This article investigates the security control problem of Markov jump systems (MJSs) under denial of service (DoS) attacks by constructing a class of discontinuous interval-dependent functional. Motivated by (1) the enhanced practicality and flexibility of multi-sensor sampled-data control with asynchronous packet arrivals and (2) the destabilizing impact of DoS-induced data loss, a resilient asynchronous sampled-data control (RASDC) scheme is designed. RASDC can send data once immediately after the attack, thereby alleviating the performance degradation caused by long-term no input. Furthermore, by utilizing the stability analysis idea of switched systems, a discontinuous interval-dependent functional is constructed, which combines the information of the resilient sampling interval and the attack interval. To overcome the analytical challenges posed by its discontinuities, the discrete-time Lyapunov stability theory, convex combination techniques, and some estimation methods are used to derive the sufficient condition for mean square asymptotic stability. In light of the stability criterion, a design algorithm for the secure controller is provided. Finally, simulation results demonstrate the effectiveness of the proposed approach.

Wireless Capsule Endoscopy (WCE) has revolutionized small intestinal disease diagnosis with its minimally invasive approach. However, its effectiveness greatly depends on image quality, influenced by bowel preparation, which is traditionally assessed subjectively by clinicians. To address this, the WCEFastViT model was developed in this study, aiming to objectively quantify bowel preparation quality in WCE. This model classifies images into four distinct quality levels and significantly accelerates the inference process through structural reparameterization. Achieving an ideal balance between latency and accuracy, it delivers classification accuracy of 92.34%, with remarkably low latencies of 2.80 milliseconds per frame on the GPU and 11.76 milliseconds on the CPU. Such advancements not only enhance the accuracy and speed of bowel preparation assessments but also offer clinicians a more objective and efficient diagnostic tool. Overall, the study demonstrates the potential of deep learning in improving medical image analysis and patient care in clinical settings.

This paper investigates the security control of a class of discrete-time Markov jump systems (DMJS). Due to the vulnerability of open communication networks to cyber attacks, a hybrid attack model is established to describe the situation where the DMJS is simultaneously suffered from deception attacks (DAs) and denial-of-service (DoS) attacks. To cope with the intermittent characteristic of DoS attacks, adaptive memory-based event-triggered control (AMETC) with a switching-like adaptive law is proposed. The designed AMETC includes historical triggered data in its triggering condition, hence allows data transmission to adjust adaptively based on the long-term change of system state. In addition, when DoS attacks are launched by attackers, the designed switching-like adaptive law can help decrease the threshold to trigger more sampled data so as to stabilize the DMJS. These features contribute to improve the tolerance of the whole control system to DoS attacks and DAs. On the basis of the AMETC, a novel Lyapunov functional is designed, and sufficient conditions are derived to ensure the asymptotic stability of DMJSs. This functional plays a crucial role in ensuring the negative definiteness of the LMIs in the stability condition. Based on the stability condition, a design algorithm for security control gains and event-trigger matrices is given. Finally, simulation results validate the effectiveness and superiority of the proposed mechanism.Note to Practitioners—This paper was motivated by existing results on memory-based event-triggered control (METC) and hybrid cyber attacks. The aim of this paper is to design a novel AMETC with a switching-like adaptive law to cope with the impact of hybrid attacks on system performance. In practical applications, if the event-triggered mechanism only considers the transient information of the system, it may lead to high peak responses of the control systems, resulting in a decrease in system performance. Therefore, the AMETC proposed in this paper considers both the transient information and the long-term state change of the system. It can adjust the data transmission rate adaptively and save transmission resources effectively. Additionally, based on the designed AMETC, sufficient conditions are obtained to ensure the safe and stable operation of DMJSs. Our theoretical analysis and simulation results show the effectiveness and superiority of the designed mechanism.

This paper investigates the secure synchronization control of Markovian jump neural networks (MJNNs) suffering from denial of service attacks (DoS attacks). The issue is presented for two reasons: 1) multiple sensors are generally used to measure the information of different state variables in practical networked control systems; 2) DoS attacks will disrupt the transmission of data packets in communication channels, thereby causing the system performance degradation. To deal with these problems, a multi-sensor-based switching event-triggered mechanism (SETM) is designed. More specifically, when the data packets are transmitted normally, the SETM will switch to an adaptive memory-based event-triggered mechanism, thereby saving the network resources. When the DoS attack occurs, the SETM will trigger immediately at the end of the DoS attack to improve the system performance. To facilitate the stability analysis, a merging time series is constructed by integrating the triggering instants of successful transmission and the ending instants of DoS attacks together. In light of the merging time series, a switched closed-loop system is established. Then, by utilizing the stability analysis idea of switched systems, a multiple Lyapunov functional is constructed, enabling the exploitation of multi-sampling. On this basis, a synchronization criterion is derived, accompanied by a co-design method for controller and the trigger matrices. Finally, the outcomes of the simulation confirm both the efficacy and the advantage of the suggested approach, especially when dealing with DoS attacks.

A modular reconfigurable robot can change its configuration adaptively according to the different environments and tasks. By adding or subtracting modules or recombining existing modules, the current configuration can be quickly changed to another one adapted to a new environment and tasks. Stable autonomous docking control directly affects the mobility, docking accuracy, and docking success rate of reconfigurable robots, and has become the focus of research in this field. In this paper, a multi-constrained trajectory planning algorithm is designed, and a multi-stage robust reconfiguration strategy is designed on the basis of this optimization algorithm. The strategy is also applied to a chain modular reconfigurable robot independently researched in the laboratory for testing, and the experimental results show that the method in this paper is effective and feasible.

In this article, the exponential synchronization of Markovian jump neural networks (MJNNs) with time-varying delays is investigated via stochastic sampling and looped-functional (LF) approach. For simplicity, it is assumed that there exist two sampling periods, which satisfies the Bernoulli distribution. To model the synchronization error system, two random variables that, respectively, describe the location of the input delays and the sampling periods are introduced. In order to reduce the conservativeness, a time-dependent looped-functional (TDLF) is designed, which takes full advantage of the available information of the sampling pattern. The Gronwall-Bellman inequalities and the discrete-time Lyapunov stability theory are utilized jointly to analyze the mean-square exponential stability of the error system. A less conservative exponential synchronization criterion is derived, based on which a mode-independent stochastic sampled-data controller (SSDC) is designed. Finally, the effectiveness of the proposed control strategy is demonstrated by a numerical example.

As an important post-translational modification (PTM), protein phosphorylation is involved in the regulation of almost all of biological processes in eukaryotes. Due to the rapid progress in mass spectrometry-based phosphoproteomics, a large number of phosphorylation sites (p-sites) have been characterized but remain to be curated. Here, we briefly summarized the current progresses in the development of data resources for the collection, curation, integration and annotation of p-sites in eukaryotic proteins. Also, we designed the eukaryotic phosphorylation site database (EPSD), which contained 1 616 804 experimentally identified p-sites in 209 326 phosphoproteins from 68 eukaryotic species. In EPSD, we not only collected 1 451 629 newly identified p-sites from high-throughput (HTP) phosphoproteomic studies, but also integrated known p-sites from 13 additional databases. Moreover, we carefully annotated the phosphoproteins and p-sites of eight model organisms by integrating the knowledge from 100 additional resources that covered 15 aspects, including phosphorylation regulator, genetic variation and mutation, functional annotation, structural annotation, physicochemical property, functional domain, disease-associated information, protein-protein interaction, drug-target relation, orthologous information, biological pathway, transcriptional regulator, mRNA expression, protein expression/proteomics and subcellular localization. We anticipate that the EPSD can serve as a useful resource for further analysis of eukaryotic phosphorylation. With a data volume of 14.1 GB, EPSD is free for all users at http://epsd.biocuckoo.cn/.

Word-of-Mouth (WoM) mode, as a new mode of task sensing in crowdsourcing, shows high efficiency in building contributor groups. To better tap the potential of WoM mobile crowdsourcing, the underlying rationale of interactions among contributors needs to be well understood. In this article, we analyze the behavior of contributors based on the Stackelberg game, and find optimal strategies for contributors. We consider two different crowdsourcing tasks announcement methods: 1) one-time parallel and 2) multitime sequential announcement ways, which form two different market scenarios. Then, we formulate two-stage and multistage contributor game models for the two scenarios, respectively. The backward induction approach is used to analyze each game, and the problems to find the optimal strategies for contributors are transformed into optimization problems. Furthermore, the Lagrange multiplier and Karush-Kuhn-Tucker (KKT) methods are used to solve the optimization problems. We theoretically prove that Stackelberg equilibrium exists and is unique. Based on the proposed theory, we design algorithms to compute the profit-maximizing contribution quantity of sensing data for each contributor. Finally, we present the detailed experimental analysis and the experimental result shows the effectiveness of the proposed algorithms.

Summary Human contact prediction is a challenging task in mobile social networks. The existing prediction methods are based on the static network structure, and directly applying these static prediction methods to dynamic network prediction is bound to reduce the prediction accuracy. In this paper, we extract some important features to predict human contacts and propose a novel human contact prediction method based on naive Bayes algorithm, which is suitable for dynamic networks. The proposed method takes the ever‐changing structure of mobile social networks into account. First, the past time is partitioned into many periods with equal intervals, and each period has a feature matrix of all node pairs. Then, with the feature matrixes used for classifiers training based on naive Bayes algorithm, we can get a classifier for each time period. At last, the different weights are assigned to the classifiers according to their importance to contact prediction, and all classifiers are weighted combination into the final prediction classifier. The extensive experiments are conducted to verify the effectiveness and superiority of the proposed method, and the results show that the proposed method can improve the prediction accuracy and TP Rate to a large extent. Besides, we find that the size of time interval has a certain impact on the clustering coefficient of mobile social networks, which further affects the prediction accuracy.

The semi-global matching algorithm is usually used for generating high-quality and real-time disparity maps in stereo vision systems. To reduce the hardware-resource consumption, we present a multi-stage pipeline hardware architecture with timesharing reuse for semi-global stereo matching. Combined with image down-sampling, jumping disparity, and a post processing, the presented architecture is used in a practical advanced driver-assistance system (ADAS), which is implemented on a Zynq-7 FPGA chip. The whole stereo matching architecture consumes 19,603 LUTs and 61.5 BRAM (36 KB), and the throughput is 2857 Million Disparity Estimation per second (MDE/S), which corresponds to a throughput of 31 fps when processing images with 1280∗960 resolution and 75 disparity levels.

With the development of OTT (Over The Top) application, cell phone users have to pay more for their Internet access. To help these users save their 3G/4G communication in smart phone, a number of Apps have been upgraded to provide none-graphic model. However, the QoS of Internet service is weakened under this model. This paper proposes an innovative method by analyzing the user's Web access pattern and mobile network status, and thus to predict the user's browsing behavior. Time Series Matrix Based Prediction (TSMP) algorithm is proposed to establish the Time Series Matrix through pretreating user information, so that each user could utilize their own time series and matrices. The experimental results that the proposed method accurately predicts the user's Web access intention. Therefore, it helps users save communication cost of their mobile devices.

We present SmartLoc, a localization system to estimate the location and the traveling distance by leveraging the lower-power inertial sensors embedded in smartphones as a supplementary to GPS. To minimize the negative impact of sensor noises, SmartLoc exploits the intermittent strong GPS signals and uses the linear regression to build a prediction model which is based on the trace estimated from inertial sensors and the one computed from the GPS. Furthermore, we utilize landmarks (e.g., bridge, traffic lights) detected automatically and special driving patterns (e.g., turning, uphill, and downhill) from inertial sensory data to improve the localization accuracy when the GPS signal is weak. Our evaluations of SmartLoc in the city demonstrates its technique viability and significant localization accuracy improvement compared with GPS and other approaches: the error is approximately 20m for 90% of time while the known mean error of GPS is 42.22m.

In wireless sensor networks, there exist many redundant sensor nodes, and activating only the necessary number of sensor nodes at any particular moment can save energy, while ensuring all targets covered and network connectivity. In this paper, we first introduce a distributed scheme for sink nodes to find K paths to each sensor nodes. Secondly, a shortest path-based algorithm is presented for the maximum set covers problem in wireless sensor networks. The algorithm partitions all nodes into possibly maximum disjointed sets, and the nodes in each set have all targets covered while ensuring the network connectivity. In the proposed algorithm, when constructing a cover set, the key idea is to select a node joining into the set if it has the shortest path to the nodes which is already in the set. At last, simulation is done, and the result shows that the proposed algorithm outperforms others.

Per-hop authentication is the most effective way to prevent DOS attacks during multihop data delivery. Although the study results show that Public Key Cryptography (PKC) is feasible on sensor nodes with limited resources, it is still very expensive to perform per-hop authentication using public key digital signature. To solve the problem that the resources of WSN is exhausted quickly by PKC, Dynamic Window Based Multihop Authentication(DWMA) for WSN is proposed in this paper. Dynamic window makes it possible to pay only a small number of authentication based on digital signature for confining DoS attacks effectively in a small scope and locating suspicious nodes quickly. Experimental results show that DWMA could save more resources than per-hop authentication for WSN, defend DoS attacks effectively and locate malicious nodes. It's an effective protocol for ensuring the resistance of DoS in routing.