Switching Networks Research Articles

Microhardness, Young’s and Shear Modulus in Tetrahedrally Bonded Novel II-Oxides and III-Nitrides

Direct wide-bandgap III-Ns and II-Os have recently gained considerable attention due to their unique electrical and chemical properties. These novel semiconductors are being explored to design short-wavelength light-emitting diodes, sensors/biosensors, photodetectors for integration into flexible transparent nanoelectronics/photonics to achieve high-power radio-frequency modules, and heat-resistant optical switches for communication networks. Knowledge of the elastic constants structural and mechanical properties has played crucial roles both in the basic understanding and assessing materials’ use in thermal management applications. In the absence of experimental structural, elastic constants, and mechanical traits, many theoretical simulations have yielded inconsistent results. This work aims to investigate the basic characteristics of tetrahedrally coordinated, partially ionic BeO, MgO, ZnO, and CdO, and partially covalent BN, AlN, GaN, and InN materials. By incorporating a bond-orbital and a valance force field model, we have reported comparative results of our systematic calculations for the bond length d, bond polarity αP, covalency αC, bulk modulus B, elastic stiffness C(=c11−c122), bond-stretching α and bond-bending β force constants, Kleinmann’s internal displacement ζ, and Born’s transverse effective charge eT*. Correlations between C/B, β/α, c12c11, ζ, and αC revealed valuable trends of structural, elastic, and bonding characteristics. The study noticed AlN and GaN (MgO and ZnO) showing nearly comparable features, while BN (BeO) is much harder compared to InN (CdO) material, with drastically softer bonding. Calculations of microhardness H, shear modulus G, and Young’s modulus Y have predicted BN (BeO) satisfying a criterion of super hardness. III-Ns (II-Os) could be vital in electronics, aerospace, defense, nuclear reactors, and automotive industries, providing integrity and performance at high temperature in high-power applications, ranging from heat sinks to electronic substrates to insulators in high-power devices.

AWGR-based all-optical switching network for distributed machine learning.

With the increase sizes of training datasets and models, the bottleneck in distributed machine learning (DML) training has shifted from computation to communication. To address this bottleneck, we propose an all-optical switching network architecture for accelerating the communication phase of DML training. Experimental results validate packets with error-free and 385 ns server-to-server low-latency communication at traffic load of 0.9. Small-scale DML training experiment deployed in the proposed architecture shows that Resnet50, Resnet101, and Vgg19 can be accelerated by 1.16x to 1.48x compared to electrical switching network. The proposed architecture demonstrates a 58.9% enhancement in cost efficiency and a 60.9% improvement in power efficiency compared to the 3-tier fat-tree architecture.

Log‐TF‐IDF and NETCONF‐Based Network Switch Anomaly Detection

ABSTRACTIn this study, we propose and evaluate a model that utilizes both log data and state data to detect abnormal conditions in network switches. Building upon our previous research and drawing inspiration from TF‐IDF used in natural language processing to measure word importance, we propose a statistical method, Log‐TF‐IDF, to quantify the rarity of each log pattern in the log data. Furthermore, based on this Log‐TF‐IDF, we introduce the AB Score, which quantifies how abnormal the current log pattern is. Our findings indicate that the AB Score is notably higher and more volatile in abnormal conditions. We confirm that anomaly detection is feasible through the AB Score, which has the advantage of being computationally efficient due to its statistical basis. We combined the metrics generated during the AB Score calculation with resource data collected with NETCONF and developed a machine‐learning model to detect abnormal conditions in network switches. We confirm that this model can detect abnormal conditions with an F1 score of 0.86 on our collected dataset, confirming its viability for detecting abnormal states in network equipment.

Single-cell transcriptomic profiling of rat iridocorneal angle at perinatal stages: Revisiting the development of periocular mesenchyme.

The periocular mesenchyme (POM) gives rise to key structures in the ocular anterior segment, and its malformation leads to anterior segment dysgenesis (ASD) with iridocorneal angle (ICA) abnormalities. However, the transcriptional profile of the POM and the regulatory mechanisms governing cell-fate decision during anterior eye and ICA development remain poorly understood. In this study, we performed a comprehensive time-series analysis by sequencing rat anterior ocular samples collected at five consecutive perinatal stages: embryonic days 16.5 and 18.5, the day of birth, and postnatal days 4 and 8, at the single-cell level and validated a portion of in silico findings with immunostaining. High-quality transcriptomes were obtained from 59,416cells with diverse embryonic origins. A prominent transcriptional shift was observed in POM cells, coinciding with anatomical alterations around the ICA shortly after birth. We illustrated the molecular signatures of five POM subclusters while tracing their developmental trajectories. Additionally, we identified key driver genes, as well as cell type-specific and stage-wise gene modules underlying lineage specification. Furthermore, the switch of regulon network and cellular crosstalk associated with POM maturation were unveiled. Lastly, we mapped ASD-relevant genes to this single-cell atlas, revealing distinct expression patterns. Collectively, this study provides a transcriptomic blueprint for understanding normal POM and ICA development, as well as a valuable reference for future research into ASD pathogenesis.

آلية إدارة مفتاح RSA المحسّنة لحماية حزم التحكم في شبكات تبديل الاندفاع البصري

Optical Burst Switch (OBS) network is a typical technology currently proposed, as an infrastructure to deal with most highly demanded communication services. OBS architecture consists of Data Burst (DB) as a payload and Burst Header Packet (BHP) as a control packet. BHP carries important information about path reservation and its corresponding DB. However, the information security (Privacy, Reliability, Confidentiality, Integrity, Availability, Authentication, and Authorization) of OBS network communication has been represented as the current issues, which affect the network performance in terms of data loss and data transmission delay. This paper focuses on security weaknesses of BHP transmission in OBS network against Data Burst Redirection (DBR) Attack. The current paper was conducted to develop a protection mechanism to ensure the confidentiality and authentication of BHP. In OBS, RSA public-key encryption algorithm has been enhanced and integrated. Moreover, the Self-Controlling key distribution technique has been implemented to ensure a high security level of key transmission between each pair of OBS nodes. Three different OBS environments have been designed and implemented. These environments were established on the bases of three different concepts; OBS Topology without Security Measures and without Security Attacks, OBS Topology under Security Attacks without Security Measures, and OBS Topology under Security Attacks with Security Measures. The obtained results are based on Burst Loss Ratio, Throughputs, and Average Delay Ratio. Such a result has successfully proved the trustworthiness and efficiency of Control Packet Protection Technique (CPPT-OBS) to prevent DBR attack. Keywords: OBS , DB, BHP, DBR, RSA, CPPT

ML-Based Dynamic Network Switching Framework for Non-Terrestrial Networks in 5G and Beyond

ML-Based Dynamic Network Switching Framework for Non-Terrestrial Networks in 5G and Beyond

A Cross-Layer Game-Theoretic Approach to Resilient Control of Networked Switched Systems Against DoS Attacks.

This article investigates the resilient control strategies of networked switched systems (NSSs) against denial-of-service (DoS) attacks and external disturbance. In the network layer, both the defender and the attacker allocate energy over multiple channels. Considering the impact of switching characteristic in the physical layer on the network layer, a dynamic regulating factor is proposed to adjust the total energy of the defender. To optimize the signal-to-interference-noise ratio and energy consumption simultaneously at each player's side, a multiobjective game problem is formulated. Furthermore, a nondominated sorting genetic algorithm framework is employed, incorporating the knee point selection mechanism to attain the Pareto-Nash equilibrium, based on which the optimal defense strategy can be derived to achieve resilience against DoS attacks. In the physical-layer, taking the dynamic packet loss caused by DoS attacks and external disturbance into account, an H∞ minimax controller containing control inputs and the switching signal is designed to guarantee the optimal performance for NSSs through the dynamic game-theoretic approach. Finally, the networked continuous stirred tank reactor system is provided to verify the effectiveness of the proposed method.

K-Nearest Neighbors with Third-Order Distance for Flooding Attack Classification in Optical Burst Switching Networks

Optical burst switching (OBS) is a network architecture that combines the advantages of packet and circuit switching techniques. However, OBS networks are susceptible to cyber-attacks, such as flooding attacks, which can degrade their performance and security. This paper introduces a novel machine learning method for flooding attack detection in OBS networks, based on a third-order distance function for k-nearest neighbors (KNN3O). The proposed distance is expected to improve detection accuracy due to higher sensitivity with respect to the distance difference between two points. The developed method is compared with seven other machine learning methods, namely standard KNN, KNN with cosine distance (KNNC), multi-layer perceptron (MLP), naive Bayes classifier (NBC), support vector machine (SVM), decision tree (DT), and discriminant analysis classifier (DAC). The methods are further assessed using five metrics: accuracy, precision, recall, F1-score, and specificity. The proposed method achieved an accuracy of 99.3%, outperforming the original KNN, MLP, and SVM, which achieved accuracies of 99%, 76.4%, and 94.7%, respectively. The results show that KNN3O is the best method for flooding attack detection in OBS networks, as it achieves the highest scores in all five metrics.

Formation control of multiple VTOL UAVs with global stability subject to switching networks

This article investigates the problem of distributed formation control for multiple vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) under a switching network with uniform joint connectivity condition. VTOL UAV, as a representative UAV, combines the VTOL capabilities of rotary-wing aircraft with the forward flight capabilities of fixed-wing aircraft. On the one hand, a novel distributed observer is developed for each VTOL UAV to asymptotically acquire the leader’s desired information. On the other hand, considering the underactuated nature of VTOL UAVs, a hierarchical framework is introduced to achieve position tracking of the desired observation positions and attitude tracking of the commanded attitudes. Compared to existing findings, a notable feature of this study is the allowance for a directed and switching communication network. Specifically, the switching network only requires joint connectivity. Additionally, the introduction of hyperbolic tangent functions ensures the boundedness of control inputs, further guarantees the global asymptotic stability of the entire closed-loop system. Simulation results validate the effectiveness of the algorithm.

Scalability of Ultralow‐Loss Calibration‐Free Silicon Photonic Mach‐Zehnder Switches

AbstractWith the rapid development of artificial intelligence (AI) based on deep neural networks, large‐scale photonic switches are essential components for the fast and efficient communication of unprecedentedly large amounts of data between processing units and memories. In this paper, a comprehensive Monte Carlo analysis is provided on the scalability of Mach–Zehnder switch (MZS) networks utilizing Benes topologies as an example, employing the transfer matrix method. The results show that iterative calibration algorithms with high time complexities are infeasible for large‐scale MZSs with significant random phase imbalances, which, instead of the excess loss, is the dominant fundamental obstacle for scaling up MZS. Therefore, calibration‐free MZSs are crucial for scaling up. To further validate the key assumptions of the Monte Carlo analysis above, ultralow‐loss 2 × 2 MZSs and 4 × 4 Benes MZSs fabricated with standard 180‐nm silicon photonics foundry processes are systematically characterized. Drawing from the statistical experimental results of random phase imbalance and excess loss, the scalability of the Benes topology is projected and concludes that it is promising to realize large‐scale, low‐excess‐loss, calibration‐free N × N photonic switches (e.g., N ≥ 64) based on these proposed MZS for agile, flexible, and scalable optical packet/burst switching (OPS/OBS) in data centers.

A Big Data Discriminatory Pricing Strategy for Takeaway Platforms Considering Different Attributional Behaviors of Users

To address the big data discriminatory pricing problem of two takeout platforms with different attribution behaviors of platform users, the equilibrium pricing of the two platforms under different attribution behaviors is analyzed and compared based on the Hotelling model, and the effects of switching costs and network effects on pricing strategies are examined. The results show that: in the single-attribution-single-attribution model, the network effect affects the user scale of the platform, and then the pricing of the platform is affected by the switching costs and the network effect, and the pricing of the platform in the first stage affects the pricing decision in the second stage; in the single-attribution-partial-multi-attribution model, the pricing of the platform is affected not only by the switching cost and the network effect but also by the switching costs and the network effect of the user base. In the single-attribution-partial-multi-attribution model, platform pricing is not only affected by switching costs and network effects but also by users' base utility, and the platform's pricing in the first stage partially affects the pricing decision in the second stage.

Dynamic control, routing, and resource assignment in multi-granular optical node topologies combining wavelength, waveband, and spatial switching for 6G transport networks [Invited

Effective management of end-to-end 6G network services is crucial, with peak capacity requirements for 6G transport connections expected to exceed 1 Tb/s. As demand for high bandwidth rises, there is a growing necessity for high-capacity optical fiber links, including ultra-wideband (UWB) and multiple fiber links within the network. Scaling up to accommodate these demands, designing wavelength-selective switches (WSSs) for such networks significantly increases the port count. To tackle this issue, we propose various multi-granular optical node (MG-ON) architectures utilizing heterogeneous wavelength, waveband, and spatial switching. We evaluate these architectures’ performance against high-capacity wavelength division multiplexed (WDM) networks through various simulation parameters.

Distributed algorithms for aggregative games with multiple uncertain Euler-Lagrange systems over switching networks

Distributed algorithms for aggregative games with multiple uncertain Euler-Lagrange systems over switching networks

Fully Event-Triggered Practical Leader-Following Consensus of Multiple Euler-Lagrange Systems Over Switching Networks.

In this article, a fully event-triggered adaptive distributed control law is developed for solving the practical leader-following consensus problem (LFCP) of a group of uncertain Euler-Lagrange (EL) systems. An event-triggered output-based distributed observer (OBDO) is established first to estimate the state variable of the leader system over jointly connected switching networks (JCSNs). Then, the event-triggered adaptive control law is designed to exclude the Zeno phenomenon and to make the steady-state consensus error smaller than any specified value by adjusting some design parameters. Compared with the existing results, the proposed event-triggered control law only depends on the output of the leader system, works over JCSNs, and, moreover, is fully event-triggered so that it can be directly implemented in a digital platform. The overall design is illustrated using a group of two-link manipulators.

Sparse Bayesian Learning for Switching Network Identification.

Learning dynamical networks based on time series of nodal states is of significant interest in systems science, computer science, and control engineering. Despite recent progress in network identification, most research focuses on static structures rather than switching ones. Therefore, this article develops a method for identifying the structures of switching networks by exploring and leveraging both temporal and spatial structural information that characterizes the switching process. The proposed method employs a new sparse Bayesian learning algorithm based on coupled hyperblocks to estimate unknown switching instants. Experimental results on benchmark artificial and real networks are elaborated to demonstrate the effectiveness and superiority of the proposed method.

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching Networks.

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

Dynamic Mobile X-Haul Reconfiguration by Fast Network Switching for Low-Latency MEC Service

Dynamic Mobile X-Haul Reconfiguration by Fast Network Switching for Low-Latency MEC Service

On containment control for discrete-time switching networks of delayed first-order systems

This paper delves into the domain of dynamic containment control, with specific focus on discrete-time perturbed and delayed systems characterised by first-order dynamics. Central to containment control is the aspiration to guide agents towards desired spatial formations while mitigating the effects of disturbances, ensuring cohesive behaviour. In this respect, we propose a containment protocol that ensures input-to-state stability with respect to the convex hull generated by the leader trajectories while guaranteeing robustness with respect to both fixed and distributed communication delays acting on the state measurements exchanged between the network nodes. Numerical simulations on a team of interconnected drones show the effectiveness of the proposed approach.

Observer-Based Event-Triggered Fuzzy Control for Switched Multi-Agent Systems with State Constraints and Sensor Faults

In this paper, we consider the output feedback event-triggered tracking control problem for a class of switched nonlinear multi-agent systems (MASs) with sensor faults and state constraints. For unknown nonlinear functions in the system, fuzzy logic systems are used to approximate them. To address sensor faults, sensor error compensation coefficients are designed to compensate for the errors caused by sensor faults. Meanwhile, state observer is designed to estimate the unmeasurable states in the system, providing necessary information for control design. Based on directed switching networks containing a directed spanning tree, an event-triggered output feedback controller is designed to enable the MAS to track the leader agent. By constructing a suitable barrier Lyapunov function, the stability of the system is analyzed, and it is proved that the consensus tracking can be achieved without violating the state constraints, the uniform boundedness of all the closed-loop system signals is ensured and the Zeno behavior can be eliminated. Finally, the effectiveness of the proposed algorithm is verified through a simulation example.

Optimized Low‐Loss Ge2Sb2Te5 Superlattice: Design, Fabrication and Application

AbstractOptical phase change materials (OPCMs) hold significant promise in photonic integrated circuits (PICs) due to their non‐volatile modulation capabilities through phase transition. Recently, integrating OPCMs with silicon devices has been leveraged to achieve multi‐level modulation in neural network computing. However, the conventional OPCM Ge2Sb2Te5 (GST), faces challenges in large‐scale PICs due to its large absorption. In this work, an optimized low‐loss GST superlattice is demonstrated, which significantly lowers crystalline attenuation coefficients from 2.69 to 0.539 dB µm−1, with negligible loss in amorphous state, while retaining excellent phase modulation capability. Additionally, 15 distinguishable extinction ratio states are achieved by manipulating irradiation energy for a hybrid Si microring resonator (MRR) embedded with GST superlattice. To further explore the potential application of GST superlattice in optical computing, an all‐optical tunable temporal differentiator is empirically demonstrated with a single hybrid MRR, achieving a broad dynamic differentiation order ranging from 0.7 to 1.3. The non‐volatile nature of OPCM facilitates low energy consumption and effectively optimizes chip density. The proposed structural design strategy to reduce the absorption loss is universal for any promising OPCMs feasible for large‐scale artificial intelligence‐driven PICs, including optical analog computing, optical switching and optical neural networks.