Miracle-dz/UniFAR-FTUs huggingface dataset:
Spinal Cord Injury Patients Exhibit Changes in Motor-Related Activity and Topographic Distribution *

Spinal Cord Injury (SCI) is a common disease that usually limits the patient’s independence by affecting their motor function. SCI patients usually present neuroplasticity, which allows brain signals transmission through spread pathways. Some innovative rehabilitation therapies, such as functional electrical stimulation (FES) or Brain-computer interfaces (BCIs) jointly with motor neuroprostheses, provide hope for functional restoration. BCIs require the analysis of event-related EEG potentials (ERPs). Movement-related cortical potentials (MRCPs) and event-related desynchroni-zation and synchronization (ERD/ERS) are the most commonly studied ERPs during motor activity. ERPs of healthy subjects may vary from SCI patients. Thus, this study aimed to compare ERPs between healthy subjects and SCI patients during upper-limb movements (forearm supination and pronation, and hand open). Differences between controls and SCI patients were shown in terms of ERPs’ amplitude as well as in topographic maps. Changes in amplitude were more substantial in ERD potentials than in MRCPs, while topographic maps showed better localization of all features in healthy patients. The level of SCI injury determines the patients’ mobility. A comparison between complete, partial and no motor function subjects showed lower values of feature’s amplitudes in the latter group.Clinical Relevance— This demonstrates the existence of significant statistical differences between healthy and SCI subjects, and might be helpful when performing SCI rehabilitation techniques such as designing BCI and neuroprostheses, or analyzing and understanding the brain plasticity process.
A Single-Parameter Model for Soft Bellows Actuators under Axial Deformation and Loading

Soft fluidic actuators are becoming popular for their backdrivability, potential for high power density, and their support for power supply through flexible tubes. Control and design of such actuators requires serviceable models that describe how they relate fluid pressure and flow to mechanical force and motion. We present a simple 2-port model of a bellows actuator that accounts for the relationships among fluid and mechanical variables imposed by the kinematics of the deforming bellows structure and accounts for elastic energy stored in the actuator's thermoplastic material structure. Elastic energy storage due to axial deformation is captured by revolving a differential strip whose linear elastic behavior is a nonlinear function of the actuator length. The model is evaluated through experiments in which either actuator length and pressure or force and pressure are imposed. The model has an error of 9.8% of the force range explored and yields insight into the effects of geometry changes. The resulting model can be used for model-based control or actuator design across the full operating range and can be exercised under either imposed force or imposed actuator length.
Robust Framework for Medical Time Series Classification and Application to Real Scenarios in Modern Bioengineering

In this study, a novel unsupervised classification framework for time series of medical nature is presented. This framework is based on the intersection of machine learning, Hilbert Spaces algebra, and signal theory. The methodology is illustrated through the resolution of three biomedical engineering problems: neuronal activity tracking, protein functional classification, and non-invasive diagnosis of atrial flutter (AFL). The results indicate that the proposed algorithms exhibit high proficiency in solving these tasks and demonstrate robustness in identifying damaged neuronal units while tracking healthy ones. Moreover, the application of the framework in protein functional classification provides a new perspective for the development of pharmaceutical products and personalised medicine. Additionally, the controlled environment of the framework in AFL simulation problem underscores the algorithm’s ability to encode information efficiently. These results offer valuable insights into the potential of this framework and lay the groundwork for future studies.Clinical relevance— The framework proposed in this study has the potential to yield novel insights into the effects of newly implanted electrodes in the brain. Furthermore, the categorization of proteins by function could facilitate the development of personalised and efficient medicines, ultimately reducing both time and cost. The simulation of atrial flutter also demonstrates the framework’s ability to encode information for arrhythmia diagnosis and treatment, which has the potential to lead to improved patient outcomes.
A 4.43 TΩ ZIN 0.0128 mm2 Cascaded Instrumentation Amplifier with Input-biased Pseudo Resistor for Implantable Brain Machine Interfaces

A cascaded instrumentation amplifier (CaIA) with input-biased pseudo resistors (IBPR) is presented for implantable brain machine interfaces (BMI). The gain distribution of two-stage cascaded amplifiers, instead of a single-stage amplifier, helps to achieve an input impedance of 4. 43TΩ at 100Hz, and maintain the small active area (0.0128 mm2). The input-biased pseudo resistors contribute to a much lower high-pass corner (fHP=0.00011Hz) compared with the conventional structure, the input-referred noise is only 3. 836μVrms integrated from 0.5Hz to 10kHz with 0. 98μW power consumption.Clinical Relevance— This establishes an area-efficient amplifier design with ultra-high input impedance (4.43TΩ at 100Hz) and hyper-low high-pass corner frequency (fHP=0.00011Hz), which is suitable for long-term monitoring of neural activities (including slow oscillations) in implantable brain-machine interfaces.
Autonomy/Dependence Alignment: A Cognitive Study of “N+V” Compound Words in Mongolian

Taking the “N+V” compounds included in Modern Mongolian as the object of study, and this paper attempts to analyze the semantics of Mongolian “N+V” compounds by applying the theoretical framework of autonomy/dependence alignment of cognitive linguistics in order to explore the cognitive motivation of their internal semantic generation. Through in-depth investigation of the internal semantic generation mechanism of Mongolian “N+V” compounds, this paper suggests that the connection between N and V in Mongolian “N+V” compounds is characterized by N as an autonomous component and V as a dependent component. This relationship is realized by the interpretation and interpreted relationship between the substructure highlighted by N and the semantic profile highlighted by V. Furthermore, the cognitive motivation of “N+V” compounds in Mongolian is autonomy/dependence alignment. This research is of great significance for the deeper understanding of Mongolian lexical composition and the promotion of Mongolian language teaching and linguistic research.
On OTFS and OFDM Radar Signal Design Based on the Ambiguity Function Analysis

Wireless communication enabled integrated sensing and communication (ISAC) attracts growing interest recently. This paper investigates the sensing ability of OFDM and OTFS signals. We derive the expression of their ambiguity functions, and show that the ambiguity surface can be shaped by the information carried. To this end, we then develop a Zak-transform based ambiguity function synthesis method and deduce the corresponding information sequence in the l2-norm case. Finally, we present the ambiguity surface of it, and compare it with the Zaddoff-Chu and gold sequences that are widely used in LTE and NR systems. Simulations validate the dependency of sensing performance on the designed sequence and the size of the time-frequency resource.
Optimization of Arrhythmia-based ECG-lead Selection for Computer-interpreted Heart Rhythm Classification

The 12-lead ECG only has 8 independent ECG leads, which leads to diagnostic redundancy when using all 12 leads for heart arrhythmias classification. We have previously developed a deep learning (DL)-based computer-interpreted ECG (CIE) approach to identify an optimal 4-lead ECG subset for classifying heart arrhythmias. However, the clinical diagnostic criteria of cardiac arrhythmia types are often lead-specific, so this study is going to explore the selection of arrhythmia-based ECG-lead subsets rather than one general optimal ECG-lead subset, which could improve the classification performance for the CIE. The DL-based CIE model previously developed was used to learn 4 common types of heart arrhythmias (LBBB, RBBB, AF, and I-AVB) for identifying corresponding optimal ECG-lead subsets. A public dataset that splits into training (approx. 70%), validation (approx. 15%), and test (approx. 15%) sets from the PhysioNet Cardiology Challenge 2020 was used to explore the study. The results demonstrated that the DL-based CIE model identified an optimal ECG-lead subset for each arrhythmia: I, II, aVR, aVL, V1, V3, and V5 for I-AVB; I, II, aVR, and V3 for AF; I, II, aVR, aVF, V1, V3, and V4 for LBBB; and I, II, III, aVR, V1, V4, and V6 for RBBB. For each arrhythmia classification, the DL-based CIE model using the optimal ECG-lead subset significantly outperformed the model using the full 12-lead ECG set on the validation set and on the external test dataset.The results support the hypothesis that using an optimal ECG-lead subset instead of the full 12-lead ECG set can improve the classification performance of a specific arrhythmia when using the DL-based CIE approach.Clinical Relevance— Using an arrhythmia-based optimal ECG-lead subset, the classification performance of a deep-learning-based model can be achieved without loss of accuracy in comparison with the full 12-lead set (p<0.05).
Do Hierarchies in a Robot Team Impact the Service Evaluation by Users?

Customer satisfaction is not only determined by how workers treat customers but also by how they treat their coworkers. In this sense, we examined whether the dynamics of robot workers influence user satisfaction. To optimize the robot team's atmosphere for Korean culture, we adopted the Korean honorific language system to express hierarchy. We set four types of relationships between two robots: Case 1) both using non honorific language, indicating intimacy and a flat hierarchy; Case 2) both using honorific language, indicating non intimacy and a flat hierarchy; Case 3) delivery robot using non honorific language and monitoring robot using honorific language, indicating non intimacy and a tall hierarchy; Case 4) vice versa. People preferred a robot team with a flat hierarchy and both robots using honorific language, common in the service industry. Interviews revealed discomfort with tall hierarchies. Case 4 was least satisfactory in service, while Case 3 was least socially intelligent. People perceive a robot team mirroring human society as socially intelligent, but negative representations like Case 4 led to less satisfaction.
A Cartesian Platform for Cooperative Multi-Robot Manipulation Tasks

For many manipulation tasks in environments such as laboratory or a kitchen, the presence of two robot arms is important to enable collaborative tasks requiring two arms (e.g. lid removal or tool use) or to improve the efficiency of scheduling of tasks. Currently, the development of multi-arm manipulation solutions has largely focused on 6 degrees of freedom articulated robot arms. However, cartesian robots have many advantages, including their precision, reliability, efficiency, and simple path planning. By developing a cartesian platform such that the end effectors of two mirrored systems can interact freely without collisions in 5 degrees of freedom, we can leverage the advantages of cartesian robots (high precision, simple planning, and low-cost hardware) and show robot cooperation. We equip each robot with end-effectors with different skills to increase the range of tasks the robots can cooperatively complete. To exploit this robotic hardware, we have developed a task-allocation and path-planning algorithm that enables these two mirror robots to work together to solve tasks collaboratively, exploiting the different skills and workspace of the two robots. We show how this robot can be used for cooperative tasks in lab automation, including pick and place, unscrewing vial caps, liquid pouring, and weighing. These demonstrate the feasibility and capabilities of the proposed robotic system for cooperative automation using cartesian robots.
An extended model-based characterization of fine-grained access control for SQL queries

In the context of a project in model-driven security that focuses on the development of model-driven techniques for building secure data-centric (web) applications, we extend, in three (inter-related) dimensions, a recently proposed model-based characterization of fine-grained access control (FGAC) authorization for SQL queries. First, we extend the FGAC policies’ underlying data models by considering association-classes. Secondly, we extend the FGAC policies’ security modeling language by considering, as protected resources, the classes and the (explicit and implicit) associations introduced by the association-classes. Thirdly, we extend the clauses that define whether a user is authorized by an FGAC policy to execute a SQL query, to cover the case of queries retrieving information related to the association-classes. To illustrate our extensions and to demonstrate their applicability, we provide examples of authorization decisions for SQL queries with respect to FGAC policies. The arte-fact comprising the implementation of this model-based characterization and an executable version of the example is available at https://doi.org/10.5281/zenodo.8176237.
A CNN-Based Routing Scheme for Minimizing TCP Flow Completion Time in SD-DCNs

Data centers play a vital role in supporting the increasing demand from the six-generation (6G) networks for cloud services, big data, artificial intelligence and other data-intensive tasks. Software-defined data center networks (SD-DCNs) represent a natural evolution of traditional data center architectures aimed at improving network utilization. In this paper, we consider routing optimization for minimizing the completion time of transmission control protocol (TCP) flows in SD-DCNs. Specifically, we propose a routing scheme based on convolutional neural networks (CNN). Under different traffic transmission scenarios in SD-DCNs, the simulation results show that the flow completion time (FCT) of our scheme is much shorter compared to the equal-cost multipath (ECMP) and the shortest path first (SPF) algorithm, particularly in scenarios involving high-demand applications.
Characterization and Modeling of Variability in Commercial Self-Directed Channel Memristors

Commercial Ag/Ge<inf>2</inf>Se<inf>3</inf>/SnSe/Ge<inf>2</inf>Se<inf>3</inf> memristors have been experimentally characterized by means of I-V plots obtained under voltage ramped signals. Variability has been measured using automatic extraction procedures. The I-V curves and variability have been modeled by means of the Dynamic Memdiode Model and the corresponding model parameters have been obtained by employing a genetic algorithm.
Analytical Representation of Four-dimensional Hemodynamics for Drug Therapy Simulation in Acute Heart Failure Treatment

Acute heart failure imperils multiple organs, including the heart. Elucidating the impact of drug therapies across this multidimensional hemodynamic system remains a challenge. This paper proposes a simulator that analyzes the impact of drug therapies on four dimensions of hemodynamics: left atrial pressure, cardiac output, mean arterial pressure, and myocardial oxygen consumption. To mathematically formulate hemodynamics, the analytical solutions of four-dimensional hemodynamics and the direction of its change are derived as functions of cardiovascular parameters: systemic vascular resistance, cardiac contractility, heart rate, and stressed blood volume. Furthermore, a drug library which represents the multi-dependency effect of drug therapies on cardiovascular parameters was identified in animal experiments. In evaluating the accuracy of our derived hemodynamic direction, the average angular error of predicted versus observed direction was 18.85[deg] after four different drug infusions for acute heart failure in animal experiments. Finally, the impact of drug therapies on four-dimensional hemodynamics was analyzed in three different simulation settings. One result showed that, even when drug therapies were simulated with simple rules according to the Forrester classification, the predicted direction of hemodynamic change matched the expected direction in more than 80% in 963 different AHF patient scenarios. Our developed simulator visualizes the impact of drug therapies on four-dimensional hemodynamics so intuitively that it can support clinicians’ decision-making to protect multiple organs.
Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors

Intelligent rehabilitation robotics (RR) have been proposed in recent years to aid post-stroke survivors recover their lost limb functions. However, a large proportion of these robotic systems operate in a passive mode that restricts users to predefined trajectories that rarely align with their intended limb movements, precluding full functional recovery. To address this issue, an efficient Transfer Learning based Convolutional Neural Network (TL-CNN) model is proposed to decode post-stroke patients’ motion intentions toward realizing dexterously active robotic training during rehabilitation. For the first time, we use Spatial-Temporal Descriptor based Continuous Wavelet Transform (STD-CWT) as input to TL-CNN to optimally decode limb movement intent patterns. We evaluated the STD-CWT method on three distinct wavelets including the Morse, Amor, and Bump, and compared their decoding outcomes with those of the commonly adopted CWT technique under similar experimental conditions. We then validated the method using electromyogram signals of five stroke survivors who performed twenty-one distinct motor tasks. The results showed that the proposed technique recorded a significantly higher (p<0.05) decoding accuracy and faster convergence compared to the common method. Our method equally recorded obvious class separability for individual motor tasks across subjects. The findings suggest that the STD-CWT Scalograms have the potential for robust decoding of motor intention and could facilitate intuitive and active motor training in stroke RR.Clinical Relevance— The study demonstrated the potential of Spatial Temporal based Scalograms in aiding precise and robust decoding of multi-class motor tasks, upon which dexterously active rehabilitation robotic training for full motor function restoration could be realized.
A Novel Random Access for Ultra-Low Latency Communications

Under the same network topology as the IEEE 802.11be multi-link operation medium access control, we propose an uplink random access that achieves ultra-low latency communication. To support real-time applications (RTAs), packets shall be transmitted immediately without the need of channel sensing or retransmission. In our proposed uplink multiple access, each transmitting station sends $L$ uncoded copies of the same packet in parallel on $L$ random channels across multiple frequency bands. Transmission is considered successful if at least one copy is received without collision. We evaluate the performance for both finite and infinite number of stations using a mathematical model to determine the likelihood of successful transmission. We compare static and adaptive number of copies for finite number of stations case. The adaptive model, which adjusts based on traffic rates, is more efficient, increasing reliability and throughput. The overall performance of the proposed system is subject to traffic density. To ensure sustained performance and reliability, we recommend stabilization by controlling the transmission probability to achieve the desired level of reliability.
Applicability of Model Checking for Verifying Spacecraft Operational Designs

Guaranteeing safety and correctness is one of the main objectives during the development of space systems. This is a challenging task, since many different engineering disciplines are involved in the development and the constituent parts of a spacecraft are highly interconnected and interdependent. Increasingly, formal methods such as model checking are applied to verify safety-critical parts of spacecraft designs and also implementation, since they may prove the absence of design errors. Generally, a major challenge for adopting model checking into the design process is its scalability. Usually, the whole state space of a system, which grows exponentially with, e.g., the number of parallel processes, must be explored.In this paper, we consider operational designs of spacecraft as they may occur during early development phases and systematically evaluate the scalability of model checking for verifying such models. For this, we created an arbitrarily scalable operational design describing the mode management of a satellite. Transformations of the models into the modeling languages of different model-checking tools enables a comparative scalability study of various model-checking algorithms. The evaluation shows promising results for symbolic model-checking approaches. A comparatively low analysis time and memory usage suggest that model checking for early operational designs can be incorporated into existing design processes.
Comparison of IDW and Physically Based IDEW Method in Hydrological Modelling for a Large Mountainous Watershed, Northwest China

Topography and spatial patterns of landscape significantly affect spatial distribution of precipitation and, in turn, hydrological modelling, especially in high elevation, mountainous watersheds of arid regions. This study incorporates a physically based inverse distance and elevation weighted (PBIDEW) method into a distributed conceptual hydrological model, distributed large basin runoff model, and compared with an inverse distance weighted (IDW) method to assess the performances of both methods in precipitation estimation for hydrological modelling at watershed scale. The PBIDEW method considers the impacts of topography using month‐dependent parameters in its interpolation of meteorological variables while the IDW method does not. Both the IDW and the PBIDEW methods are evaluated and compared in hydrological modelling at different spatial resolutions in the upper reach of the Heihe River Watershed, Northwest China. Results show that the IDW method underestimated the areal precipitation, and the PBIDEW method produced more realistic precipitation estimations in the study area. Both methods have some limitations, the performance of the IDW method was mainly influenced by the availability of observation data, while that of the PBIDEW method was mainly influenced by the representation of topographical information. Considering more detailed information for precipitation estimates, the PBIDEW method performed better at finer spatial resolution. Overall, the PBIDEW method, using month‐dependent physical interpolation parameters, seems more suitable for precipitation estimation in hydrological simulations in data‐scarce, high elevation and topographically complex mountainous watersheds in arid area. Copyright © 2017 John Wiley & Sons, Ltd.