Terahertz (THz) frequency band has been widely studied and is recognized as a promising candidate for centimeter-level localization. However, the limited coverage of THz networks may result in localization failures, while a heterogeneous deployment of millimeter-wave (mmWave) and THz radio units (RUs) offers a viable solution to mitigate this issue. This paper presents a theoretical framework for evaluating the performance limits of localization systems in mmWave and THz heterogeneous networks. In this architecture, the mmWave RUs serve as macro base stations (BSs), while the THz RUs function as micro BSs distributed around each mmWave RU. By leveraging the standard tools of stochastic geometry to model the spatial distributions of the RUs and ambient obstacles, the localizability of a target is computed to evaluate the probability of achieving sufficient signal-to-interference-plus-noise ratio for localization in both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. Furthermore, the Cram é r-Rao lower bounds in both LoS and NLoS scenarios are analytically derived to characterize the overall positioning performance. Numerical results demonstrate that the hybrid deployment strategy significantly improves both the network coverage and localization accuracy compared to mmWave-only and THz-only networks.

In the realm of motorcycle extreme sports, wheel-ground friction significantly influences vehicle safety. This study presents a robust and precise method for identifying motorcycle tire friction by leveraging an advanced observation scheme. The proposed observer combines cascaded flatness-based observer and extended Kalman filter, employs a robust fixed-time exact differentiator to estimate the first- and second-order derivatives of the signal. This approach ensures adaptability to environmental parameter variations while effectively attenuating measurement noise and external disturbances. The robustness and accuracy of the proposed method are validated through simulations on the BikeSim platform, incorporating external shock disturbances and varying road conditions.

To travel in the uneven and multi-obstacle terrain autonomously, an unmanned reverse tricycle vehicle, which mainly includes the steering, active tilting and driving actuators, is designed. A kinematic modeling framework incorporates terrain-induced camber, pitch, and tilting angles to characterize the vehicle’s dynamic behavior. The analytical investigation focuses on caster angle optimization and demonstrates how a multi-link parallelogram chassis architecture achieves kinematic decoupling between steering and tilting functions. To validate the potential of travelling in uneven terrain, the strait forward traveling across the road bank with near ’0’ inclination, active tilting assistant turning on the level surface, and the challenging counter-gradient slope turning experiments are carried. Experimental validation encompasses three critical scenarios: straight line forward traversal across road bank with near-zero lateral inclination, active tilt-assisted turning on planar surfaces, and counter-gradient slope turning. These trials substantiate the vehicle’s capability to maintain stability while executing complex maneuvers across uneven terrain.

Accurate prediction of mmWave time-varying channels is essential for mitigating the issue ofchannel agingin highly dynamic scenarios. Existing channel prediction methods have limitations: classical model-based methods often struggle to track highly nonlinear channel dynamics due to limited expert knowledge, while emerging data-driven methods typically require substantial labeled data for effective training and often lack interpretability. To address these issues, this paper proposes a novel hybrid method that integrates a data-driven neural network into a conventional model-based workflow based on a state-space model (SSM), implicitly tracking complex channel dynamics from data without requiring precise expert knowledge. Additionally, a novel unsupervised learning strategy is developed to train the embedded neural network solely with unlabeled data. Theoretical analyses and ablation studies are conducted to interpret the enhanced benefits gained from the hybrid integration. Numerical simulations based on the 3GPP mmWave channel model corroborate the superior prediction accuracy of the proposed method, compared to state-of-the-art methods that are either purely model-based or data-driven. Furthermore, extensive experiments validate its robustness against various challenging factors, including among others severe channel variations.

Received signal strength (RSS) measurements can be converted to the distance estimates between the emission source and the sensors to construct a system of linear equations, thereby allowing for the use of the weighted linear least squares (WLLS) estimators for location estimation. However, estimating the squared distances from the RSS measurements governed by the log-normal shadowing effect presents a major challenge in such approaches. In this paper, we propose a linear minimum mean square error (LMMSE) estimator of the squared distance between the emission source and the sensor first. Then a LMMSE-aided WLLS (LMMSE-WLLS) location estimator and its unbiased counterpart are presented for source localization. Furthermore, their estimation performance are analyzed in terms of mean square error (MSE) and covariance. It is found that the proposed LMMSE-aided WLLS location estimators have better estimation performance than existing WLLS estimators. Numerical examples also demonstrate the performance superiority of the proposed location estimators for source localization.

Deep state-space models (DSSMs) have gained popularity in recent years due to their potent modeling capacity for dynamic systems. However, existing DSSM works are limited to single-task modeling, which requires retraining with historical task data upon revisiting a forepassed task. To address this limitation, we propose continual learning DSSMs (CLDSSMs), which are capable of adapting to evolving tasks without catastrophic forgetting. Our proposed CLDSSMs integrate mainstream regularization-based continual learning (CL) methods, ensuring efficient updates with constant computational and memory costs for modeling multiple dynamic systems. We also conduct a comprehensive cost analysis of each CL method applied to the respective CLDSSMs, and demonstrate the efficacy of CLDSSMs through experiments on real-world datasets. The results corroborate that while various competing CL methods exhibit different merits, the proposed CLDSSMs consistently outperform traditional DSSMs in terms of effectively addressing catastrophic forgetting, enabling swift and accurate parameter transfer to new tasks.

Rigid robotic arms have strong load capacity and mature control schemes, but they are often not suitable for working in narrow spaces with multiple obstacles. Flexible robotic arms can bend more freely, but they have weaker load capacity and are not suitable for fine manipulation. In view of this, inspired by the biological structure of the Nycticorax Violaceus, a novel scalable flexible robotic arm for addressing complex spatial fault-tolerant manipulation requirements is proposed in this paper. The robotic arm can be concealed within a shell at the end of a rigid arm without affecting the normal use of the rigid arm. When needed, it can extend from the shell of the rigid arm, serving as a flexible auxiliary operating end attached to the rigid arm base for large curvature bending. This paper presents the structural design of a flexible arm and provides kinematic and dynamic models for the flexible arm extension operation. Through a case study, the kinematic and dynamic models proposed in this paper are validated.

Despite the dominance of unsupervised and self-supervised anomaly detection methods in the current satellite fault diagnosis domain, supervised anomaly detection offers a superior alternative for high-sensitivity detection and lightweight deployment requirements specific to subsystems or components, such as attitude control systems (ACSs). This article addresses the issues of over-design and insufficient accuracy in the CNN network design for satellite ACS fault diagnosis by introducing the modified particle swarm optimization-advanced convolution blocks-based CNN (MPSO-ACBCNN) method. First, we present the ACBCNN, a lightweight, flexible-layer CNN architecture. This architecture leverages advanced convolution blocks (ACBs), which incorporate numerous efficient design elements to enhance feature extraction capabilities within power spectral density (PSD) graphs of various fault samples, and employs classical dense connection methods to prevent the issue of gradient vanishing. Second, we devise the MPSO-ACBCNN algorithm to optimize the ACBCNN fault diagnosis architecture for specified ACS using MPSO. In MPSO-ACBCNN, several optimizations to the canonical PSO are implemented, including the fitness design that balances the tradeoff between total parameter quantity and the training effectiveness, and methods to ensure feasible solutions, etc. Finally, numerical experimental results demonstrate the effectiveness and superiority of MPSO-ACBCNN in fault diagnosis for ACS.

Safe driving and stable paths are essential for the autonomous vehicle navigation, that large and fast steering angle should be avoided. This paper addresses a local path generation problem for autonomous vehicles while the least maximum curvature and the shortest length are obtained with limited curvature rate. The properties of the novel feasible paths based on continuous curvature turns are investigated. And a simple and fast computational method is presented to solve the problem by iterative procedure. The simulation in this paper shows that, compared with recent researches, the performance of least maximum curvature and shortest length is obtained by the novel local path planner, and the driving behavior is closer to the human driver operation.

Most researches on balance control of the bicycle robots are for the situation that the bicycle robot is with constant forward velocity and constant feedback gain, but are not appropriate to be employed for time varying forward velocity situation. In this paper, the nonlinear Euler-Lagrange model of the bicycle robot and the simplified nonlinear descriptor state space model are firstly deduced. A polynomial controller, rather than a constant gain feedback one, is proposed, which constituting the nonlinear closed-loop descriptor system. The sufficient condition on examining the stability of closed-loop system, together with one alternative method on designing a polynomial controller utilizing the SOSTool, are then proposed. By this work, the polynomial controller is broadened to be applied on nonlinear descriptor system, and on the balance and forward control of bicycle robot with time varying forward velocity. One numerical example shows the capacity of the control scheme proposed in this paper.

In this paper, the problem of robust backstepping control for a three-degree-of-freedom experimental helicopter is investigated by using output feedback. The proposed control strategy can estimate the angular velocity through a state observer and achieve the attitude tracking of the elevation and pitch angles respectively in the case of using only the angular position sensor. The system unmodeled dynamics, parameter uncertainties, and external perturbation are addressed via robust backstepping technique. It's shown by Lyapunov stability analysis that the closed-loop system can be stabilized by the proposed controller with high control accuracy. The experimental results are provided to verify the effectiveness and advantage of the proposed control methodology.

This paper investigates the coupled disturbance reconstruction, state estimation and trajectory tracking problems for a class of nonlinear systems. The sliding mode observer and the state feedback approaches are utilized to solve these problems. Considering only a partial of the system states are measured but the disturbances couple with time-varying parameters that the observer is designed by transforming the original system into a descriptor one. Hence, from the estimated values of system states and the decoupled disturbance, one can reconstruct the coupled disturbance. Upon these estimates, a state feedback based fault tolerant controller is designed such that the system states converge to the desired trajectory. Finally, to verify the validity of our scheme, a numerical simulation together with an experiment of 3-DOF robot are offered.

Collaborative spectrum sensing has been recognized as a promising way to ameliorate the sensing performance in cognitive radio networks. Unfortunately, it also introduces some system overhead to users, and as a result some selfish secondary users might be unwilling to contribute to collaborative spectrum sensing. In this paper, we propose a new selfishness model in cluster-based collaborative spectrum sensing, which is referred to Overclaim Selfishness (OS). An OS group may gain benefit by sharing nominally equal but actually much less sensing reports than it declares. To deal with this problem, we propose an Overclaim Selfishness Detection Scheme (OSDS) to detect the potential OS groups. We find that a single secondary user tends to have one special type of sensing reports correlated with his physical location, thus the cluster number estimated by OSDS should be no much less than the number of users the group contains. Further, we adopt an incentive scheme to stimulate rational groups to behave honestly. Finally, a real world experiment is adopted to demonstrate the effectiveness of our proposed scheme OSDS.

We present a novel feature-based non-rigid image registration algorithm using a small number of automatically extracted points and their associated local salient region features. Our automatic registration is a hybrid approach co-optimizing point-based and image-based terms. Motivated by the paradigm of the TPS-RPM algorithm [6], we develop the RHDM (Robust Hybrid Deformable Matching) algorithm by alternatively optimizing correspondences and transformations for registration. The local salient region features and the geometric features, together with the softassign and deterministic annealing techniques, are used for solving correspondences. Thin-plate splines are used for generating a smooth non-rigid spatial transformation. Our algorithm is built to be extremely robust to feature extraction errors. A new dynamic outlier rejection mechanism is described for rejecting outliers and generating accurate spatial mappings. A local refinement technique is used for correcting non-exactly matched correspondences arising from image noise and irregular deformations. In contrast with the TPS-RPM algorithm, which can handle only outliers in one point set, our algorithm is able to handle a considerable number of outliers in both point sets. The experimental results demonstrate the robustness and accuracy of our algorithm.

In the short time since publication of Boykov and Jolly's seminal paper [2001], graph cuts have become well established as a leading method in 2D and 3D semi-automated image segmentation. Although this approach is computationally feasible for many tasks, the memory overhead and supralinear time complexity of leading algorithms results in an excessive computational burden for high-resolution data. In this paper, we introduce a multilevel banded heuristic for computation of graph cuts that is motivated by the well-known narrow band algorithm in level set computation. We perform a number of numerical experiments to show that this heuristic drastically reduces both the running time and the memory consumption of graph cuts while producing nearly the same segmentation result as the conventional graph cuts. Additionally, we are able to characterize the type of segmentation target for which our multilevel banded heuristic yields different results from the conventional graph cuts. The proposed method has been applied to both 2D and 3D images with promising results.

This paper proposes a new, efficient surface representation method for surface matching. A feature carrier for a surface point, which is a set of two-dimensional (2-D) contours that are the projections of geodesic circles on the tangent plane, is generated. The carrier is named point fingerprint because its pattern is similar to human fingerprints and plays a role in discriminating surface points. Corresponding points on surfaces from different views are found by comparing their fingerprints. The point fingerprint is able to carry curvature, color, and other information which can improve matching accuracy, and the matching process is faster than 2-D image comparison. A novel candidate point selection method based on the fingerprint irregularity is introduced. Point fingerprint is successfully applied to pose estimation of real range data.

We propose a novel algorithm to smooth and simplify simultaneously range images and also triangle meshes derived from those images. These data sets often suffer from noise and over-sampling. To overcome these issues, smoothing from image processing and simplification from computer graphics attempt to minimize noise and reduce complexity, respectively. Typically, these algorithms are separate and distinct steps, but we combine them into one algorithm. We employ surface normal voting to generate robust orientation estimates and then extend the quadric error metric framework to smooth noise while simplifying the surface. We demonstrate the capabilities of this algorithm with both synthetic and real data. The proposed algorithm provides significant noise smoothing improvement when compared to the standard Garland and Heckbert (1998) quadric simplification algorithm.

Triangle meshes are widely used in representing surfaces in computer vision and computer graphics. Although 2D image processing-based edge detection techniques have been popular in many application areas, they are not well developed for surfaces represented by triangle meshes. This paper proposes a robust edge detection algorithm for triangle meshes and its applications to surface segmentation and adaptive surface smoothing. The proposed edge detection technique is based on eigen analysis of the surface normal vector field in a geodesic window. To compute the edge strength of a certain vertex, the neighboring vertices in a specified geodesic distance are involved. Edge information are used further to segment the surfaces with the watershed algorithm and to achieve edge-preserved, adaptive surface smoothing. The proposed algorithm is novel in robustly detecting edges on triangle meshes against noise. The 3D watershed algorithm is an extension from previous work. Experimental results on surfaces reconstructed from multi-view real range images are presented.

In this paper, we describe a robust method for the estimation of curvature on a triangle mesh, where this mesh is a discrete approximation of a piecewise smooth surface. The proposed method avoids the computationally expensive process of surface fitting and instead employs normal voting to achieve robust results. This method detects crease discontinuities on the surface to improve estimates near those creases. Using a voting scheme, the algorithm estimates both principal curvatures and principal directions for smooth patches. The entire process requires one user parameter-the voting neighborhood size, which is a function of sampling density, feature size, and measurement noise. We present results for both synthetic and real data and compare these results to an existing algorithm developed by Taubin (1995).

This paper proposes a new efficient surface representation method for the application of surface matching. We generate a feature carrier for the surface point, which is a set of 2D contours that are the projection of geodesic circles onto the tangent plane. The carrier is named point's fingerprint because its pattern is similar to human fingerprint and discriminating for each point. Each point's fingerprint carries the information of the normal variation along geodesic circles. Corresponding points on surfaces from different views are found by comparing fingerprints of the points. This representation scheme includes more local geometry information than some previous works that only use one contour as the feature carrier. It is not histogram based so that it is able to carry more features to improve comparison accuracy. To speed up the matching, we use a novel candidate point selection method based on the shape irregularity of the projected local geodesic circle. The point's fingerprint is successfully used to register both synthetic and real 2 1/2 data.

We propose an adaptive regularization algorithm for smoothing dense range images using a novel, first order stabilizing function. The stabilizer we suggest is based upon minimizing the reconstructed surface area and is derived in the native, spherical coordinate system of the range scanner. This allows adjustments to be made along only the direction of measurement, thereby preventing the data overlapping problem that can arise in dense images. Adaptation is achieved by adjusting the regularization parameter according to the results of 2D edge analysis. Results indicate effective noise suppression along with well preserved edges and details in the reconstructed, 3D surfaces.