Miracle-dz/UniFAR-FTUs huggingface dataset:
Haptic Interface Device Using Cable Tension Based on Ultrasonic Phased Array

This paper presents a new haptics interface device based on cable-driven parallel manipulators and an ultrasonic phased array. Unlike previous studies, our method combines force and tactile feedback at the same time. It consists of two main parts: force feedback generated by cable tension and tactile feedback provided by an ultrasonic phased array. While these parts have no direct interference, combining them can offer a synergistic effect on haptic perception. Through a series of experiments, the tactile rendering algorithm for this device is established. The output pressure of ultrasonic phased array is dependent on input command value, modulation frequency, modulation waveform, and the position of the focal point in the workspace. The results of psychophysical experiments are evaluated to determine the absolute threshold of perceivable ultrasonic tactile feedback when the cable-driven force acts on fingers. Finally, we carry out a test to confirm the accuracy advantage of our system in the virtual environment. The results of our study indicate that this device has a wide range of applications in the field of aerial haptic display.
Omnidirectional Depth Extension Networks

Omnidirectional 360° camera proliferates rapidly for autonomous robots since it significantly enhances the perception ability by widening the field of view (FoV). However, corresponding 360° depth sensors, which are also critical for the perception system, are still difficult or expensive to have. In this paper, we propose a low-cost 3D sensing system that combines an omnidirectional camera with a calibrated projective depth camera, where the depth from the limited FoV can be automatically extended to the rest of recorded omnidirectional image. To accurately recover the missing depths, we design an omnidirectional depth extension convolutional neural network (ODE-CNN), in which a spherical feature transform layer (SFTL) is embedded at the end of feature encoding layers, and a deformable convolutional spatial propagation network (D-CSPN) is appended at the end of feature decoding layers. The former re-samples the neighborhood of each pixel in the omnidirectional coordination to the projective coordination, which reduce the difficulty of feature learning, and the later automatically finds a proper context to well align the structures in the estimated depths via CNN w.r. t. the reference image, which significantly improves the visual quality. Finally, we demonstrate the effectiveness of proposed ODE-CNN over the popular 360D dataset, and show that ODE-CNN significantly outperforms (relatively 33% reduction in depth error) other state-of-the-art (SoTA) methods.
Axial Stray Flux Sensor Proposal for Three-Phase Induction Motor Fault Monitoring by Means of Orbital Analysis

This paper proposes a new form of stray flux sensor, placed on the inner side of the machine rear end plate, in order to monitor the presence and evolution of some most common faults in three-phase induction motors. The fault indicator is based on analysis of orbits built from time flux signals collected by the sensor. The effectiveness of this methodology was checked considering three types of faults under three different load conditions: incipient inter-turn short circuits, unbalanced voltage supplies and misalignment, all with 0%, 50% and 100% loads. The experimental results showed a viability of the technique for diagnosing and monitoring of induction motors. This can be adapted and used in predictive maintenance in industry.
Monocular Visual-Inertial Odometry in Low-Textured Environments with Smooth Gradients: A Fully Dense Direct Filtering Approach

State of the art visual-inertial odometry approaches suffer from the requirement of high gradients and sufficient visual texture. Even direct photometric approaches select a subset of the image with high-gradient areas and ignore smooth gradients or generally low-textured areas. In this work, we show that taking all image information (i.e. every single pixel) enables visual-inertial odometry even on areas with very low texture and smooth gradients, inherently interpolating and estimating the scene with no texture based on its informative surrounding. This information propagation is only possible as we estimate all states and their uncertainties (robot pose, extrinsic sensor calibration, and scene depth) jointly in a fully dense filter framework. Our complexity reduction approach enables real-time execution despite the large size of the state vector. Compared to our previous basic feasibility study on this topic, this work includes higher order covariance propagation and improved state handling for a significant performance gain, thorough comparisons to state-of-the-art algorithms, larger mapping components with uncertainty, self-calibration capability, and real-data tests.
Design and Analysis of Grid-Interactive DFIG Based WECS for Regulated Power Flow

This article presents the sharing of reactive power between two converters of a doubly fed induction generator (DFIG) based wind energy conversion system interacting with the grid. The rotor side converter (RSC) control of DFIG is designed for sharing of reactive power at below rated wind speeds, which essentially reduces the amount of rotor winding copper loss. However, at rated wind speed, the RSC control is designed to maintain the unity power factor at stator terminals and to extract rated power without exceeding its rating. Further, the reduction in rotor winding copper loss due to reactive power distribution is demonstrated with an example. Moreover, the grid side converter (GSC) control is designed to feed regulated power flow to the grid along with reactive power support to DFIG and to the load connected at point of common coupling. Moreover, the GSC control is designed to compensate load unbalance and load harmonics. The battery energy storage connected at dc link of back-to-back converters, is used for maintaining the regulated grid power flow regardless of wind speed variation. The system is modeled and its performance is simulated under change in grid reference active power, varying wind speed, sharing of reactive power, and unbalanced nonlinear load using SimPowerSystems toolbox of MATLAB. Finally, a prototype is developed to verify the system steady state and dynamic performance. Moreover, system voltages and currents are found sinusoidal and balanced, and their total harmonic distortions are as per the IEEE 519 standard.
Characterisation of Self-locking High-contraction Electro-ribbon Actuators*

Actuators are essential devices that exert force and do work. The contraction of an actuator (how much it can shorten) is an important property that strongly influences its applications, especially in engineering and robotics. While high contractions have been achieved by thermally- or fluidically-driven technologies, electrically-driven actuators typically cannot contract by more than 50%. Recently developed electro-ribbon actuators are simple, low cost, scalable electroactive devices powered by dielectrophoretic liquid zipping (DLZ) that exhibit high efficiency (~70%), high power equivalent to mammalian muscle (~100 W/kg), contractions exceeding 99%. We characterise the electro-ribbon actuator and explore contraction variation with voltage and load. We describe the unique self-locking behaviour of the electro-ribbon actuator which could allow for low-power-consumption solenoids and valves. Finally, we show the interdependence of constituent material properties and the important role that material choice plays in maximising performance.
Modeling Electromagnetic Devices Using Reluctance Scaled Magnetic Circuit Equations

Magnetic-circuit-derived models are commonly used to simulate electromagnetic devices due to their simplicity and relative accuracy in basic applications. Lumped parameter models (LPMs) based on magnetic circuit parameters offer computational speed advantages relative to other numerical method-based modeling techniques. However, situations can arise, whereby the accuracy of these LPMs is compromised even when non-linear effects are accounted for. In this research, an analytical technique is described whereby a factor that can reduce the magnetic circuit-based models’ accuracy is identified and corrected using a technique referred to as reluctance scaling. This technique allows electromagnetic device operational models to be derived using less computationally complex numerical methods than commonly used alternatives, while achieving similar levels of accuracy.
A Waveguide-Fed Transmitting Antenna for High Efficiency Microwave Power Transmission

In this paper, a transmitting antenna fed by rectangular waveguide are intend for the operation at relatively high power levels in practical use, such as mid-range wireless charging. The proposed transmitter is composed of square patches on a 1.5mm thick dielectric substrate. The square patches are connected by main feeder and excited by rectangular feeding patches in the middle of the antenna. Single antenna is fed by a rectangular waveguide and sized for 370×480 mm (14.6×18.9 inch). The transmitting antenna is investigated by simulation and experimentally. The simulated results indicated that this antenna achieves 30dB gain and 150MHz bandwidth from 5.74 to 5.89 GHz. Experimental results are in accordance with those of the simulation results. The electromagnetic field distributions of antenna radiation direction have been characterized both in horizontal and vertical sections. Further analysis of the power density of the improved transmitting antenna is given.
An Actor-Critic Approach for Legible Robot Motion Planner

In human-robot collaboration, it is crucial for the robot to make its intentions clear and predictable to the human partners. Inspired by the mutual learning and adaptation of human partners, we suggest an actor-critic approach for a legible robot motion planner. This approach includes two neural networks and a legibility evaluator: 1) A policy network based on deep reinforcement learning (DRL); 2) A Recurrent Neural Networks (RNNs) based sequence to sequence (Seq2Seq) model as a motion predictor; 3) A legibility evaluator that maps motion to legible reward. Through a series of human-subject experiments, we demonstrate that with a simple handicraft function and no real-human data, our method lead to improved collaborative performance against a baseline method and a non-prediction method.
Voxel-based General Voronoi Diagram for Complex Data with Application on Motion Planning

One major challenge in Assembly Sequence Planning (ASP) for complex real-world CAD-scenarios is to find appropriate disassembly paths for all assembled parts. Such a path places demands on its length and clearance. In the past, it became apparent that planning the disassembly path based on the (approximate) General Voronoi Diagram (GVD) is a good approach to achieve these requirements. But for complex real-world data, every known solution for computing the GVD is either too slow or very memory consuming, even if only approximating the GVD.We present a new approach for computing the approximate GVD and demonstrate its practicability using a representative vehicle data set. We can calculate an approximation of the GVD within minutes and meet the accuracy requirement of some few millimeters for the subsequent path planning. This is achieved by voxelizing the surface with a common error-bounded GPU render approach. We then use an error-bounded wavefront propagation technique and combine it with a novel hash table-based data structure, the so-called Voronoi Voxel History (VVH). On top of the GVD, we present a novel approach for the creation of a General Voronoi Diagram Graph (GVDG) that leads to an extensive roadmap. For the later motion planning task this roadmap can be used to suggest appropriate disassembly paths.
Fixed-Time Synchronization of Delayed Fractional-Order Memristor-Based Fuzzy Cellular Neural Networks

In this article, we discussed the fixed-time synchronization of fractional-order memristor-based fuzzy cellular neural networks (FMFCNNs) with time-varying delays. Based on the differential inclusion theory and discontinuous state feedback control technique, some useful novel criteria of the fixed-time synchronization for FMFCNNs are derived by constructing appropriate Lyapunov function. The results in our paper improve and generalize current literature research results significantly. Finally, the effectiveness of our control schemes for the fixed-time synchronization is demonstrated through numerical simulations.
Load Balancing Algorithm for Real-Time Ray Tracing of Dynamic Scenes

In this article, we propose a load balancing algorithm that accelerates ray tracing effectively and simply manner. The algorithm was developed in a hybrid system consisting of a CPU and hardware dedicated to ray tracing. Tree-building is processed on the CPU, and rendering is executed by the ray tracing-dedicated hardware. Because these components operate independently of each other, the final performance in terms of frames per second (FPS) is determined based on the time spent on tree-building or rendering, whichever process takes longer. This characteristic of a hybrid system is reflected in the developed algorithm, which dynamically adjusts tree-build parameters at every frame, thereby minimizing the interval between tree-building and rendering times. These effects ultimately increase FPS performance. Experiments at a resolution of $1920\times1080$ involving various dynamic scenes indicated that FPS performance improved by an average of 75.3% when the proposed algorithm was used.
Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Model predictive control (MPC) has established itself as a promising control methodology in power electronics. This survey paper highlights the most relevant MPC techniques for power electronic systems. These can be classified into two major groups, namely, MPC without modulator, referred to as direct MPC, and MPC with a subsequent modulation stage, known as indirect MPC. Design choices, and parameters that affect the system performance, closed-loop stability, and controller robustness are discussed. Moreover, solvers, and control platforms that can be employed for the real-time implementation of MPC algorithms are presented. Finally, the MPC schemes in question are assessed, among others, in terms of design, and computational complexity, along with their performance, and applicability depending on the power electronic system at hand.
A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities

Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false-(positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery. The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent feature-extraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions. However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches. This research also explores various well-known databases using ”Breast Cancer” keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.
Energy Management for Medical Rescue Robot

This paper mainly centers on the energy management of the medical rescue mobile robot with different payloads in the uncertain road conditions. Efficient energy usage is a crucial issue because energy consumption will increase with the application and expansion of robotics. The power consumption of the robot system is affected by the task and its operating environment. This paper designs a six-wheel-drive medical rescue mobile robot using a hybrid system. Aiming at the wheel driving conditions of mobile robots, a power-adaptation control strategy is proposed, and its structure and driving form is introduced. Firstly, based on a power-adaptation control strategy, a nonlinear model predictive control (NMPC) algorithm is proposed to optimize the power adaptive control strategy. Non-linear model predictive control uses a non-linear model to represent the medical rescue robot model and external characteristic constraints. Secondly, the AVL CRUISE module is used to build a dynamic model of a medical rescue mobile robot, and it is jointly simulated with MATLAB/Simulink to simulate the energy consumption of a medical rescue robot under Urban Driving Cycle (UDC) standard operating conditions. Finally, the NMPC method is used to solve the system and compared with the power-adaptation control strategy. The simulation results show that compared with the power adaptive control strategy, the fuel consumption of combustion engine of the NMPC is improved by 26.4%.
LabVIEW based Smart Pillow

Day by day mental stress is increasing at an alarming rate. As a result frequent occurrence of health issues like insomnia can be seen. In this approach, we have focused on sleeping disorder, insomnia and provided a solution to overcome this disorder by levelling the mental stress by means of various human body parameters such as Electroencephalography (EEG) Signal, Heart Rate, GSR and Electrocardiography (ECG) Signal. The main aims of the work is to design a Pillow based on the level of the stress recognized with the help of two human body parameters which are ECG and GSR. The concept of geomagnetic field which is considered to have an effect on our sleep cycles is being implemented. Smart Pillow is designed to create electromagnetic field which is equivalent to geomagnetic field and helps an individuals to attain a peaceful and deep sleep.
Modeling of Shape Memory Alloy Actuated System Using a Modified Rate-dependent Prandtl-Ishlinskii Hysteresis Model

Shape memory alloys (SMAs) are widely used as actuators to construct various robotics systems, such as, minimally invasive surgery robots, micro in-pipe robots and biomimetic micro robots. However, the nonlinear hysteresis behaviors in SMA actuators seriously degrade the control accuracy and desired performances of SMA-actuated control systems. In this paper, an experimental platform based on SMA actuator is conducted. To make the platform capable of achieving precise control, the complex hysteresis characteristics of the SMA actuators are studied. The SMA actuator in this study exhibits complicated nonlinear properties with saturation nonlinearity and asymmetric hysteresis influenced by both the duty cycle of the input voltage and the load mass, which are different from the classical rate-dependent hysteresis behaviors. To explore the nonlinear properties of the SMA actuated system, open-loop experiments under different duty cycles and loads are testified, and a modified Rate-dependent Prandtl-Ishlinskii (RDPI) model is developed to enhance modeling abilities. Fitting effects of the modified RDPI model is validated by the comparison between the modeling output and experimental data.
Biophysical Parameters During Radiofrequency Catheter Ablation of ScarMediated Ventricular Tachycardia

Ablation Effectiveness and Biophysical Parameters. Background: There is a paucity of data on biophysical parameters during radiofrequency ablation of scar-mediated ventricular tachycardia (VT). Methods and Results: Data were collected from consecutive patients undergoing VT ablation with openirrigation. Complete data were available for 372 lesions in 21 patients. The frequency of biophysical parameter changes were: >10 reduction (80%), bipolar EGM reduction (69%), while loss of capture was uncommon (32%). Unipolar injury current was seen in 72% of radiofrequency applications. Both EGM reduction and impedance drop were seen in 57% and a change in all 3 parameters was seen in only 20% of lesions. Late potentials were eliminated in 33%, reduced/modified in 56%, and remained after ablation in 11%. Epicardial lesions exhibited an impedance drop (90% vs. 76%, P = 0.002) and loss of capture (46% vs. 27%, P < 0.001) more frequently than endocardial lesions. Lesions delivered manually exhibited a >10 impedance drop (83% vs. 71%, P = 0.02) and an EGM reduction (71% vs. 40%, P < 0.001) more frequently than lesions applied using magnetic navigation, although loss of capture, elimination of LPs, and a change in all 3 parameters were similarly observed. Conclusions: VT ablation is inefficient as the majority of radiofrequency lesions do not achieve more than one targeted biophysical parameter. Only one-third of RF applications targeted at LPs result in complete elimination. Epicardial ablation within scar may be more effective than endocardial lesions, and lesions applied manually may be more effective than lesions applied using magnetic navigation. New technologies directed at identifying and optimizing ablation effectiveness in scar are clinically warranted. (J Cardiovasc Electrophysiol, Vol. 25, pp. 1165-1173, November 2014)
Impact of luminal and systemic endotoxin exposure on gut function, immune response and performance of chickens

The gastrointestinal tract is a large reservoir of both Gram positive and negative bacteria, of which the Gram negative bacteria act as a source of lipopolysaccharide (LPS). Luminal LPS, commonly known as endotoxin, can enter systemic circulation via paracellular and transcellular permeation. It has been shown that endotoxin exposure can increase intestinal paracellular permeability and alter intestinal structure and function, resulting in impaired absorption and utilisation of nutrients with negative impact on both poultry heath and growth. Acute exposure to large amounts of endotoxin suppresses feed intake in chickens and activation of the innate immune system. Substantial evidence suggests that endotoxin is recognised via toll-like receptor (TLR)-4, located on the surface of immune cells, which activates kinases to enhance the transcription of pro-inflammatory cytokines such as tumour necrosis factor alpha (TNF-α) and interleukin (IL)-1 and 6 which mediate the inhibitory effect of endotoxin on the intestinal nutrient absorption. Inflammation can divert energy and nutrients away from growth supp and into supporting the immune system responses, leading to growth suppression and lowered feed efficiency. In addition, sustained exposure to endotoxin may elicit tolerance, which can make animals refractory against secondary Gram-negative infections. In this review, the effects of endotoxin on intestinal barrier functions and the subsequent negative impacts on the production of chickens are summarised. Overall, this review combines the current knowledge of the chicken's intestinal responses to Gram negative bacterial infection, illustrating the importance of gut health in defence against bacterial infections and improving poultry health, growth and welfare.