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Coordination effectively enhances the detection capabilities of sensors towards cluster targets. However, due to the dense distribution of cluster members and the cooperative interaction among them, continuous tracking of these targets becomes challenging. To address these issues, we propose a novel multi-sensor continuous-discrete Poisson multi-Bernoulli mixture filter, abbreviated as MS-CDPMBM-CT filter. Based on the cooperative interaction rules, we use the multivariate stochastic differential equation (SDE) to model the dynamics of cluster members in continuous time. To adapt to the Bayesian recursive process, we derive a Gaussian state transition form of the above model in the state space. In addition, by utilizing a cluster structure undirected graph and a multi-dimensional assignment (MDA) algorithm, we improve the multi-sensor continuous-discrete PMBM filter, which can iteratively estimate the posterior probability density of the target based on the continuous time state model and discrete multi-sensor measurements. During prediction, the cluster structure is exploited to provide cooperative interaction inputs that constrain member motions. During the update, the partitioning problem of multi-sensor measurements is transformed into the MDA problem for updating the global hypotheses. Simulation experiments show that the proposed method can better adapt to the cluster interaction behavior, distinguish the members within the cluster, and achieve high filtering accuracy. [ABSTRACT FROM AUTHOR]

In recent years, with the wide application and popularization of artificial intelligence algorithm in the field of multisensor information processing, it has been a research hotspot to solve the problem of sensor alliance formation in the battlefield environment by using multisensor cross-cueing technology. Based on the establishment of the multisensor hybrid dynamic alliance model and objective function, a multisensor cross-cueing algorithm based on dynamic discrete particle swarm optimization (DDPSO) with sensitive particles is proposed and a mechanism of "predict re-predict" is proposed in the process of sensor handover. Simulations have verified the good convergence effect and small detection error of multisensor cross-cueing technology in solving alliance formation problems. Meanwhile, compared with "measurement and then update" and "predict and update" mechanisms, the proposed mechanism is more suitable to the changing combat environment. At the same time, to some extent, it also shows that the artificial intelligence algorithm is more suitable for multisensor information processing. [ABSTRACT FROM AUTHOR]

The acquisition of high signal-to-noise ratio (SNR) detection results for targets in infrared images is of great significance for early warning detection and precision guidance. To address the issue of detecting cluster targets in complex backgrounds, a detection method combining the Facet kernel and adaptive distance regularization level set evolution (ADRLSE) is proposed. Firstly, local order-statistics and mean filters are applied to remove high-brightness noise and smooth the image. Subsequently, the Facet kernel filter is applied to enhance the target while suppress the background. Candidate target pixels are obtained through adaptive threshold segmentation. Additionally, based on the edge characteristics of cluster targets, a novel edge detection function is proposed that adaptively determines the parameters using the contour volume (CV) and peak signal-to-noise ratio (PSNR) as references. The enhanced mapping of the target region is obtained by extracting the cluster target contour using ADRLSE. The ADRLSE map is weighted using the target map obtained from the Facet kernel to further enhance the target, and the target is detected through thresholding operations. Finally, the separation result of a single target within a cluster is obtained through a novel adaptive k -means clustering algorithm. Simulation experiments demonstrate that our method has high detection accuracy for cluster targets and can accomplish detection tasks such as cluster contour extraction and single target division. [ABSTRACT FROM AUTHOR]

This article investigates the robust adaptive control system design for the longitudinal dynamics of a flexible air‐breathing hypersonic vehicle (FAHV) subject to parametric uncertainties and control input constraints. A combination of back‐stepping and nonlinear disturbance observer (NDO) is utilized for exploiting an adaptive output‐feedback controller to provide robust tracking of velocity and altitude reference trajectories in the presence of flexible effects and system uncertainties. The dynamic surface control is introduced to solve the problem of "explosion of terms." A new NDO is developed to guarantee the proposed controller's disturbance attenuation ability and to performance robustness against uncertain aerodynamic coefficients. To deal with the problem of actuator saturation, a novel auxiliary system is exploited to compensate the desired control laws. The stability of the presented NDO and controller is analyzed. Simulation results are given to demonstrate the effectiveness of the presented control strategy. [ABSTRACT FROM AUTHOR]

Sensor deployment is one of the major concerns in multisensor networks. This paper proposes a sensor deployment approach using improved virtual force algorithm based on area intensity for multisensor networks to realize the optimal deployment of multisensor and obtain better coverage effect. Due to the real-time sensor detection model, the algorithm uses the intensity of sensor area to select the optimal deployment distance. In order to verify the effectiveness of this algorithm to improve coverage quality, VFA and PSOA are selected for comparative analysis. The simulation results show that the algorithm can achieve global coverage optimization better and improve the performance of virtual force algorithm. It avoids the unstable coverage caused by the large amount of computation, slow convergence speed, and easily falling into local optimum, which provides a new idea for multisensor deployment. [ABSTRACT FROM AUTHOR]

The trajectory prediction of the hypersonic glide vehicle (HGV) can provide hit point information for early warning systems, which is of great significance for near‐space defence operations. However, the heavy‐tailed noise caused by abnormal environmental disturbance and abrupt changes in vehicle trajectory seriously affects the accuracy of HGV trajectory prediction. To solve the problem of trajectory prediction for HGV under heavy‐tailed noise, we propose an adaptive multivariate Student's t‐process regression method called aEM‐MVTPR. Firstly, a heavy‐tailed noise model is developed using the Student's t‐distribution. Secondly, a multivariate Student's t‐process regression (MVTPR) for HGV trajectory prediction is derived, and the method can learn the HGV trajectory time series features and mine the correlations among the trajectory variables. Finally, to further improve the method's robustness, we use the accelerated expectation maximisation algorithm and Pearson correlation analysis to adaptively estimate and adjust the initial values of the MVTPR parameters. The simulation experimental results show that the proposed method has more accurate predicted values of trajectory variables than multivariate Gaussian process regression (MVGPR) under heavy‐tailed noise conditions. In addition, the ability of the aEM‐MVTPR to adaptively adjust the parameter makes it more robust than the MVTPR under different noise environments. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of motion intention recognition for cluster targets with splitting behaviour and lack of motion prior information. This is a challenge to the classical Bayesian inference based intention recognition algorithms because they rely heavily on a priori knowledge. In order to solve these problems, a joint algorithm of deep echo state network optimized by adaptive genetic algorithm (AGADESN) and DBSCAN clustering algorithm is proposed in this paper. We use improved Olfati-Saber model with direction noise to generate cluster motion and use the cluster motion data to drive AGADESN algorithm to predict cluster destination, which achieves higher destination prediction accuracy than DESN algorithm. We innovatively design the motion similarity distance (MSD) and take the destination prediction output as one of the distance inputs, alleviating the lack of differentiation among different cluster targets caused by only relying on speed and position distance at the early stage of cluster motion. Based on the MSD, DBSCAN clustering algorithm is used to identify clusters in the field of view to determine whether splitting behaviour occurs. Simulation results demonstrate the effectiveness of the proposed algorithm in cluster target motion intention recognition and its superiority over DESN algorithm and DBSCAN algorithm only based on speed and position distance. [ABSTRACT FROM AUTHOR]

The Asian water plantain, Alisma orientale (Sam.) Juzep, is a traditional Chinese medicinal plant. The dried tubers of the Alisma orientale, commonly referred to as Alismatis rhizome (AR), have long been used in traditional Chinese medicine to treat a variety of diseases. Soil properties and the soil microbial composition are known to affect the quality and bioactivity of plants. Here, we sought to identify variations in soil fungal communities and soil properties to determine which would be optimal for cultivation of A. orietale. Soil properties, heavy metal content, and pesticide residues were determined from soils derived from four different agricultural regions around Shaowu City, Fujian, China, that had previously been cultivated with various crops, namely, Shui Dao Tu (SDT, rice), Guo Shu Tu (GST, pecan), Cha Shu Tu (CST, tea trees), and Sang Shen Tu (SST, mulberry). As fungi can either positively or negatively impact plant growth, the fungal communities in the different soils were characterized using long-read PacBio sequencing. Finally, we examined the quality of A. orientale grown in the different soils. Our results show that fungal community diversity of the GST soil was the highest with saprotrophs the main functional modes in these and SDT soils. Our data show that GST and SDT soils were most suitable for A. orientale growth, with the quality of the AR tubers harvested from GST soil being the highest. These data provide a systematic approach at soil properties of agricultural lands in need of replacement and/or rotating crops. Based on our findings, GST was identified as the optimal soil for planting A. orientale, providing a new resource for local farmers. [ABSTRACT FROM AUTHOR]

Background: Indigo alkaloids, such as indigo, indirubin and its derivatives, have been identified as effective antiviral compounds in Baphicacanthus cusia. Evidence suggests that the biosynthesis of indigo alkaloids in plants occurs via the shikimate pathway. The enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) is involved in plant metabolism; however, its underlying putative mechanism of regulating the production of indigo alkaloids is currently unknown. Results: One gene encoding EPSPS was isolated from B. cusia. Quantitative real-time PCR analysis revealed that BcEPSPS was expressed at the highest level in the stem and upregulated by methyl jasmonate (MeJA), salicylic acid (SA) and abscisic acid (ABA) treatment. The results of subcellular localization indicated that BcEPSPS is mainly expressed in both the plastids and cytosol, which has not been previously reported. An enzyme assay revealed that the heterogeneously expressed BcEPSPS protein catalysed the generation of 5-enolpyruvyl shikimate-3-phosphate. The overexpression of BcEPSPS in Isatis indigotica hairy roots resulted in the high accumulation of indigo alkaloids, such as indigo, secologanin, indole and isorhamnetin. Conclusions: The function of BcEPSPS in catalysing the production of EPSP and regulating indigo alkaloid biosynthesis was revealed, which provided a distinct view of plant metabolic engineering. Our findings have practical implications for understanding the effect of BcEPSPS on active compound biosynthesis in B. cusia. [ABSTRACT FROM AUTHOR]

• In IR imaging, UAV cluster targets include a lack of detail characteristics, inadequate form features, and a low signal-to-noise ratio. • The weighted tri-layer sliding window local contrast algorithm is capable of resolving the problem of detecting UAV cluster targets. • In terms of SCRG, BSF, and ROC curves, the algorithm in this paper surpasses previous algorithms. Infrared images of unmanned aerial vehicle (UAV) cluster targets are characterized by the low signal-to-noise ratio (SCR), closely spaced, and limited detail characteristics, which exacerbate the detection complexity of contemporary infrared (IR) small target detection algorithms. This paper proposes a weighted tri-layer sliding window local contrast (NTLLCM) algorithm for UAV cluster targets detection. Firstly, the IR imaging characteristics of UAV cluster targets are modeled and evaluated. Secondly, after evaluating the limitations of LCM, the local contrast of the filtered images is calculated by designing a tri-layer sliding window structure with core, middle, and outermost layers. The local contrast is then weighted utilizing a new regional intensity level (NRIL) strategy to suppress the background further, and the target is detected by adaptive threshold segmentation. Finally, a considerable number of experimental results illustrate the superiority of this algorithm in detecting UAV cluster targets under sophisticated backgrounds, especially the proposed algorithm detects multiscale targets utilizing single-scale windows instead of using multiscale windows, which significantly decreases the computationally costs. Furthermore, the algorithm can reduce the overlapping effect of UAV cluster target imaging and correctly detect closely spaced targets. [ABSTRACT FROM AUTHOR]

Baphicacanthus cusia (Nees) Bremek is an herb widely used for the clinical treatment of colds, fever, and influenza in Traditional Chinese Medicine. The roots, stems and leaves can be used as natural medicine, in which indigo and indirubin are two main active ingredients. In this study, quantification of indigo, indirubin, indican and adenosine among various tissues of B. cusia was conducted using HPLC-DAD. Leaves have significantly higher contents than stems and roots (380.66, 315.15, 20,978.26, 4323.15 μg/g in leaves, 306.36, 71.71, 3,056.78, 139.45 μg/g in stems, and 9.31, 7.82, 170.45, 197.48 μg/g in roots, respectively). De novo transcriptome sequencing of B. cusia was performed for the first time. The sequencing yielded 137,216,248, 122,837,394 and 140,240,688 clean reads from leaves, stems and roots respectively, which were assembled into 51,381 unique sequences. A total of 33,317 unigenes could be annotated using the databases of Nr, Swiss-Prot, KEGG and KOG. These analyses provided a detailed view of the enzymes involved in indican backbone biosynthesis, such as cytochrome P450, UDP-glycosyltransferase, glucosidase and tryptophan synthase. Analysis results showed that tryptophan synthase was the candidate gene involved in the tissue-specific biosynthesis of indican. We also detected sixteen types of simple sequence repeats in RNA-Seq data for use in future molecular mark assisted breeding studies. The results will be helpful in further analysis of B. cusia functional genomics, especially in increasing biosynthesis of indican through biotechnological approaches and metabolic regulation. [ABSTRACT FROM AUTHOR]

In order to build the sensor network facing the task of multisensor crossing cueing, the requirements of initiating cueing and being cued are analyzed. Probability theory is used when building models, then probability of sensor cueing in the case of target moving is given, and, after that, the best distance between two sensors is calculated. The operational environment is described by normal distribution function. In the process of distributing sensor network, their elements, operational environment demand of cueing, and the probability of sensor network coverage are considered; then the optimization algorithm of sensor network based on hypothesis testing theory is made. The simulation result indicates that the algorithm can make sensor network which is required. On the basis of that, the two cases, including targets that make linear motion and orbit motion, are used to test the performance of the sensor network, which show that the sensor network can make uninterrupted detection on targets through multisensor cross cuing. [ABSTRACT FROM AUTHOR]

This paper investigates the design of a novel estimation-free prescribed performance non-affine control strategy for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) via back-stepping. The proposed control scheme is capable of guaranteeing tracking errors of velocity, altitude, flight-path angle, pitch angle and pitch rate with prescribed performance. By prescribed performance, we mean that the tracking error is limited to a predefined arbitrarily small residual set, with convergence rate no less than a certain constant, exhibiting maximum overshoot less than a given value. Unlike traditional back-stepping designs, there is no need of an affine model in this paper. Moreover, both the tedious analytic and numerical computations of time derivatives of virtual control laws are completely avoided. In contrast to estimation-based strategies, the presented estimation-free controller possesses much lower computational costs, while successfully eliminating the potential problem of parameter drifting. Owing to its independence on an accurate AHV model, the studied methodology exhibits excellent robustness against system uncertainties. Finally, simulation results from a fully nonlinear model clarify and verify the design. [ABSTRACT FROM PUBLISHER]