Publications

In this paper, periodic and chaotic behaviors of DC–DC converters under certain parametric conditions are simulated, experimentally verified, and analyzed. Motivated by the work of J.H.B. Deane and D.C. Hamill in 1996, where chaotic phenomena are useful in suppressing electromagnetic interference (EMI) by adjusting the parameters of the DC–DC converter and making it operate in chaos, a chaos-based pulse width modulation (CPWM) is proposed to distribute the harmonics of the DC–DC converters continuously and evenly over a wide frequency range, thereby reducing the EMI. The output waves and spectral properties of the EMI are simulated and analyzed as the carrier frequency or amplitude changes with regard to different chaotic maps. Simulation and experimental results are given to illustrate the effectiveness of the proposed CPWM, which provides a good example of applying chaos theory in engineering practice.

It is important for a networked-system to have both good controllability and robustness, where the former measures the ability that the networked system can be properly steered via control to any target state form any initial state within a finite time duration, while the later reflects how well this system can regain its controllability after being destructively attacked. Empirical observations suggest that multi-loop structures are beneficial for enhancing the controllability robustness. The Henneberg-growth mechanism in social networks offers a natural manner to generate multi-clique structures that can be developed to multi-loops by assigning proper edge directions. In this paper, a series of random polygon networks are generated, forming random triangle, rectangle, pentagon and hexagon networks. Then, their controllability robustness is investigated. A realistic measure is designed for characterizing their controllability robustness, which can be used to filter out trivial performances after the network is severely destructed, so that the computation and analysis become much more efficient. Extensive simulation results suggest that, for random polygon networks, 1) non-loop polygon structures are inferior to the loop polygons, confirming that the multi-loop structures are indeed beneficial for controllability robustness; 2) polygons with more sides possess better controllability robustness; and 3) the correlation between controllability robustness and connectivity robustness is weak, implying that the two objectives cannot be enhanced in the same way.

As an enhanced version of Walsh-code-based multiaccess differential chaos shift keying (DCSK-WC), the differentially DCSK-WC (DDCSK-WC) benefits from better error performance and stronger robustness against inter-symbol interference in multipath fading channels. In this paper, the Bahl-Cocke-Jelinek-Raviv (BCJR) decoding algorithm is applied in the detection of DDCSK-WC to form DDCSK-WC-BCJR. Theoretical analyses and simulation results demonstrate that the BCJR detector achieves a significant performance gain with respect to the conventional generalized-maximum-likelihood detector, while retaining the hardware complexity almost unchanged. To further improve the performance, protograph-based low-density-parity-check channel codes are incorporated into the DDCSK-WC-BCJR system so as to construct a serial concatenated coding scheme. However, the capacity-approaching protograph code in additive white Gaussian noise (AWGN) channels performs poorly in the DDCSK-WC-BCJR system over multipath fading channels. To tackle this problem, a new protograph code is designed with the help of a modified extrinsic information transfer analysis. The proposed code has decoding thresholds gap within 1.0 dB way from to the system capacity limits. Besides, the performance superiority of the protograph-coded DDCSK-WC-BCJR architecture is illustrated over a practical UWB channel. More importantly, the proposed scheme does not require channel state information as in uncoded-DCSK schemes, which makes it extremely promising for low-cost and low-complexity wireless communication applications.

Community detection plays a significant role in network analysis. However, it also faces numerous challenges like adversarial attacks. How to further improve the performance and robustness of community detection for real-world networks has raised great concerns. In this paper, we propose a concept of adversarial enhancement for community detection, and present two adversarial enhancement algorithms: one is named adversarial enhancement via genetic algorithm (AE-GA), in which the modularity and the number of clusters are used to design a fitness function to solve the resolution limit problem; and the other is called adversarial enhancement via vertex similarity (AE-VS), integrating multiple information of community structures captured by diverse vertex similarities, which scales well on large-scale networks. The two algorithms are tested along with six existing community detection algorithms on four real-world networks. Comprehensive experimental results show that, by comparing with two traditional enhancement strategies, our methods help six community detection algorithms achieve more significant performance improvement. Moreover, experiments on the corresponding adversarial networks indicate that our methods can rebuild the network structure destroyed by adversarial attacks to certain extent, achieving stronger defense against community detection deception.

Li–Yorke chaos of linear differential equations in a finite-dimensional space with a weak topology is introduced. Based on this topology on the Euclidean space, a flow generated from a linear differential equation is proved to be Li–Yorke chaotic under certain conditions, which is in sharp contract to the well-known fact that linear differential equations cannot be chaotic in a finite-dimensional space with a strong topology.

Differential-chaos-shift-keying cooperative communication (DCSK-CC) has attracted a great deal of attention in the past five years, because it can overcome the negative effect of wireless multipath fading. However, the conventional DCSK-CC system suffers from a major disadvantage of low data rate due to the resource consumption in cooperation. To address the issue, this paper proposes an efficient cooperative scheme, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partial-sequence CC (PS-CC) scheme</i> , for the DCSK-CC system. In the PS-CC framework, the source and relays only transmit a partial chaotic sequence corresponding to one bit to the destination, while consuming the same transmission energy as DCSK-CC. To validate the feasibility of the DCSK PS-CC system, this paper carries out the error performance, diversity order, and data rate analyses under different cooperative conditions over multipath fading channels. Both numerical and simulated results show that the proposed PS-CC system outperforms the DCSK-based non-cooperative system and DCSK-CC systems over the range of signal-to-noise ratio under study. Moreover, the proposed system can enhance data rate and reduce the computational overhead of the relays as compared with the DCSK-CC system. In addition, it will be demonstrated that the DCSK PS-CC system preserves its advantages in typical ultra-wideband (UWB) channels. Overall, the PS-CC appears to be a desirable cooperative scheme for those applications featured by high-data-rate, low-power, and low-complexity, such as wireless sensor networks using the transmitted reference UWB technique.

Based on the Hodgkin–Huxley and Hindmarsh–Rose models, this paper proposes a geometric phenomenological model of bursting neuron in its simplest form, describing the dynamic motion on a mug-shaped branched manifold, which is a cylinder tied to a ribbon. Rigorous mathematical analysis is performed on the nature of the bursting neuron solutions: the number of spikes in a burst, the periodicity or chaoticity of the bursts, etc. The model is then generalized to obtain mixing burst of any number of spikes. Finally, an example is presented to verify the theoretical results.

No abstract is provided for this article.

Graph classification is an important task widely applied to biochemistry, social networks, and other fields. Since it is a data-dependent problem, insufficient training data will deteriorate the performance of graph classification models. To address this issue, various data augmentation methods have been proposed. However, most existing methods tend to destroy the topological features, leading to a negative impact on information propagation and semantics. While the null model generates new data with the same topological features as the original graph and helps capture the latent information based on topology. Hence, combining the null model with data augmentation for graph classification may be useful in helping models learn graph representations. This paper introduces a novel null model-based augmentation technique for graph classification. Specifically, four standard and four approximate null model-based augmentation methods are developed to verify the effectiveness of the technique. Experimental results on benchmark datasets demonstrate significant performance improvements with the proposed technique. Depending on the design mechanisms of the null models, standard augmentation methods outperform the approximate ones. These findings emphasize the critical role of non-trivial features in enhancing augmentation models for different network structures, providing a new perspective on data augmentation for studying graph classification methods.