Event-triggered impulsive control for stability of stochastic delayed complex networks under deception attacks

Yang, Ni; Zhang, Shuo; Su, Huan

doi:10.1016/j.engappai.2023.105953

Cited by 20 publications

(5 citation statements)

References 40 publications

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“…Although vision-based neuromorphic intelligence has impressively developed both academically and commercially [55]- [57], one-dimensional sensing modalities like audio and haptic-based sensing lag behind due to limited application scenarios. However, these studies are rapidly emerging [46], [58], particularly in robotics [10], [59], [60], and have shown impressive latency and energy performance using SNNs for inferences [4], [5], offering valuable insights for more ubiquitous scenarios. Table I highlights several studies exploring SNNs for ubiquitous computing with low-dimensional signals, including their encoding schemes, training approaches, and evaluation metrics.…”

Section: Related Workmentioning

confidence: 99%

“…Spiking neuron networks (SNNs) are gaining popularity for processing two-dimensional event streams from Dynamic Vision Sensor (DVS) [7]- [9], partly because DVS is the only commercially available event sensor. Prior works have shown SNNs deliver impressive performance on neuromorphioc platforms for one-dimensional signals like electroencephalogram (EEG) and electromyography (EMG) in terms of latency and power consumption [3], [10]- [12]. In ubiquitous computing, the advantage of energy and low latency of neuromorphic somatosensory and computing systems [13], [14] are highly beneficial for battery-operated devices with limited hardware resources [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Spiking Neural Network on Neuromorphic Platform For Human Activity Recognition

Bian,

Magno

2023

Proceedings of the 2023 International Symposium on Wearable Computers

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Spiking neural networks (SNNs), a brain-inspired computing paradigm, are emerging for their inference performance, particularly in terms of energy efficiency and latency attributed to the plasticity in signal processing. To deploy SNNs in ubiquitous computing systems, signal encoding of sensors is crucial for achieving high accuracy and robustness. Using inertial sensor readings for gym activity recognition as a case study, this work comprehensively evaluates four main encoding schemes and deploys the corresponding SNN on the neuromorphic processor Loihi2 for post-deployment encoding assessment. Rate encoding, time-to-first-spike encoding, binary encoding, and delta modulation are evaluated using metrics like average fire rate, signalto-noise ratio, classification accuracy, robustness, and inference latency and energy. In this case study, the time-to-first-spike encoding required the lowest firing rate (2%) and achieved a comparative accuracy (89%), although it was the least robust scheme against error spikes (over 20% accuracy drop with 0.1 noisy spike rate). Rate encoding with optimal value-to-probability mapping achieved the highest accuracy (91.7%). Binary encoding provided a balance between information reconstruction and noise resistance. Multi-threshold delta modulation showed the best robustness, with only a 0.7% accuracy drop at a 0.1 noisy spike rate. This work serves researchers in selecting the best encoding scheme for SNN-based ubiquitous sensor signal processing, tailored to specific performance requirements.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating Spiking Neural Network on Neuromorphic Platform For Human Activity Recognition

Bian,

Magno

2023

Proceedings of the 2023 International Symposium on Wearable Computers

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“…In recent years, a novel approach called event-triggered impulsive control (ETIC) has been proposed. ETIC integrates the benefits of impulsive control and event-triggered control to effectively reduce update frequency and enhance resource utilization in resource-limited environments [19][20][21][22]. In [21], the authors investigate the fault-tolerant secure containment control of multi-agent systems by using an impulsive scheme within an event-triggered framework.…”

Section: Introductionmentioning

confidence: 99%

“…In [21], the authors investigate the fault-tolerant secure containment control of multi-agent systems by using an impulsive scheme within an event-triggered framework. In [22], the authors consider the stability of delayed stochastic systems under deception attacks, adopting event-triggered impulsive control. The input of the impulsive control signal may be affected by the past state due to the delay factor.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered delayed impulsive control for quasi‐synchronization of complex dynamic systems with packet loss and parameter mismatch

Li,

Jiang

2023

IET Control Theory & Appl

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This paper studies the event triggered delayed impulsive control strategy for quasi‐synchronization for complex dynamical systems with parameter mismatch and packet loss. By constructing a comparative lemma with time delays and Lyapunov function, a few sufficient synchronization conditions are proposed by Cauchy matrix with time delays to analyze the system. Meanwhile, the possibility of packet loss is also considered and the quasi‐synchronization of the system is investigated by using both event triggered delayed impulsive control and self‐triggered impulsive control mechanisms. Theoretical analysis results are validated by numerical simulations.

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“…Complex networks are ubiquitous in modern science and play an important role in a wide range of fields, from social networks and biological systems to transportation and communication networks [1][2][3][4][5][6][7] . One of the most intriguing properties of complex networks is the emergence of small-world behavior, characterized by a short path length between nodes.…”

Section: Introductionmentioning

confidence: 99%

Uncovering the hidden structure of small-world networks

Achahbar,

Lachgar

2023

Preprint

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The small-world (SW) network model introduced by Watts and Strogatz has had a tremendous impact on the study of complex systems, leading to the emergence of network science as an interdisciplinary field. While the Newman-Watts model is commonly used to analyze SW networks by adding a few randomly placed shortcuts to a regular lattice. We carefully study related previous works and conclude that scaling of different relevant quantities is not convincing. Here, we demonstrate that the small-world property arises mainly from the existence of clusters of nodes linked by shortcuts rather than just the mean number of shortcuts. Introducing the mean degree of clusters linked by shortcuts as a new parameter resolves the scaling ambiguity, providing a more accurate description of the network. Our findings provide a new framework for analyzing SW networks, emphasizing the importance of considering emergent structures in complex systems. We also construct a phase diagram of the crossover transition from the small to the large world, yielding valuable insights into the nature of complex networks and highlighting the power of emergence in shaping their behavior.

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Event-triggered impulsive control for stability of stochastic delayed complex networks under deception attacks

Cited by 20 publications

References 40 publications

Evaluating Spiking Neural Network on Neuromorphic Platform For Human Activity Recognition

Evaluating Spiking Neural Network on Neuromorphic Platform For Human Activity Recognition

Event‐triggered delayed impulsive control for quasi‐synchronization of complex dynamic systems with packet loss and parameter mismatch

Uncovering the hidden structure of small-world networks

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