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Formation flight holds significant potential for various applications involving aerial robot swarms. However, current methodologies lack the capability to autonomously execute large-scale formation flights in densely populated environments. To bridge the gap, a decentralized and asynchronous formation flight planner is proposed based on a graph-based formation metric and tight representations of kino-dynamically feasible trajectories for collision avoidance. The planner handles formation path planning along with dynamic obstacles and intervehicle collision avoidance using minimum volume convex hulls for agent trajectories. The employed formation metric is invariant to rotation, translation, and scaling, granting greater flexibility in formation coordination. A decoupled and distributed trajectory optimization framework is proposed to enhance the computational feasibility of large-scale formation flights. Moreover, to mitigate issues relating to communication delays between agents, asynchronous execution of a finite horizon navigation framework with usage of sparse trajectory control points for trajectory segments is employed. Simulations with multiple agents, static and dynamic obstacles support the robustness of the planner to formation flights in real world. The planner demonstrates goal/waypoints achievement and formation adherence capabilities that are assessed and compared using a popular quantifiable formation similarity metric. Furthermore, the paper also serves as a guideline to build upon trajectory planning frameworks for tight formation control in cluttered, dynamic environments.

This paper proposes a parallel implementation of the Bron-Kerbosch algorithm, which finds all maximal cliques in large, complex graphs using CPU and thread-level parallelism and distributed memory with multiple cores. With the growing size and complexity of modern graphs, sequential algorithms can become impractical, making parallel computation a promising solution for reducing duration and enhancing scalability. Decomposing the graph into smaller components and distributing the workload across multiple threads using task parallelism is the proposed method. To optimise the algorithm’s efficacy across processes, load-balancing techniques are also investigated. The modified Bron-Kerbosch algorithm is implemented and evaluated on a variety of large graphs, exhibiting significant runtime and scalability enhancements over the sequential version. The conclusion of the paper discusses the prospective applications of the parallel Bron-Kerbosch algorithm in a variety of domains, including social network analysis, bio informatics, and network security. Overall, this research contributes to the expanding body of work on parallel computing and graph analysis by emphasising the advantages of CPU and thread-level parallelism for efficiently solving complex computational problems.

Graph Attention Networks (GAT) is a type of neural network architecture designed to effectively model and process data represented as graphs. GATs leverage the concept of attention mechanisms to learn the importance of different nodes in a graph when performing tasks such as node classification or link prediction. By assigning attention weights to neighboring nodes, GATs can capture the relevance and influence of different graph elements in a localized manner. In this paper, we present Graph Oriented Attention Networks (GOAT) as a novel approach for attention calculation in graph-related tasks, where attention is specifically directed towards a particular destination node during each iteration. This approach aims to enhance the interpretability and control of attention within a graph neural network. By focusing attention on a specific destination node, the model can explicitly consider the influence of that particular node on the different graph nodes, providing insights into its role and importance within the graph structure. This approach is particularly valuable in scenarios where understanding the impact of individual nodes on specific targets is critical: in our case, to predict a heuristic for the $\text{A}^{\ast} $ search algorithm, we are focusing the attention on the destination node. Experimental results indicate that this new approach effectively improves both the performance and interpretability of graph-based models, facilitating a more fine-grained analysis of graph dynamics and the factors influencing specific nodes.

Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet distinguishing it from later stage dementia is intricate due to subtle symptoms. The primary objective of this study is to adopt a complex network perspective to unravel the underlying pathophysiological mechanisms of dementia-related disorders. Leveraging the extensive availability of electroencephalogram (EEG) data, our study focuses on the meticulous identification and analysis of EEG-based brain functional network (BFNs) associated with dementia-related disorders. To achieve this, we employ the Phase Lag Index (PLI) as a connectivity measure, offering a comprehensive view of neural interactions. To enhance the analytical rigor, we introduce a data-driven threshold selection technique. This innovative approach allows us to compare the topological structures of the formulated BFNs using complex network measures quantitatively and statistically. Furthermore, we harness the power of these BFNs by utilizing them as pre-defined graph inputs for a Graph Convolution Network (GCN-net) based approach. The results demonstrate that graph theory metrics, such as the rich-club coefficient, transitivity, and assortativity coefficients, effectively distinguish between MCI, Alzheimer’s disease (AD) and vascular dementia (VD). Furthermore, GCN-net achieves high accuracy (95.07% delta, 80.62% theta) and F1-scores (0.92 delta, 0.67 theta), highlighting the effectiveness of EEG-based BFNs in the analysis of dementia-related disorders.

In this paper, an age of information (AoI) oriented data collection scheme is proposed for wireless sensor networks with energy-constrained unmanned aerial vehicles (UAVs). Different from traditional approaches that focus on one single UAV, we advocate the activation of multiple candidate UAVs to improve the AoI performances. However, coordinating multiple UAVs complicates the data collection strategies of each UAV, rendering AoI minimization a formidable challenge. To address the intractable formulated optimization problem, we propose a heuristic two-step algorithm based on greedy search for multiple UAVs. Specifically, we address the coordination of multiple UAVs through a novel algorithm grounded in graph theory and kernel K-means methods. Concurrently, we devise an energy-constrained trajectory planning algorithm to optimize data collection for each activated UAV. The numerical results validate the accuracy of the proposed algorithm, and demonstrate that both average AoI and energy consumption per UAV can be reduced when more energy-constrained UAVs are involved in the data collection process for wireless sensor networks.

Graph theory within power electronics, developed over a 50-year span, is continually evolving, necessitating ongoing research endeavors. Facing with the never-been-seen explosion of graph-structured data, the state-of-the-art deep learning technique-Graph Neural Networks (GNNs), becomes the leading trend in machine learning within just recent five years and demonstrated surprisingly broad and prominent benefits covering from new drug discovery to better IC design. However, its promising applications in Power Electronics are still rarely discussed and its full potential remains unexplored. Addressing this gap, this review paper is the first to outline GNNs’ general workflow in power electronics, laying the groundwork and examining current GNN methodologies within the field. To bridge the gap in the sparse GNN literature within this domain, we also provide extended discussions on leveraging insights from GNN-aided circuit design to enrich power electronics research. Our work includes in-depth GNN-based case studies that demonstrate promising applications from converters to system-level power electronics, showcasing GNNs’ unique benefits and untapped possibilities (e.g., accurate component design, voltage predictions on IEEE-13 bus and 118 bus systems). Additionally, we provide a comprehensive survey of GNNs’ latest and successful applications, emphasizing their impact on energy-centric sectors, such as transportation electrification, smart grids. Considering the interdisciplinary nature of power electronics in modern energy systems, our review highlights the potential of GNNs emerge as a promising tool to decode the intricate behavior and dynamics of power electronics systems, and we hope such synergies between advanced AI methodologies like GNNs with the ever-evolving graph theory can lead to more powerful tools, novel methodologies, and advancements in the power electronics community.

In this paper, we propose a load-balancing algorithm for small-cell integrated access and backhaul (IAB) networks operating in the millimeter wave (mmWave) band. With the help of mmWave communications, ultra-dense small cell deployment is a key technology for future networks, but it leads to high fiber installation costs. IAB has emerged as a cost-effective and flexible solution because it uses the multi-hop wireless backhaul to the core network via macro base stations (BSs). Multi-hop transmission and spectrum sharing between access and backhaul links cause an unbalanced load across BSs under IAB. To tackle the unbalanced load problem, we propose a graph-based load balancing algorithm for IAB by estimating and adapting to network load status. The weight of the graph is represented as the cost of the link between two BSs and is defined to reflect the cell load and link capacity. The proposed algorithm estimates the upcoming load and adjusts the network transmission schedule to balance the load inside the network. Through simulations, performance is evaluated in various environments. The simulation results show that the proposed algorithm not only distributes the load across small cells more evenly but also increases network throughput by 22.3% and 32.1% with 16.3% and 28.7% higher in UEs satisfaction when compared to MaxMinThroughput and Backpressure algorithms.

In a platoon communication scenario, the Platoon Leader (PL) vehicle interacts with the gNodeB (gNB) to obtain some assigned radio resources and then the obtained radio resources are allocated to the PL vehicle and its Platoon Member (PM) vehicles. The high transmission delay in the platoon network affects the quality of vehicles’ connectivity. Therefore, the issues of improving radio resource’s spectral efficiency and stability of the vehicle platooning with limited radio resources need to be tackled. This work proposes the 2-STage Resource Allocation (2-STRA) method for multi-platoon communication to reduce the transmission latency of platoon vehicles, i.e., it can improve the data transmission rates of platoon vehicles for the conditions of various speeds and various numbers of subchannels. In the $1^{st}$ stage, a greedy algorithm that jointly considers subchannel allocation and power control for PL vehicles’ resource allocation is devised. In the $2^{nd}$ stage, the resource allocation for PM vehicles, which is based on the subchannels that are not allocated to PL vehicles, is resolved. A tripartite hypergraph, in which a tripartite hyperedge links:I 1) cluster of PM vehicles that use the same subchannel; 2) individual cellular user; and 3) subchannel, is adopted to devise the PM vehicles’ resource allocation algorithm. The simulation results show that the proposed 2-STRA method can have better performance in terms of spectral efficiency and the sum of data transmission rate comparing with the other methods.

This article delves into the challenges of ensuring stability (in some sense) and robustness in large-scale second-order consensus networks (SOCNs) and autonomous vehicle platoons in the discrete-time domain. We propose a graph-theoretic methodology for designing a state feedback law for these systems in a discrete-time framework. By analyzing the behavior of the solutions of the networks based on the algebraic properties of the Laplacian matrices of the underlying graphs and on the value of the update cycle (also known as the time step) of each vehicle, we provide a necessary and sufficient condition for the stability of a linear second-order consensus network in the discrete-time domain. We then perform an $\mathcal {H}_{2}$-based robustness analysis to demonstrate the relationship between the $\mathcal {H}_{2}$-norm of the system, network size, connectivity, and update cycles, providing insights into how these factors impact the convergence and robustness of the system. A key contribution of this work is the development of a formal framework for understanding the link between an $\mathcal {H}_{2}$-based performance measure and the restrictions on the update cycle of the vehicles. Specifically, we show that denser networks (i.e., networks with more communication links) might require faster agents (i.e., smaller update cycles) to outperform or achieve the same level of robustness as sparse networks (i.e., networks with fewer communication links) - see Fig. 1. These findings have important implications for the design and implementation of large-scale consensus networks and autonomous vehicle platoons, highlighting the need for a balance between network density and update cycle speed for optimal performance. We finish the article with results from simulations and experiments that illustrate the effectiveness of the proposed framework in predicting the behavior of vehicle platoons, even for more complex agents with nonlinear dynamics, using Quanser's Qlabs and Qcars.

In this paper, we present a novel approach to estimating multiple time delays (TDs) in sensor arrays that is robust to outliers of TD measurements. These measurements are typically obtained from the peak of the cross correlation of two sensor signals but may contain outliers due to noise, significantly degrading the performance of downstream applications. To address this issue, we propose an approach to leverage only the best minimum-necessary TD measurements. First, we describe the general properties of TDs and show that the degree of freedom of TDs is the number of sensors minus one for a signal source, which indicates that the full set of TDs is redundant in this case. We then consider selecting nonredundant TDs from all measurements given the necessary and sufficient condition to reconstruct all TDs uniquely. We represent this condition by utilizing the graph theory, and then, formulate an optimization problem to select the optimal nonredundant TDs while satisfying the condition above. We reduce this problem to the problem of finding a minimum spanning tree and propose an efficient algorithm for TD estimation. Experimental evaluation shows that our method successfully eliminates outliers while ensuring that all TDs can be restored.

The gap between software development requirements and available resources of software developers continues to widen. This requires changes in the development and organization of software development. This study introduced a quantitative software development management methodology that estimates the relative importance and risk of functionality retention or abundance, which determines the final value of the software product. The final value of a software product is interpreted as a function of its requirements and functionalities, represented as a computational graph (called a software product graph). The software product graph allows the relative importance of functionalities to be estimated by calculating the corresponding partial derivatives of the value function. The risk of not implementing functionality was estimated by reducing the product’s final value. This model was applied to two EU projects, CareHD and vINCI. In vINCI, functionalities with the most significant added value to the application are developed based on the implemented model, and those related to the least value are abandoned. Optimization was not implemented in the CareHD project, and proceeded as initially designed. Consequently, only 71% of the CareHD potential value was achieved. The proposed model enables rational management and organization of software product development with real-time quantitative evaluation of functionalities impacts and, assessment of the risks of omitting them without a significant impact. Quantitative evaluation of the impacts and risks of retention or abundance is possible based on the proposed algorithm, which is the core of the model. This model is a tool for the rational organization and development of software products.

In a network system, there inevitably be a few connection failures at nodes, such as delay. Once a failure occurs, the network administrator must detect failure sources as soon as possible to maintain communication over the network. Group testing is a method for detecting failure nodes in networks using a small number of measurements, provided that the measurement matrix is constructed appropriately. A promising method for constructing measurement matrices is given by the binary correlation matrices. This study analyzes the performance limitation of group testing based on the binary correlation measurement matrix. We derive the upper and lower bounds of the minimum number of measurements needed for network detection. Moreover, we propose a sufficient condition of network topology, under which the failure vertices in the network can be detected with optimal performance, and we also provide a detection scheme with guaranteed exactness for the network. Numerical example indicates that for the network that satisfies the proposed sufficient condition, the administrator can exactly detect the failure vertices with optimal performance by using our proposed detection scheme.

Sailboat weather routing is a highly complex problem in terms of both the computational time and memory. The reason for this is a large search resulting in a multitude of possible routes and a variety of user preferences. Analysing all possible routes is only feasible for small sailing regions, low-resolution maps, or sailboat movements on a grid. Therefore, various heuristic approaches are often applied, which can find solutions within an acceptable time, sacrificing their optimality and accuracy. In this study, we propose a different approach based on the parallel implementation of an exact algorithm. Specifically, we present a Sailing Assistance Application (SAA) utilizing a deterministic approach and show how it can be parallelized in a cloud environment to reduce its execution time. The potential of the proposed parallelization method goes beyond the particular presented solution; it can be used to improve the performance of other weather routing tools such as collision avoidance and related applications.

This paper proposes a new distributed AC state estimation method. Different from the popular distributed state estimation (DSE) methods based on area partitioning method, the proposed method is a truly distributed method in which the power system is not required to be divided into smaller areas and a centralized state estimator in each area is not needed. In order to achieve fully DSE, the information propagation algorithm is introduced in this paper to help the distributed local state estimators share the measurement data. The information propagation algorithm is developed based on consensus protocol. The proof of the convergence of the information propagation algorithm is provided in this paper. Then, the AC state estimation method is integrated with the information propagation algorithm to realize the proposed method. The proposed method is tested in different standard power system models. The results show that the proposed method reaches the similar accuracy as the traditional centralized state estimation methods and performs faster and more accurate than the existing DSE methods.

Weighted Petri nets are common tools for modeling and validating discrete event systems involving resource allocation, such as flexible manufacturing systems (FMSs). A subclass of weighted Petri nets called a system of sequential systems with shared resources (S4R) has the power for modeling complex FMSs where the execution of an operation may require multiple resource types and multiple units of some resource types. Deadlock resolution is a crucial issue for the operation of an FMS. A direct and efficient policy is developed in this paper for detecting deadlock markings by extracting a weighted resource flow graph (WRFG) from an S4R and recovering them by synthesizing a recovery-transition-based controller. This study contributes to the field with five folds: 1) with S4R, an efficient integrated policy is put forward for robust supervisor synthesis; 2) it enhances an algorithm for extracting the WRFG from an S4R to reveal the shared resource competitions by different processes; 3) to detect partial deadlock markings, a technique for finding weighted circular wait graphs (WCWGs) in WRFG is presented; 4) with WCWGs, an algorithm is designed for the design of recovery-transition-based controller such that the resulting controlled system becomes deadlock-free; and 5) it presents a comprehensive analysis to demonstrate the proposed method by using the Integrated Net Analyzer (INA). With the proposed policy, it is not necessary to generate a reachability graph, making the method efficient. Finally, the performance of the proposed policy is illustrated by some commonly used examples.

The security of in-vehicle networks has become an important issue as automobiles become more connected and automated. In this paper, we propose a graph-based intrusion detection and classification system, named G-IDCS, which aims to enhance the security of the in-vehicle controller area network (CAN) protocol. Existing intrusion detection systems (IDSs) using graph theory suffer from limitations, such as requiring a large number of CAN messages for detection and being unable to classify attack types despite analyzing numerous messages. Meanwhile, machine learning or deep learning-based systems have limited sensitivity to environmental changes such as attack type change due to model overfitting, and are unable to provide explanations for classification decisions. Using various graph features, our threshold-based intrusion detection method overcomes these limitations by integrating a threshold-based IDS and a machine learning-based attack type classifier. Our threshold-based intrusion detection method of G-IDCS reduces the number of CAN messages required for detection by more than 1/30 and improves the accuracy of combined attack detection by over 9% compared to an existing intrusion detection method that uses graph theory. Furthermore, unlike existing machine learning and deep learning-based intrusion detection systems, our threshold classifier is robust to changes in attack types and can provide explanations for the features used in attack detection. In addition, our machine learning-based attack type classifier outperforms existing techniques in all performance metrics and can serve as a digital forensic tool for investigating cyber attacks on in-vehicle networks. Using the classifier to identify attack types can facilitate the design of corresponding protection methods, thereby enhancing the security of in-vehicle networks.

Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed.

Navigation and traffic services such as Google Maps, Bing Maps, and Apple Maps have become increasingly popular for their ability to calculate the shortest path, provide real-time traffic updates, recommend nearby points of interest, and suggest multi-modal route options based on user constraints. However, while these services offer convenience and efficiency, they may not always prioritize user safety. In response to this concern, recent research have begun to address safety issues in navigation and traffic services. To the best of our knowledge, none of these are capable of adapting to dynamic, conflicting safety features and real-time user feedback. A recent algorithm called SPaFE has been introduced to incorporate crowd-sourced and historical data, but it does not prioritize the most recent feedback or consider updated crime reports. It also does not account for distance and performs poorly in areas with insignificant or zero feedback. In light of the preceding, we introduce CrowdSPaFE, a population-based algorithm that adapts to dynamic crime reports, the most recent feedback, navigation in locations with negligible feedback, and a tradeoff between distance and safety considerations. Lastly, our empirical results demonstrate that the CrowdSPaFE algorithm outperforms the state-of-the-art.

Modern distribution grids are transforming due to the increasing amount of load and generation based on DC technologies, whose interconnection is facilitated by the upcoming AC-DC distribution networks. Their fault management is challenged by new issues related to the power transferred among AC and DC sub-networks. In light of this, the present paper proposes a novel Service Restoration (SR) algorithm, specifically tailored for AC-DC distribution grids, that optimizes the re-energization by establishing priorities of disconnected bus groups and computing, as candidate solutions, hierarchical combinations of normally open and normally closed switches. To overcome the limitations of existing approaches, a Multiple Criteria Decision Analysis (MCDA) is proposed to combine and objectively prioritize different SR goals (i.e., the use of telecontrolled switches, the minimization of power losses, and the applicability of the proposed solutions in a defined time horizon), and ultimately make the grid operator benefit from the possibility to flexibly tune different operational objectives. The proposed algorithm allows to effectively discriminating competing solutions, i.e., whence and how to re-energize disconnected buses, and complies with the time requirements for field implementation. The results prove the crucial role of DC sub-networks, associated to the control of power injection from AC-DC converters, in enhancing the SR by improving its targets and increasing the number of re-energized loads. Ultimately, the effect of MCDA comparison parameters on the SR outcomes is quantitatively investigated via global sensitivity analysis, whose adoption is recommended for supporting the grid operator in the algorithm implementation.

The pervasiveness of complex technological infrastructures and services coupled with the continuously evolving threat landscape poses new sophisticated security risks. These risks are mostly associated with many diverse vulnerabilities related to software or hardware security flaws, misconfigurations and operational weaknesses. In this scenario, a timely assessment and mitigation of the security risks affecting technological environments are of paramount importance. To cope with these compelling issues, we propose an AI-assisted methodological framework aimed at evaluating whether the target environment is vulnerable or safe. The framework is based on the combined application of graph-based and machine learning techniques. More precisely, the components of the target together with their vulnerabilities are represented by graphs whose analysis identifies the attack paths associated with potential security threats. Machine learning techniques classify these paths and provide the security assessment of the target. The experimental evaluation of the proposed framework was performed on 220 artificially generated Active Directory environments, half of which injected with vulnerabilities. The results of the classification process were generally good. For example, the F1-score obtained by the Random Forest classifier for the assessment of vulnerable networks was equal to 0.91. These results suggest that our approach could be applied for automating the security assessment procedures of complex networked environments.

Distance relays are critical components in protection systems of power grids that can be attacked by cyber-attackers. Indeed, a cyber-attacker injects fake data into a distance relay to pretend a fault has happened, and the distance relay must be tripped. Thus, a new powerful approach, named Multi-Agent Distributed Deep Learning (MADDL) method is proposed to tackle cyber-attacks in distance relays. Unlike centralized methods, the protection system with several distance relays is mapped to a multi-agent distributed system by employing the graph theory, in which the distance relays are considered as the agents of the multi-agent system or the nodes of the considered graph. Each agent is only connected to the neighboring agents to exchange voltage and current data. Then, a deep neural network as a cyber-attack detection structure is assumed for each agent that utilizes the local voltage and current data and the received data from the neighboring agents to detect the attacks. Hence, the considered detection structures are tuned by employing train data, obtained by simulating the grid in different types of faults. Then, the tuned detection structures are evaluated by a test dataset, including data from the grid under various faults and the normal situation by injecting fake data as cyber-attacks. The developed method has been employed for three different case studies, including IEEE 6-bus, IEEE 14-bus, and IEEE 118-bus power grids. According to the simulation results, the proposed algorithm has succeeded in identifying more than 99.88% of faults and cyber-attacks.

Epilepsy is one of the most chronic brain disorder recorded from since 2000 BC. Almost one-third of epileptic patients experience seizures attack even with medicated treatment. The menace of SUDEP (Sudden unexpected death in epilepsy) in an adult epileptic patient is approximately 8–17% more and 34% in a children epileptic patient. The expert neurologist manually analyses the Electroencephalogram (EEG) signals for epilepsy diagnosis. The non-stationary and complex nature of EEG signals this task more error-prone, time-consuming and even expensive. Hence, it is essential to develop automatic epilepsy detection techniques to ensure an appropriate identification and treatment of this disease. Nowadays, graph-theory has been considered as a prominent approach in the neuroscience field. The network-based approach characterizes a hidden sight of brain activity and brain-behavior mapping. The graph-theory not even helps to understand the underlying dynamics of EEG signals at microscopic, mesoscopic, and macroscopic level but also provide the correlation among them. This paper provides a review report about graph-theory based automated epilepsy detection methods. Furthermore, it will assist the expert's neurologist and researchers with the information of complex network-based epilepsy detection and aid the technician for developing an intelligent system that improving the diagnosis of epilepsy disorder. [ABSTRACT FROM AUTHOR]

Polycentric urban structures determine the combination and correlation of urban resources. In the past, nighttime light data were often used to identify the center locations, but the borders of polycentric urban regions (PURs) could not be obtained. Using multisource remote sensing data and graph, this research proposes an effective method for polycentric structure identification. First, we regard nighttime light data as a continuous mathematical surface, which can be constructed as nighttime light intensity graphs (NLIGs). Then, the space-optimized Girvan–Newman (SGN) method is proposed to detect the communities, and the eigenvector centrality (EC) and gray value are used to discover the central node of each community. Finally, the geographical location mapping (GLM) between Landsat 8 data segmentation objects and nodes is established, and the PURs and centers can be mapped to the communities and central nodes. This study took Shenyang, Chengdu, and Xi’an as study areas and used monthly Visible Infrared Imaging Radiometer-National Polar-orbiting Partnership (NPP-VIIRS) data in April 2019 and Landsat 8 data in January and August 2019. The average accuracies of PURs and centers identified by the proposed method were 86.24% and 72.5%, respectively. The developed method can provide technical support and data support for urban planning. [ABSTRACT FROM AUTHOR]

Developing complex engineered systems (CES) poses significant challenges for engineers, managers, designers, and businesspeople alike due to the inherent complexity of the systems and contexts involved. Furthermore, experts have expressed great interest in building a foundation of “theories” that describe the patterns underlying how development process qualities shape system outcomes. This article contributes to that foundation in two ways. First, it identifies the core elements of CES development processes (CESDPs) through a literature review. Then, it proposes the ComplEX System Integrated Utilities Model (CESIUM), a novel framework for exploring how numerous system and development process characteristics may affect the performance of CES. CESIUM creates abstract representations of a system architecture, the corresponding engineering organization, and the new product development process through which the organization designs the system. It does so by representing the system as a network of interdependent artifacts designed by agents. Simulated agents iteratively design their artifacts through optimization and share information with other agents, thereby advancing the CES toward a solution. Hence, this article makes it possible for researchers to compare how development process characteristics shape system outcomes. [ABSTRACT FROM AUTHOR]

Surface electromyogram (sEMG) is widely used in active rehabilitation control for stroke patients. However, the accuracy of movement recognition using sEMG signals is affected by abnormal states such as muscular fatigue and muscle weakness. In this article, a multimodal fusion strategy of electroencephalogram (EEG) and sEMG is proposed to improve the accuracy and robustness of hand motion recognition. It is an end-to-end approach based on a graph theory, in which the temporal signals of EEG and sEMG are considered as the features of nodes, and the functional connectivity is considered as the weights of edges. Four topologies, namely, 2EnMe, 2EwMe, 5EnMe, and 5EwMe, are proposed, and two standardization methods are tested for each topology. Then, three functional connectivity methods are investigated, namely, Pearson coefficient, mutual information, and coherence. Ten rounds of fivefold cross validation show that graph convolutional network (GCN)-2EnMe with the Pearson coefficient and min–max standardization is the best fusion model. At the fatigue levels of 0% and 30%, the achieved average accuracies are, respectively, 93.86% and 91.23%, which are significantly higher than those when using a parallel fusion method and a single-modality model. Moreover, the accuracy decrease ratio (ADR) of GCN-2EnMe is 2.80%, which is considerably better than that of a convolutional neural network (CNN) with parallel fusion (7.87%) and a CNN with single-modality sEMG (11.15%). The results show that the proposed novel EEG and sEMG fusion method has the potential to improve the accuracy and reliability of active control for stroke rehabilitation. [ABSTRACT FROM AUTHOR]

A generalized approach to obtain random expander graphs is proposed, which includes the growth of the network by adding to it any suitable random regular network, iteratively, in special cases, a node, an edge. We show that the proposed algorithm can produce good expanders (Ramanujan graphs) that are used to design fast, scalable communication networks. The qualitative and numerical analysis of the produced communication networks is performed on the basis of predefined structural and spectral measures and metrics, for example, mean-first-passage-time, eigenratio, clustering coefficient, and average path length. Apart from that, a simple message passing communication protocol is simulated over the proposed growing expander graphs and other state-of-the-art network topologies, BAM, ERM, SWN, CSWN and SWRN models, and delay is calculated. Results show that the constructed random expanders have low network latency, high convergence rate and robustness. [ABSTRACT FROM AUTHOR]

Demonstrated Spectral Efficiency (SE) gain by In Band Full Duplex (IBFD) makes it an attractive radio technology. Owing to inherent complexities in IBFD, Hybrid IBFD Cellular Network (HICN) is opted in practice because it limits IBFD capability only to Base Station and continues with legacy Half Duplex (HD) User Equipments (UEs). Since sharing frequencies within UE groups maximizes sum-SE in a HICN, we formulate a grouped UEs maximization problem for achieving maximum frequency sharing. Unlike heuristic search methods developed in literature, in this paper, we build a mathematical framework for UE grouping. Three grouping algorithms are analytically derived in closed-forms with varying performance and time complexity trade-offs. The optimal algorithm provides benchmarking on these performance measures. Using structural advantages of a HICN, we derive reduced search-optimal algorithm that reduces time complexity while guaranteeing optimal grouping, whereas near-optimal algorithm achieves close-to-optimal grouping with substantially reduced time complexity for real-time cellular systems. We further introduce the concept of ineligible UEs, which uniformly reduces time complexity of all three algorithms without impacting their grouping performance. Extensive simulations reveal that the proposed near-optimal algorithm achieved a median sum-SE of 93.5% of theoretically doubling maximum over legacy HD systems, thus emerging as a prospective solution. [ABSTRACT FROM AUTHOR]

The design of decoding algorithms is a significant technological component in the development of fault-tolerant quantum computers. Often design of quantum decoders is inspired by classical decoding algorithms, but there are no general principles for building quantum decoders from classical decoders. Given any pair of classical codes, we can build a quantum code using the hypergraph product, yielding a hypergraph product code. Here we show we can also lift the decoders for these classical codes. That is, given oracle access to a minimum weight decoder for the relevant classical codes, the corresponding $[[n,k,d]]$ quantum code can be efficiently decoded for any error of weight smaller than $(d-1)/2$. The quantum decoder requires only $O(k)$ oracle calls to the classical decoder and $O(n^{2})$ classical resources. The lift and the correctness proof of the decoder have a purely algebraic nature that draws on the discovery of some novel homological invariants of the hypergraph product codespace. While the decoder works perfectly for adversarial errors, that is errors of weight up to half the code distance, it is not suitable for more realistic stochastic noise models and therefore can not be used to establish an error correcting threshold. [ABSTRACT FROM AUTHOR]

A vertex subset $S$ of a graph $G$ constitutes a 1-perfect code if the one-balls centered at the nodes in $S$ effect a vertex partition of $G$. This paper considers the quad-cube $CQ_{m}$ that is a connected $(m+2)$ -regular spanning subgraph of the hypercube $Q_{4m+2}$ , and shows that $CQ_{m}$ admits a vertex partition into 1-perfect codes iff $m=2^{k}-3$ , where $k\ge 2$. The scheme for that purpose makes use of a procedure by Jha and Slutzki that constructs Hamming codes using a Latin square. The result closely parallels the existence of a 1-perfect code over the dual-cube, which is another derivative of the hypercube. [ABSTRACT FROM AUTHOR]

In large-scale applications, parts of harmonic data are inevitably lost during transmission. This study presents an approach for the recovery of missing harmonic data based on the spectral graph theory. The proposed methodology involves graph theory for constructing a Laplacian matrix and a graph signal reconstruction function, the merging K-means algorithm for building a priori information model, and the accelerated segmentation Bregman iterative algorithm for solving the reconstruction function. Compared with existing methods on data recovery in power systems, the method maintains a good recovery accuracy when the data correlation of measurement units is low and the prior information is little. The proposed method has good anti-noise performances and low computational complexity. The feasibility and accuracy of the proposed method are verified through simulation and field recorded data. [ABSTRACT FROM AUTHOR]

As natural habitats protection has become a global priority, smart sensing-nets are ever-increasingly needed for effective environmental observation. In a practical monitoring network, it is critical to deploy sensors with sufficient automated intelligence and motion flexibility. Recent advances in robotics and sensors technology have enabled automated mobile sensors deployment in a smart sensing-net. Existing deployment algorithms can be employed to calculate adequate destinations (goals) for sensors to perform respective monitoring tasks. However, given the calculated goal positions, the problem of how to actually coordinate a fleet of robots and schedule moving paths from random initials to reach their goals safely, without collisions, remains largely unaddressed in the wireless sensor networking (WSN) literature. In this paper, we investigate this problem and propose polynomial-time collision-free motion algorithms based on batched movements to ensure all the mobile sensors reach their goals successfully without incurring collisions. We observe that the grouping (batching) strategy is similar to the coloring procedure in graph theory. By constructing a conflict graph, we model the collision-free path scheduling as the well-known k-coloring problem, from which we reduce to our k-batching problem (determining the minimum number of required batches for a successful deployment) and prove its NP completeness. Since the k-batching problem is intractable, we develop CFMA (collision-free motion algorithm), a simple yet effective batching (coloring) heuristic mechanism, to approximate the optimal solution. Performance results show that our motion algorithms outperform other existing path-scheduling mechanisms by producing 100% sensors reachability (success probability of goals reaching), time-bounded deployment latency with low computation complexity, and reduced energy consumption. Note to Practitioners— This research was originally motivated by an oceanography project, which studied marine microbes by sending a team of tiny robots (sensors) randomly scattered on the ocean floor. For hard-to-access habitats like deserts or oceans, where manual placement of sensors is costly or impossible, automatically scheduling robots movements to calculated positions from random initials is essential for an effective monitoring. Our contribution is unique in two ways. First, traditional path-planning research focuses more on independent robots navigating the environment, whereas we target on the network automation problem, coordinating a fleet of robots working together to perform an environmental sensing task. Second, we observe that existing movement methods are too complicated and energy-consuming due to the calculation on-the-go nature. To provide a practical solution, we suggest and design our motion algorithms from a new perspective: pre-scheduled batched movements. Our approach requires a central server for grouping (batching) calculations, but can effectively reduce computation complexity and save significant energy expenditure exerted on resource-limited sensors. The proposed collision-free motion algorithm (CFMA) is a suboptimal yet efficient batching solution, which can be easily applied in real-life open-space monitoring networks. Insights from this foundational research will facilitate future opportunities to implement and verify our method in operational monitoring testbeds. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is undoubtedly at the core of the future of intelligent transportation. It will prevail over the road ecosystem, and it will have a huge impact on our lives throughout the provision of seamless connectivity among diverse transportation means. For the network to operate efficiently, the data needs to be quickly spread throughout the network, which requires low computational and bandwidth overheads. However, the dynamics of vehicular environments due to frequent node mobility poses many challenges to realize efficient data dissemination. This work addresses this type of problem by proposing a novel clustering algorithm at the edge of the network and an efficient message routing approach, which is known as Group’n Route (GnR). Both mechanisms resort to machine learning and graph metrics that reflect the social relationships between the nodes. Our performance evaluation reveals that the clustering algorithm yields stable results with varying road scenarios, which are becoming an advisable approach in the presence of mobile IoV nodes. Also, the designed routing protocol achieves two orders of magnitude smaller overhead and almost double the delivery rate when it is compared to traditional routing protocols, which thereby justify that the combination of our two proposed clustering and routing methods are a plausible alternative to support IoV communications in real-world setups. [ABSTRACT FROM AUTHOR]

In this article, we devote to solve the consensus problem for nonlinear multiagent system under energy consumption constraint. A novel control protocol which combines impulse control with event-triggered mechanism is proposed. By setting the event decision time at pulse time, it can not only reduce triggered times but also avoid the occurrence of “Zeno behavior.” In order to reduce the conservatism of control protocol, input saturation constraint and state saturation constraint are both discussed, for the limited receiving channel of the agent and limited sampling range of the sampler, respectively. Further, the dynamic model of multiagent systems with fuzzy information is proposed for the uncertain factors in actual environment. Finally, for the sake of proving the effectiveness about the control protocols, some simulation examples are presented. [ABSTRACT FROM AUTHOR]

In this article an autonomous distributed-consensus system is evaluated with regard to its efficacy for establishing a common detection statistic among a network of passive acoustic hydrophones. The notion of consensus among sensors is briefly reviewed, and an algorithm motivated by diffusion of information among network neighbors is developed within the context of spectral graph theory. Relative entropy derived at each node provides information that the network averages to reach a joint detection decision. As the expected value of a log-likelihood ratio, relative entropy is the appropriate detection statistic for this application because it is nondimensional, additive, and insensitive to calibration. These properties facilitate the fusion of information among heterogeneous sensors. Seven nodes of an acoustical receiver network were moored on the New Jersey coastal shelf in May 2019, while towed and autonomous sources provided target signals at 900–1050 Hz. The convergence rate of the system to a detection statistic was found to be limited by slow acoustic communications, and convergence to a global consensus was often disrupted by communication breakdowns. When failures occurred, the network successfully demonstrated robustness by dissociating into viable subnetworks, each of which converged to a valid detection statistic. [ABSTRACT FROM AUTHOR]

We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. With the advances of deep learning, current link prediction methods commonly compute features from subgraphs centered at two neighboring nodes and use the features to predict the label of the link between these two nodes. In this formalism, a link prediction problem is converted to a graph classification task. In order to extract fixed-size features for classification, graph pooling layers are necessary in the deep learning model, thereby incurring information loss. To overcome this key limitation, we propose to seek a radically different and novel path by making use of the line graphs in graph theory. In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task. Experimental results on fourteen datasets from different applications demonstrate that our proposed method consistently outperforms the state-of-the-art methods, while it has fewer parameters and high training efficiency. [ABSTRACT FROM AUTHOR]

In this article, we discuss fast finite-time consensus (FFTC) problems for uncertain nonlinear multiple mechanical systems with actuator faults and time-varying asymmetric output constraints. In order to guarantee the constraints are satisfied, an appropriate nonlinear mapping (NM) is employed to transform the original system with output constrains into an corresponding unconstrained one. Combining neural networks technology, graph theory, fast finite-time control theory, and backstepping technology, the actuator faults are considered to propose a distributed adaptive finite-time consensus (FTC) protocol, which can guarantee the position errors as well as the velocity errors reaching a region in finite time. Finally, an illustrative example is presented to support the obtained theoretical results. [ABSTRACT FROM AUTHOR]

This article investigates the distributed optimal cooperation problems for multiple high-order systems, in which the dynamics of each agent is allowed to be subject to unknown nonlinearities. To eliminate the effect caused by unknown nonlinearities, a nonlinearity estimator is developed based on agents’ states, which successfully reconstructs the nonlinear dynamics if the unknown nonlinearities are bounded. And to minimize the sum of multiple local nonlinear cost functions with Lipschitz-type gradients, a couple of static and adaptive state-dependent algorithms are designed, respectively, where each agent may only have access to its own local cost function. It is challenging to solve such an optimal cooperation problem as the performance of the whole multiagent network is evaluated by the sum of all local performance functions. In order to fulfill the goal of cooperative optimization, a state-dependent distributed optimal cooperation algorithm is proposed first. By utilizing tools from the Lyapunov stability theory and convex optimization analysis, it is proven that the considered distributed optimal cooperation problem for high-order nonlinear systems can be solved by the proposed optimal cooperation algorithm if the state-dependent parameters are suitably selected. It is noted that the selections of the state-dependent parameters depend on some global information of the multiagent systems. Furthermore, by incorporating the proposed optimal cooperation algorithm with adaptive parameters strategy, the optimal cooperation problem is solved in a fully distributed manner. Finally, a numerical simulation is shown to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the impact of information flow topologies on the resilience of distributed algorithms that are widely used for estimation and control in vehicle platoons. In the state of the art, the influence of information flow topology on both internal and string stability of vehicle platoons has been well studied. However, understanding the impact of information flow topology on cyber-security tasks, e.g., attack detection, resilient estimation and formation algorithms, is largely open. By means of a general graph theory framework, we study connectivity measures of several platoon topologies and we reveal how these measures affect the ability of distributed algorithms to reject communication disturbances, to detect cyber-attacks, and to be resilient against them. We show that the traditional platoon topologies relying on interaction with the nearest neighbor are very fragile with respect to performance and security criteria. On the other hand, appropriate platoon topologies, namely $k$ -nearest neighbor topologies, are shown to fulfill desired security and performance levels. The framework we study covers undirected and directed topologies, ungrounded and grounded topologies, or topologies on a line and on a ring. We show that there is a trade-off in the network design between the robustness to disturbances and the resilience to adversarial actions. Theoretical results are validated via simulations. [ABSTRACT FROM AUTHOR]

This article investigates the containment control problem of nonlinear multiagent systems subject to actuator nonlinearity, unmeasured states, and unknown external disturbances. The predictor technique is employed for each subsystem, and the prediction error is utilized to update the adaptive law. The problem of actuator nonlinearity with dead zone and saturation input is solved by introducing an auxiliary control signal. Incorporating the state observer and disturbance ones, an extended state observer is proposed to compensate the effect of unmeasurable and unknown disturbances, simultaneously. Based on Lyapunov theory and graph theory, it is shown that the devised control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Moreover, the followers can enter the dynamic convex hull spanned by the dynamic leaders. A simulation example with comparison analysis is provided to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

As an effective method to improve the reliability, the advanced communication infrastructure also exposes the islanded AC microgrid (MG) to potential privacy threats. Although the existing differential-privacy based secondary controls provide the privacy protection through adding noise, the noise would bring about large harmonic components thereby degrading the power quality. To tackle with this problem, a novel integrated privacy-preserving distributed fixed-time (PDF) secondary control of the islanded AC MG is proposed for the accurate frequency restoration and active power sharing. The state decomposition strategy and the additional virtual proportional (AVP) coefficient are innovatively designed to achieve the privacy preservation, where the active power ratio of each DG is decomposed into several sub-states. The integrated PDF secondary control only requires each DG to transmit the first sub-state of active power ratio through communication network, which would not increase the communication burden. The designed privacy-preserving mechanism can protect the initial value and real-time running values simultaneously when the AVP coefficient is not disclosed to the external eavesdropper. The real-time simulations based on the NI-PXI real-time simulator are conducted to validate the effectiveness and advantages of the integrated PDF secondary control. [ABSTRACT FROM AUTHOR]

In this article, quadrotor unmanned aerial vehicles (QUAVs) are organized as a two-layer structure, where the first layer is leader QUAVs and the second layer is follower QUAVs. In this structure, only the leader QUAVs can receive the desired tracking information of position and attitude. Although the followers cannot obtain the given tracking information directly, they can obtain the corresponding information from leaders and other followers through the communication network based on the graph theory. In terms of this case, a distributed formation-containment (FC) control method is proposed to handle the related flight problems. We aim to develop a formation control for the leader QUAVs and a containment control for the follower QUAVs with the graph theory. Furthermore, a neural network (NN) technique is utilized to cope with the uncertainty of each QUAV. In order to guarantee good flight performance when tracking, the finite-time stability theorem is introduced into the control design to make each QUAV achieve satisfactory tacking performance in finite time. Finally, numerical simulations are conducted in the platform of two-layer 16 QUAVs to validate the feasibility and effectiveness of the proposed NN-based finite-time FC control. [ABSTRACT FROM AUTHOR]

Multichip silicon carbide power modules with integrated snubbers are promising for large-capacity converters with high speed and cost efficiency. In the design stage, the parasitic inductance of module layout generally attracts the primary efforts of designers because of its severe impact on dynamic performance, e.g., voltage overshoot and transient current imbalance. However, due to the complexity of the heterogeneous layout, the solution space and development efficiency are always limited by the traditional manual design approach. Thus, this article proposes a generative method to optimize the layout autonomously. First, a graph model is built to describe heterogeneous layouts with all interconnectivity and design constraints retained. Based on the model, integer programming is introduced to generate layout templates with variable geometric topologies. Then, coupled with a self-developed discrete extractor, the Pareto-front is obtained by genetic algorithm, providing a tradeoff boundary for loop inductance and branch mismatch. The proposed method is systematic, flexible, and requires few designers’ expertise. A 1200 V/240 A half-bridge module is designed and fabricated to validate its capability. The experimental results show that the loop inductance of 5.59 nH is achieved, and less than 5% of the transient current imbalance is realized under 6.2 A/ns turn-on in the rated. [ABSTRACT FROM AUTHOR]

In the expansion of power distribution networks, the periodic task of determining the location, number, and capacity of the distribution transformers is still an open issue that needs to be systematically coped with. In this paper, a comprehensive approach for solving in practice the mentioned task is presented. The proposed methodology is based on an improved clustering technique, which considers not only the actual distances between consumers but also their electrical demand. Graph theory is exploited to represent the feasible paths of the complete system, so that, the distances and the obtained locations are accurate, and feasible in practice, respectively. The proposed objective function minimizes the total annual costs of the system, by considering investments, transformer losses, and operational costs. An approximate expression to calculate the maximum distance between each transformer and its furthest customer to accomplish voltage regulation has been obtained. Consequently, the outputs of the proposed model are the correct number of distribution transformers to be installed, their optimal locations and capacities, and the best consumer grouping with acceptable voltage drops. In order to validate the proposal, this methodology is applied to an urban system with 106 consumers, 23 vertexes, and 31 streets with promising results. [ABSTRACT FROM AUTHOR]

This article focuses on quasi-synchronization of fuzzy heterogeneous complex networks. An aperiodically intermittent control strategy based on discrete-time state observations (aperiodically intermittent discrete-time state observations control for short) is designed. Different from intermittent control strategies referred in existing literatures, the control duration of the control strategy used in this article is based on discrete-time state observations, which makes the control used in this article somewhat less demanding and more effective. Under the aperiodically intermittent discrete-time state observations control strategy, a valid approach combining Lyapunov method with graph theory is proposed in this article. Throughout this article, a theorem and a corollary to the quasi-synchronization criterion of fuzzy heterogeneous complex networks are established. The results show that when the control gain is larger, the convergence domain is smaller. Finally, an illustrative example is presented and the simulation of this example shows the feasibility and validness of the obtained results. [ABSTRACT FROM AUTHOR]

Feature evaluation is an important issue in constructing a feature selection algorithm in kernelized fuzzy rough sets, which has been proven to be an effective approach to deal with nonlinear classification tasks and uncertainty in learning problems. However, the feature evaluation function developed with kernelized fuzzy rough sets cannot better reflect the affinity relationship of samples and is time-consuming. To overcome these drawbacks, in this article, the problem of feature selection with kernelized fuzzy rough sets is studied based on the spectral graph theory. First, the within-class and between-class sample similarity matrices by using kernelized fuzzy approximation operators are constructed. Two operators, which can capture the affinity relationship of samples, are then introduced based on the sample similarity matrices. The proposed operator can be regarded as the sum of the weighted kernelized fuzzy approximation operators. Second, based on the ratio criterion, a feature evaluation function and its corresponding feature selection algorithm FRKF are presented, which can effectively evaluate the importance of features. Third, to illustrate the performance of the proposed algorithm, extensive experiments have been carried out to compare FRKF and other well-known feature selection methods, including the feature ranking methods and feature subset selection methods on various classification tasks. The experimental results on real-world datasets demonstrate that FRKF achieves the high performances in terms of the robustness, efficiency, and effectiveness. [ABSTRACT FROM AUTHOR]

Transcranial alternating current stimulation (tACS) is an emerging non-invasive neuromodulation technique, which could be potentially applied to enhance stroke rehabilitation. In this study, we aimed to characterize the frequency-specific effects of tACS in chronic stroke. Therefore, concurrent tACS and functional magnetic resonance imaging (fMRI) were conducted in 13 chronic stroke individuals. Resting-state and task-based fMRI were collected for each subject under different frequencies (10 Hz, 20 Hz, Sham). Task-based fMRI showed that increased activation was found in the ipsilesional precentral area during paretic hand movements after 10 Hz tACS, while increased activation was found in the contralesional precentral area during non-paretic hand movements after both 10 and 20 Hz tACS. Resting-state seed-based functional connectivity (FC) analysis showed that 10 Hz tACS mainly modulated FC within motor-related regions, while 20 Hz tACS also modulated regions beyond the motor-related areas. Graph theory analysis further demonstrated the functional interaction modulated by tACS in the whole-brain level. Taken together, our results showed that tACS might exhibit frequency-specific modulation in chronic stroke. 20 Hz tACS facilitates the functional interaction between the sensorimotor regions and brain regions involved in executive control, while 10 Hz and sham tACS has limited effect on motor-related brain activity. Our results reveal the neural response process under external current stimulation, providing new insight into the neuromodulation mechanism of tACS in a lesioned brain.

Control systems of modular multilevel converters can be partitioned into several levels. Such partitioning is becoming relevant in multi-vendor high-voltage direct-current systems where one party may deliver the converter hardware and associated control, whereas another party may deliver the system-related control. However, the exact control system partitioning has not been subject to research. This paper presents a method for partitioning control systems into two levels with minimum dependencies between the two levels. Furthermore, the impact of specific control designs on the partitioning and integration effort is discussed using an example. The presented method is useful for the design of multi-vendor control systems for modular multilevel converters whose partitioning otherwise would be based on engineering intuition.

This article introduces structural information potential (SIP), a measure of information based on the potential of structures to be informative about their content. An example of this concept is the clustered appearance that typically characterizes the first page of scientific articles, which summarizes the article’s contents and provides additional data, yielding potentially the largest and most diverse amount of information from a single page in the shortest time with the least effort. This characteristic makes SIP particularly well-adapted to triage tasks (i.e., rapid decision-making under conditions of uncertainty and limited resources), an application illustrated by means of a case study on classifying document images. The SIP method consists in unifying the Shannon entropy, the Fourier transform, the fractal dimension, and the golden ratio into a single equation and several algorithmic components. While the application domain is document images, the concept has generic character. The method results in a mathematically and perceptually coherent pattern space, characterized by continuous transition between uniform, clustered, and regular configurations, and corresponding to a structural information potential with a well-defined maximum. The maximum SIP leads to the identification of shapes and patterns with minimal structural redundancy, termed “fluorescent objects” as a complement to regular graphs and the Platonic solids.

The smart grid connects components of power systems and communication networks in an interdependent two-way system that delivers electricity to consumers and collects data that enables it to react to usage levels and interference from threats, such as cyber-attacks. In this paper, we propose a novel cyber-attack failure propagation model in smart grids. Our realistic failure propagation model addresses the system’s heterogeneity by assigning different roles to its components. We define rules for and interdependencies of failure propagation and propose a new approach to studying cascading failures. In addition, our graph model identifies the most-vulnerable nodes. The model implements power flow analysis to guarantee that all transmission lines work below capacity and remove lines exceeding capacity. The model also considers that control packets could encounter different delays regarding the communication network structure and investigates the impact of communication delay on the failure of power components. Our results establish that by considering both power and communication characteristics and interdependencies, cascading failures can be modeled more accurately. We show that when we run the power flow analysis, there are a negligible number of failed nodes, which means that our model accurately identifies system failures.

Quadrotor Unmanned Aerial Vehicles (UAVs) are generally underactuated systems and when load is attached for transportation purposes, the system complexity increases. Therefore, the need to appropriately control such systems becomes paramount as they usually navigate in cluttered environments. In this work, we conceptualize the problem of cooperative tracking control for a Multi-agent UAV load system (MUAVLs) whereby each UAV is divided into global position and local attitude subsystems. To ensure that formation is maintained in a desired path, Neural Network Graph-theoretic Distributed Adaptive Control (NNGDAC) is used for the position subsystem with a modified virtual force artificial potential field for obstacle avoidance. Another Adaptive Feedback Linearization (AFBL) controller is also designed for the attitude subsystem which is verified by simulation results.