Network System Research Articles

Carrier frequency synchronization for WLAN systems based on MIMO-OFDM-IM

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) with index modulation (IM) MIMO-OFDM-IM is a modulation technique that has garnered significant interest in recent times owing to its attraction of smooth transition to greener communications, enhancing efficiency in terms of energy, spectrum and compatibility to previous existing standards without the need for drastic changes in the physical layer. It is also proved that even though OFDM-IM provides better immunity to carrier frequency offset compared to traditional OFDM, the sensitivity to frequency error is still a major issue and it has to be resolved on priority basis to fully exploit the potential offered by the IM technique. In this work, we propose a novel non-data-aided algorithm that efficiently estimates and eliminates the offset present in the received signal. The algorithm has been analyzed for the MIMO-OFDM-IMbased wireless local area network system using the high throughput task group (TGn) MIMO channel models in the presence of AWGN. In addition, it is compared with the other popular blind algorithms based on both pilots and inactive data tones. The simulation plots depict a clear improvement of the new estimator over the existing methods not only in the low signal-to-noise ratio (SNR) regions but also in the high SNR region.

Analysis of changes in river connectivity during the urbanization process: a case study of the southeastern plain of Yinzhou, China.

Affected by human activities, the naturally occurring river network in the southeastern plain of Yinzhou has gradually evolved into a natural-artificial composite water system, and changes in river connectivity due to changes in river network systems have caused water security problems, including urban flooding. To clarify the river connectivity change and its relationship with the urbanization process, this paper discusses an evaluation method for river connectivity based on complex networks and cellular automata (CA) from the perspective of complex systems, quantitatively analyzes the spatial-temporal characteristics of the structural and functional river connectivity in the study area during the 1990s-2020s, and reveals the impact of river nodes and chains on the connectivity level under the disturbance of natural or human factors. The results contained the following revelations: ① River connectivity showed a decreasing trend in the initial and rapid development stages of urbanization from the 1990s to the 2010s and a limited increasing trend in the optimization and upgrading stages from the 2010s to the 2020s. ② River network degradation and ongoing connectivity decline are found in the northeastern part of the study area. The highest river connectivity exists in Dongqianhukaifaqu. ③ The number of river nodes and chains should be maintained at approximately 80% for normal river connectivity. The nodes of high degree in the inflow area are listed in the key protection areas. ④ Changes in river connectivity are significantly correlated with the urbanization process. Changes in the functional connectivity level affect the magnitude of a flood. This study provides a theoretical basis for river network connectivity improvement and flood prevention in plain areas.

Dynamic event‐triggered model‐free adaptive control for networked control systems with random packet loss

AbstractA novel dual‐channel dynamic event‐triggered model‐free adaptive control method with compensation is proposed for nonlinear networked control systems (NCSs) subjected to random packet loss. In order to tackle the issue of redundant data transmission, a dual‐channel triggering control framework is designed, where data transmission only occurs upon meeting the triggering conditions. Compared with the traditional static event‐triggered schemes with fixed triggering thresholds, the proposed method allows for dynamically adjustable triggering thresholds. This means that the number of data transmissions in the forward and feedback channels can be further reduced. In addition, considering the detrimental impact of random packet loss and output event‐triggered on the system, compensation is applied to the system's output data using the linear data model of the nonlinear system, which is directly derived from I/O data without incorporating any other mechanistic model information, thereby ensuring optimal control performance. In contrast to existing results, the proposed control strategy can enhance system performance while conserving network communication resources. The effectiveness and superiority of the proposed scheme have been validated through two simulations.

Taming explosive growth through linear augmentation

Abstract Nonlinear coupling among dynamical systems has found widespread applications in real-world systems. Such interactions may lead to an undesired explosive growth of dynamical variables. We employ a control technique to drive such unstable dynamics (explosive growth) of dynamical systems to stable dynamics through the scheme of linear augmentation. The efficacy of our scheme in suppressing the explosive growth of trajectories in a network of limit cycles is illustrated and the dynamics of the un-augmented and augmented networked systems are discussed. We also introduce the concept of ``dynamic augmentation" and exhibited the robustness of our scheme by successfully controlling the explosive growth of chaotic trajectories. Our results may be useful in controlling and preventing explosive growths of trajectories in a wide range of devices in the engineering fields.

Incidence and Dynamics of CRC Stage Migration: A Regional vs. a National Analysis

Background/Objectives: Due to an increased rate of surveillance colonoscopy, we aim to determine the impact of stage migration on the incidence and overall survival (OS) of patients who underwent pathological staging of colorectal cancer (CRC) at our Health Network System. Methods: Two datasets were included: subjects from the tumor registry at a regional Comprehensive Cancer Center (n = 1385) and subjects from the Surveillance, Epidemiology, and End Results (SEER) national database (n = 202,391). Results: A significant increase in the diagnosis of CRC Stage 1 and 4 was observed, with a decrease in stage 2, and no change in Stage 3 in the National datasets (p < 0.01). There was an increase in Stage 4 CRC diagnosis, with a concurrent decrease in stage 2, and no changes in stages 1 and 3 in the regional dataset (p < 0.05). OS followed the expected and progressive decrease in OS by stage (from 1 to 4, p < 0.01). Conclusions: The present findings confirmed CRC stage migration in our Health Network System, along with a national trend conducive to an increased OS for early CRC stages.

Deep learning-based post-disaster energy management and faster network reconfiguration method for improvement of restoration time

Natural disasters in the world often cause power outages. To improve post-disaster response and restoration time, a method for energy management and network reconfiguration in emergencies has been proposed, where energy storages systems (ESSs) in distribution networks are used to support the system balance immediately and then the network reconfiguration is accelerated based on deep learning techniques. First, when power failures occur, the outputs of ESSs are calculated optimally in terms of the proposed optimization model under the constraints of minimizing line losses. After the injections of ESSs in emergencies, the network reconfiguration is still needed due to the limitation of ESS capacities. To avoid calculations of power flow repeatedly, a deep-learning based reconfiguration method for distribution networks has been proposed, where the deep-learning model is trained offline and can output the parameters of power flow immediately when a state combination of distribution generators (DGs), loads and power lines, which is an effective way to accelerate the network reconfiguration. If many reconfiguration schemes are found, a decision-making method with preferences are used to select one according to different situations. Finally, cases are designed, and simulation results show that the calculation time of a reconfiguration schemes are reduced significantly by almost one hundred multiples.

Discovery and Prediction Study of the Dominant Pharmacological Action Organ of Aconitum carmichaeli Debeaux Using Multiple Bioinformatic Analyses.

Herbs, such as Aconitum carmichaeli Debeaux (ACD), have long been used as therapies, but it is difficult to identify which organs of the human body are affected by the various compounds. In this study, we predicted the organ where the drug predominantly acts using bioinformatics and verified it using transcriptomics. We constructed a computer-aided brain system network (BSN) and intestinal system network (ISN). We predicted the action points of ACD using network pharmacology (NP) analysis and predicted the dockable proteins acting in the BSN and ISN using statistical-based docking analysis. The predicted results were verified using ACD-induced transcriptome analysis. The predicted results showed that both the NP and docking analyses predominantly acted on the BSN and showed better hit rates in the hub nodes. In addition, we confirmed through verification experiments that the SW1783 cell line had more than 10 times more differentially expressed genes than the HT29 cell line and that the dominant acting organ is the brain, using network dimension spanning analysis. In conclusion, we found that ACD preferentially acts in the brain rather than in the intestine, and this multi-bioinformatics-based approach is expected to be used in future studies of drug efficacy and side effects.

Feeder–Line Concept Development For Public Transportation Network In Urban Area Based On O-D Matrix And Performance Evaluation

The management of the public transport network system is very important because it affects the future of the city and supports the activities of the community in travelling and movement. This research aims to develop a feeder-line concept on the public transport network based on O-D Matrix and Operational Performance Evaluation. The research method used is the operational performance standard indicator approach based on the Directorate General of Land Transportation SK 687/AJ.206/DRJD/2002 and the O-D Matrix method in determining new route recommendations. This research uses secondary data as well as primary surveys to assess operational performance. The results show that the level of operational performance of public transport in the research area there are still several routes below the standard with a total of routes operating from D1 to D8 of which 75% of the routes are in good condition and the other 25% of the routes have substandard assessment results with a total value of ?18. Thus, it is recommended that rerouting to be carried out on routes whose operational performance is still below standard, namely routes D7, and D8 based on the characteristics of the feeder-line concept to BRT Corridor 2.

Farm Monitoring System with Drones and Optical Camera Communication.

Drones have been attracting significant attention in the field of agriculture. They can be used for various tasks such as spraying pesticides, monitoring pests, and assessing crop growth. Sensors are also widely used in agriculture to monitor environmental parameters such as soil moisture and temperature. Due to the high cost of communication infrastructure and radio-wave modules, the adoption of high-density sensing systems in agriculture is limited. To address this issue, we propose an agricultural sensor network system using drones and Optical Camera Communication (OCC). The idea is to transmit sensor data from LED panels mounted on sensor nodes and receive the data using a drone-mounted camera. This enables high-density sensing at low cost and can be deployed in areas with underdeveloped infrastructure and radio silence. We propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. From computer simulations, we confirmed that the proposed algorithm reduces total flight time by 30% compared to a shortest-path algorithm. We also conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, to demonstrate the effectiveness of the proposed system. We collected 5178 images of LED panels with a drone-mounted camera to train YOLOv5 for object detection. With simple On-Off Keying (OOK) modulation, we achieved sufficiently low bit error rates (BERs) under 10-3 in the real-world environment. The experimental results show that the proposed system is applicable for drone-based sensor data collection in agriculture.

Barrier Polarity Reversal Based on Interfacial Modification of Au Nanoparticles for Nonvolatile Multilevel Memory and Optoelectronic Synapses.

Optoelectronic synaptic devices, integrating light sensing and information processing capabilities, have emerged as advantageous tools for the implementation of visual neuromorphic computing. However, the transient light-triggered response characteristic typically results in unstable memory retention times and restricted current response ranges, posing significant challenges to the development and practical application of neural network systems. In response to these limitations, this study developed a nonvolatile optoelectronic memory based on the indium tin oxide (ITO)/Au nanoparticles (NPs)/amorphous Ga2O3 (a-Ga2O3)/Pt structure. Unlike conventional optoelectronic memories, this device features a modification with Au NPs that markedly enhances the Schottky barrier height at the interface. Au NPs function as a charge-trapping layer for sensitive and large-scale modulation of the barrier by the light field, thereby enabling the nonvolatile reversal of the device's barrier polarity. This innovative approach enables controllable multilevel data storage with an ultra large on/off ratio (∼104) and excellent retention capability exceeding 12,000 s. Additionally, the device emulates essential synaptic functions and demonstrates potential application values in visual weak signal perception and image memory. This study introduces a mechanism for Schottky barrier polarity control and presents a promising strategy for the development of future high-performance integrated devices and optoelectronic synaptic elements.

Understanding internal dynamics of state anxiety during COVID‐19 pandemic: Seven‐wave longitudinal findings via panel network analysis

AbstractResearch on state anxiety has long been dominated by the traditional psychometric approach that assumes anxiety symptoms have a common cause. Yet state anxiety can be conceptualized as a network system. In this study, we utilized data from the COVID‐Dynamic dataset from waves 7 to 13, collected at three‐week intervals from June 6, 2020, to October 13, 2020, and included 1,042 valid participants to characterize the internal dynamics of state anxiety. Using the Gaussian graphical model along with strength centrality, we estimated three network models of state anxiety. The between‐subjects and contemporaneous network showed numerous positive relations between items and some unexpected negative relations. Three communities were identified in the between‐subjects network, and two communities were identified in the contemporaneous network. The temporal network showed the coexistence of positive and negative predictions between items after three weeks. Several items exhibited significant positive autocorrelations after three weeks. These findings have implications for anxiety theory and clinical interventions at between‐subjects and within‐subjects levels.

Triplex event-triggered recursive quantizer-based networked control system under communication delay and packet dropouts

In light of network congestion, communication delay, and packet dropouts, a triplex event-triggered recursive quantizer-based networked control system (TERQNCS) is proposed in this paper. To constrain quantization error while alleviating network congestion, the recursive quantizer is constructed in the TERQNCS. Considering it is insufficient to adequately alleviate network congestion only with either the quantizer or the event trigger, a triplex event-triggered mechanism is designed through the recursive quantizer. For revealing the impacts of communication delay and packet dropouts, four Bernoulli random variables are adopted to model the network channels in the TERQNCS. Then, the model-based predictive controller is designed by virtue of the predictive states, estimated states, and Bernoulli-based model. The proposed TERQNCS can achieve the desired system performance under communication delay and packet dropouts while adequately alleviating network congestion and constraining quantization error. The stability of the TERQNCS is proved by theoretical analysis. Besides, the advantages of the TERQNCS are substantiated by a tunnel diode circuit networked control system.

Network Intrusion Detection based on LSTM

Network intrusion detection, as an important means of ensuring daily network security, its accuracy and response speed are crucial for defending against network attacks. This article explores and implements deep learning based network intrusion detection techniques, particularly the application of Long Short Term Memory (LSTM) networks in detecting network intrusion behavior. The aim is to solve the problems of gradient vanishing and exploding in traditional RNNs, improve the emergency response capability of network systems, and enhance the reliability and security of networks. The study used the KDD99 dataset to demonstrate the effectiveness of the LSTM model in network intrusion detection. The experimental results show that the constructed LSTM model achieves an accuracy of 88.01% in network intrusion detection tasks, demonstrating high accuracy and feasibility.

Event-Based Finite-Time $$H_\infty $$ Security Control for Networked Control Systems with Deception Attacks

Event-Based Finite-Time $$H_\infty $$ Security Control for Networked Control Systems with Deception Attacks

Waiting Time Control Chart for M/G/1 Retrial Queue

Retrial queues are used extensively to model many practical problems in service systems, call centers, data centers, and computer network systems. The average waiting time is the main observable characteristic of the retrial queues. Long queues may cause negative impacts such as waste of manpower and unnecessary crowding leading to suffocation, and can even cause trouble for customers and institutions. Applying control chart technology can help managers analyze customers’ waiting times to improve the effective performance of service and attention. This paper pioneers the developing and detailed study of a waiting time control chart for a retrial queue with general service times. Two waiting time control charts, the Shewhart control chart, and a control chart using the weighted variance method are constructed in this paper. We present three cases for the Shewhart control chart in which the service time obeys special distributions, such as exponential, Erlang, and hyper-exponential distributions. The case of an exponentially distributed service time is also presented for the control chart using the weighted variance method. Based on the numerical simulations conducted herein, managers can better monitor and analyze the customers’ waiting times for their service systems and take preventive measures.

Information protection and cyber security in the public and financial sectors

The public sector faces increasing challenges in the field of cyber security and information protection, stimulated by the rapid development of digital technologies. A significant increase in the need for reliable protection of state data and information systems requires state institutions to implement complex information protection measures. The purpose of the study is to analyze modern approaches and technologies in the field of cyber security and the use of encryption, cryptography, and the use of cloud technologies that can be used in the public sector for effective information protection. The research methodology involves consideration of strategies that allow to increase the level of security and ensure the protection of critical infrastructure. The results of the study emphasize the need to adapt to the rapidly changing digital environment and involve modern technological solutions to create a reliable information network system in the public sector. The findings of the study draw attention to the importance of global cooperation and increased investment in the field of countering digital attacks, demonstrating examples of effective international cooperation and support in this direction. The need for the integration of international standards and practices in order to create a global digital space has been revealed. A promising area of research is the analysis of the impact of the latest technologies on data storage strategies, which contributes to the identification of ways for further innovations in the industry and the creation of flexible information network systems capable of effectively responding to changing conditions and challenges of modern cyberspace.

Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

In recent years, Advanced Persistent Threat (APT) attacks on network systems have increased through sophisticated fraud tactics. Traditional Intrusion Detection Systems (IDSs) suffer from low detection accuracy, high false-positive rates, and difficulty identifying unknown attacks such as remote-to-local (R2L) and user-to-root (U2R) attacks. This paper addresses these challenges by providing a foundational discussion of APTs and the limitations of existing detection methods. It then pivots to explore the novel integration of deep learning techniques and Explainable Artificial Intelligence (XAI) to improve APT detection. This paper aims to fill the gaps in the current research by providing a thorough analysis of how XAI methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), can make black-box models more transparent and interpretable. The objective is to demonstrate the necessity of explainability in APT detection and propose solutions that enhance the trustworthiness and effectiveness of these models. It offers a critical analysis of existing approaches, highlights their strengths and limitations, and identifies open issues that require further research. This paper also suggests future research directions to combat evolving threats, paving the way for more effective and reliable cybersecurity solutions. Overall, this paper emphasizes the importance of explainability in enhancing the performance and trustworthiness of cybersecurity systems.

Optimal Control Strategy to Analyse the Networked Control System Stability

It is crucial to preserve the networked control systems (NCS) transient performance and stability because adding uncertain parameters degrades system performance and introduces instability. Thus, the analysis of NCS system performance under uncertain conditions, such as disturbance, is the focus of this paper. The performance of NCS is demonstrated using control action and some appropriate stability conditions. The simulation diagram in result section displays effectiveness of proposed methodology and shows a comparative analysis of the response signal with and without control action along with disturbance in NCS. Experiments conducted in the MATLAB Simulink environment demonstrate the efficacy of the suggested methodology.

Formation Control with Feedback Nash Strategy for Multiple Unmanned Aerial Vehicles in Unknown Environments

In the paper, the formation control problem in unknown environments for networked multi-unmanned aerial vehicle systems (UAVSs) is resolved under a nonlinear differential game (NL-DDG) framework. The main challenge of this framework is how to obtain feedback Nash strategies, which typically overly rely on global information and cannot ensure the existence of Nash strategies in unknown environments. Toward this goal, we initially design collision avoidance rules to ensure the safety of each UAV. Subsequently, we utilize an inverse optimal control method to construct the NL-DDG that incorporates both formation control and collision avoidance costs, enabling the derivation of analytical forms of Nash strategies relying solely on local information and minimizing performance metrics. In addition, the existence of feedback Nash strategy can be guaranteed with an undirected and connected information topology, which represents the optimality for the UAVSs. Moreover, we analyze the stability of the closed-loop system. Finally, the simulation results validate the effectiveness of the proposed scheme.

Developing an Early Warning System for Financial Networks: An Explainable Machine Learning Approach.

Identifying the influential variables that provide early warning of financial network instability is challenging, in part due to the complexity of the system, uncertainty of a failure, and nonlinear, time-varying relationships between network participants. In this study, we introduce a novel methodology to select variables that, from a data-driven and statistical modeling perspective, represent these relationships and may indicate that the financial network is trending toward instability. We introduce a novel variable selection methodology that leverages Shapley values and modified Borda counts, in combination with statistical and machine learning methods, to create an explainable linear model to predict relationship value weights between network participants. We validate this new approach with data collected from the March 2023 Silicon Valley Bank Failure. The models produced using this novel method successfully identified the instability trend using only 14 input variables out of a possible 3160. The use of parsimonious linear models developed by this method has the potential to identify key financial stability indicators while also increasing the transparency of this complex system.