Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of the Varying Lateral Clearances on the Pressure Ripple of Internal Gear Pumps in Biped Robots

- Internal gear pumps (IGPs) are commonly applied in biped robots’ hydraulic actuation systems serving as the power unit. The pump’s outlet pressure ripple does harm to accurate motion controls and is usually lowered by adopting an accumulator. This paper investigates the effects of the varying lateral clearances between the gear and the floating plate on the pressure ripple. It aims to provide advices on manufacturing the gears to lower the pump’s outlet pressure ripple so that the accumulator could be smaller, which is of great importance to weight and space saving in biped robots’ design. A mathematical model was established for evaluating the outlet pressure considering the lateral clearances’ varying phenomenon. Moreover, the outlet pressure was measured by high-frequency pressure sensors for verifying the effects of the varying lateral clearances. The simulated and experimental results suggest that the outlet pressure concerning varying lateral clearances has small ripples characterized by high frequency and big ripples characterized by low frequency. Additionally, the pressure ripple, especially the big ripple, can be lowered by well manufacturing the gear pair’s lateral sides, ensuring the consistency of the tooth width of the gear.
Utilization of the V2X Radio Channels Temporal Stationarity for Communication and Radar Sensing

In this paper, we discuss the possibilities of utilizing the stationarity of the vehicle to everything (V2X) radio channel for applications in the field of communication as well as passive radar. The stationarity can be interpreted as a measure of how severely the radio channel changes. We compute it in the temporal domain using the generalized local scattering function (GLSF) and its collinearity. Knowing about the stationarity can enable us to adapt communication and passive radar systems to make them more efficient and reliable at the same time.
Real-Time Drift-Free Path Recording and Replaying Based On Virtual Image Matching For MAVs

This paper presents an efficient method to achieve real-time drift-free path replaying for Micro Aerial Vehicle (MAV). The drift-free path replaying method is proposed for precise autonomous path following in GPS-denied environments. In this paper, we present solutions on how to effectively select recording images during flying and drive the MAV to fly automatically to target position. During the path replaying, a strategy named triggered virtual image matching is used for reducing the computational cost and eliminating the drift. Time of image selection is reduced to negligible due to above strategy. Moreover, we use optical flow method for features matching between current image and the virtual image which is precise and faster. Experimental results show that our approach can achieve autonomous returning and precise path following in indoor scene and outdoor scene.
Distributed Acoustic Source Tracking in Noisy and Reverberant Environments With Distributed Microphone Networks

In this paper, an improved distributed unscented Kalman particle filter (DUKPF) is proposed for the problem of tracking a single moving acoustic source in noisy and reverberant environments with distributed microphone networks. The conventional DUKPF employs the unscented Kalman filter (UKF) for its proposal of particle sampling, whereas the UKF incorporates one single observation from a certain localization function, which is vulnerable to noise or reverberation. To alleviate this problem, multiple observations are extracted from the localization function at each node and incorporated into the state update of the UKF via the probability data association (PDA) technique, yielding the PDA-UKF. Next, employing the PDA-UKF for the proposal of particle sampling, the improved DUKPF is further developed. Finally, the improved DUKPF is adapted for the acoustic source tracking problem, and a distributed acoustic source tracking method is presented. Simulation results reveal that the improved DUKPF achieved better tracking performance than the conventional DUKPF in different noisy and reverberant conditions.
Robust Identification of Visual Markers Under Boundary Occlusion Condition*

Visual markers are widely used in indoor marker-based positioning systems to achieve higher speed and accurate positioning performance. However, the classic mark identification methods have certain limitations when encountering complex conditions. Especially, if the marker’s boundary is blocked, it is almost impossible to identify the marker. In this paper, we redefine the identification task to a classification task based on CNN method. We also do some image transformations to create the dataset for our task. We train our dataset by transfer learning based on Google’s Inception-V3 CNN model. The experimental results show that the classification method can handle the boundary occlusion problem well, which is also proved to be useful for other complex conditions.
Asymptotic Stabilization of USVs With Actuator Dead-Zones and Yaw Constraints Based on Fixed-Time Disturbance Observer

This paper investigates the problem of asymptotic stabilization of underactuated surface vehicles (USVs) with actuator dead-zones and yaw constraints. By transforming the system model into two subsystems, a backstepping control method based on a Barrier Lyapunov Function (BLF) is introduced to stabilize the transformed system and prevent the USV from reaching the boundaries of yaw angle and velocity constraints. A fixed-time disturbance observer is involved in the USV controller to deal with actuator dead-zones and disturbances. The method is extended to cover the case in which the yaw constraints are time-varying and the initial states exceed the constraint regions. Numerical simulations have shown the effectiveness of the presented method.
Full-Drive Decoupled Bionic Finger: Structure and Experimental Trials*

This paper presents the design and pilot experiment of a novel full-drive decoupled bionic finger with four degrees of freedom (DOFs). Each joint of the proposed finger can operate independently, without interference from its company. Three pneumatic artificial muscles and a steering engine serve as the drivers for all the four joints, which ensures their independent motions. More specifically, the pneumatic artificial muscles drive the distal interphalangeal (DIP), middle interphalangeal (MIP) and proximal interphalangeal (PIP) joints, who can realize flexion. The steering engine drives the metacarpophalangeal (MP) joint, which performs the adduction and abduction motion. Tendons transfer power from the pneumatic artificial muscles and drive the front three joints with compliance. Different from the traditional tendon drive systems, we proposed a new N type tendon drive scheme, which enables the decoupling between the joints with just one single tendon driving single joint. The MP joint is driven separately by the steering engine and has no influence from other joints. With the characteristics of full-drive and decoupling, the bionic finger has less demand for sophisticated mathematical model and control algorithm, and can achieve precise position and orientation through relatively simple controller. Finally, the motion and grasping experiments demonstrated the full-drive and decoupling features, which implies the possibility for future application in dexterous hand development and practice.
Internet-of-Things Sensors Wirelessly Powered by Electrically Small Huygens Dipole Rectenna

Internet-of-Things (IoT) sensors are expected to be ubiquitous in a future smart and sustainable society. Due to the exponential growth of device numbers, powering IoT devices by far field wireless power transfer (WPT) is becoming a necessary trend. This paper introduces two IoT sensors (light and temperature) that are wirelessly powered by a highly compact and efficient electrically small Huygens dipole rectenna. The entire system seamlessly integrates two subsystems. One is the metamaterial-inspired electrically small Huygens antenna that organically combines capacitively loaded loop (CLL) and Egyptian axe dipole (EAD) radiators together. The other is the sensor-augmented rectifier in which the output DC voltage varies as a function of the sensor impedance. The developed wirelessly powered systems can sense light or temperature levels and, when attached to an alarm, can send a warning signal if a threshold value is exceeded. Their prototypes were fabricated and tested. The measured results agree very well with their simulated values. The prototypes are highly compact and electrically small (ka = 0.73). They represent ideal candidates for many emerging IoT wireless sensor applications.
Unsupervised Segmentation Of Urban 3D Point Cloud Based On Lidar-image

As one of the most vital fields in 3D scene understanding, segmentation of lidar point cloud has verity of applications such as unmanned vehicle. In order to deal with the problem of limited local computing resources, this paper proposes a two-step unsupervised learning-free method to segment sparse 3D point cloud with low computational demands. The 3D point cloud data is projected into a lidar-specific 2D coordinate system called lidar-image. For unmanned vehicles, road is a special kind of object, so the algorithm divides road firstly by using improved lidar-histogram. The algorithm further examines the geometric connection between adjacent points in 3D space, thus dividing non-road points into independent subsets. Experiments on KITTI dataset validate the proposed algorithm outperforms similar methods.
Automatic Hair Segmentation in Complex Background

Fine hair area segmentation, which allows the individual processing of a person’s hair, has important applications in digital entertainment simulation. This paper proposes a method to automatically and precisely extract hair contour under complex background. Our method divides hair semantic segmentation into two phases. In the first stage, an improved BiseNet network was used to segment the rough hair area automatically. In the second stage, we generated corresponding trimaps through the output of the previous stage and input them into a small convolutional network together with the original input to refine the rough segmentation and get more accurate edges. Applying this method to a large number of examples, the experiment results show that this method can not only automatically extract hair region effectively, but also have good effect on the processing of hair edge under complex background.
Challenges in Statistical Analysis: Yesterday, Today, and Tomorrow: (Invited Paper)

Verification of custom circuit designs for parametric variation defects has become a must-have procedure for today's nanometer designs. Statistical analysis of circuits poses a challenge because the simulation effort is often high, and the accuracy of some statistical analysis methods is difficult to assess. This paper gives an overview of various approaches to address statistical parametric analysis with respect to their efficiency and reliability.
A Monocular Visual Odometry Combining Edge Enhance with Deep Learning

Deep learning-based monocular visual odometry (VO) methods have recently shown highly promising results. However, most of these methods do not produce reliable results because of the insufficient features when the environment is low-textured and ignore the importance of features of images. This paper proposes a monocular VO method with edge feature extraction and deep Recurrent-Convolutional Neural Network (RCNN). This solution embedded traditional geometry algorithm in deep learning-based VO, in order to enhance the influence of edge feature information in images. Our method can have good performance in low-textured environments.
Homogenization Method Based on Cauer Circuit via Unit Cell Approach

This article proposes a novel homogenization method based on the unit cell approach which provides the continued fraction and, equivalently, the Cauer circuit representation of the complex permeability of fine structure materials. The proposed method makes it possible to perform the homogenization analysis in time domain. It is shown that the proposed method provides a more accurate resistance factor in comparison to the Dowell method and other classical methods.
A Reconfigurable Balanced Filtering Antenna with High Common-Mode Suppression

A reconfigurable planar balanced quasi-Yagi antenna integrated with a bandpass filtering response is presented in this paper. The proposed antenna consists of a balanced stepped-impedance microstrip-slotline transition structure, a driver dipole, a pin diode, a parasitic strip and a bandpass filtering unit. A differential-mode (DM) assband with a sharp filtering performance is realized by introducing a microstrip stub-loaded resonator (SLR). By pin diode over the open-stub of SLR, the effective electrical length of the antenna can be changed, leading to an electrically tunable operating bandwidth. The antenna maintains radiation performance similar to that of the conventional quasi-Yagi antenna. Meanwhile, there are two radiation nulls on both sides of the passband to enhance the selectivity greatly. For the common-mode (CM) signals, the microstrip-slotline transition structure can achieve a good wideband CM suppression without affecting the DM ones, thus simplify the design procedure.
AM2FNet: Attention-based Multiscale & Multi-modality Fused Network

How to infer the 3D geometries and 3D semantic labels for each unit in a scene, including visible surfaces and occluded parts, is an important issue in many robotic fields. In recent years, there exists some studies on segmenting and completing 3D scene from 2D information. Most of them complete a scene from a single depth image. Compared with the depth image, the RGB image contains more color features and contour features, which can help to semantic labeling. However, how to design an effective strategy to fuse RGB and depth features is a challenge issue. Our paper presents an attention-based multi-scale & multi-modality fused network, called AM2FNet, which includes six modules: depth feature module, color feature module, 3D integration module for multi-modality feature fusion, 3D refinement module for multi-scale feature fusion, attention modules, semantic mapping module. The integration module and the refinement module work together in 3D space to fuse color and depth features at low-level, middle-level and high-level in a top-down fashion. In addition, we use an attention module to efficiently bias input-related features. Experimental results show that our proposed network can generate higher-quality semantic scene completion (SSC) results and scene completion (SC) results, and outperforms the state-of-the-art methods on real NYU and synthetic NYUCAD datasets. Meanwhilethe contribions of single modules have been illustrated.
A Luneburg Lens antenna Composed of Gradient Index Metamaterials based on 3D printing

A Ku-band Luneburg lens antenna composed of gradient index metamaterials is presented made by 3D printing. The focusing condition of the lens, requiring a gradient refractive index, is achieved by using the Photonic crystal structure. A dielectric column diamond structure is chosen as the Photonic crystal unit cell since its permeability and permittivity can keep constants when the unit cell size is much smaller than the working wavelength. A standard retrieval procedure is used to determine the relationship between the dielectric column radius of the unit cells and the refraction index. Therefore, a new kind of Luneburg lens composed of the diamond structure dielectric column arrays is constructed. Simulation results have shown that the proposed Luneburg lens, which operating in Ku-band, has an excellent focusing ability.
Humanoid Robot Locomotion Control based on Perceptive Model

A humanoid locomotion control method with human-like perceptive model is described in this paper. Traditionally the locomotion problem of biped robot is divided into temporal periodic gait generation and real-time stabilizing control. However due to unmeasurable accumulated errors from simplified dynamic model and other uncertain disturbances, the spatial and temporal deviation from the motion plans askes for a new perceptive locomotion planning and control system which is decoupled with time, rather than adhering to a rigid time schedule regardless of current dynamic balance state. This method also gives the right of interpretation of non-time motion plan of gait to the motion control layer based on a designed perceptive reference, which is directly ensure dynamic balancing and high response rate to the errors. The simulation and experiment on a full-size humanoid robot show the success of the method.
A Low-Complex Compressive Sensing Based Thermal Sensor Placement for Multicore Systems

As the complexity of the multicore system grows, the large workload diversity results in serious thermal problems. In practical way, the number of placed thermal sensors is usually limited due to the manufacturing cost. In recent years, the compressive sensing (CS) theory is proven as an efficient way to reconstruct the original signals by using fewer sampling data. However, due to the high computational complexity during signal reconstruction, the compressive sensing theory is not appropriate to apply to the real-time temperature monitoring in the current multicore systems. In this paper, we first propose a grid-based sensor placement approach to allocate the number-limited thermal sensors on the multicore systems. On the other hand, we adopt an algorithmic transformation of Matrix Inversion Bypass (MIB) to reduce the computational complexity of the Orthogonal Matching Pursuit (OMP)-based signal reconstruction approach, which is a wide use signal reconstruction approach in the compressive sensing theory. The experimental results show that the proposed approach can reduce 52% average full-system temperature reconstruction error compared with the previous non-CS-based approaches. Besides, we can also reduce 22%-27% execution time compared with the current CS-based temperature reconstruction algorithm.
Ray-Launching Narrowband Analysis of UAV-to-Ground Propagation in Urban Environment

Unmanned Aerial Vehicles (UAV) enabled communications are emerging as a promising solution for the next-generation wireless systems, e.g. to extend coverage and capacity of 5G mobile radio networks. In this paper the narrowband properties of air-to-ground propagation in UAV-aided communication systems are investigated by means of fast ray launching simulations carried out in a dense urban reference environment.
Object-Aware Hybrid Map for Indoor Robot Visual Semantic Navigation*

In order to achieve an intuitive interaction and visual semantic navigation for the indoor robot, we propose a novel object-aware hybrid map. The existing map is usually a metric map, lacking semantics for interaction. We combine objects in the indoor environment with the metric map to constitute a hybrid map. The map consists of a 3D object semantic map and a 2D occupancy grid map, which transfers human commands to the grid map through object semantics, thereby enabling autonomous navigation for the robot. We utilize ORB-SLAM2 for continuous pose estimation and 3D mapping. 2D object detection in key-frames is conducted based on YOLO v3. The object point clouds in multiple perspectives are merged and a 3D bounding box of the object is estimated. These objects construct a 3D semantic map. Furthermore, we project a 3D point cloud map into a 2D plane in order to get an occupancy grid map. Finally, these two maps are combined forming an object-aware hybrid map. We conduct experiments in real environments in order to verify the feasibility and robustness of the hybrid map for robot semantic navigation.
A 2D Pneumatic Soft Robot with Suckers for Locomotion

Soft robots have a great application potential in agriculture, military and disaster search and rescue and a flexible locomotivity is required for soft robots. This paper presents the design and fabrication procedure of a soft pneumatic robot that can move in different directions with various gaits and is called 2D-SSR. The robot is composed of two soft actuators, which perform pure 2D bending, and three rigid connectors with three suckers. Based on specific sequences of seven air channels and the derived geometric kinematics, various gaits including moving forward and turning were simulated. The critical conditions of periodic locomotion were analyzed and the relationship between air pressure and bending angle was acquired in some tests. In order to verify the feasibility of the development of the new soft robot and gait simulations, the experiments of moving forward and turning were executed on both the single-module robot and double-module robot.
Performance of Fiber Reinforced Composite Strengthened Concrete Interface

The finite element method was adopted for investigation, the effects on the performance of the fiber reinforced polymer composite materials strengthened concrete structure. The finite element formula was conducted in terms of Mindlin first order shear deformational theory. And then the effects of the bond layer length, the width and numbers of bond layers adhesive fiber reinforced polymer on the the interfacial performance were discussed. Numerical results show that bond layer parameters of FRP strengthened concrete interface can affect adhesive performance.
A High-Efficiency Reconfigurable Complimentary Electric-LC (CELC) Metamaterial Element

A high-efficiency reconfigurable metamaterial element for coding holographic antennas, consisting of a complimentary electric LC (CELC) resonator, is proposed in this paper. The low efficiency problem of a traditional reconfigurable CELC element is demonstrated in terms of the power dissipation, before the structure introduction of the proposed element. Simulation showed that the proposed element has a lower ohmic loss when it is switched on, at least 20 times less than that of traditional one, thus increasing the efficiency.
Hand Gesture Recognition Based on Deep Neural Network and sEMG Signal

The physiological electrical signal of human body is the direct response from human behavior intention. By analyzing and interpreting the physiological electrical signal of human body, human subject consciousness can be effectively recognized. In order to enable people with physical disabilities to effectively control peripherals according to their own intentions, this research is based on the classification and recognition of sEMG hand movements. The position of sEMG electrode on the muscle is determined. sEMG signals of 8 kinds of common hand movements in daily life are successfully collected and data collection of sEMG signals is completed. The VGG16 is used to construct the convolutional neural network structure suitable for sEMG gesture recognition. At the same time, a highly biomimetic robotic hand is built according to the bone structure of human hand to complete 8 gestures as an actuator, and STM32 microprocessor is used to control it. Through the comparison and analysis with two VGG16 networks with different convolution kernel sizes, the results show that the VGG16 network structure with convolution kernel size of 3×3 adopted by this method has a high accuracy for the recognition of 8 kinds of hand movements.