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Motivation Peptide identification of data-independent acquisition (DIA) mass spectrometry applying the peptide-centric approach heavily relies on the spectral library matching, such as the fragment intensity similarity. If the intensity similarity is calculated through all possible fragment ions of a targeted peptide instead of just a few fragment ions provided by the spectral library, the matching will be more comprehensive and reliable, and thus the identification will be more confident. In addition, the emergence of high precision spectrum predictors, like Prosit, also makes it possible to capitalize on the predicted spectrum, which contains all possible fragment ion intensities, to calculate the intensity similarity for DIA data. Results In this work, we propose Alpha-Tri, a neural-network-based model to calculate intensity similarity as a post-processing score using the predicted spectrum, measured spectrum and correlation spectrum (triple-spectrum). The predicted spectrum is generated by Prosit, the measured spectrum is retrieved from the apex of the chromatograms of all possible fragment ions and the correlation spectrum is used to indicate the present probabilities of these fragment ions as the link between the precursor and its fragment ions is lost in DIA. By adopting a data-driven method, Alpha-Tri is able to learn the intensity similarity from the triple-spectrum. This learned value is appended to initial scores from DIA-NN, allowing the ensuing statistical validation tool to report more peptides at the same false discovery rate (FDR). In our evaluation of the HeLa dataset with gradient lengths ranging from 0.5 to 2 h, Alpha-Tri delivered 3.0–7.2% gains in peptide detections at 1% FDR. On LFQbench dataset, a mixed-species dataset with known ratios, Alpha-Tri identified more peptides and proteins fell within the valid ratio ranges by up to 8.6% and 7.6%, respectively, compared with DIA-NN solely. Availability and implementation The original datasets for benchmarks are downloaded from the ProteomeXchange with the identifiers PXD005573, PXD000954 and PXD002952. Source code is available at https://github.com/YuAirLab/Alpha-Tri.

Wetland ecology monitoring is an important technical guarantee for the protection and restoration of fragile wetland environments. In view of the degradation of the functional vegetation Suaeda heteroptera ( S. heteroptera ) in the Liao River Estuary wetland, an online monitoring method named the Wetland Image Monitoring System (WIMS) was established to obtain real-time, continuous, high spatial and temporal resolution data about the area and height of S. heteroptera . Based on the monitoring data, we confirmed the best model to describe the change process of the area and height for S. heteroptera . The results showed that the growth of S. heteroptera could be divided into three stages: in April and May, the area and height of the S. heteroptera increased rapidly; from June to September, the growth rate slowed down; after October, the area and height of S. heteroptera gradually stabilized. The modeling results showed that the Bertalanffy model had the best simulation performance on the change of the area. While for the height simulation, the segment model composed of the linear and Gompertz models could effectively reduce the simulation error and improve the fit accuracy compared with the single model. In addition, the WIMS has the potential to capture other important ecological factors in small regions, such as benthic animals, birds, waterlogging condition, etc. This observing method can provide high-resolution and abundant data for further exploration of the degradation of typical ecosystems under climate change and human activities.

In this paper, an efficient and probabilistic complete planning algorithm called Composite-space RRT is presented to address motion planning with soft constraints for spherical wrist manipulators. Firstly, we propose a novel configuration space termed Composite Configuration Space (“Composite Space” for short), which is composed of the joint space and the task space. Then, collision-free paths are generated in the composite space by the Rapidly-exploring Random Trees (RRT) algorithm. Finally, the planned paths in the composite space are mapped into the corresponding joint-space paths by a local planner. As the analytical inverse kinematics (IK) of the spherical wrist is used in the local planner, the proposed Composite-space RRT algorithm is characterized by high efficiency and no numerical iteration. Moreover, this approach can effectively improve the smoothness of the end-effector orientation path. The effectiveness of the proposed algorithm is demonstrated on the Willow Garage’s PR2 simulation platform with two typical orientation-constrained cases.

Instance segmentation in videos, which aims to segment and track multiple objects in video frames, has garnered a flurry of research attention in recent years. In this paper, we present a novel weakly supervised framework with \textbf{S}patio-\textbf{T}emporal \textbf{C}ollaboration for instance \textbf{Seg}mentation in videos, namely \textbf{STC-Seg}. Concretely, STC-Seg demonstrates four contributions. First, we leverage the complementary representations from unsupervised depth estimation and optical flow to produce effective pseudo-labels for training deep networks and predicting high-quality instance masks. Second, to enhance the mask generation, we devise a puzzle loss, which enables end-to-end training using box-level annotations. Third, our tracking module jointly utilizes bounding-box diagonal points with spatio-temporal discrepancy to model movements, which largely improves the robustness to different object appearances. Finally, our framework is flexible and enables image-level instance segmentation methods to operate the video-level task. We conduct an extensive set of experiments on the KITTI MOTS and YT-VIS datasets. Experimental results demonstrate that our method achieves strong performance and even outperforms fully supervised TrackR-CNN and MaskTrack R-CNN. We believe that STC-Seg can be a valuable addition to the community, as it reflects the tip of an iceberg about the innovative opportunities in the weakly supervised paradigm for instance segmentation in videos.

Excessive use of chemical fertilizers has led to a reduction in the quality of arable land and environmental pollution. Using green manure to replace chemical fertilizers is one of the most effective solutions. To study the effect of green manure on the requirement for nitrogen fertilizer in oilseed rape, a field experiment with maize–oilseed rape rotation was conducted. Green manure was intercropped between rows of maize and returned after the maize harvest, with no green manure intercropped as control. Different nitrogen fertilizer treatments (0, 65%, 75% and 100% N rates, respectively) were applied during the oilseed rape season. The results showed that with a 35% reduction in nitrogen application rate, the rapeseed grain yield was significantly higher with the maize intercropping with green manure returned to the field than with the maize monocropping treatment at the same nitrogen level. Under conditions of intercropping and return of green manure, compared with the full standard rate of nitrogen fertilizer treatment, a reduction in nitrogen application of 25–30% in the rape season had no significant effect on rape yield. The agronomic efficiency of nitrogen fertilizer on oilseed rape increased significantly, by 47.61–121%, with green manure incorporation. In addition, green manure incorporation significantly increased the soil organic matter content and the soil-available nitrogen content when chemical nitrogen fertilization was abandoned. Benefit analysis showed that a 25–35% reduction in chemical nitrogen fertilizer applied to oilseed rape crops could be achieved by intercropping green manure in the maize season before the sowing of rapeseed in the experimental area. In the long-term, this measure would increase nitrogen utility, reduce production costs, and have concomitant environmental benefits of improving the quality of cultivated land.

As recognized in psychological research, there is often a difference between an agent’s expressed opinion and private opinion (or belief). This occurs for different reasons, such as political correctness or peer pressure. The opinion expressed by an agent is the result of pressure to follow the (average) opinions expressed by the group to which the agent belongs, or to follow group norms. The agent’s private opinion is unknown to others, but evolved under the influence of other agents’ expressed opinions. This paper proposes an opinion formation model based on the theory of bounded confidence, and studies the dynamic process of expressed and private opinions in time-varying networks. At the same time, the self-persuasion effect of agents in the dissonance between expressed and private opinions is considered. Here, group pressure establishes the motive force from private opinion to expressed opinion, while self-persuasion establishes the reverse connection. We find that group pressure can effectively reduce the gap of opinions between the group, but does not always promote consensus. Furthermore, the self-persuasion effect of agents can ensure the realization of group consensus.

In this paper, we propose a game of the Byzantine Generals, which is a coordination game of agents seeking consensus by strategically transmitting information on a sequence of time-varying communication graphs. The first scenario of the game is where the generals cannot communicate with others at the same "level" in the communication graph. The second scenario is where those generals can. In either scenario, we examine the influences of the number of traitors and the decision rule held by the generals on equilibrium predictions of the game.

As the pervasive, standardized format for interchange and deposition of raw mass spectrometry (MS) proteomics and metabolomics data, text-based mzML is inefficiently utilized on various analysis platforms due to its sheer volume of samples and limited read/write speed. Database-developed solution guarantees the efficiency of random file reading, but nevertheless the efforts in compression and third-party software support are insufficient, while most research on compression algorithms rarely provides flexible random file reading scheme. Under the premise of ensuring the efficiency of decompression, we propose an encoding scheme “Stack-ZDPD” that is optimized for storage of raw MS data, designed for the format “Aird”, a computation-oriented format with fast accessing and decoding time, where the core compression algorithm is “ZDPD”. Stack-ZDPD reduces the volume of data stored in mzML format by around 80% or more, depending on the data acquisition pattern, and the compression ratio is approximately 30% compared to ZDPD for data generated using Time of Flight technology. Our approach is available on Airdpro, for file conversion and the Java-API Aird SDK, for data parsing.

Robotic grasp detection is a fundamental problem in robotic manipulation. The conventional grasp methods, using vision information only, can cause potential damage in force-sensitive tasks. In this paper, we propose a tactile–visual based method using a reproducible sensor to realize a fine-grained and haptic grasping. Although there exist several tactile-based methods, they require expensive custom sensors in coordination with their specific datasets. In order to overcome the limitations, we introduce a low-cost and reproducible tactile fingertip and build a general tactile–visual fusion grasp dataset including 5,110 grasping trials. We further propose a hierarchical encoder–decoder neural network to predict grasp points and force in an end-to-end manner. Then comparisons of our method with the state-of-the-art methods in the benchmark are shown both in vision-based and tactile–visual fusion schemes, and our method outperforms in most scenarios. Furthermore, we also compare our fusion method with the only vision-based method in the physical experiment, and the results indicate that our end-to-end method empowers the robot with a more fine-grained grasp ability, reducing force redundancy by 41%. Our project is available at https://sites.google.com/view/tvgd

Motivation The peptide-centric identification methodologies of data-independent acquisition (DIA) data mainly rely on scores for the mass spectrometric signals of targeted peptides. Among these scores, the coelution scores of peak groups constructed by the chromatograms of peptide fragment ions have a significant influence on the identification. Most of the existing coelution scores are achieved by artificially designing some functions in terms of the shape similarity, retention time shift of peak groups. However, these scores cannot characterize the coelution robustly when the peak group is in the circumstance of interference. Results On the basis that the neural network is more powerful to learn the implicit features of data robustly from a large number of samples, and thus minimizing the influence of data noise, in this work, we propose Alpha-XIC, a neural network-based model to score the coelution. By learning the characteristics of the coelution of peak groups derived from the being analyzed DIA data, Alpha-XIC is capable of yielding robust coelution scores even for peak groups with interference. With this score appending to initial scores generated by the accompanying identification engine DIA-NN, the ensuing statistical validation can report the identification result and recover the misidentified peptides. In our evaluation of the HeLa dataset with gradient lengths ranging from 0.5 to 2 h, Alpha-XIC delivered 9.4–16.2% improvements in the number of identified precursors at 1% false discovery rate. Furthermore, Alpha-XIC was tested on LFQbench, a mixed-species dataset with known ratios, and increased the number of peptides and proteins fell within valid ratios by up to 16.4% and 17.8%, respectively, compared to the initial identification by DIA-NN. Availability and implementation Source code is available at https://github.com/YuAirLab/Alpha-XIC.

Identification of peptides in mass spectrometry-based proteomics typically relies on spectra matches. As MS/MS spectra record presence and intensity of fragment ions, the match should take both fragment presence similarity and intensity similarity into consideration. Fragment presence similarity can be calculated with the help of fragment presence prediction such as theoretical enumeration of all possible fragment ions or selecting non-zero intensity ions from the result of fragment intensity prediction, but neither of these two methods is accurate enough. In this work, we developed a deep neural network based model, Alpha-Frag, to predict precisely the fragment ions that should be present for a given peptide. Alpha-Frag modelled fragment presence prediction as a multi-label classification task and trained with ProteomeTools dataset. In terms of intersection over union (IoU), Alpha-Frag achieved an average of >0.7 and outperformed the benchmarks across the validation datasets. Furthermore, fragment presence similarity was calculated based on presence prediction and incorporated into the peptide statistical validation tools as an additional score to improve peptide identifications. Our preliminary experiments show that this score led to a maximum increase of 26.8% (FDR 0.1%) and 21.6% (FDR 1%) for the DDA and the DIA identification, respectively.Significance StatementA better prediction of fragmentation for peptides in mass spectrometry (MS) is beneficial to the peptide identification. As the MS/MS spectra record two-dimensional information of fragment ions derived from precursors, mass-to-charge ratio (m/z) and their corresponding intensities, besides the fragment intensity prediction, it is necessary to study the presence prediction. Although the presence prediction can be realized by enumerating all the possible fragmentation patterns of a peptide with equal probability or by selecting non-zero intensity fragment ions from the result of fragment intensity prediction, neither of these two methods is accurate enough. In this study, deep learning is leveraged to precisely predict the fragment ions of a given peptide. Based on the fragment presence prediction, fragment presence similarity between experimental spectra and predicted spectra can be calculated which is proved to promote the peptide detections both for DDA and for DIA data.

In this paper, we will study coupled dynamical systems and investigate dimension properties of the subspace spanned by solutions of each individual system. Relevant problems on collinear dynamical systems and their variations are discussed recently by Montenbruck et al. in [1] , while in this paper we aim to provide a unified analysis to derive the dimensional-invariance principles for networked coupled systems, and to generalize the invariance principles for networked systems with more general forms of coupling terms. To be specific, we consider two types of coupled systems, one with scalar couplings and the other with matrix couplings. Via the rank-preserving flow theory , we show that any scalar-coupled dynamical system (with constant, time-varying, or state-dependent couplings) possesses the dimensional-invariance principles, in that the dimension of the subspace spanned by the individual systems’ solutions remains invariant. For coupled dynamical systems with matrix coefficients/couplings, necessary and sufficient conditions (for constant, time-varying, and state-dependent couplings) are given to characterize dimensional-invariance principles. The proofs via a rank-preserving matrix flow theory in this paper simplify the analysis in [1] , and we also extend the invariance principles to the cases of time-varying couplings and state-dependent couplings. Furthermore, subspace-preserving property and signature-preserving flows are also developed for coupled networked systems with particular coupling terms. These invariance principles provide insightful characterizations to analyze transient behaviors and solution evolutions for a large family of coupled systems, such as multiagent consensus dynamics, distributed coordination systems, formation control systems, etc.

This paper proposes three different distributed event-triggered control algorithms to achieve leader–follower consensus for a network of Euler–Lagrange agents. We first propose two model-independent algorithms for a subclass of Euler–Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work studying event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities, which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information, and bounds on the initial conditions. When the Euler–Lagrange agents have dynamics that include the vector of gravitational potential forces, an adaptive algorithm is proposed. This requires more information about the agent dynamics but allows for the estimation of uncertain parameters associated with the agent self-dynamics. For each algorithm, a trigger function is proposed to govern the event update times. The controller is only updated at each event, which ensures that the control input is piecewise constant and thus saves energy resources. We analyze each controller and trigger function to exclude Zeno behavior.

The recently proposed DeGroot-Friedkin model describes the dynamical evolution of individual social power in a social network that holds opinion discussions on a sequence of different issues. This paper revisits that model, and uses nonlinear contraction analysis, among other tools, to establish several novel results. First, we show that for a social network with constant topology, each individual's social power converges to its equilibrium value exponentially fast, whereas previous results only concluded asymptotic convergence. Second, when the network topology is dynamic (i.e., the relative interaction matrix may change between any two successive issues), we show that each individual exponentially forgets its initial social power. Specifically, individual social power is dependent only on the dynamic network topology, and initial (or perceived) social power is forgotten as a result of sequential opinion discussion. Last, we provide an explicit upper bound on an individual's social power as the number of issues discussed tends to infinity; this bound depends only on the network topology. Simulations are provided to illustrate our results., Comment: Extended version of submitted journal paper. Includes additional simulation details

Vision and voice are two vital keys for agents' interaction and learning. In this paper, we present a novel indoor navigation model called Memory Vision-Voice Indoor Navigation (MVV-IN), which receives voice commands and analyzes multimodal information of visual observation in order to enhance robots' environment understanding. We make use of single RGB images taken by a first-view monocular camera. We also apply a self-attention mechanism to keep the agent focusing on key areas. Memory is important for the agent to avoid repeating certain tasks unnecessarily and in order for it to adapt adequately to new scenes, therefore, we make use of meta-learning. We have experimented with various functional features extracted from visual observation. Comparative experiments prove that our methods outperform state-of-the-art baselines., Comment: 8 pages, 6 figures, 2 tables, accepted at 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)

With the precision of mass spectrometer going higher and the emergence of data independence acquisition (DIA), the file size is increasing rapidly. Beyond the widely-used open format mzML (Deutsch 2008), near-lossless or lossless compression algorithms and formats have emerged. The data precision is often related to the instrument and subsequent processing algorithms. Unlike storage-oriented formats, which focusing more on lossless compression and compression rate, computation-oriented formats focus as much on decoding speed and disk read strategy as compression rate. Here we describe “Aird", an opensource and computation-oriented format with controllable precision, flexible indexing strategies and high compression rate. Aird uses JavaScript Object Notation (JSON) for metadata storage, multiple indexing, and reordered storage strategies for higher speed of data randomly reading. Aird also provides a novel compressor called Zlib-Diff-PforDelta (ZDPD) for m/z data compression. Compared with Zlib only, m/z data size is about 65% lower in Aird, and merely takes 33% decoding time.AvailabilityAird SDK is written in Java, which allow scholars to access mass spectrometry data efficiently. It is available at https://github.com/Propro-Studio/Aird-SDK AirdPro can convert vendor files into Aird files, which is available at https://github.com/Propro-Studio/AirdPro

We consider the problem of robotic grasping by 2. 5D image data sampling from a real sensor. We design an encoder-decoder neural network to predict grasping policy in real-time which enhances the robustness for the policy generation at different observation heights by fusing depth image and RGB image. We propose an open-loop algorithm to realize robotic grasp operation and evaluate our method in a physical robotic system. The result shows that our method is competitive with the state-of-the-art in grasp performance, real-time and model size. The video is available in https://youtu.be/Wxw_r5a8qV0.

A concise and enantioselective procedure for the synthesis of optically active indolines has been developed through intramolecular condensation, deprotection and palladium-catalyzed asymmetric hydrogenation in a one-pot process with up to 96% ee. A strong Bronsted acid played an important role in both the formation of indoles and asymmetric hydrogenation process. This strategy could be scaled-up with excellent reactivity and enantioselectivity.

In this note, a switching-type delay-adaptive predictor feedback controller is proposed to globally stabilize linear systems with unknown input delay. The idea of our method is to take advantage of the robustness of feedback laws to delay estimate error that is sufficiently small. We first prove that, when the delay estimate is sufficiently close to the true delay, there is a Lyapunov–Krasovskii functional that is strictly decreasing, and asymptotic regulation is achievable. Then, with the help of the Lyapunov–Krasovskii functional as a supervisor, the switching logic plays the role of adjusting the estimated delay online to make it sufficiently close to the true delay. The result of this note shows that, global adaptive stabilization of linear systems with unknown input delay is achievable, with a logic-based switching control law.

The distributed averaging problem is a consensus problem whose objective is to devise a protocol which will enable all the members of a group of autonomous agents to compute the average of the initial values of their individual consensus variables in a distributed manner. Periodic gossiping is a deterministic method for solving the distributed averaging problem by stipulating that each pair of agents which are allowed to gossip, do so repeatedly in accordance with a prespecified periodic schedule. Agent pairs which are allowed to gossip correspond to edges on a given connected, undirected graph. In general, the rate at which the agents' consensus variables converge to the desired average value depends on the order in which the gossips occur over a period. The main contributions of this paper are first to characterize the classes of periodic gossip sequences which have the same convergence rate and second to prove that if the graph of allowable gossips is a tree with each edge restricted to gossiping once per period, the convergence rate is quite surprisingly, fixed and invariant over all possible periodic gossip sequences the graph allows. To arrive at these results, a new and unusual graph theoretic concept, namely the transfer function of a node of an undirected graph, is used. Among all the trees with the same number of edges, optimal tree structures, which yield the fastest convergence rate, can then be sought.

This paper analyses the DeGroot-Friedkin model for evolution of the individuals’ social powers in a social network when the network topology varies dynamically (described by dynamic relative interaction matrices). The DeGroot-Friedkin model describes how individual social power (self-appraisal, self-weight) evolves as a network of individuals discuss opinions on a sequence of issues. We seek to study dynamically changing relative interactions because interactions may change depending on the issue being discussed. Specifically, we study relative interaction matrices which vary periodically with respect to the issues. This may reflect a group of individuals, e.g. a government cabinet, that meet regularly to discuss a set of issues sequentially. It is shown that individuals’ social powers admit a periodic solution. Initially, we study a social network which varies periodically between two relative interaction matrices, and then generalise to an arbitrary number of relative interaction matrices.

This paper studies the problem of optimal coverage control over a convex bounded region by a group of unicycle-type mobile agents with constant speeds. We further assume that the constant speeds for different agents may be non-identical. Thus, a unicycle is controlled solely via steering of its orientation in which a virtual centre is defined for each unicycle. A basic controller is proposed which asymptotically drive the virtual centres to a Centroidal Voronoi configuration, thus achieving the optimal coverage objective. At the Centroidal Voronoi configuration, each unicycle executes steady-state circular orbit about its virtual centre. To overcome a drawback of the basic controller that requires a careful selection of initial conditions, a modified switching controller is proposed which achieves a Centroidal Voronoi configuration for arbitrarily chosen initial conditions. Simulations for both the basic and modified controllers are presented for a five-unicycle group.

Self-localization and formation control tasks are considered when each agent in a multiagent formation observes its neighbors but does not communicate. Each agent is restricted to a predefined motion type on a 2-D plane and collects bearing-only measurements over a time interval to localize neighboring agents. The localization process is used by a three agent formation to achieve velocity consensus combined with formation shape control. Simulations are provided and noisy bearing measurements are investigated.

Summary This paper provides unified solutions for distributed attitude and translation consensus problems for networked fully actuated rigid bodies under the fixed and undirected communication topology with the tool of unit dual quaternion. We investigate two kinds of consensus, that is, leaderless consensus and leader-following consensus with a static leader. Firstly, the dynamics of rigid bodies are presented by unit dual quaternion. The control inputs of rigid bodies are also obtained from unit dual quaternion. Secondly, we propose a distributed consensus law in the form of dual quaternion to guarantee that the attitudes and translations of all rigid bodies reach consensus, respectively, without a leader. Thirdly, the leader-following consensus problem with a static leader is studied. With the proposed leader-following consensus law, the states of all rigid bodies converge to the corresponding states of the static leader, including the attitude and the translation. Finally, numerical examples are provided to validate the effectiveness of the theoretical results. Copyright © 2017 John Wiley & Sons, Ltd.

Summary This paper proposes a distributed model-independent algorithm to achieve leaderless consensus on a directed network where each fully-actuated agent has self-dynamics described by Euler–Lagrange equations of motion. Specifically, we aim to achieve consensus of the generalised coordinates with zero generalised velocity. We show that on a strongly connected graph, a model-independent algorithm can achieve the consensus objective at an exponential rate if an upper bound on the initial conditions is known a priori. By model-independent, we mean that each agent can execute the algorithm with no knowledge of the equations describing the self-dynamics of any agent. For design of the control laws which achieve consensus, a control gain scalar and a control gain matrix are required to satisfy several inequalities involving bounds on the matrices of the agent dynamic model, bounds on the Laplacian matrix describing the network topology and the set of initial conditions; design of the algorithm therefore requires some knowledge on the bounds of the agent dynamical parameters. Because only bounds are required, the proposed algorithm offers robustness to uncertainty in the parameters of the multiagent system. We systematically show that additional relative velocity information improves the performance of the controller. Numerical simulations are provided to show the effectiveness of the algorithm. Copyright © 2016 John Wiley & Sons, Ltd.

This article discusses cooperative stabilization control of rigid formations via an event-based approach. We first design a centralized event-based formation control system, in which a central event controller determines the next triggering time and broadcasts the event signal to all the agents for control input update. We then build on this approach to propose a distributed event control strategy, in which each agent can use its local event trigger and local information to update the control input at its own event time. For both cases, the triggering condition, event function, and triggering behavior are discussed in detail, and the exponential convergence of the event-based formation system is guaranteed.

This work focuses on the construction of rigid formation from non-rigid ones in the two-dimensional space. Analogously to operations of Henneberg sequence aiming to guarantee the minimal rigidity of formation, two new operations are introduced, allowing one to sequentially build any rigid graph by connecting non-rigid ones. A systematic construction sequence is developed based on proposed operations, and is shown to be able to restore rigidity by introducing minimum number of new edges during the construction process. Further applications of the proposed operations are also presented, one of which is successfully employed in the problem of persistence analysis of directed graphs, and can verify the persistence of a given graph with a speed two times faster comparing with existing solution.

In this paper, we propose a discontinuous distributed model- independent algorithm for a directed network of Euler- Lagrange agents to track the trajectory of a leader with nonconstant velocity. We initially study a fixed network and show that the leader tracking objective is achieved semiglobally exponentially fast if the graph contains a directed spanning tree. By model independent, we mean that each agent executes its algorithm with no knowledge of the parameter values of any agent's dynamics. Certain bounds on the agent dynamics (including any disturbances) and network topology information are used to design the control gain. This fact, combined with the algorithm's model independence, results in robustness to disturbances and modeling uncertainties. Next, a continuous approximation of the algorithm is proposed, which achieves practical tracking with an adjustable tracking error. Last, we show that the algorithm is stable for networks that switch with an explicitly computable dwell time. Numerical simulations are given to show the algorithm's effectiveness.

In this paper, a novel bias-reduction method is proposed to analytically express and reduce the bias arising in sensor network localization problems, thereby improving the localization accuracy. The proposed bias-reduction method mixes Taylor series and a maximum-likelihood estimate and leads to an easily calculated analytical bias expression in terms of a known maximum-likelihood cost function. In contrast to existing contributions, this paper considers a sensor network as a whole when the bias is investigated by introducing the geometric structure of the sensor network into the proposed bias-reduction method via the rigidity matrix, a concept drawn from graph theory. The maximum-likelihood cost function is related to the rigidity matrix resulting in the final analytical expression of localization bias in terms of the rigidity matrix. Another important contribution of this paper is that, in addition to the presented simulation results, experimental results obtained by using a wireless localization system, which is developed based on reconfigurable software-defined radios (SDRs), are also provided to verify the performance of the proposed bias-reduction method. Both the simulation and experimental results demonstrate that the proposed method can reduce the bias, thereby improving the localization accuracy in different scenarios.