Real-time Approach Research Articles

Spatial participatory planning for urban logistics: A GIS-enhanced Real-Time Spatial Delphi approach

Effective urban logistics planning plays a crucial role in effectively managing infrastructure and transportation networks, contributing to the mitigation of negative externalities associated with last-mile deliveries and fostering city sustainability. Among the various facets of supply chain management, selecting appropriate locations in urban areas for logistics facilities, such as automated parcel lockers and smart loading bays, stands out. However, the complexity of decision-making in this context, involving diverse stakeholders with potential conflicting objectives, poses a significant challenge in reaching a common consensus. This paper proposes an innovative approach to address this issue by adopting a spatial participatory process that combines GIS models and Real-Time Spatial Delphi to identify suitable locations for two logistic facilities. The case study is the city of Dublin (Ireland), where an Urban Living Lab of the H2020 project SENATOR is based. A modified Delphi is used, first gathering expert opinions on preferences and objectives. These insights are then used to develop a spatial analysis using GIS, ranking areas according to the expert preferences. These outcomes form the basis for the Real-Time Spatial Delphi, where stakeholders and experts working on the project collaboratively engage to achieve a spatial consensus on the territory. The findings of this study provide valuable information for stakeholders, policymakers, local authorities, and governmental bodies to implement specific actions. Through the active involvement of key actors in the decision-making process, this approach ensures that the selected locations for logistics facilities align with the needs of all parties involved.

Adaptive Hybrid Prediction Model for Adapting to Data Distribution Shifts in Machining Quality Prediction

Abstract The accuracy of data-driven intelligent prediction for machining quality relies on the training samples. However, in actual applications, the continuous operation of machining equipment leads to gradual distribution shifts between the process data and the training samples for modeling. The shifts result in a degradation in the performance of predictive model, previous studies have often overlooked this issue. To tackle with the intricate problem, this research proposes a real-time model optimization approach. Firstly, a method for detecting machining data distribution shifts based on the two-sample Kolmogorov-Smirnov (KS) test is proposed. Then, an adaptive hybrid prediction model (AHPM) capable of real-time optimization is developed. This model consists of a deep neural network (DNN) and a broad learning system (BLS). DNN plays a primary role in prediction within the hybrid model with excellent generalization capability. BLS quickly completes optimization prior to DNN with its unique parameter update mechanism to compensate for prediction loss. Experimental results indicate that AHPM achieves the shortest optimization time while maintaining high accuracy, with post-optimization error reduction rates for MSE, MAE, and MAPE all exceeding 10%. In the test of application to actual machining cases, accuracy improved by 8.88% compared to traditional methods without optimization.

Modulating subjective pain perception with decoded Montreal Neurological Institute-space neurofeedback: a proof-of-concept study.

Pain is a complex emotional experience that still remains challenging to manage. Previous functional magnetic resonance imaging (fMRI) studies have associated pain with distributed patterns of brain activity (i.e. brain decoders), but it is still unclear whether these observations reflect causal mechanisms. To address this question, we devised a new neurofeedback approach using real-time decoding of fMRI data to test if modulating pain-related multivoxel fMRI patterns could lead to changes in subjective pain experience. We first showed that subjective pain ratings can indeed be accurately predicted using a real-time decoding approach based on the stimulus intensity independent pain signature (SIIPS) and the neurologic pain signature (NPS). Next, we trained participants (n = 16) in a double-blinded decoded fMRI neurofeedback experiment to up- or downregulate the SIIPS. Our results indicate that participants can learn to downregulate the expression of SIIPS independently from NPS expression. Importantly, the success of this neurofeedback training was associated with the perceived intensity of painful stimulation following the intervention. Taken together, these results indicate that closed-loop brain imaging can be efficiently conducted using a priori fMRI decoders of pain, potentially opening up a new range of applications for decoded neurofeedback, both for clinical and basic science purposes. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

A Multi-View Real-Time Approach for Rapid Point Cloud Acquisition and Reconstruction in Goats

The body size, shape, weight, and scoring of goats are crucial indicators for assessing their growth, health, and meat production. The application of computer vision technology to measure these parameters is becoming increasingly prevalent. However, in real farm environments, obstacles, such as fences, ground conditions, and dust, pose significant challenges for obtaining accurate goat point cloud data. These obstacles lead to difficulties in rapid data extraction and result in incomplete reconstructions, causing substantial measurement errors. To address these challenges, we developed a system for real-time, non-contact acquisition, extraction, and reconstruction of goat point clouds using three depth cameras. The system operates in a scenario where goats walk naturally through a designated channel, and bidirectional distributed triggering logic is employed to ensure real-time acquisition of the point cloud. We also designed a noise recognition and filtering method tailored to handle complex environmental interferences found on farms, enabling automatic extraction of the goat point cloud. Furthermore, a distributed point cloud completion algorithm was developed to reconstruct missing sections of the goat point cloud caused by unavoidable factors such as railings and dust. Measurements of body height, body slant length, and chest circumference were calculated separately with deviation of no more than 25 mm and an average error of 3.1%. The system processes each goat in an average time of 3–5 s. This method provides rapid and accurate extraction and complementary reconstruction of 3D point clouds of goats in motion on real farms, without human intervention. It offers a valuable technological solution for non-contact monitoring and evaluation of goat body size, weight, shape, and appearance.

Exploring the use of wearable sensors for assessing risk factors during orthopedic surgeries: A protocol for data collection

During orthopedic surgeries, surgeons are generally exposed to prolonged periods of standing, awkward and sustained body postures, and forceful movements, which can increase the likelihood of developing work-related musculoskeletal disorders (WRMSD). Therefore, this study proposes a protocol to measure parameters related to physical risk factors contributing to lower limb WRMSD, during orthopedic surgery procedures. The protocol development was preceded by an initial phase of understanding and specifying the context of use, followed by pre-tests in laboratory environment. It integrates a motion capture system, using inertial measurement units (IMU) to collect posture data from hip, knee, and ankle, and electromyography system (EMG) to measure and record data from muscle activity of biceps femoris, rectus femoris, and gastrocnemius lateralis. Pre-tests provided insights for protocol optimization, estimating a 3-hour data collection session per surgery due to sensor battery limitations, streamlining the process by placing EMG sensors before IMU and refining thigh sensor placement strategies. The protocol presents an opportunity for a real-time and quantitative approach to monitor surgeon's exposure to risk factors contributing to lower limb WRMSD while performing surgical procedures. Two months after pre-tests, the protocol implementation began in a real work context. The study's final outcomes fall outside the paper's scope.

Modeling of artificial intelligence-based respiratory motion prediction in MRI-guided radiotherapy: a review.

The advancement of precision radiotherapy techniques, such as volumetric modulated arc therapy (VMAT), stereotactic body radiotherapy (SBRT), and particle therapy, highlights the importance of radiotherapy in the treatment of cancer, while also posing challenges for respiratory motion management in thoracic and abdominal tumors. MRI-guided radiotherapy (MRIgRT) stands out as state-of-art real-time respiratory motion management approach owing to the non-ionizing radiation nature and superior soft-tissue contrast characteristic of MR imaging. In clinical practice, MR imaging often operates at a frequency of 4Hz, resulting in approximately a 300 ms system latency of MRIgRT. This system latency decreases the accuracy of respiratory motion management in MRIgRT. Artificial intelligence (AI)-based respiratory motion prediction has recently emerged as a promising solution to address the system latency issues in MRIgRT, particularly for advanced contour prediction and volumetric prediction. However, implementing AI-based respiratory motion prediction faces several challenges including the collection of training datasets, the selection of prediction methods, and the formulation of complex contour and volumetric prediction problems. This review presents modeling approaches of AI-based respiratory motion prediction in MRIgRT, and provides recommendations for achieving consistent and generalizable results in this field.

A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs

In the field of Brain Machine Interface (BMI), the process of translating motor intention into a machine command is denoted as decoding. However, despite recent advancements, decoding remains a formidable challenge within BMI. The utilization of current decoding algorithms in the field of BMI often involves computational complexity and requires the use of computers. This is primarily due to the reliance on mathematical models to address the decoding issue and perform subsequent output calculations. Unfortunately, computers are not feasible for implantable BMI systems due to their size and power consumption. To address this predicament, this study proposes a pioneering approach inspired by hyperdimensional computing. This approach first involves identifying the pattern of each stimulus by considering the normal firing rate distribution of each neuron. Subsequently, the newly observed firing pattern for each input is compared with the patterns detected at each moment for each neuron. The algorithm, which shares similarities with hyperdimensional computing, identifies the most similar pattern as the final output. This approach reduces the dependence on mathematical models. The efficacy of this method is assessed through the utilization of an authentic dataset acquired from the Frontal Eye Field (FEF) of two male rhesus monkeys. The output space encompasses eight possible angles. The results demonstrate an accuracy rate of 51.5% while exhibiting significantly low computational complexity, involving a mere 2050 adder operators. Furthermore, the proposed algorithm is implemented on a field-programmable gate array (FPGA) and as an ASIC designe in a standard CMOS 180 nm technology, underscoring its suitability for real-time implantable BMI applications. The implementation required only 2.3 Kbytes of RAM, occupied an area of 2.2 mm2, and consumed 9.32 µW at a 1.8 V power supply. Consequently, the proposed solution represents an accurate, low computational complexity, hardware-friendly, and real-time approach.

Molecular epidemiology and population genetics of Schistosoma mansoni infecting school-aged children situated along the southern shoreline of Lake Malawi, Malawi.

In areas of low disease endemicity, highly sensitive diagnostic tools to identify, diagnose, and monitor intestinal schistosomiasis transmission are needed to reliably measure the burden and risk of infection. Here, we used highly sensitive molecular diagnostic methods to investigate Schistosoma mansoni prevalence and transmission along the southern shoreline of Lake Malawi, five years post-disease outbreak. Faecal and urine samples were provided by school-aged children situated along the southern shoreline of Lake Malawi. Kato-Katz faecal-egg microscopy and point-of-care circulating cathodic antigen (POC-CCA) rapid diagnostic tests were then performed to diagnose infection with S. mansoni. Urine-egg microscopy was also used to diagnose infection with Schistosoma haematobium. In addition, Schistosoma miracidia were isolated from faecal material using a standard miracidium hatching technique. A two-step real-time PCR approach was then used to diagnose infection with S. mansoni using DNA isolated from faecal samples. Furthermore, isolated miracidia were genotyped to species level through PCR and Sanger sequencing. Phylogenetic analyses were then carried out to identify which previously defined S. mansoni cox1 lineage group S. mansoni miracidia were most closely related to. The measured prevalence of S. mansoni infection varied considerably depending on which diagnostic assay was used. When compared to real-time PCR, faecal-egg microscopy had a sensitivity of 9% and a specificity of 100%. When POC-CCA 'trace' results were considered positive, POC-CCA had a sensitivity of 73% and a specificity of 81% when compared to real-time PCR. However, when considered negative, POC-CCA sensitivity was reduced to 56%, whereas specificity was increased to 90%. In addition, a high degree of S. haematobium DNA was detected in DNA isolated from faecal samples and motile S. haematobium miracidia were recovered from faecal samples. Schistosoma mansoni miracidia were closely related to two independent cox1 lineage groups, suggesting multiple recent introduction and colonisation events originating from surrounding east African countries. Intestinal schistosomiasis is now highly prevalent along the southern shoreline of Lake Malawi just five years post-disease outbreak. In addition, a high prevalence of urogenital schistosomiasis persists. The revision of ongoing schistosomiasis control programmes in this area is therefore recommended. Our study also highlights the need for reliable diagnostic assays capable of distinguishing between Schistosoma species in multispecies co-endemic areas.

A Practical, Adaptive, and Scalable Real-Time Control Approach for Stormwater Storage Systems

Traditionally, urban stormwater infrastructure systems consist of passive infrastructure that is not actively controlled in response to rainfall events. Recently, real-time control (RTC) has been considered as a means to significantly increase the capacity and lifespan of these systems. This paper introduces the target flow control systems (TFCS) approach, which can use real-time control of systems of storages to achieve the desired flow conditions at the locations of interest. The first distinctive feature of this approach is that it does not require calibration to catchment-specific data, unlike existing approaches. This means that the TFCS approach is generally applicable to different catchments and is able to respond to future changes in runoff due to land use and/or climate change. The second distinctive feature is that the approach only requires storage-level information measured in real time with the aid of low-cost pressure sensors. This means that the approach is practical and relatively easy to implement. In addition to the introduction of the novel TFCS approach, a key innovation of this study is that the approach is tested on three case studies, each with different physical configurations and stormwater management objectives. Another key innovation is that the TFCS approach is compared to five RTC approaches, including three of the best-performing advanced approaches from the literature. Comparisons of multiple RTC approaches that consider both performance and practicality across multiple case studies are rare. Results show that the TFCS approach is the only one of the five control approaches analysed that has both the best overall performance and the highest level of practicality. The outcomes highlight the potential of the TFCS approach as a practical RTC approach that is applicable to a wide range of catchments with different stormwater management objectives. By maximizing the performance of existing stormwater storages, the TFCS approach can potentially extend the lifespan of existing infrastructure and avoid costly upgrades due to increased runoff caused by land use and climate change.

Comparative study of improved control strategies for DFIG in wind energy conversion systems: A dSpace real-time implementation approach

This research paper focuses on the comparison of two distinct strategies for direct power control (DPC) of a doubly fed induction generator (DFIG) in wind energy conversion systems (WECSs). The study places particular emphasis on algorithms based on low-pass filters for the estimation of stator and rotor flux, which have demonstrated good performance characteristics. Detailed theoretical analysis and implementation procedures of the proposed methods are presented. To evaluate and compare the performance of the control strategies, MATLAB/Simulink software is utilized for comprehensive analysis. The obtained results, both quantitative and qualitative, from this comparative analysis are expected to be of great interest to researchers engaged in the field of DFIG-based WECSs. Furthermore, real-time validation is conducted to demonstrate the effectiveness of the proposed control strategy, specifically focusing on the DPC approach based on stator flux estimation. A dSpace 1104 real-time card is employed and tested with a wind turbine emulator. The experimental results obtained through this validation process provide evidence of the efficiency and validity of the proposed control strategy. Overall, this research contributes to the advancement of DPC strategies for DFIG-based WECS by comparing and evaluating different control methods, providing in-depth theoretical analysis, and validating the proposed approach through real-time experimentation. The findings affirm the efficiency and effectiveness of the proposed control strategy, further establishing its value in practical applications.

On the degree of parallelism for parallel real-time tasks

The degree of parallelism, which measures how a task can execute concurrently, is an important characterization in scheduling. This paper studies the degree of parallelism in the domain of real-time scheduling of parallel tasks, including the DAG task model and the conditional DAG task model. The definition of the degree of parallelism for DAG tasks is clarified; the definition and computing algorithm of the degree of parallelism for conditional DAG tasks are proposed. By leveraging the degree of parallelism, new response time bounds are derived and simple but effective real-time scheduling approaches are presented. This research is the first work to study the degree of parallelism for conditional DAG tasks and explore its benefits in real-time scheduling. Experimental results demonstrate that the proposed scheduling approaches significantly outperform existing state-of-the-art methods.

Efficient Real-Time Droplet Tracking in Crop-Spraying Systems

Spray systems in agriculture serve essential roles in the precision application of pesticides, fertilizers, and water, contributing to effective pest control, nutrient management, and irrigation. These systems enhance efficiency, reduce labor, and promote environmentally friendly practices by minimizing chemical waste and runoff. The efficacy of a spray is largely determined by the characteristics of its droplets, including their size and velocity. These parameters are not only pivotal in assessing spray retention, i.e., how much of the spray adheres to crops versus becoming environmental runoff, but also in understanding spray drift dynamics. This study introduces a real-time deep learning-based approach for droplet detection and tracking which significantly improves the accuracy and efficiency of measuring these droplet properties. Our methodology leverages advanced AI techniques to overcome the limitations of previous tracking frameworks, employing three novel deep learning-based tracking methods. These methods are adept at handling challenges such as droplet occlusion and varying velocities, ensuring precise tracking in real-time potentially on mobile platforms. The use of a high-speed camera operating at 2000 frames per second coupled with innovative automatic annotation tools enables the creation of a large and accurately labeled droplet dataset for training and evaluation. The core of our framework lies in the ability to track droplets across frames, associating them temporally despite changes in appearance or occlusions. We utilize metrics including Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) to quantify the tracking algorithm’s performance. Our approach is set to pave the way for innovations in agricultural spraying systems, offering a more efficient, accurate, and environmentally responsible method of applying sprays and representing a significant step toward sustainable agricultural practices.

A real-time eye movement-based computer interface for people with disabilities

It is costly to develop systems that enable individuals exposed to Amyotrophic Lateral Sclerosis and similar diseases that directly affect the neuromotor ability to communicate with the outside world. In this study, a budget friendly, high-accuracy, software-based, gaze-controlled, real-time virtual keyboard approach that can enable these people to communicate effectively is proposed. The proposed application requires only a computer and a webcam and has a user-friendly interface that meets the basic daily needs of individuals with disabilities. Since the proposed system does not require an extra action such as blinking, it makes it possible to use computers in advanced stage patients who cannot blink their eyes. The application which uses a deep learning-based facial landmark detector, determines the letters the user focuses on the screen and converts thoughts into text. The part of the screen that the user focuses on is determined with a new selection approach inspired by the K-Nearest Neighbors algorithm. This approach, which does not require blinking, offers high speed and accuracy. In the tests, a typing speed of 23.33 characters per minute is achieved with an accuracy rate of 95.12%. It is anticipated that the study will increase computer accessibility for disabled individuals with limited mobility and contribute to the development of real-time eye tracking systems.

Red-emitting carbon dots for fluorescent and smartphone-assisted dual-mode detection of Cu(II), Hg(II), and Fe(III) and investigation of the sensing mechanism

Red-emitting carbon dots (R-CDs) have triggered ever-increasing interest in biochemical sensing due to their deep tissue penetration and minimal biological auto-fluorescence interference. Herein, dual-emitting intrinsic R-CDs are fabricated and well-characterized. R-CDs display ratiometric and colorimetric sensing properties for heavy metal ions (HMIs, Cu2+, Hg2+, and Fe3+), with the corresponding detection limits of 24.5, 74.2, and 170 nM, respectively. Additionally, smartphone imaging colorimetry based on red-green-blue (RGB) value variations is employed to achieve real-time and on-site detection of these HMIs. Crucially, discrimination of these HMIs is facilitated by principal component analysis. Furthermore, R-CDs demonstrate effective optical sensing of these HMIs in tap/lake water and human urine with satisfactory recoveries (95.82–104.8 %). The underlying mechanism of optical sensing, which involves metal ion-triggered aggregation-induced quenching, is elucidated by spectroscopic, morphological analysis, and quantum mechanical calculations. This work presents a feasible real-time approach for the fluorescent and smartphone-assisted dual-mode detection of HMIs.

Adaptive Variational Quantum Computing Approaches for Green's Functions and Nonlinear Susceptibilities.

We present and benchmark quantum computing approaches for calculating real-time single-particle Green's functions and nonlinear susceptibilities of Hamiltonian systems. The approaches leverage adaptive variational quantum algorithms for state preparation and propagation. Using automatically generated compact circuits, the dynamical evolution is performed over sufficiently long times to achieve adequate frequency resolution of the response functions. We showcase accurate Green's function calculations using a statevector simulator on classical hardware for Fermi-Hubbard chains of 4 and 6 sites, with maximal ansatz circuit depths of 65 and 424 layers, respectively, and for the molecule LiH with a maximal ansatz circuit depth of 81 layers. Additionally, we consider an antiferromagnetic quantum spin-1 model that incorporates the Dzyaloshinskii-Moriya interaction to illustrate calculations of the third-order nonlinear susceptibilities, which can be measured in two-dimensional coherent spectroscopy experiments. These results demonstrate that real-time approaches using adaptive parametrized circuits to evaluate linear and nonlinear response functions can be feasible with near-term quantum processors.

Influence analysis of position error to the operating characteristics of opposed-piston free-piston engine generator with position observation optimization

Stable operation is a crucial challenge for opposed-piston free-piston engine generator (FPEG). Due to the absence of mechanical constraints, variations position-based operation parameters would have a significantly impact on the operation performance of the system. This paper conducted a comprehensive investigation into the effects of position fluctuations on the system's dynamic characteristics and thermodynamic performance by leveraging a detail system model with stable operation. Additionally, a novel real-time position observation approach for the mover was proposed, leveraging observation coils. The findings indicate that the opposed-piston FPEG system can quickly recover to stable operational state within a few cycles for a one-time position error, if there is no misfire in the power cylinder. The acceptable range for stable operation of the opposed-piston FPEG is between +35 % and −48 % for the one-time position error. With the continuous position error varying from +20 % to −30 %, there is a noticeable decrease in the compression ratio, operating frequency, and overall system performance. A significant linear relationship has been found between the flux linkage value of the observation coil and the position of the mover. The maximum difference between the observed position calculated by the proposed approach and the actual position was below 4 %.

Direct quadruplex real-time VPCR for simultaneous rapid detection of Pinelliae Rhizoma, Rhizoma Pinelliae Pedatisectae and Rhizoma Typhonii Flagelliformis

Known for its health benefits, Pinelliae Rhizoma is frequently utilized in many botanical dietary supplements and herbal products. However, the substantial demand has led to the frequent adulteration with morphologically similar species, such as Rhizoma Pinelliae Pedatisectae and Rhizoma Typhonii Flagelliformis. Addressing this challenge, a direct quadruplex real-time VPCR approach has been developed in this study. Firstly, a direct lysis protocol has been implemented to facilitate the rapid release of genomic DNA within only 2 min. Secondly, specific primers and probes were designed for Pinellia ternata (Thunb.) Breit., Pinellia pedatisecta Schott and Typhonium flagelliforme (Lodd.) Blume, respectively. A conserved sequence on the ITS was also included as an internal control to mitigate the potential false negatives arising from DNA extraction or DNA amplification errors. Furthermore, the amplification process has been refined through a VPCR heating model, curtailing the detection to a scant 30 min. After a series of optimizations, the developed direct quadruplex VPCR allows for the simultaneous detection of these three plant materials, with each material having a detection sensitivity as low as 1 pg/µL. At last, the method's applicability was validated by using several commercial samples previously identified by Sanger sequencing.

DBDAA: A real-time approach to Dynamic Banker’s Deadlock Avoidance Algorithm with optimized time complexity

Effective resource allocation is crucial in operating systems to prevent deadlocks, especially when resources are limited and non-shareable. Traditional methods like the Banker’s algorithm provide solutions but suffer from limitations such as static process handling, high time complexity, and a lack of real-time adaptability. To address these challenges, we propose the Dynamic Banker’s Deadlock Avoidance Algorithm (DBDAA). The DBDAA introduces real-time processing for safety checks, significantly improving system efficiency and reducing the risk of deadlocks. Unlike conventional methods, the DBDAA dynamically includes processes in safety checks, considerably decreasing the number of comparisons required to determine safe states. This optimization reduces the time complexity to O(n) in the best-case and O(nd) in the average and worst-case scenarios, compared to the O(n2d) complexity of the original Banker’s algorithm. The integration of real-time processing ensures that all processes can immediately engage in safety checks, improving system responsiveness and making the DBDAA suitable for dynamic and time-sensitive applications. Additionally, the DBDAA introduces a primary unsafe sequence mechanism that enhances the acceptability and efficiency of the algorithm by allowing processes to participate in safety checks repeatedly after a predetermined amount of system-defined time. Experimental comparisons with existing algorithms demonstrate the superiority of the DBDAA in terms of reduced safe state prediction time and increased efficiency, making it a robust solution for deadlock avoidance in real-time systems.

Bio-Inspired Motion Emulation for Social Robots: A Real-Time Trajectory Generation and Control Approach.

Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through one-on-one interactions. To optimize the interaction between social robotic platforms and humans, it is crucial for these robots to identify and mimic human motions in real time. This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. Once identified, the corresponding reactions of the robots are executed by moving their joints along specific trajectories derived through temporal alignment and stored in a pre-selected motion library. In this study, we developed a multi-axial robotic arm integrated with a motion identification model to interact with humans by emulating their movements. The robotic arm follows pre-selected trajectories for corresponding interactions, which are generated based on identified human motions. To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. This integrated system ensures that the robotic arm can achieve adequate controlled outcomes, thus validating the feasibility of such an interactive robotic system in providing effective bio-inspired motion emulation.

Regional multi-station real-time time transfer using an undifferenced multi-GNSS network solution

Real-time time transfer is the basis for the time synchronization and establishment and maintenance of time scales. In this contribution, we present a regional multi-station real-time time transfer approach, in which observations from multi-global navigation satellite system (GNSS) received at regional multi-station are integrated to conduct an undifferenced network solution. Consequently, the simultaneous estimation of receiver clock offsets for multiple stations becomes possible, enabling the execution of real-time time transfer. One week of observation data from five multi-GNSS Experiment (MGEX) stations located in Europe are selected to generate four links (distances from 919.7 to 2153.4 km), and four schemes are designed, i.e., GPS-only, BDS-3-only, Galileo-only, and GPS/BDS-3/Galileo (GCE) solutions, respectively. Experiment results show that the mean number of observations, time dilution of precision (TDOP) values, and standard deviation (STD) of clock offset difference time series between the Center for Orbit Determination in Europe (CODE) and the estimated solutions for four time links are 90, 76, 72, 239 and 0.34, 0.38, 0.39, 0.21 and 0.039, 0.050, 0.043, 0.036 ns for GPS-only, BDS-3-only, Galileo-only, and GCE solutions, respectively. When the averaging time is shorter than 7680 s, the frequency stability of GCE solutions shows the best performance. The mean frequency stability of four time links at 7680 s is 6.59 × 10–15,7.25 × 10–15, 6.88 × 10–15 and 5.96 × 10–15 for GPS-only, BDS-3-only, Galileo-only, and GCE solutions, respectively. The GCE solution shows the best performance among the four schemes in terms of the number of observations, TDOP, STD, and frequency stability. The experiment results demonstrate that this approach is suitable for regional multi-station real-time time transfer.