This paper introduces a novel multi-sensor fusion SLAM algorithm named FLARE-SLAM, designed for mobile robots operating in complex environments. This algorithm addresses challenges associated with uneven LiDAR measurement signals and their random distribution. First, we enhance the stability of feature extraction by refining the curvature calculation strategy for LiDAR point clouds and incorporating contextual information from the sensor array. Second, we introduce an adaptive residual optimization weight distribution mechanism, grounded in the principle of uniform residual optimization, to boost the algorithm’s adaptability across various environments. Extensive evaluations on the KITTI dataset confirm that FLARE-SLAM constructs a global map with enhanced consistency and accuracy, achieving an absolute trajectory error of 0.53% and an absolute rotation error of 0.19∘/100m. Additionally, we validate the robustness of the algorithm through real-world testing in diverse outdoor and indoor settings.

Most current light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) methods are based on static conditions, but real-world scenarios often violate this prior assumption. To address the existing challenges, this article proposes DO-Removal, an online LiDAR-inertial odometry that removes dynamic objects. Specifically, the method uses ground fitting results as a reference, takes point cloud measurements with significant geometric features as seed points for region growing, and uses clustering results to determine the confidence of dynamic element point cloud segmentation, thereby separating dynamic and static elements. Additionally, this article proposes a multiline LiDAR point cloud feature extraction method that considers context beams simultaneously, enhancing the significance of the extraction results. It also implements a residual optimization function based on distance truncation, distinguishing contributions by confidence, and adaptively weighting features at different distances. Finally, extensive testing was conducted on the KITTI dataset and a self-collected dataset, achieving competitive results with absolute trajectory error and absolute rotation error reduced to 0.51% and 0.19°/100 m, respectively.

This article addresses the finite-time neural predefined performance control (PPC) issue for state-constrained nonlinear systems (NSs) with exogenous disturbances. By integrating the predefined-time performance function (PTPF) and the conventional barrier Lyapunov function (BLF), a new set of time-varying BLFs is designed to constrain the error variables. This establishes conditions for satisfying full-state constraints while ensuring that the tracking error meets the predefined performance indicators (PPIs) within a predefined time. Additionally, the incorporation of the nonlinear disturbance observer technique (NDOT) in the control design significantly enhances the ability of the system to reject disturbances and improves overall robustness. Leveraging recursive design based on dynamic surface control (DSC), a finite-time neural adaptive PPC strategy is devised to ensure that the closed-loop system is semi-globally practically finite-time stable (SPFS) and achieves the desired PPIs. Finally, the simulation results of two practical examples validate the efficacy and viability of the proposed approach.

This article presents a novel command-filter-based finite-time fuzzy preassigned performance adaptive control approach for nonlinear systems with exogenous disturbances and asymmetric time-varying constraints. Combining the asymmetric barrier Lyapunov function (ABLF) with preassigned performance control (PPC), it is ensured that the state$x_{1}$satisfies the required asymmetric time-varying state constraints (ATSCs), and the system output y can track the desired signal with achieving preassigned performance indices (PPIs). The command-filter-based control design not only circumvents the computing complexity in the backstepping but also reduces the conservativeness of the assumption regarding the desired signal and the time-varying asymmetric constraints. Additionally, the nonlinear disturbance observer method (NDOM) is employed to effectively estimate unknown exogenous disturbances and enhance the robustness of the closed-loop system. Through rigorous theoretical analysis, the proposed finite-time command filter-based control method effectively ensures that all variables are bounded within a finite time. The practicality and feasibility of the proposed approach are further validated by simulation results.

Piecewise Linear Approximation (PLA) is one of the most widely used approaches for representing a time series with a set of approximated line segments. With this compressed form of representation, many large complicated time series can be efficiently stored, transmitted and analyzed. In this article, with the introduced concept of "semi-connection" that allowing two representation lines to be connected at a point between two consecutive time stamps, we propose a new optimal linear-time PLA algorithm SemiOptConnAlg for generating the least number of semi-connected line segments with guaranteed maximum error bound. With extended experimental tests, we demonstrate that the proposed algorithm is very efficient in execution and achieves better performances than the state-of-art solutions.

This article studies a nonlinear disturbance observer (NDO)-based finite-time fuzzy adaptive dynamic surface control (DSC) of nonlinear systems (NSs) with external disturbances and preassigned performance indices. By constructing a type of finite-time preassigned-performance function (FTPF), the tracking error variable is confined within the boundaries of the FTPF such that the performance metrics, for instance, steady-state error, and convergence time, could be satisfied. Incorporating fuzzy adaptive control with the NDO technique, an effective composite NDO (CNDO) scheme is built to reckon the unknown composite disturbances, including unknown external disturbances and fuzzy approximation errors, which implies that fuzzy approaching errors can be further cut down. It is confirmed that the closed-loop system is semiglobally practically finite-time stable, as well as the tracking error and convergence time satisfy the predefined performance indices. In the end, the validity of the CNDO-based finite-time adaptive DSC scheme has been evidenced by a practical example model.

Piecewise Linear Approximation (PLA) is one of the most widely used approaches for representing a time series with a set of approximated line segments. With this compressed form of representation, many large complicated time series can be efficiently stored, transmitted and analyzed. In this article, with the introduced concept of “semi-connection” that allowing two representation lines to be connected at a point between two consecutive time stamps, we propose a new optimal linear-time PLA algorithm SemiOptConnAlg for generating the least number of semi-connected line segments with guaranteed maximum error bound. With extended experimental tests, we demonstrate that the proposed algorithm is very efficient in execution time and achieves better performances than the state-of-art solutions.

Current approaches for the security of embedded software mainly focused on some specific platforms. In this paper, a security model based on code obfuscation is applied to embedded software. A control flow flattening algorithm is used to implement an automated obfuscator, which obfuscates C code first, and does source-to-source conversions to protect software on different platforms. The effectiveness of code obfuscation is evaluated by a multi-level quantitative model proposed in this paper. Related experiments are carried out on the NUC140VE3CN board and MC9S12XEPIOOMAG board, which are typical hardware platforms used in the application domain of automotive. The result of experiments shows that for one thing, the quantitative value of the effectiveness of the obfuscated program is obviously higher than that of the original program, namely the strength for software to keep it from being reversed is greater, and the overhead of time and space is acceptable; for another, the efficiency of the evaluation model is also demonstrated.

Brain Computer Interface (BCI) has been an emerging topic in recent years. Specially, Artificial Intelligence (AI) is becoming a hot research area in recent years. However, many BCI techniques utilize invasive interfaces to brains (animal or human), which could cause potential risks for experimental subjects. EEG (Electroencephalography) technique has been used extensively as a non-invasive BCI solution for brain activity study. Many psychological work has suggested that human brains can generate some recognizable EEG signals associated with some specific activities. This paper suggests a novel EEG recognition method, i.e. Segmented EEG Graph using PLA (SEGPA), that incorporates improved Piecewise Linear Approximation (PLA) algorithm and EEG-based weighted network for EEG pattern recognition, which can be used for machinery control. The improved PLA algorithm and EEG-based weighted network technique incorporates the data sampling and segmentation method. This research proposes a potentially efficient method for recognizing human's brain activities that can be used for machinery or robot control.

This paper studies the problem of optimizing the sum of multiple agents' local convex objective functions, subject to global convex inequality constraints and a convex state constraint set over a network. Through characterizing the primal and dual optimal solutions as the saddle points of the Lagrangian function associated with the problem, we propose a distributed algorithm, named the distributed primal-dual subgradient method, to provide approximate saddle points of the Lagrangian function, based on the distributed average consensus algorithms. Under Slater's condition, we obtain bounds on the convergence properties of the proposed method for a constant step size. Simulation examples are provided to demonstrate the effectiveness of the proposed method.

The success of current trust and reputation systems is on the premise that truthful feedbacks are obtained. However, without appropriate mechanisms, silent and lying strategies usually yield higher payoffs for peers than truthful feedback strategies. Thus, to ensure trustworthiness, incentive mechanisms are critically needed for a reputation system to encourage rational peers to provide truthful feedbacks. In this paper, we model the feedback reporting process in reputation system as a reporting game. We propose a Wage-based Incentive Mechanism (WIM) for enforcing truthful report in self-interested P2P networks. We design, implement, and analyze incentive mechanisms and players' strategies. The extensive simulation results demonstrate that the proposed incentive mechanisms reinforce truthful feedbacks and achieve optimal welfare.

With emerging Internet-scale open content and resource sharing, social networks, and complex cyber-physical systems, trust issues become prominent. In this paper, we propose a trust vector based scheme (VectorTrust) for aggregation of distributed trust scores. Leveraging a Bellman-Ford based algorithm for fast trust score aggregation, VectorTrust features localized and distributed concurrent communication. A Vector Trust-enabled system is decentralized by nature and does not rely on any centralized server or centralized trust aggregation. We design, implement, and analyze trust aggregation and trust management strategies. To evaluate the performance, we design and implement a VectorTrust simulator (VTSim) in an unstructured P2P network. The analysis and simulation results demonstrate the efficiency, accuracy, scalability and robustness of VectorTrust scheme. On average, VectorTrust converges faster and involves less computational complexity than most existing trust schemes. VectorTrust remains robust and tolerant to malicious peers and malicious behaviors. With dynamic growth of P2P network scales and topology complexities, VectorTrust scales well with reasonable overheads (O(lgN) communication overheads) and fast convergence speed (about O(lgN) steps).

In this paper, we propose an autonomic management framework (ASGrid) to address the requirements of emerging large-scale applications in hybrid grid and sensor network systems. To the best of our knowledge, we are the first who proposed the autonomic sensor grid system concept in a holistic manner targeted at non-trivial large applications. To bridge the gap between the physical world and the digital world and facilitate information analysis and decision making, ASGrid is designed to smooth the integration of sensor networks and grid systems and efficiently use both on demand. Under the blueprint of ASGrid, we present several building blocks that fulfill the following major features: (1) Self-configuration through content-based aggregation and associative rendezvous mechanisms; (2) Self-optimization through utility-based sensor selection and model-driven hierarchical sensing task scheduling; (3) Self-protection through ActiveKey dynamic key management and S3Trust trust management mechanisms. Experimental and simulation results on these aspects are presented.

In this paper, we present a group trust management system S3Trust for collaborative services in peer-to-peer grid systems. S3Trust is based on personalized trust rating and selective aggregation algorithms. Leveraging the robustness of a simplistic but elegant co-constraint aggregation algorithm (inspired by H-index) under incomplete and uncertain circumstances, S3Trust offers a robust and lightweight reputation evaluation mechanism for both individual and group trusts with minimal communication and computation overheads. The five phases of S3Trust scheme are presented in detail, including trust recording, local trust evaluation, trust query phase, spatial-temporal update phase, and group reputation evaluation phases. Simulation results demonstrate that S3Trust is robust and can efficiently aggregate cooperative groups in systems with malicious users.