNs) to glean features for pattern recognition over time. By extracting high-level spatial information from genetic data, CNN can better identify prospective medication candidates. When tested on several benchmark datasets, the proposed framework demonstrates superior performance over traditional neural networks across the board regarding prediction accuracy, convergence speed, and model durability. As a powerful tool, this hybrid approach can simplify drug discovery, leading to more efficient drug development.

Spatially varying nanophotonic neural networks.

The explosive growth in computation and energy cost of artificial intelligence has spurred interest in alternative computing modalities to conventional electronic processors. Photonic processors, which use photons instead of electrons, promise optical neural networks with ultralow latency and power consumption. However, existing optical neural networks, limited by their designs, have not achieved the recognition accuracy of modern electronic neural networks. In this work, we bridge this gap by embedding parallelized optical computation into flat camera optics that perform neural network computations during capture, before recording on the sensor. We leverage large kernels and propose a spatially varying convolutional network learned through a low-dimensional reparameterization. We instantiate this network inside the camera lens with a nanophotonic array with angle-dependent responses. Combined with a lightweight electronic back-end of about 2K parameters, our reconfigurable nanophotonic neural network achieves 72.76% accuracy on CIFAR-10, surpassing AlexNet (72.64%), and advancing optical neural networks into the deep learning era.

Peer Interaction Dynamics and Second Language Learning Trajectories During Study Abroad: A Longitudinal Investigation Using Dynamic Computational Social Network Analysis

AbstractUsing computational Social Network Analysis (SNA), this longitudinal study investigates the development of the interaction network and its influence on the second language (L2) gains of a complete cohort of 41 U.S. sojourners enrolled in a 3‐month intensive study‐abroad Arabic program in Jordan. Unlike extant research, our study focuses on students’ interactions with alma mater classmates, reconstructing their complete network, tracing the impact of individual students’ positions in the social graph using centrality metrics, and incorporating a developmental perspective with three measurement points. Objective proficiency gains were influenced by predeparture proficiency (negatively), multilingualism, perceived integration of the peer learner group (negatively), and the number of fellow learners speaking to the student. Analyses reveal relatively stable same‐gender cliques, but with changes in the patterns and strength of interaction. We also discuss interesting divergent trajectories of centrality metrics, L2 use, and progress; predictors of self‐perceived progress across skills; and the interplay of context and gender.

Distributed computing in the asynchronous LOCAL model

The LOCAL model is among the main models for studying locality in the framework of distributed network computing. This model is however subject to pertinent criticisms, including the facts that all nodes wake up simultaneously, perform in lock steps, and are failure-free. We show that relaxing these hypotheses to some extent does not hurt local computing. In particular, we show that, for any task T associated to a locally checkable labeling (lcl), if T is solvable in t rounds by a deterministic algorithm in the LOCAL model, then T remains solvable by a deterministic algorithm in O(t) rounds in an asynchronous variant of the LOCAL model whenever t=O(polylogn). This improves the result by Castañeda et al. [TCS, 2019], which was restricted to 3-coloring the rings. More generally, the main contribution of this paper is to show that, perhaps surprisingly, asynchrony and failures in the computations do not restrict the power of the LOCAL model, as long as the communications remain synchronous and failure-free. To this end, this paper introduces a new distributed renaming technique to provide nodes with consistent identifiers.

Semantic Guided Matting Net

Abstract Human matting refers to extracting human parts from natural images with high quality, including human detail information such as hair, glasses, hats, etc. This technology plays an essential role in image synthesis and visual effects in the film industry. When the green screen is not available, the existing human matting methods need the help of additional inputs (such as trimap, background image, etc.), or the model with high computational cost and complex network structure, which brings great difficulties to the application of human matting in practice. To alleviate such problems, we use a segmentation network as the foundation and use multiple branches to achieve human segmentation, contour detail extraction, and information fusion. We also propose a foreground probability map module, which uses the feature maps in the segmentation network to pre-estimate the foreground probabilities of each pixel and obtain Semantic Guided Matting Net. Under the condition that only a single image is needed as the input, the human matting task can be realized by making full use of the semantic information in the image. We validate our method on the P3M-10k dataset. Compared with the benchmark, our method has made significant improvements in various evaluation indicators.

Enhancing Urban Surveillance with Fog Computing, Mobile Cloud, and Big Data Analytics in 5G Networks

The new emerging applications in 5G network, in the context of the Internet of Everything (IoE), will introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog Computing and Cloud Computing, with their service orchestration mechanisms offer virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G will use the benefits of centralized high performance computing cloud centers, cloud and fog RANs and distributed peer-to-peer mobile cloud that will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates a model for fog and cloud hybrid environment service orchestration mechanisms for 5G network in terms of energy efficiency per user for different payloads.