In this paper, the time-varying formation tracking (TVFT) problem of multi-leader multi-agent systems (MASs) under sampled-data control is studied. First, an improved TVFT criterion is derived by employing a general looped-functional and a zero integral functional. Then a new TVFT controller is designed based on our proposed method. Unlike the existing works, the TVFT controller can allow for bigger sampling intervals of multi-leader MASs. The results demonstrate that the states of the followers can form the designated formation and rotate around the convex combination formed by the multiple leaders. In addition, we generalize the results of TVFT to the sampled-data consensus for MASs, a sampled-data consensus controller which can reduce communication consumption is designed. Meanwhile, the values of the allowable maximum sampling intervals (AMSIs) are calculated. Finally, the superiority and availability of the theoretical results are verified by two simulation experiments.Note to Practitioners—The motivation of this paper is to address the problem of TVFT for MAS with multiple leaders. It has been applied to several practical engineering systems, e.g., satellite formation control and unmanned air vehicles (UAVs) formation control. Note that information exchange is a prerequisite to ensure safe and stable control of multi-UAV formation. However, continuous-time communication is difficult to be realized in the actual environment, and it will inevitably consume a lot of energy. In addition, sampled-data controller for MAS with a small sampling interval may cause network congestion. Therefore, a sampled-data controller which allows for a bigger sampling interval for MAS is designed to save network resources in this paper. Through simulation experiments, it can be proved that the followers not only track the leaders, but also form the designated formation.

This brief investigates the stability of neural networks with time-varying delays. Novel stability conditions are derived by employing free-matrix-based inequality and introducing the variable-augmented-based free-weighting matrices in the estimation of the derivative of the Lyapunov-Krasovskii functionals (LKFs). Both techniques avoid the appearance of the nonlinear terms of the time-varying delay. Especially, the time-varying free-weighting matrices associated with the derivative of the delay and the time-varying S-Procedure related to the delay and its derivative are combined to improve the presented criteria. Finally, numerical examples are given to illustrate the benefits of the presented methods.

Federated learning (FL) as an emerging edge intelligence paradigm allows clients to jointly train a model without exchanging raw data. Due to its excellent performance in privacy protection, FL has practical application in many areas. However, data heterogeneity across clients is a prevalent phenomenon and poses a significant challenge to FL. Although many FL algorithms have been proposed to address the issue of performance deterioration under non-independent and identically distributed (Non-IID), the improvement in performance is not significant. In this paper, we propose knowledge discrepancy-aware federated learning (KDAFL). It evaluates the local model with regard to each class and overall learning effect based on the discrepancy between local and global knowledge. In this way, each client assigns a new weight to each category of cross-entropy and decides if knowledge distillation is to be conducted. The awareness of discrepancy allows the client to adjust the local training according to the characteristics of the knowledge it learned, thus better solving the Non-IID issue. Extensive experiments have demonstrated the effectiveness of KDAFL, particularly in terms of the improvement of global model accuracy compared to other state-of-the-art algorithms.

Emerging iris recognition techniques are highly dependent on high-resolution iris images. However, existing iris recognition methods cannot effectively extract local texture features in low-resolution application scenarios, resulting in low recognition accuracy below expected. In this paper, we propose DBANet, a novel dual branch attention-based deep neural network for biological iris recognition that can achieve high accuracy for both high and low-resolution images. Specifically, we first design a spatial feature module with a small stride to preserve lower-level spatial detail features. Then, since the high-level feature can provide rich global context information, we propose a context feature module to generate high-level features. Finally, we develop a novel spatial attention module to fuse features generated by the above modules. We conduct the experiments on UBIRIS. v2, CASIA-V4-Distance, and MICHE-I datasets. Experimental results show that as compared to state-of-art methods, our proposed method can reduce equal error rates by up to 38.7%, 53.4%, and 71.9%, respectively.

With the development of robotics, the underwater robot platform has been widely used in the Internet of Underwater Things (IoUT). An underwater robot platform equipped with multiple sensors is used as a mobile collector to build a reliable information collection system for IoUT. This article presents the mechatronic design, fabrication, modeling, control, simulation, and experiments of a robot IoUT platform to enable the smart ocean. Inspired by the design of both fin-actuated swimming of fish and buoyancy-driven gliding of underwater glider, a novel multilink gliding fish robot is proposed. The multilink gliding fish robot, which is called FishBot in this article, can swim flexibly and glide energy efficiently in three dimensions. In the FishBot, the body and/or caudal fin (BCF) with three degrees of freedom and buoyancy-driven system was equipped as the main propulsion device. Besides, a pair of pectoral fins was equipped to assist in regulating the gliding attitude and enhance the FishBot maneuverability in the vertical plane. The dynamic model that consists of cruise swimming motion, pure-pitching swimming motion, and 3-D swimming motion for control is established. Moreover, a behavioral control framework is developed to achieve a variety of fish-like swimming behaviors and gliding motion. Meanwhile, we proved the stability of the linear quadratic regulation controller and the locomotion controller is provided with exponential stability. The validity of the proposed model and the designed controller is demonstrated by numerical simulations. Finally, a series of experiments involving different fish-like behaviors and gliding motion elucidates the powerful locomotion ability of the FishBot.

This paper investigates the problem of extended dissipativity for Markovian jump neural networks (MJNNs) with a time-varying delay. The objective is to derive less conservative extended dissipativity criteria for delayed MJNNs. Toward this aim, an appropriate Lyapunov-Krasovskii functional (LKF) with some improved delay-product-type terms is first constructed. Then, by employing the extended reciprocally convex matrix inequality (ERCMI) and the Wirtinger-based integral inequality to estimate the derivative of the constructed LKF, a delay-dependent extended dissipativity condition is derived for the delayed MJNNs. An improved extended dissipativity criterion is also given via the allowable delay sets method. Based on the above-mentioned results, the extended dissipativity condition of delayed NNs without Markovian jump parameters is directly derived. Finally, three numerical examples are employed to illustrate the advantages of the proposed method.

This Innovative Practice Work in Progress Paper presents a multi-dimension training scheme to improve the innovation capacity of postgraduate and undergraduate students collaboratively. The current training of postgraduate and undergraduate education schemes cannot form a mutually beneficial relation, not suitable for the training of innovation capacity on postgraduate and undergraduate students collaboratively. There are various problems that need to be addressed. The main strategy of the proposed scheme is utilizing real research programs to establish an integrated team mixed with both postgraduate and undergraduate students, focusing on stimulating their innovation capacity in scientific research. In the scheme, we designed a multi-level joint training mechanism based on the mixed pedagogy, the collaborative resource pool platform, the multi-role and team-based innovation talent incubation mechanism, and the preparatory postgraduate student selection and training goal achievement evaluation mechanism. We applied the scheme to the training process of postgraduate students from the majors of Software Engineering, Computer Software and Theory, Computer Application, Computer Technology and Electronic Information Engineering, and undergraduate students from the majors of Computer Science and Technology, Information Security and Communication Engineering in Nanjing University of Posts and Telecommunications. The training results approve the feasibility and effectiveness of the scheme.

The population of open education resource and online self-studying mode have been brought great and profound changes to traditional higher education system. The introduction of Massive Open Online Course (MOOC) not only presents a convenient ways to involved in top university education courses, but also proposes an alternative method to obtain university experience and credit certification. This paper gives a brief but comprehensive review of MOOC application in China including its status quo, opportunities and challenges.

This paper has designed a kind of WIFI multi-node heart rate variability (HRV) analysis system, consisting of several ElectroCardioGram (ECG) acquisition nodes and an analysis platform based on ARM Cortex-A8 processor. The node oversamples ECG signal through a photoelectric ear clip and filters it with FIR window before sending it to the platform with specific format package by WIFI module. The platform extracts ECG signal of each node from the received packages in real time and RR intervals are acquired from ECG signal by differential threshold algorithm so as to calculate time domain parameters. Before performing frequency domain analysis on RR intervals, the baseline wander is removed from RR intervals by db6 wavelet and RR intervals is resampled by cubic spline interpolation. All algorithm for each node can be done in short period by means of multi-thread and NEON technique. The system is promising for multi-node with real-time and stable operation and accurate analysis result, which can be widely used in medical care, the collective movement training and psychological testing.

In the large scale multimedia interactive applications over the Internet, data can be categorized into the streaming data (e.g. the audio and the video streaming) and the sporadic data (e.g. the annotation in a whiteboard). The mesh-pull overlay fits for the streaming data, for it is resilient in heterogeneous network, while the tree-push overlay is more suitable for the sporadic data, for its easy-to-manage structure can solve the problem of packet loss and disorder. To address the transmission of both types of data, a Hybrid Mesh/Tree Overlay (HyMTO) is proposed in this paper, where the sporadic data and the streaming data are transmitted through different overlays with a novel synchronous scheme. HyMTO can provide the resilient, reliable and ordered transmission for both types of data respectively. Simulations are presented to evaluate the effectiveness and efficiency of HyMTO and a practical e-conference system based on HyMTO is implemented and deployed to demonstrate its practicability and reasonability.

In this paper, a class of impulsive interval neural networks with discrete and distributed time-varying delays is discussed. Several new sufficient conditions are obtained ensuring the existence, uniqueness, and global exponential stability of periodic solution (i.e., stationary oscillation) for the addressed models based on inequality analysis techniques. The obtained results can be checked easily by the linear matrix inequality control toolbox in MATLAB. Finally, two numerical examples are given to show the effectiveness of our results.

Layered streaming in P2P networks has become a hot topic recently. However, the "layered" feature makes the data scheduling quite different from that for nonlayered streaming, and it hasn't been systematically studied yet. In this paper, first, according to the unique characteristics caused by layered coding, we present four objectives that should be addressed by scheduling: throughput, layer delivery ratio, useless packets ratio, and subscription jitter prevention; then a three-stage scheduling approach LayerP2P is designed to request data, where the min-cost flow model, probability decision mechanism, and multiwindow remedy mechanism are used in Free Stage, Decision Stage, and Remedy Stage, respectively, to collaboratively achieve the above objectives. With the basic version of LayerP2P and corresponding experiment results achieved in our previous work, in this paper, more efforts are put on its mechanism details and analysis to its unique features; besides, to further guarantee the performance under sharp bandwidth variation, we propose the enhanced approach by improving the Decision Stage strategy. Extensive experiments by simulation and real network implementation indicate that it outperforms other schemes. LayerP2P has also been deployed in PDEPS Project in China, which is expected to be the first practical layered streaming system for education in P2P networks.

Virtual Actors now play an important role in computer-generated films, Virtual Environments, Telecooperative work, and multimedia. In order to make these actors realistic, it is essential to represent their body shape during the motion. In this paper we present different methods for representing realistic deformations for virtual humans with various characteristics: sex, age, height, weight. Our methods based on a combination of metaballs and splines could be applied to frame-by-frame computer generated-films and Virtual Environments. Several examples are presented: autonomous actors, animation based on flock of birds, networked Virtual Environments.

Abstract We describe the HUMANOID environment dedicated to human modeling and animation for general multimedia, VR, and CAD applications integrating virtual humans. We present the design of the system and the integration of the various features: generic modeling of a large class of entities with the BODY data structure, realistic skin deformation for body and hands, facial animation, collision detection, integrated motion control and parallelization of computation intensive tasks.