Memory-Event-Triggered H∞ Output Control of Neural Networks With Mixed Delays

Mixed-Zero-Sum-Game-Based Memory Event-Triggered Cooperative Control of Heterogeneous MASs Against DoS Attacks

This article studies the problem of memory event-triggered cooperative adaptive control of heterogeneous nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks based on the multiplayer mixed zero-sum (ZS) game strategy. First, a neural-network-based reinforcement learning scheme is structured to obtain the Nash equilibrium solution of the proposed multiplayer mixed ZS game scheme. Then, a memory-based event-triggered mechanism considering the historical data is proposed. This effectively avoids incorrect triggering information caused by unknown external factors. Moreover, thanks to the idea of switching topology, the mixed ZS game problem under the influence of node-based DoS attacks is solved efficiently. In accordance with the Lyapunov stability theory, it is proved that all signals of heterogeneous MASs are bounded, all heterogeneous followers can track the trajectory of the leader during the no-attack period, the attacked follower can achieve stabilization control during the attack period, and the remaining nonattacked followers can achieve cooperative control during the attack period. Finally, the effectiveness of the designed memory-event-triggered-based mixed ZS game cooperative control strategy is tested by the given simulation results.

Trends in DNN Model Based Classification and Segmentation of Brain Tumor Detection

Background:

Due to the complexities of scrutinizing and diagnosing brain tumors from MR images, brain tumor analysis has become one of the most indispensable concerns. Characterization of a brain tumor before any treatment, such as radiotherapy, requires decisive treatment planning and accurate implementation. As a result, early detection of brain tumors is imperative for better clinical outcomes and subsequent patient survival.

Introduction:

Brain tumor segmentation is a crucial task in medical image analysis. Because of tumor heterogeneity and varied intensity patterns, manual segmentation takes a long time, limiting the use of accurate quantitative interventions in clinical practice. Automated computer-based brain tumor image processing has become more valuable with technological advancement. With various imaging and statistical analysis tools, deep learning algorithms offer a viable option to enable health care practitioners to rule out the disease and estimate the growth.

Methods:

This article presents a comprehensive evaluation of conventional machine learning models as well as evolving deep learning techniques for brain tumor segmentation and classification.

Conclusion:

In this manuscript, a hierarchical review has been presented for brain tumor segmentation and detection. It is found that the segmentation methods hold a wide margin of improvement in the context of the implementation of adaptive thresholding and segmentation methods, the feature training and mapping requires redundancy correction, the input data training needs to be more exhaustive and the detection algorithms are required to be robust in terms of handling online input data analysis/tumor detection.

Adaptive Leader-Following Consensus Tracking Control of Multiple UAVs Subject to Deception Attacks

This article is concerned with the problem of leader-following consensus tracking control for multi-unmanned aerial vehicle (UAV) systems under deception attacks and actuator saturation. While guaranteeing the expected control performance, a novel adaptive event-triggered scheme (AETS) is developed to reduce the frequency of data transmission among UAVs and reduce the release of redundant data, where the adaptive threshold can be adjusted online according to the dynamic error instead of giving a default value. With the help of a new Lyapunov function, sufficient conditions and security criteria are developed to ensure that all following UAVs could reach secure consensus asymptotically with a leader UAV. Lastly, an illustrative example is presented to validate the effectiveness of the designed method.

Green Demand Aware Fog Computing: A Prediction-Based Dynamic Resource Provisioning Approach

Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance.

Resilient Adaptive Event-Triggered Load Frequency Control of Network-Based Power Systems against Deception Attacks

This paper investigates the problem of networked load frequency control (LFC) of power systems (PSs) against deception attacks. To lighten the load of the communication network, a new adaptive event-triggered scheme (ETS) is developed on the premise of maintaining a certain control performance of LFC systems. Compared with the existing ETSs, the proposed adaptive ETS can adjust the number of triggering packets, along with the state changes in the presence of deception attacks, which can reduce the average data-releasing rate. In addition, sufficient conditions can be derived, providing a trade-off between the limited network communication resources and the desired control performance of PSs. Finally, an application case is presented for the PSs to demonstrate the advantages of the proposed approach.