With the increasing demand for internet of things (IoT) applications, especially for location-based services, how to locate passive mobile targets (MTs) with minimal beam adjustment has become a challenge. Resonant beam systems are considered promising IoT technologies with advantages such as beam self-alignment and energy concentration. However, resonant systems are difficult to apply to multi-user scenarios due to co-frequency interference. To establish a resonant system for multi-target localization, this paper designs an innovative resonant system architecture based on frequency division multiple access (FDMA), which enables a base station (BS) to establish connections with multiple mobile targets (MTs) with different carriers, and establishes a multi-channel cyclic model through a retro-directive array (RDA) to achieve one-to-many electromagnetic wave propagation and MTs direction of arrival (DOA) estimation through echo signals. Simulation results show that the proposed system supports resonant establishment between the BS and multiple MTs. This helps the BS maintain high DOA estimation accuracy when faced with multiple passive MTs, ensuring that the DOA error is less than 1° within a range of 5 m and 50° field of view, and the accuracy is higher than that of active beamforming localization systems under the same conditions.

With the explosive growth of the Internet of Things (IoT), simultaneous wireless information and power transfer (SWIPT) has emerged as a core solution for powering low-energy devices. To address the limitations of single-user adaptation and the high cost and power consumption caused by high local oscillator (LO) frequencies of up to 60 GHz, this paper proposes a low LO frequency resonant beam system (LLF-RBS) for multi-user self-aligning SWIPT. Our three-mixer phase conjugation (TMPC) circuit architecture reduces the requirement for key LO frequencies to the 28 and 32 GHz bands, representing an approximate 50% reduction. This architecture enables simultaneous phase conjugation, frequency translation, and information modulation within a low-cost hardware envelope. We theoretically prove that the system’s multi-user beamforming mechanism is physically equivalent to a power iteration algorithm, guaranteeing spontaneous convergence to the channel’s principal eigenmodes without explicit channel state information or digital beamforming control. Simulation results demonstrate that the LLF-RBS achieves adaptive self-alignment and watt-level power transfer for multiple users, attaining a downlink spectral efficiency of up to 21.4 bps/Hz.

For amputees with powered transfemoral prosthetics, navigating obstacles or complex terrain remains challenging. This study addresses this issue by using an inertial sensor on the sound ankle to guide obstacle-crossing movements. A genetic algorithm computes the optimal neural network structure to predict the required angles of the thigh and knee joints. A gait progression prediction algorithm determines the actuation angle index for the prosthetic knee motor, ultimately defining the necessary thigh and knee angles and gait progression. Results show that when the standard deviation of Gaussian noise added to the thigh angle data is less than 1, the method can effectively eliminate noise interference, achieving 100% accuracy in gait phase estimation under 150 Hz, with thigh angle prediction error being 8.71% and knee angle prediction error being 6.78%. These findings demonstrate the method’s ability to accurately predict gait progression and joint angles, offering significant practical value for obstacle negotiation in powered transfemoral prosthetics.

With the rapid development of the internet of things (IoT), location-based services are becoming increasingly prominent in various aspects of social life, and accurate location information is crucial. However, RF-based indoor positioning solutions are severely limited in positioning accuracy due to signal transmission losses and directional difficulties, and optical indoor positioning methods require high propagation conditions. To achieve higher accuracy in indoor positioning, we utilize the principle of resonance to design a triangulation-based resonant beam positioning system (TRBPS) in the RF band. The proposed system employs phase-conjugation antenna arrays and resonance mechanism to achieve energy concentration and beam self-alignment, without requiring active signals from the target for positioning and complex beam control algorithms. Numerical evaluations indicate that TRBPS can achieve millimeter-level accuracy within a range of 3.6m without the need for additional embedded systems.

This article focuses on the trajectory tracking problem of mobile manipulators (MMs). Firstly, we construct a position and orientation model predictive tracking control (POMPTC) scheme for mobile manipulators. The proposed POMPTC scheme can simultaneously minimize the tracking error, joint velocity, and joint acceleration. Moreover, it can achieve synchronous control for the position and orientation of the end-effector. Secondly, a finite-time convergent neural dynamics (FTCND) model is constructed to find the optimal solution of the POMPTC scheme. Then, based on the proposed POMPTC scheme, a non-singular fast terminal sliding model (NFTSM) control method is presented, which considers the disturbances caused by the floating-base on the manipulator at the dynamic level. It can achieve finite-time tracking performance and improve the anti-disturbances ability. Finally, simulation and experiments show that the proposed control method has the advantages of strong robustness, fast convergence, and high control accuracy.

The rapid advancement of the next generation of communications and internet of things (IoT) technologies has made the provision of location-based services for diverse devices an increasingly pressing necessity. Localizing devices with/without intelligent computing abilities, including both active and passive devices is essential, especially in indoor scenarios. For traditional RF positioning systems, aligning transmission signals and dealing with signal interference in complex environments are inevitable challenges. Therefore, this paper proposed a new passive positioning system, the RF-band resonant beam positioning system (RF-RBPS), which achieves energy concentration and beam alignment by amplifying echoes between the base station (BS) and the passive target (PT), without the need for complex channel estimation and time-consuming beamforming and provides high-precision direction of arrival (DoA) estimation for battery-free targets using the resonant mechanism. The direction information of the PT is estimated using the multiple signal classification (MUSIC) algorithm at the end of BS. The feasibility of the proposed system is validated through theoretical analysis and simulations. Results indicate that the proposed RF-RBPS surpasses RF-band active positioning system (RF-APS) in precision, achieving millimeter-level precision at 2m within an elevation angle of 35°, and an error of less than 3cm at 2.5m within an elevation angle of 35°.

Age of information (AoI) is a new performance index proposed recently, which is used to describe the freshness of information. This paper studies$A$oI minimization for the unmanned aerial vehicle (UAV)-assisted cognitive radio networks (CRN), where the primary users and secondary users share the authorized spectrum, and UAV as a relay adopts time division duplex (TDD) scheme for uplink and downlink transmissions. We focus on AoI minimization by jointly optimizing the UAV flight trajectory and resource allocation. Since the optimization problem is non-convex, we use an efficient successive convex approximation iterative algorithm to solve the optimal solution, while satisfying the conditions of interference threshold and feasibility constraints. Simulation results show that the proposed joint design scheme can effectively improve the freshness of information.

The irregular distribution of drones in space poses great challenges to the design and deployment of the networks. In this paper, we use point process theory and stochastic geometry model to develop a drone heterogeneous network (HetNet) framework. In order to better reflect the real scene, we assume that the macro base stations are two-dimensionally distributed on the ground, while the drones as small base stations are three-dimensionally distributed in the air. We assume that the channel model follows the Nakagami-m fading, and the user is associated with the base station by the maximum bias received power (Max-BRP), which is necessary to reduce the pressure on the terrestrial macro base station. Based on these, we give mathematical expressions for the association probability and outage probability between the user and each base station. The numerical results show that the proposed hybrid modeling method can better capture the actual base station deployment. In the ultra-dense drone network, setting an appropriate bias factor can effectively relieve the pressure of the macro base station and improve the network performance.

Aiming at the problem that single‐network hopping signals have not fully utilised its frequency domain sparse characteristic in the parameter estimation, this study proposes a parameter estimation of frequency hopping (FH) signal based on multi‐measurement vector sparse Bayesian learning (MSBL) in modulation wideband converter (MWC). Since the FH signal is sparse in the frequency domain, the authors apply the MSBL method to estimate its parameters. After the signal is sampled by the MWC, the MSBL algorithm is used to reconstruct its support set. Then the time–frequency ridge method is used to estimate the signal's hop duration, time‐hopping, and carrier frequency based on the time–frequency map. Simulation experiments show that under the condition of low signal‐to‐noise ratio, the parameter estimation performance in the case can be improved by up to 65% and anti‐noise performance can be improved up to 6 db compared with the existing method. The result is very close to the Nyquist full sampling and can greatly improve the accuracy of the FH signal parameter estimation in the MWC system and relieve the pressure of the hardware.

OBJECTIVE: Cohort definition is a bottleneck for conducting clinical research and depends on subjective decisions by domain experts. Data-driven cohort definition is appealing but requires substantial knowledge of terminologies and clinical data models. Criteria2Query is a natural language interface that facilitates human-computer collaboration for cohort definition and execution using clinical databases. MATERIALS AND METHODS: Criteria2Query uses a hybrid information extraction pipeline combining machine learning and rule-based methods to systematically parse eligibility criteria text, transforms it first into a structured criteria representation and next into sharable and executable clinical data queries represented as SQL queries conforming to the OMOP Common Data Model. Users can interactively review, refine, and execute queries in the ATLAS web application. To test effectiveness, we evaluated 125 criteria across different disease domains from ClinicalTrials.gov and 52 user-entered criteria. We evaluated F1 score and accuracy against 2 domain experts and calculated the average computation time for fully automated query formulation. We conducted an anonymous survey evaluating usability. RESULTS: Criteria2Query achieved 0.795 and 0.805 F1 score for entity recognition and relation extraction, respectively. Accuracies for negation detection, logic detection, entity normalization, and attribute normalization were 0.984, 0.864, 0.514 and 0.793, respectively. Fully automatic query formulation took 1.22 seconds/criterion. More than 80% (11+ of 13) of users would use Criteria2Query in their future cohort definition tasks. CONCLUSIONS: We contribute a novel natural language interface to clinical databases. It is open source and supports fully automated and interactive modes for autonomous data-driven cohort definition by researchers with minimal human effort. We demonstrate its promising user friendliness and usability.

The equipment used in kinematic calibration schemes is generally expensive and time-consuming, and requires skilled engineers. This paper proposes a new measurement method for identifying kinematic errors on a six degree of freedom industrial robot with a single laser displacement sensor. The three-dimensional deviations of the end-effector are calculated by an algorithm with data collected from the scanning procedure. Then, the kinematic parameters are identified by combining the joint angles, the deviations involved error information and an error model. The accuracy and efficiency are improved by the laser displacement sensor and the simple scanning procedure. The method presented in this paper can also be expanded to other serial industrial robots. The experimental results validate the effectiveness of the proposed method.