Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Validation of a Miniature Fiber Bragg Grating-Enabled High-Sensitivity Torque Sensor

This paper presents a Fiber Bragg Grating (FBG)-based torque sensor with high sensitivity and miniature size for MIS instruments and multi-finger hands. The proposed sensor design mainly consists of a torque-sensitive flexure structure and two diagonally arranged optical fibers embedded with FBG sensing elements. An improved hollow hexaform structure with flexible hinges has been utilized to design the torque-sensitive flexure with an excellent linear measurement range and a solid capacity to resist non-torsion loading. Two optical fibers with an FBG inscribed each adopt a two-point gluing configuration with a tightly stretched status, and they are arranged diagonally along the hyperboloid surface. This configuration can obtain improved sensitivity and resolution and avoid the drawbacks of FBG chirping and low repeatability. Finite element modeling (FEM)-enabled simulation has been implemented to perform design optimization for further sensor sensitivity improvement and performance investigation. The prototyped sensor has been calibrated and achieves a high resolution of 0.062N<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>mm within [−100, 100N<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>mm] and excellent linearity with a small linearity error of 0.68%. The crosstalk experiments on the bending moment of Mx and My and the axial force have been conducted to validate the anti-interference capacity on the basis of the small values of 0.47%, 0.31%, and 1.78%. The proposed design also supports convenient customization to achieve the adjustable measuring range and torque sensitivity, which has been validated by another sensor version with a resolution of 0.26 <inline-formula> <tex-math notation="LaTeX">$\text{N}\cdot $ </tex-math></inline-formula>mm within [−500, 500N<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>mm].
Non-Invasive Glucose Measurement Using Sub-Terahertz Sensor, Time Domain Processing, and Neural Network

This paper reports a non-invasive sub-Terahertz glucose concentration measurement system consisting of Sensor Unit (SU) and Processing Unit (PU). The SU. uses waveguide probe sensors to obtain the <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> parameters of glucose samples of different concentrations. These <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> parameters depend on the dielectric properties of glucose samples. The frequency-dependent permittivity values of various glucose samples are theoretically estimated using the double–Debye model for sensitivity and uncertainty investigations. The glucose sample concentration is used in the range 70–145 mg/dl to mimic healthy human bodies’ blood glucose levels, ranging from 70 to 140 mg/dl. The <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> parameters obtained through SU are converted to the time domain to obtain real-valued impulse responses, which are normalized in PU, making the input data suitable for analysis using Levenberg–Marquardt (LM) algorithm-based Back Propagation Neural Network. The proposed SU. provides a sensitivity of 2 dB for 15 mg/dl change in glucose concentration, and PU exhibits an accuracy of ±5%, which falls within the clinical range specified for non-invasive based monitoring systems for diabetes. Overall, SU provides high sensitivity towards blood glucose measurement, and PU enhances the measurement system’s readability by forming a non–linear relationship between <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> parameters and glucose concentration values
Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

Research on lower limb exoskeleton (LLE) for rehabilitation have developed rapidly to meet the need of the population with neurologic injuries. LLEs for rehabilitation include therapeutic LLEs that aim to restore walking ability for patients, and assistive LLEs that offer support on activities in daily life. A substantial part of them can serve both purposes. However, these devices are yet to reach the final goal of performing human-machine joint movement agilely and smartly. Control strategy plays an important role in achieving their designed goal. At present, control strategies face three major challenges: how to detect human intention, how to do motion control with given intentions, and how to optimize control parameters to suit different individuals. As a contribution, this paper offers an overview on the state-of-the-art control strategies for rehabilitation LLEs by classifying them into eight categories, each of which is presented with a technical summary and tabulated information of representative papers. Moreover, current approaches addressing the three challenges are discussed in a macroscopic perspective. Finally, it has been explored which requirements the future control strategies should meet for maximizing the performance of rehabilitation LLEs.
Dynamical Movement Primitives of Crossing over Obstacles for a Lower-Limb Prosthesis

This paper proposes a trajectory generation of prosthesis to help amputee crossing over obstacles. Dynamical movement primitives (DMPs) are popular methods of reproducing trajectory for learning control. In the basic of DMPs, a novel term of obstacle is added to generate the trajectory in real-time. This term includes multiple point obstacle sources to reflect spatial size of obstacles. Each point obstacle is used to calculate direction relative to current position. Moreover, the direction vector and velocity vector are considered into term of obstacle. Besides, DMPs can generate effective trajectory through obstacle term parameter adjusting regardless of the obstacles in the front, middle or behind position in a step. Therefore, our method solves the inability crossing over obstacles under some scenarios and helps the prosthesis adapt to the various environment. Gaussian mixture regression is used to conjunction with DMPs for movement representation, which can reproduce new trajectory from multiple sets of original trajectories. Finally, simulations for DMPs were performed to demonstrate the flexibility of DMPs with obstacle term, which can generate desired trajectory of crossing over obstacles in various situations, indicating our method has a potential to facilitating prosthesis motion control.
Automatic Attention Learning Using Neural Architecture Search for Detection of Cardiac Abnormality in 12-Lead ECG

In this article, a new attention learning method based on neural architecture search (NAS) is introduced to detect cardiovascular diseases (CVDs). Cardiac abnormalities are a series of CVDs that threaten human health. Electrocardiography (ECG) is a useful tool to express the status of cardiac activity and detect cardiac abnormalities. In recent years, the convolutional neural network and attention mechanism have been applied in ECG classification with good feature-learning ability. However, there are a large number of hyperparameters that must be manually adjusted, so that they have limited ability to learn temporal and channel information from feature maps in the network. Therefore, in this article, a new attention learning method based on NAS is proposed, so that the network can better learn the temporal and channel information in the ECG and perform better than artificially designed architectures. The results were verified using the 12-lead ECG dataset of the China Physiological Signal Challenge (CPSC) 2018, which contains eight different CVDs and the proposed method performs better than state-of-the-art methods.
Application of SMES Technology in Improving the Performance of a DFIG-WECS Connected to a Weak Grid

Wind Turbine Generator (WTG) has become one of the most popular renewable-based power generation that is broadly connected to electricity grids worldwide. Till the year 2019, the total WTGs installed worldwide reached about 650.8GW. The WTG is interfaced to the electricity grid through power electronic converters with a proper control algorithm to facilitate a smooth power delivery as well as maintaining the system voltage and frequency stability during wind intermittency. However, power grids are usually subjected to load expansion which affects the stiffness of the grid and hence its stability. A weak electricity grid exhibits voltage instability that may affect the performance of WTGs and in some cases may lead to serious damages to the wind turbines and the entire system. In this paper, superconducting magnetic energy storage (SMES) technology based on fuzzy logic controller is implemented to effectively resolve this issue and improve the overall performance of WTGs. Hysteresis current and fuzzy logic-based control system is proposed to control the energy exchange between the SMES coil and the investigated system. Results show the effectiveness of the SMES to improve the overall system performance and along with the fault ride-through capability of the doubly-fed induction generator (DFIG).
Brain Source Localization with covariance fitting approaches

The techniques like fMRI, CT scans, etc are used to localize the activity in the brain. Though these techniques have a high spatial resolution they are very expensive and uncomfortable for the patients. On the other hand, EEG signals can be obtained quite comfortably but suffer from low spatial resolution. A lot of research is being done to effectively extract spatial information from EEG signals. Many inverse techniques like MNE, LORETA, sLORETA, etc are available. All these methods can detect only a few sources and their performance degrades at low SNR. In this paper, covariance-based methods are used to estimate the location of brain activity from EEG signals such as SPICE (sparse iterative covariance-based estimation), and LIKES (likelihood-based estimation of sparse parameters). Intense simulation work has been presented to show that the proposed methods outperform the state-of-the-art methods.
Performance Investigation of NOMA Versus OMA Techniques for mmWave Massive MIMO Communications

The fifth-generation (5G) of cellular technology is currently being deployed over the world. In the next decade of mobile networks, beyond 5G (B5G) cellular networks with the under-development advanced technology enablers are expected to be a fully developed system that could offer tremendous opportunities for both enterprises and society at large. B5G in more ambitious scenarios will be capable to facilitate much-improved performance with the significant upgrade of the key parameters such as massive connectivity, ultra-reliable and low latency (URLL), spectral efficiency (SE) and energy efficiency (EE). Equipping non-orthogonal multiple access (NOMA) with other key drivers will help to explore systems’ applicability to cover a wide variety of applications to forge a path for future networks. NOMA empowers the networks with seamless connectivity and can provide a secure transmission strategy for the industrial internet of things (IIoT) anywhere and anytime. Despite being a promising candidate for B5G networks a comprehensive study that covers operating principles, fundamental features and technological feasibility of NOMA at mmWave massive MIMO communications is not available. To address this, a simulation-based comparative study between NOMA and orthogonal multiple access (OMA) techniques for mmWave massive multiple-input and multiple-output (MIMO) communications is presented with performance discussions and identifying technology gaps. Throughout the paper, aspects of operating principles, fundamental features and technological feasibility of NOMA are discussed. Also, it is demonstrated that NOMA not only has good adaptability but also can outperform other OMA techniques for mmWave massive MIMO communications. Some foreseeable challenges and future directions on applying NOMA to B5G networks are also provided
Attitude Estimation of SINS on Dynamic Base With Decoupling Gravity Vector

Accurate and fast estimation of attitude is an important performance criteria for Strapdown Inertial Navigation System (SINS). Although many algorithms for SINS attitude estimation have been proposed by related researchers, most of them take the gravity vector as a known quantity. However, the gravity vector is inaccurate or even unknown in many cases, we have to use normal gravity instead of true gravity, which will bring errors to the mechanics equation of SINS, and reduce accuracy of attitude estimation. The coupling of gravity and attitude has been widely concerned by scholars and lack of effective solutions. In this paper, a new coarse attitude estimation method for dynamic base with decoupling the gravity vector is proposed. The method does not require the participation of gravity vector in attitude estimation process. Numerical simulation based on field test data is employed to verify the effectiveness and superiority of the proposed method in land vehicle navigation. The results indicate that the method improved the error caused by inaccurate gravity vector in traditional SINS attitude estimation and it provides a new idea for the measurement of vertical deviation.
A 4-bit Mixed-Signal MAC Array with Swing Enhancement and Local Kernel Memory

Modern deep neural networks require energy- and area-efficient multi-bit multiply-accumulate (MAC) functions. In-memory computing (IMC) with analog accumulation has shown the potential to outperform purely digital solutions but lacks efficient multi-bit computation. In this work, we explore the design of a mixed-signal, charge-sharing compute array that performs 4-bit MAC operations within one clock cycle. Its key features include 7×4 bit kernel memory in each MAC cell, as well as a per-kernel binary combiner that simplifies the unit cell design and enables efficient in-column ADC integration. In addition, it leverages a differential switching scheme that improves the signal swing by 4× relative to single-ended schemes, thereby reducing ADC energy. Post-layout simulations in 28 nm CMOS indicate an energy efficiency of 6.4 fJ/MAC and a compute density of 3.17 TOPS/mm2 for an input activation vector size of 160.
A Review of Bilateral Teleoperation Control Strategies with Soft Environment

In the past two decades, bilateral teleoperation with haptic feedback has attracted great research and application interests in both robotics and other areas. Initially triggered by the need to handle dangerous and remote distance tasks such as nuclear materials manipulation and space exploration, bilateral teleoperation has found its way into other applications as a result of development of control theory, robotic technology (both hardware and software) and latest breakthrough in artificial intelligence and machine learning. Consequently, bilateral teleoperation is found facing new challenges brought by these new applications. One major and obvious change is the working environment for the slave manipulator: different from rigid or solid contact environments which are reasonably assumed in early applications in industrial, nuclear and aerospace applications, the slave environment is now more complex and often the objects in contact are much softer in term of stiffness and can not be described by simple elastic model if good teleoperation performance (accurate and transparent) is expected. In this paper, the research of bilateral teleoperation system considering soft environment in recent 20 years has been surveyed for the first time in literature, to the knowledge of the authors. Following the difference in real applications, in this review the definition of soft environment covers linear elastic environment with much lower stiffness than conventional industrial environment and nonlinear complex soft environment with/out time-varying characteristics. Accordingly, the surveyed control strategies and structures in recent literature to improve the stability and accuracy of bilateral teleoperation with soft environment are classified and explained. Finally, the main applications, current challenges and future perspectives of bilateral teleoperation with soft environment are discussed.
Heart Sounds Classification Based on Feature Fusion Using Lightweight Neural Networks

Heart sounds are an important basis for evaluating heart disease. In recent years, auxiliary diagnosis technology of cardiovascular disease based on the detection of heart sound signals has become a research hotspot. A lightweight automatic heart sound classification method is presented in this study. It only needs to perform simple preprocessing on the heart sound data, and a neural network model is constructed to automatically extract the time–frequency features of heart sound data: where the convolution module extracts the frequency-domain characteristics, the loop module extracts the time-domain characteristics, and the features are then fused in series and parallel. Finally, the classification and recognition of heart sounds are realized based on the fusion features. At the same time, methods, such as group convolution, global average pooling, and gated loop mechanisms, are used to reduce the parameters and training time of the neural network. The experimental data from the PhysioNet database are used for testing. The recognition accuracy of the series feature fusion heart sound classification model is 95.00%, and the model size is 0.6 MB. In addition, the parallel feature fusion heart sound classification model is 95.50%, and the model size is 1.36 MB. Therefore, the lightweight heart sound classification model designed in this study contains fewer parameters and higher recognition accuracy. This makes it suitable for deployment in embedded devices. It also has important research significance for the development of portable heart sound detection equipment.
A Comparison between Class-E DC-DC Design Methodologies for Wireless Power Transfer

We consider the design of Wireless Power Transfer (WPT) systems based on inductive links and focus on recent works where the whole WPT system (i.e. both energy transmitter and energy receiver) is designed as an isolated resonant class-E DC-DC converter characterized by a loosely-coupled transformer. The aim of this work is to compare the classic WPT design approach with a novel one, which allows achieving the same performance with a significant reduction in the number of reactive components of the circuit, with beneficial effects in terms of system complexity, size, and cost. We will also show that such a reduction in the number of reactive components leads to improved performance robustness to variations in the inductive link coupling factor.
Resonance Suppression Method for Grid-Connected Converter With LCL Filter Under Discontinuous PWM

This paper presents a resonance suppression method that reduces current oscillation incurred by the LCL-filtered grid-connected converter’s discontinuous PWM (DPWM). First, the cause of LCL resonance is analyzed with an aspect of frequency and time domain for DPWM scheme. Then, voltage references are manipulated to decrease the resonance problem. In the proposed method, pole voltage references at the edge of offset voltage are slightly adjusted to reduce the current oscillation. In addition, a perturb and observe (P&O) technique is adopted to search the magnitude of an edge voltage. Through the simulation and experimental tests, the current oscillation is dramatically reduced while keeping the advantages of DPWM scheme. The proposed method can minimize the switching frequency under the grid harmonics regulation.
Challenges, Applications and Design Aspects of Federated Learning: A Survey

Federated learning (FL) is a new technology that has been a hot research topic. It enables the training of an algorithm across multiple decentralized edge devices or servers holding local data samples without exchanging them. There are many application domains in which considerable properly labeled and complete data are not available in a centralized location (e.g., doctors’ diagnoses from medical image analysis). There are also growing concerns over data and user privacy, as artificial intelligence is becoming ubiquitous in new application domains. As such, much research has recently been conducted in several areas within the nascent field of FL. Various surveys on different subtopics exist in the current literature, focusing on specific challenges, design aspects, and application domains. In this paper, we review existing contemporary works in related areas to understand the challenges and topics emphasized by each type of FL survey. Furthermore, we categorize FL research in terms of challenges, design factors, and applications, conducting a holistic review of each and outlining promising research directions.
Characterization of Catalytic Materials through a Facile Approach to Probe OH Groups by Solid-State NMR

A facile NMR approach based on dipolar filtering (DF) and spin echo (SE) is proposed to study commercially available dealuminated USY zeolites, such as H-CBV760, H-CBV720, and NH4-CBV712 of nominal Si/Al ratio of 30, 15, and 6, respectively. The proposed 1H DF-SE magic angle spinning (MAS) NMR approach provides the substantial suppression of water signal intensity in partly hydrated samples providing an opportunity to obtain the signal of other surface groups. 1H DF-SE MAS NMR technique has been demonstrated its usability for quantitative (semiquantitative) analysis of dried, i.e., partly hydrated, samples. It is essential to use this approach when calcination under vacuum used as a reference procedure leads to drastic surface changes. Moreover, the technique is applicable for qualitative analysis of fully hydrated samples. This method is found to be extremely sensitive to the residual ammonium content in zeolite structure even in transformed to H form by calcination. Finally, the framework stacking fault.
Fast Multi-Focus Image Fusion Using Inner Channel Prior

In this paper, we propose a simple but effective sharpness prior—inner channel prior—to detect the focused area of multi-focus images. The inner channel prior is a kind of location feature of natural image sharpness. It is based on a key observation—the high-frequency information of the saturation channel is located inside the object. The inner channel prior indicates the focus degree inside objects, which can obtain better color fidelity and sharper object. Using the proposed prior combined with the multi-scale image fusion, we can directly obtain a sharp image with an extended depth of field focusing on all the objects. Results on a variety of defocused image sequences demonstrate the availability of the proposed prior. In addition, the proposed prior is insensitive to overexposure and can better maintain the color of multi-focus images.
Surrogate Model based Co-Optimization of Deep Neural Network Hardware Accelerators

In this paper, we present an ASIC based on 22FDX/FDSOI technology for the detection of atrial fibrillation in human electrocardiograms using neural networks. The ASIC consists of a RISC-V core for supporting software components and an application-specific machine learning IP core (ML-IP), which is used to implement the computationally intensive inference. The ASIC was designed for maximum energy efficiency. A special feature of the ML-IP is its modular, generic and scalable design of the ML-IP which allows to specify the quantization of each computational operation, the degree of parallelization and the architecture of the neural network. This in turn allows the use of ML-based optimization techniques to perform co-optimization for hardware design and architecture of the neural network (NNs). Here, a multi-objective optimization of the overall system is performed with respect to computational efficiency at a given classification accuracy and speed by using a multi-objective optimization, which is carried out using a probabilistic surrogate model. This model tries to find the optimal neural network architecture with a minimum number of training, simulation and evaluation steps.