The multiuser haptic-enabled robotic system (M-Hers) facilitates shared control among human operators through task-dependent authority allocation, where interaction relationships are typically dictated by task requirements. However, some of these relationships can be nonpassive, generating excess energy that violates passivity constraints and compromises system stability. To address this, we first introduce the interaction architecture (IA) to formalize how operators influence task execution. Based on this framework, we propose a tank-based two-layer task model that ensures system passivity despite nonpassive IAs. This model comprises a virtual object (VO) layer for task rendering and a virtual system (VS) layer that passively executes nonpassive IA behaviors. The VS layer uses a global energy tank to compensate for IA-induced energy violations and modify the VO model when tank energy is depleted. This structure decouples task rendering from low-level robotic control, enabling seamless integration of an arbitrary number of robots with heterogeneous dynamics and control modes. Simulation and experimental results validate the proposed method’s scalability, flexibility, and effectiveness in preserving passivity while accurately realizing diverse IAs. This approach paves the way for scalable and easy-to-deploy control framework that supports multiuser haptic interaction.

Despite extensive investigations into the multi-user haptic-enabled robotic system (M-Hers), achieving scalable control design in the presence of non-passive human operators remains a key challenge. This is primarily due to the increasing complexity of stability conditions and interaction coupling as the number of operators grows. In this study, we address this challenge in two steps. First, we introduce the individual interaction environment (IIE) to isolate the passivity violations, which facilitates the independent control design for each human-robot subsystem, thereby enhancing the scalability with respect to the number of subsystems. Second, within the IIE framework, we identify passivity-violating components caused by partners' active behaviors and propose a novel augmented tank-based controller (ATBC) to guarantee passive IIE while maintaining high rendering accuracy. Specifically, the ATBC employs an energy-related power regulation strategy to enhance interaction safety and a time-varying control gain to mitigate the negative effects of power regulation on rendering fidelity. We validated the proposed method through collaborative haptic tasks on a customized M-Hers composed of three robots in four different scenarios. Comparative studies demonstrate that our approach effectively ensures IIE passivity in the presence of active human behaviors, while ensuring high reproducibility and achieving a favorable balance between passivity and rendering accuracy.

Robot-assisted ankle rehabilitation training imitating physician's professional techniques is highly important for promoting personalized training and improving clinical outcomes. In this work, we propose a two-level kernelized movement primitives (2-level-KMP) imitation learning algorithm under the kernelized movement primitives (KMP) framework, which reproduces physician's experience and optimizes the imitation trajectory during rehabilitation, to realize physician-level performance in robot-assisted ankle rehabilitation training. First, a KMP process combined with a Bayesian optimizer is used to imitate the rehabilitation trajectory. Second, the other KMP process is used to smooth the imitation trajectory further. Then the two KMP processes combined with patient-in-the-loop optimization (PILO) realize temporal rehabilitation adaptation. Finally, the 2-level-KMP algorithm is reproduced on a parallel ankle rehabilitation robot (PARR), which enables the patient's passive rehabilitation training to be empirical and adaptive. Ten ankle dysfunction patients were involved in clinical experiments, with the results showing that the proposed algorithm can accurately reproduce physician's trajectories and modulate trajectories based on patient's feedback. After ten rehabilitation exercises, the number of modulation points calculated from patient's torque feedback decreases by 85.19% on average compared with the beginning stage. A comparison between the 2-level KMP algorithm and existing algorithms shows that the 2-level-KMP algorithm can better ensure smoothness and retain the shape of the trajectory during trajectory modulation, ensuring the safety of ankle rehabilitation and retaining the experience of the physician.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) network and its low-complexity user-centric (UC) alternative rely on accurate and up-to-date channel state information (CSI) for combining and/or precoding a large number of signals received by the distributed antenna array to achieve their anticipated performance gains. When serving highly mobile Internet of Things (IoT) devices, limited Line-of-Sight (LoS) information and nonnegligible channel aging (CA) effects will undermine CSI acquisition and inevitably degrade system performance. As a result, fundamental limit assessment and performance degradation mitigation under imperfect CSI are important for practical system designs. In this article, we focus on the performance of UC CF-mMIMO IoT systems in the interaction of sufficient and insufficient LoS knowledge, nonisotropic Non-LoS components, and heterogeneous CA effects. The novel and exact closed-form expressions for uplink spectral efficiency (SE) under the impaired CSI are derived. Numerical results verify the correctness of the SE expressions and reveal that the system parameters, such as resource block length and pilot overhead, should be optimally preconfigured according to environmental information and transmission tasks to alleviate the performance degradation caused by the imperfect CSI. Finally, a UC soft handover scheme is designed to enhance the mobility support of CF-mMIMO IoT systems in practical implementation.

We investigate the effect of spatially and temporally correlated channels on the transmission performance of multicell multiuser massive multiple-input–multiple-output (MIMO) vehicular networks in generic nonisotropic scattering environments. A new channel model is established to evaluate the harmfulness of the nonisotropic-scattered Angle-of-Departure/Angle-of-Arrival (AoD/AoA) spread and the high mobility of users on the uplink transmission. We derive the expressions of achievable spectral efficiency (SE), taking into account the effects of Line-of-Sight propagation, channel aging, and pilot contamination. Specifically, two novel receive combining schemes, namely, the aging-aware maximum ratio combining and the aging-aware minimum-mean-square error combining, are presented to mitigate the SE decline caused by outdated channel state information. A low-complexity pilot assignment algorithm is proposed to suppress pilot contamination. We find that the quasi-static assumption of the channel may be unsafe for the system design of the vehicular networks even within a single transmission block period lasting from hundreds of microseconds to a few milliseconds. We observe that there exists an optimal block size$C_{\mathrm {opt}}$that maximizes area SE. Especially,$C_{\mathrm {opt}}$can be expressed as a function of movement speed, AoD spread, and AoA spread. Numerical results are presented to validate the efficacy of the proposed schemes and highlight the importance of correct performance evaluation for practical massive MIMO system designs.

This article proposes a method for enhancing vehicle monocular 3-D perception using vehicle reidentification (Re-ID) and collaborative vehicle infrastructure systems (CVIS), aimed at enhancing the perception range and safety of the majority of intelligent connected vehicles currently using cameras. The method initially employs a monocular 3-D perception approach to extract images and rough 3-D information of traffic targets from the vehicle side. Following that, an adaptive compression method called Adaptive-FALSH is introduced, which, combined with vehicle Re-ID technology, enables efficient compression and correlation of vehicle Re-ID features. Ultimately, a perception fusion method dubbed Hamming registration Hamming fusion is proposed, merging monocular 3-D detection results from the vehicle side and high-precision perception results from the roadside. This method quickly extend roadside perception results in real-time to the vehicle side, thereby enhancing the vehicle’s perception range. Experimental results demonstrate that this method efficiently merges 3-D target perception information from both vehicle and road sides using just a few hundred bytes, without the need for high-precision maps and global positioning system assistance. While reducing communication data volume, this method also effectively widens the perception range of intelligent connected vehicles.

We demonstrate Rock, a system for cleaning relational data. Rock highlights the following unique features: (1) it extends logic rules by embedding machine learning models as predicates, to benefit from both ML and logic deduction; (2) it supports entity resolution, conflict resolution, timeliness deduction and missing data imputation in a unified process; and (3) it provides parallelly scalable algorithms for rule discovery, error detection and error correction, in batch and incremental modes. We will demonstrate Rock for its (a) easy-to-use interface, (b) scalability when cleaning large datasets, (c) accuracy for detecting and correcting errors across multiple tables, and (d) applications at banks and HR departments.

The efficient exchange of perception information between vehicles and infrastructure is crucial for implementing vehicle-infrastructure (VI) collaborative intelligent driving. To address the high real-time requirements of VI communication and lack of intelligence and flexibility in VI cooperation, this study proposes an efficient collaborative perception method based on vehicle re-identification for VI collaboration scenarios. The real-time requirements of such scenarios are addressed and a lightweight vehicle re-identification network called ShuffleBNLSH is designed. This network is combined with a hash algorithm to quickly generate ID information for collaborative sensing targets. Based on the state of the VI communication channel, the network can adaptively extract the bit features of the perceived vehicle target, adjust the feature length, and quickly perform feature matching for vehicle target re-identification. To rapidly fuse the VI collaborative perception information combined with the re-identification results and LiDAR 3D perception information from the vehicle and infrastructure, we designed a mini-ICP algorithm that can automatically select feature points and perform point-cloud registration. Experimental results show that the amount of data transmitted by a single target in cooperative sensing can be as small as hundreds of bits during the fusion of sensing targets on the vehicle and infrastructure sides. This reduces the bandwidth requirements for fusing perception targets, accelerates feature transmission and matching, and expands the perception range of VI collaborative autonomous vehicles without GPS information.

Cell-free (CF) massive multiple-input multiple-output network and its low-complexity user-centric (UC) alternative rely on accurate channel knowledge for combining and precoding to achieve their claimed performance gains. When serving vehicular users with high mobility, the non-negligible channel aging effect will inevitably degrade system performance, and accurate performance evaluation is essential to system design. In this paper, we investigate the aging uplink (UL) spectral efficiency (SE) in the UC CF networks with non-isotropic scattering conditions. We adopt the von Mises distribution to model the angle-of-departure (AoD), resulting in an analytically tractable channel autocorrelation function that allows us to analyze the time-varying properties of the channel for an arbitrary AoD spread and the user’s moving direction. We derive a closed-form signal-to-interference-and-noise ratio expression with large-scale fading decoding (LSFD) for an achievable UL SE. The simulated results in a 3GPP-recommended vehicular network scenario show that there is an optimal subframe length to achieve the maximum area average SE. The LSFD cooperative strategy of the UC CF network significantly increases the optimal subframe length in the non-isotropic scattering environment, which reduces the pilot overhead and improves the spectrum utilization eventually.

Physically-coupled bimanual tasks (activities where a force effect occurs between two human limbs) involve the coordination and cooperation of bilateral arms. Such uncertain contribution of two arms is often studied under static configuration, which is not sufficient to typify all activities of daily life (ADLs). This study aims to investigate people's bilateral force production and control in dynamic tasks. Experiments were conducted with a customized robotic system that is characterized with two handles and programmable force fields between them. Fourteen healthy right-handed human volunteers were instructed to generate force with each hand when performing predefined trajectory tracking tasks, in which the sum of forces contributed by the left and the right hand is required to equal a target force. Significant asymmetry was found in the force output between bilateral hands. With the homologous muscles activated synchronously, the contribution of the left hand was larger, while when the non-homogenous muscles were activated synchronously, the laterality was subject to the moving direction. In addition, when considering the force difference between two hands in terms of direction and magnitude, the former decreased with the increase of the target force, but the latter was more sensitive to moving directions. The results reveal the unique characteristics of non-isometric force control tasks compared with isometric ones.

Electric motors have been widely used as the actuators of robot and automation systems. This paper aims at achieving the high-precision position control of motor drive systems. For this purpose, a robust control scheme is presented by combining the internal model principle, the sliding mode technique and the extended state observer (ESO). The PID-type controller is firstly designed by using the internal model control (IMC) rules. Since the analysis of the IMC system is performed via a sliding surface, a robust sliding mode control (SMC) law is then synthesized to enhance the control ability of the system to uncertainties. However, this robust solution should make a trade-off between the chattering attenuation and the control accuracy. To handle this drawback, a linear ESO is employed to compensate the modeling errors for a higher control accuracy. The stability analysis is provided via a Lyapunov-based method, and the superiority of the proposed approach was validated by comparative experiments on a motor drive platform.

This paper presents a new automatically quantitative facial features classification system for TCM spirit diagnosis based on facial features. Facial diagnosis is an important diagnostic method in TCM (Traditional Chinese Medicine) and has been used for a long time. However, this traditional diagnostic method is mainly based on observation by TCM doctors and their personal experience. To develop quantification methods for TCM is very important. First, we capture facial features from video images through some algorithms, and then we use some classification models to analyze them. Finally, IG (Information Gain) is used to analyse which kind of features has good performance. Experiment results show that our system has high classification accuracy.