Miracle-dz/UniFAR-FTUs huggingface dataset:
Proportional Control of a Soft Cable-Driven Exoskeleton via a Myoelectrical Interface Enables Force-Controlled Finger Motions

We report new electromyography (EMG) and finger motion data that address the question of whether a proportional EMG control of a soft cable-driven exoskeleton enables force-controlled finger motions. To investigate this question, we conducted experiments with a self-built state- of-the-art cable-driven exoskeleton for a single finger and a healthy subject. The finger exoskeleton weighed only 44 g and was built from low-cost materials (ultra-high-molecular- weight polyethylene cord, DC motors, 3D-printed components). The goal of this study was to show whether the subject can remotely control a finger of their left hand (slave), to which the exoskeleton is attached, with a finger of their right hand (master) based on the EMG activity of the forearm muscles. The results show that the proportional EMG control enables force-controlled motions of the slave finger, while it was ensured that the healthy subject did not manipulate the data. Moreover, the data show that this type of control allows a modulation of the fingertip forces.
SIW Multi-polarized Cavity Antenna Based on Polarization Synthesis of Multi-slots

SIW multi-polarized cavity antennas have been widely concerned because of their simple structure. Main mode (TE10 mode) is mostly used in this type of antenna at present, however, the number of slots on cavity surface is limited, which leads to low gain of the cavity. Based on SIW transmission mode, multi-inputs with inverse phase is used to excite high-order mode. According to the field distribution in cavity, the direction and phase of the radiation electric field of each slot on the cavity surface are analyzed, and a method of polarization direction synthesis is analyzed theoretically. A SIW multi-polarized antenna based on TE220 mode is designed by this method, and the slot number of radiation cell is confirmed by discussing the influence of the number on the dispersion characteristics, transmission and radiation performances of the cavity. What’s more, the antenna gain is promoted by forming a 2×2 array. The measured results show that this array has good radiation performance with relative bandwidth of 10%, gain of 11.7dBi at 9.7GHz, and cross polarization ratio greater than 10dB, which verifies the effectiveness of the polarization synthesis method.
The Effect of Transcutaneous Spinal Cord Stimulation on Standing Postural Control in Healthy Adults

A im: To determine the effects of pulsed current transcutaneous spinal cord stimulation (TSCS) on both the spinal circuitry and the neuromotor response observed after a trunk force perturbation. Methods: A group of young healthy adults performed two experiments to study the effects of TSCS. In experiment I, the effect of TSCS on the spinal circuitry was assessed using the Hoffman-reflex (H-reflex) recruitment curve and recovery cycle. In Experiment II, the effect of TSCS on the response to trunk force perturbation was assessed with both stimulation on and off. The force perturbations were delivered by a robotic upright trunk support trainer (RobUST). Results: 30 Hz monophasic pulsed current TSCS induced spinal plasticity and increased spinal excitability. This TSCS protocol also increased muscle activation in response to specific pelvic force perturbations. These results suggest that TSCS can modulate the integration of voluntary descending commands and sensory signals and alter muscle activation during a postural task, even in neurologically intact subjects. We plan to use these results to develop training protocols to improve balance during standing in patients with spinal cord injury (SCI).
An S/C Dual-Band Dual-Polarization Array for Synthetic Aperture Radar

This paper proposes a dual-band dual-polarization array that operates in S-band (3 GHz) and C-band (5.3 GHz) which can be applied to Synthetic Aperture Radar (SAR) system. In this paper, a stacked dual-band dual-polarization microstrip patch antenna is designed as a radiating element, and then a dual-band dual-polarization array is formed by this element. The cross-polarization level of the dual-band array is suppressed by using the "pair-wise anti-phase" technique. A 2×2 prototype array is designed and simulated, and the proposed array covers the band of 2.94 GHz-3.06 GHz and 5.19 GHz-5.49 GHz. The polarization isolation is better than -16 dB in both bands. The cross-polarization is lower than -55 dB and -40 dB at S-band and C-band, respectively. The designed array has the characteristics of high aperture efficiency and symmetrical structure, which can be extended into a larger array.
Strong Magnetic Field Dependence of Micro-YBCO Superconductor Levitated Above Halbach Guideway

For a regular-geometry superconductor (SC), due to a nonuniform external magnetic field, the effect of the field dependence (i.e., <inline-formula> <tex-math notation="LaTeX">${J} _{c}$ </tex-math></inline-formula> anisotropy and <inline-formula> <tex-math notation="LaTeX">${J} _{c}$ </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">${H}$ </tex-math></inline-formula>) property) on the levitation force is complex and indistinct. For a relative microsample, its applied field is uniform, and the phenomenon caused by the field dependence should be more obvious. A novel instrument with a parallel-structure is therefore developed to accurately measure the micro force, and the device achieves a 4 mN precision. Based on this specialized apparatus, the experimental results present several rare phenomena caused by the field dependence: 1) force attenuation (near the guideway during the SC descent) occurs in the small-width SC at the <inline-formula> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula>-axis <inline-formula> <tex-math notation="LaTeX">$\bot {H}$ </tex-math></inline-formula> condition, which is attributed to <inline-formula> <tex-math notation="LaTeX">${J} _{c}$ </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">${H}$ </tex-math></inline-formula>); 2) the maximum levitation force (MLF) position offset of bulks do not appear in the stack. , which does not appear in stacks; and 3) for the stack, the MLF curve varies in a sinusoidal way with the lateral position and fits well with the applied field <inline-formula> <tex-math notation="LaTeX">$\lvert {B}_{z}\rvert $ </tex-math></inline-formula>, which depicts that the anisotropic feature of the stack is much stronger than that of the bulk. An analytical critical-state model (CSM) based on the assumptions of a uniform external field and saturated magnetization is proposed for describing these results that accurately reflect the experimental phenomenon. In addition, the finite element simulation of the micro SC in COMSOL is discussed, and a force formulation from this analytical model improves the calculated result.
Channel Parameter Extraction Based on BP-Neural Network in NLOS Environment

The extraction of wireless channel parameters is important for channel modeling and wireless resource scheduling in complex environments. Based on QuaDriGa studies the application of back-propagation neural network in MIMO channel parameters extraction, and compares with the effect of traditional space-alternating generalized expectation-maximization algorithm. The results show that the back-propagation neural network model algorithm works well and can be used for channel parameter extraction in future complex scenarios.
Model-based Control for Gait Assistance in the Frontal Plane

There is a growing interest in developing devices for human gait assistance. Most research focuses on the sagittal plane's assistance, assuming that walking is predominantly a sagittal plane motion. However, movements in the frontal plane have particular importance for balance control as well as load carrying. This paper studies hip abduction assistance and proposes using a simple human locomotion model as a guideline for designing and controlling assistive devices. We introduce the force modulated compliant hip (FMCH) model in the frontal plane, which applies the ground reaction force to tune the hip abductor/adductor stiffness. The effects of this model-based control approach on walking gait are investigated through leg muscles' activation, metabolic costs, and adaptability to a new condition (carrying 38 kg extra load) for seven healthy young subjects using an experiment-based simulation in OpenSim. Our results show 6.72 ± 0.6% and 11 ± 0.3% reduction in metabolic cost for walking at a freely selected speed and while load carrying, respectively. Also, compensating for increased muscle activation while carrying extra load due to the hip compliance adjustment by leg force feedback approves the adaptability of the proposed control approach.
Vein-Based Coalitions for Multi-Agent Pattern Formation Tasks

This letter explores the vein trait of the structural formation task and extracts four typical parallel-straight vein structures for generating coalitions. Then, the Vein-Based Multi-Agent Pattern Formation (VB-MAPF) method is proposed to resolve task and path planning on coalitions. Besides, the presented rectifying operation guarantees the optimal coalition structure, and the vein-based pruned conflict-based search reduces the searching space for collision-free path planning. Compared with typical solvers, the inter-performance and the intra-performance orders show the efficiency and scalability of the VB-MAPF method in terms of makespan, average distance, and maximum distance on dozens of coalitions and thousands of agents.
2.5D Laser-Cutting-Based Customized Fabrication of Long-Term Wearable Textile sEMG Sensor: From Design to Intention Recognition

The surface electromyography(sEMG) sensor is widely used as a human-machine interface in wearable systems. Although numerous studies have applied compact sEMG systems to wearable devices, these are inconvenient and not suitable for long-term use. Herein, we introduce a 2.5D laser cutting method to accelerate customized sensor fabrication from design to production. The customized textile-based sensor provides high wearing comfort and improves the sensor signal quality through stable contact. We implemented a foam-filled electrode to ensure solid skin-electrode contact even during perspiration and varying pressure conditions, and evaluated its performance experimentally. The sensor-integrated garments one for the leg and one for the arm were fabricated with the proposed design method for further evaluation and application. Consistent sensor performance was demonstrated during squats and running (i.e., perspiration) while wearing the leg sensor. The sensor sleeve for the arm was integrated with intention recognition algorithms for hand gesture recognition. A Convolutional Neural Network (CNN) architecture was employed to classify 28 hand gestures, including finger and wrist motions. The average classification accuracy of five subjects achieved 93.21%, and further increased to 94.34% after perspiration.
Commitment over Multiple-Access Channels

The problem of multi-user commitment is one in which multiple users first commit to individual messages with a bookmaker and later reveal their messages. The objective of the bookmaker is then to decide whether the revealed messages correspond to the committed messages. We study a specific multi-user commitment model in which the users and the bookmaker have access to a noiseless channel, as well as a noisy multiple-access channel whose inputs are controlled by the users and whose output is observed by the bookmaker. When the users are non-colluding and the channel is non-redundant, we fully characterize the commitment capacity region. When the users are colluding, we derive an achievable region and a tight converse for the sum-rate. In both cases our proposed achievable commitment schemes are constructive.
A Model for Simulation of Human Sinoatrial Node Action Potential

Upon discovery of new biological mechanisms inside cardiomyocytes modern models of sinoatrial node action potential have been steadily increasing in complexity. Researchers who design complex models try to implement as many independent currents as possible to keep their models up to date. This paper presents an improved parallel conductance model with parameters suitable for simulation of human sinoatrial node action potential. In this model the independent membrane currents were replaced by four main current groups (K-related, Na-related, Ca-related and transporter-related) to form four total currents. The resulting total currents were compared with corresponding total currents formed from the sum of independent currents from a different, more complex model. This approach can be used to simulate sinoatrial node action potentials similar to those in more complex models while keeping a reduced number of parameters and differential equations.
Glass Objects Detection Based on Transformer Encoder-Decoder

Glass objects detection is a relatively new and challenging work in the field of computer vision. This work will also have an impact on other visual tasks, such as depth estimation, instance segmentation and saliency detection. Compared with using high-precision sensors for glass object detection, the vision based detection method is more economical and robust. In this paper, we focus on the task of glass object detection in the field of image segmentation. Traditional semantic segmentation methods mostly used the model based on convolutional neural network to complete the task. On this basis, we introduce the Transformer encoder-decoder architecture to expand the size of receptive field, make full use of different levels of context information and improve the detection effect of glass objects. In this paper, the mixed model of convolutional neural network and Transformer is used to realize the detection of glass objects in the Pytorch environment, and the model is trained and evaluated on the Trans10K-v2 dataset. In the experiment, the input image resolution is 512 × 512, the final pixel accuracy is 93.94%, and the mean intersection over union is 71.32%.
Foreseer: Efficiently Forecasting Malware Event Series with Long Short-Term Memory

With the rise in malware attacks on modern computer systems, there is a critical need for the computer security defenses to not only detect the presence of adversaries accurately, but also have the ability to do so at the earliest possible time to avoid suffering from extensive system damages. Currently, malware defenders have to accumulate sufficient amounts of system event samples to detect the malware presence and identify them with increased confidence. Unfortunately, in many instances, a longer wait may have already resulted in the loss of valuable time toward stopping the malicious activity, and may even nullify the usefulness of any potential defensive actions.In this paper, we present Foreseer, a framework that can help proactive defenses through forecasting the raw event series needed for early detection of malware. To accomplish this goal, we leverage Long Short-Term Memory (LSTM) neural networks aided with attention mechanisms, that can memorize complex sequential patterns and use this information to predict the future events with high accuracy. Foreseer design adopts a joint hardware-software optimization approach to improve its efficiency by using symbolic representation for its event series, deploying runtime machine code optimizations using Just-in-Time Python compilers, hardware accelerated mixed precision quantization available in Nvidia GPUs. We evaluate the efficacy of Foreseer framework using RanSAP, an open dataset for ransomware storage access patterns. Our experimental results show that Foreseer is able to synthesize ransomware-related features with low errors for most dominant factors used in detection (such as the size of a block accessed by a sample, shannon entropy), and cuts the time to predict the malware presence by about 40%.
Comparative Analysis Of JavaScript And WebAssembly In The Browser Environment

As World Wide Web is evolving, larger and high-performance applications are being entirely run on the browsers. Web applications have their own advantages like they are more accessible and platform independent. JavaScript was the only programming-language which was historically supported to be ran on the web browsers, but it is quite limited to high-performance applications as it is dynamically-typed and interpreted language. So, as the high-performance applications started to come to web there have been always a need for another language which could run in the browser environment but also take advantage of system resources. WebAssembly was one such effort by the vendors of different browsers coming together. WebAssembly is claimed to be portable and size and time efficient binary format which could be compiled to run on the web browsers at near native speed. This paper will try to verify the claim by running various experiments on both WebAssembly and JavaScript and measuring resource used and time taken by those programs to execute and will later do a comparative analysis between the both.
A Data-based Approach to Simultaneously Align Local and Global Frames between an Inertial Measurement Unit (IMU) and an Optical Motion Capture System

Motion tracking algorithms using inertial measurement units (IMU) are commonly evaluated against ground truth measurements from optical motion capture systems (OMC). A fair comparison between IMU and OMC requires accurate frame alignment between the two systems. Existing methods address the local and global frame misalignments as separate issues, either relying on various assumptions or precise calibration measurements. In this work, we propose an assumption-free data-based method that simultaneously aligns both local and global frames via quaternion-based least squares optimization. For performance evaluation, we compared the proposed method with methods based on commonly accepted assumptions. Using human kinematics data from 6 participants, the proposed method produced the best alignment results with error less than 1.5° and its error profile was the least correlated with motion. We then conducted sensitivity analysis on input data characteristics and showed the necessity of using data with sufficient range of motion to ensure alignment accuracy. Lastly, we demonstrated that the proposed method could be used for isolating IMU drift during dynamic movements. The proposed alignment method could serve as a valuable tool for developing and evaluating IMU-based motion tracking algorithms.
Sensor fusion in autonomous vehicle using LiDAR and camera Sensor

This paper presents a sensor fusion methodology for autonomous vehicles (AVs) using Light Detection and Ranging (LiDAR) and camera. Mostly sensors like camera or LiDAR is used only as the sensor for visual perception in AVs. But the hindrance comes during bad weather conditions, dim light or night time. To alleviate this problem, a method which combines both LiDAR and camera sensor using odometry is explored in this paper. The study also attempts to employ Extended Kalman Filter (EKF) to reduce error in position estimate of the vehicle.
Automated Pulmonary Function Measurements from Preoperative CT Scans with Deep Learning

Lung resections are the most effective treatment option for early stage lung cancer. Clinicians determine whether a patient is operable and the extent a lung can be resected based in part on the patient's pulmonary function parameters. In this study, we investigate the feasibility of generating forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC) values from preoperative chest computed tomography (CT) scans. Our study population includes 546 individuals who had lung cancer surgery at an oncology specialty clinic between 2009 and 2015. All CT studies and pulmonary function tests (PFTs) were collected within 90 days before a subject's operation. We measure pulmonary function with convolutional neural network and recurrent neural network models, extracting image embeddings from axial CT slices with a ResNet-50 network and generating FEV1 and FVC measurements using a bidirectional long short-term memory regressor. We show that combining feature vectors extracted from mediastinal and lung Hounsfield unit windows and taking a multi-label regression approach improves performance over training with embeddings from only one window or single-task networks trained to measure only FEV1 or FVC values. Our work generates PFT measurements end-to-end and is trained with only computed tomography scans and pulmonary function labels with no manual slice selection, bounding boxes, or segmentation masks.
Triband Circularly Polarized Unidirectional Stacked Patch Array Antenna Enabled by Broadband Feed Network

In this work, the design of a triband circularly polarized antenna which consists of a stacked patch antenna array and a wideband quadrature four-port feed network is reported. Using a grounded shunt λg/4 stub in the main line of a conventional phase shifter, four signals with equal amplitude and orthogonal phase of 0°, 90°, 180°, and 270° can be achieved in a frequency bandwidth greater than 67% from 5 to 10 GHz. Benefiting from stacked microstrip patches and the sequentially rotated feed layout, the proposed antenna exhibits a low profile and symmetrical unidirectional radiation beam. The overall electrical size of the antenna is $1.6 \times 1.4 \times 0.06\;{\lambda _0}^3$ (λ0 is the free space wavelength at 6.0 GHz). In addition, the antenna features the same circular polarization status at three bands, the 3 dB axial ratio bandwidths are 15.2% (5.4-6.2 GHz), 11.4% (6.7-7.4 GHz), 15.3% (7.7-9.0 GHz), and the forward peak gains are 8.3, 9.8, 6.3 dBic at 6, 7, and 8.5 GHz, respectively.
Online Event-Triggered Adaptive Optimal Control of Nonlinear Large-Scale Systems

In this work, we proposed a new online decentralized event-triggered control method which is applicable to some of large-scale systems with nonlinear inter-connection affected by unknown inside system dynamics. This work first designs a recognizer based on neural network to rebuild the uncertain internal dynamics in interconnected system. In the presence of an event triggering mechanism, we next study an approximate optimal control method by adopting the adaptive critic learning method. In this paper, the decentralized event trigger conditions are influenced by only partial state messages of the relevant subsystems, so are controllers. Thus this approach eliminates some problems arising from the process of transmitting status information between subsystems via wireless communication networks. By using Lyapunov's theorem, we show that the state and critical weight estimation errors of the developed closed-loop control system are uniformly ultimately bounded. At last, two cases confirm the validity and suitability of the approach which designed in this paper.