Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Fiber Sensors Based on Core-Offset Structure: A Review

This review focuses on the preparation methods and wide applications of optic fiber sensors based on core-offset structure. Firstly, the classification and preparation methods of core-offset structures are introduced. Then, the applications of different types of sensors based on the core-offset structure are investigated, including the measurement of physical parameters such as temperature, strain, refractive index (RI), bending, magnetic field by Mach-Zehnder interferometer (MZI), cascaded sensor, long period fiber grating (LPFG), fiber Bragg grating (FBG), Fabry-Perot interferometer (FPI) and Surface plasmon resonance (SPR). Compared with the traditional fiber sensor, core-offset structure sensor with a non-axisymmetric characteristic has excellent sensing characteristics, especially in the recognition of bending direction and high RI sensitivity. Furthermore, the design scheme, working principle and sensing characteristics of different core-offset sensors are also briefly described. Finally, the existing challenges and development direction of optic fiber sensors based on core-offset structure is discussed and prospected.
Robot Interaction Studio: A Platform for Unsupervised HRI

Robots hold great potential for supporting exercise and physical therapy, but such systems are often cumbersome to set up and require expert supervision. We aim to solve these concerns by combining Captury Live, a real-time markerless motion-capture system, with a Rethink Robotics Baxter Research Robot to create the Robot Interaction Studio. We evaluated this platform for unsupervised human-robot interaction (HRI) through a 75-minute-long user study with seven adults who were given minimal instructions and no feedback about their actions. The robot used sounds, facial expressions, facial colors, head motions, and arm motions to sequentially present three categories of cues in randomized order while constantly rotating its face screen to look at the user. Analysis of the captured user motions shows that the cue type significantly affected the distance subjects traveled and the amount of time they spent within the robot’s reachable workspace, in alignment with the design of the cues. Heat map visualizations of the recorded user hand positions confirm that users tended to mimic the robot’s arm poses. Despite some initial frustration, taking part in this study did not significantly change user opinions of the robot. We reflect on the advantages of the proposed approach to unsupervised HRI as well as the limitations and possible future extensions of our system.
Gyrubot: nonanthropomorphic stabilization for a biped

Demands on leg degrees of freedom and control precision for bipedal robotics are steadily increasing, especially for the tasks involving walking on a rough terrain. In this paper we present an alternative, as well as a working proof-of-concept. Meet gyrubot: a 5-link almost planar bipedal robot with a torso complemented by a nonanthropomorphic stabilization system, capable of blindly walking through uneven areas. Despite being almost planar, the robot does not need any support in the frontal plane! This paper describes the mechanical design and the architecture of the controllers. We also provide the experimental evidence of the ability of gyrubot to navigate across non-flat terrains.
Interior Void Classification in Liquid Metal Using Multi-Frequency Magnetic Induction Tomography With a Machine Learning Approach

Identification of gas bubble, void detection and porosity estimation are important factors in many liquid metal processes. In steel casting, the importance of flow condition and phase distribution in crucial parts, such as submerged entry nozzle (SEN) and mould raises the needs to observe the phenomena. Cross-section of flow shapes can be visualised using the magnetic induction tomography (MIT) technique. However, the inversion procedure in the image reconstruction has either limited resolution or involving post-processing stages degrading its real-time capability. Additionally, when quantifying the void fraction or porosity, the image may not be required. This work proposes an interior void classifier based on multi-frequency mutual induction measurements with eutectic alloy GaInSn as a cold liquid metal model contained in a 3D printed plastic miniature of an SEN. The sensors consist of eight coils arranged in a circle encapsulating the column, providing combinatorial detection on conductive surface and depth. The datasets are induced voltage collections of several non-metallic inclusions (NMI) patterns in liquid metal static test and used to train a machine learning model. The model architectures are a fully connected neural network (FCNN) for 1D; and a convolutional neural network (CNN) for 2D data. The classifier using 1D data has been trained to provide 95% accuracy on this dataset. On the other hand, CNN classification using multi-dimensional data produces 96% of test accuracy. Refined with representative flow scenarios, the trained model could be deployed for an intelligent online control system of the liquid metal process.
Conquering Textureless with RF-referenced Monocular Vision for MAV State Estimation

The versatile nature of agile micro aerial vehicles (MAVs) poses fundamental challenges to the design of robust state estimation in various complex environments. Achieving high-quality performance in textureless scenes is one of the missing pieces in the puzzle. Previously proposed solutions either seek a remedy with visual loop closure or leverage RF localizability with inferior accuracy. None of them support accurate MAV state estimation in textureless scenes. This paper presents RFSift, a new state estimator that conquers the textureless challenge with RF-referenced monocular vision, achieving centimeter-level accuracy in textureless scenes. Our key observation is that RF and visual measurements are tied up with pose constraints. Mapping RF to feature quality and sift well-matched ones significantly improves accuracy. RFSift consists of 1) an RF-sifting algorithm that maps 3D UWB measurements to 2D visual features for sifting the best features; 2) an RF-visual-inertial sensor fusion algorithm that enables robust state estimation by leveraging multiple sensors with complementary advantages. We implement the prototype with off-the-shelf products and conduct large-scale experiments. The results demonstrate that RFSift is robust in textureless scenes, 10x more accurate than the state-of-the-art monocular vision system. The code of RFSift is available at https://github.com/weisgroup/RFSift.
FlowDriveNet: An End-to-End Network for Learning Driving Policies from Image Optical Flow and LiDAR Point Flow

Learning driving policies using an end-to-end network has been proved a promising solution for autonomous driving. Due to the lack of a benchmark driver behavior dataset that contains both the visual and the LiDAR data, existing works solely focus on learning driving from visual sensors. Besides, most works are limited to predict steering angle yet neglect the more challenging vehicle speed control problem. In this paper, we propose a novel end-to-end network, FlowDriveNet, which takes advantages of sequential visual data and LiDAR data jointly to predict steering angle and vehicle speed. The main challenges of this problem are how to efficiently extract driving-related information from images and point clouds, and how to fuse them effectively. To tackle these challenges, we propose a concept of point flow and declare that image optical flow and LiDAR point flow are significant motion cues for driving policy learning. Specifically, we first create an enhanced dataset that consists of images, point clouds and corresponding human driver behaviors. Then, in FlowDriveNet, a deep but efficient visual feature extraction module and a point feature extraction module are utilized to extract spatial features from optical flow and point flow, respectively. Additionally, a novel temporal fusion and prediction module is designed to fuse temporal information from the extracted spatial feature sequences and predict vehicle driving commands. Finally, a series of ablation experiments verify the importance of optical flow and point flow and comparison experiments show that our flow-based method outperforms the existing image-based approaches on the task of driving policy learning.
Design and Analysis of Complex Fractional-order PID Controllers

This paper is focused on the design of a complex fractional-order differentiator and integrator for the order $\alpha+j\beta$. Furthermore, for these differentiators and integrators' practical realization, an approximation using a curve fitting-based iterative algorithm is proposed. The design of various complex fractional PID controllers using the proposed differentiator and integrator has been presented. The simulation study results show that the designed controllers with complex orders have produced a steady behavior equivalent to fractional controllers' behavior. The step response characteristics on the process plant also show that the designed controllers performed well in controlling the process.
Camera Relocalization using Deep Point Cloud Generation and Hand-crafted Feature Refinement

Visual localization plays an indispensable role in robotics. Both learning and hand-crafted feature based methods for relocalization process keep their effectiveness and weakness. However, current algorithms seldom consider these two kinds of features under one framework. In this paper, focusing on this task, we propose a novel relocalization framework for RGB or RGB-D data source, which is composed of coarse localization process by learning features and pose refinement by hand-crafted features. In particular, coarse stage contains deep point cloud generation and registration. In this stage, instead of regressing camera pose directly, the paper novelly designs a neural network called PGNet to construct sparse point cloud with RGB or RGB-D as inputs. Further more, by means of training set, hand-crafted feature space is established. Based on the obtained camera pose in coarse stage, accurate point-to-point correspondences are set up through searching the space. Then accurate camera pose is obtained by applying RANSAC to correspondences or solving PnP. Finally, experiments on both outdoor and indoor benchmark datasets demonstrate state-of-the-art performance over other existing methods.
Beyond the "Dynamic range" approach In noise evaluation for Terahertz Time domain spectrometers

THz–TDS is used for a myriad of purposes. Still, today, the usual way to compare different systems in terms of signal to noise ratio is to look at the dynamic range. We propose here a more reliable method based on statistics and will show results on two systems (Toptica and Menlo).
Collaborative Learning of Multiple-Discontinuous-Image Saliency Prediction for Drone Exploration

Most of the existing saliency prediction research focuses on either single images or videos (or more precisely multiple images in sequence). However, to apply saliency prediction to drone exploration that has to consider multiple images from different view angles or localizations to determine the direction to explore, saliency prediction over multiple discontinuous images is required. In this paper, we propose a deep relative saliency model (MS-Net) for such an application. MS-Net starts with a single-image saliency feature extraction network for each image separately and then integrate these images by using a GCN-based mechanism called multi-image saliency fusion that learns relative saliency information among all the images. Finally, it predicts the saliency of each image by considering the relative information. Because there are no existing saliency prediction datasets with such multiple discontinuous images, we randomly cropped a large number of sub-images from 360° images of the existing 360° image saliency datasets to build our own dataset for both training and evaluation. Experimental results showed that the proposed MSNet considerably outperformed both single-image and video saliency prediction methods and could achieve comparative performance to that of 360° image saliency prediction even with only limited field-of-views, i.e., five sub-images, considered.
Anytime Fault-tolerant Adaptive Routing for Multi-Robot Teams

The Correlated Team Orienteering Problem (CTOP) is a routing problem where the objective is to determine a set of routes that maximizes the summation of collected rewards in the environment while respecting the vehicles’ budget. However, solutions to this problem usually consider static instances and may produce poor results in dynamic real-world scenarios. In this paper, we propose an approach to deal with the execution of missions planned as a CTOP instance, especially when vehicles of the team are prone to failure and may not complete their routes. The main contribution of this paper is a novel anytime heuristic that iteratively adapts the initial set of routes, allowing to increase the overall robustness of the mission and still collect the most profitable rewards. The methodology was thoroughly evaluated considering different scenarios and in all the cases was able to achieve comparable or better results in terms of reward than planning a new set of routes, however, spending considerably less time.
Tightly-Coupled Perception and Navigation of Heterogeneous Land-Air Robots in Complex Scenarios

In unstructured and unknown environments, heterogeneous robots must be able to perceive the environment, coordinate with each other and complete tasks collaboratively with onboard sensors. In this paper, a tightly-coupled perception and navigation framework is proposed for heterogeneous land-air robots, which forms a closed loop of perception-navigation for heterogeneous robots. The key novelty of this work is the proposing of a unified framework to formulate the cooperative mapping and navigation problem, as well as the derivation of high-level coordination strategy and low-level goal-oriented navigation within a fully integrated approach. To provide a comprehensive understanding of the environment, a flexible probabilistic map fusion algorithm is applied to merge local maps generated by hybrid robots. The proposed UAV-UGV hybrid system is validated in challenging experiments, proving its robustness and effectiveness in practical tasks.
Fully automatic brain tumor segmentation using DeepLabv3+ with variable loss functions

Glioma is found to be the deadliest and rapidly growing brain tumor in adults. The proposed pixel-level-based segmentation framework along with combined loss resolves the class imbalance issue. The proposed methodology is implemented on FLAIR MRI modalities from the training dataset from Brain Tumor Segmentation 2020 (BraTS 2020) challenge. Initially, the input dataset is pre-processed using numerous operations such as resizing, and normalization, etc. The pre-processed dataset is split into 90% training set, (80% training set and 10% validation), and 10% of the testing set. The training set is augmented to reduce the data overfitting issue. The proposed methodology is implemented with DeepLab v3+ with different baseline networks such as ResNet18, and MobileNetv2. Also, the pixel classification layer is replaced with different loss functions to efficiently segment the small size tumors. Further, the proposed methodology with combined loss obtained an average dice index of 0.92±0.5, mean IOU of 0.93, global accuracy of 99.67%, and F1-score of 0.94±0.2.
Design of Imaging System and Tomography Detection Method for Radial Tires Structure Under X-Ray Short-Scan Mode

X-ray computed tomography (CT) is a widely popular nondestructive testing (NDT) technology for engineering, which also plays an important role in the NDT testing for radial tires. The steel wires in radial tires are densely arranged and regularly distributed, which usually needs high-energy X-rays to penetrate completely. Due to the limitation of tire size and mechanical structure, it is sometimes impossible to scan full-360°. In this article, a scheme of alternating short-scan tomography for circumferential slices on one side of the tire is proposed, and a set of special X-ray NDT instrumentation for radial tires is developed. This solution solves the problem of transilluminating the entire tire in traditional CT scans and can effectively reduce the maximum penetration energy and radiation dose. Furthermore, the winding problem of high-voltage cable and oil cooling service pipeline in the process of rotation is well avoided. Then, a short-scan CT reconstruction method with the offset rotation center is deduced, which can effectively eliminate the image artifacts caused by the connection line between the rotating center of the turntable and the focus of the ray source that is not perpendicular to the imaging plane of the detector. Computer simulations and experimental results prove the correctness of the proposed compensation symmetry filling reconstruction algorithm. The proposed method can effectively improve the reconstruction accuracy and suppress the reconstruction artifacts. Based on this, the requirements of mechanical assembly precision can be relaxed, which has a certain engineering significance.
Real-time active detection of targets and path planning using UAVs

This article proposes a new method that enables Unmanned Aerial Vehicles (UAVs) to actively find targets and shoot photographs of them in an unknown environment, while successfully avoiding surrounding obstacles and planning optimize routes. Owing to the limited computing ability on the UAVs, we obtained the point cloud data of surrounding objects, and selected the best segmentation method of the point cloud to perform real-time semantic segmentation on the collected point cloud data. The point cloud data with semantic attributes were merged into voxels. We reconstruct the real-time distance and angle between the surface of obstacles and the surrounding obstacles through Euclidean Signed Distance Fields (ESDFs), and adjust the gimbal angle and focal length of UAVs and use the two-dimensional image recognition to shoot the photographs of the target precisely. Considering the increasing scale of UAVs power inspections, we can improve the efficiency of fine inspections of power transmission lines by using the method we proposed.
Benchmarking Real-Time Capabilities of ROS 2 and OROCOS for Robotics Applications

Numerous robotic and control applications have strict real-time requirements, which, when violated, result in reduced quality of service or, in case of safety critical applications, might even have catastrophic consequences. To ensure that certain real-time constraints are satisfied, roboticists have relied on real-time safe frameworks, environments and middleware. With the introduction of ROS 2, alongside kernel patches such as PREEMPT_RT, there is an abundance of solutions to pick from. This paper compares OROCOS and ROS 2 over PREEMPT_RT and vanilla Linux kernels in a variety of benchmarks and draws conclusions on their performance in real-time critical applications. The outcome of the benchmark shows comparable performances under normal conditions. However, when the system is under stress both frameworks suffer in different fashions. Furthermore, the results show an accumulating error which over time violates the real-time requirements in both frameworks. These findings are paramount in conducting real world application with real-time constraints.
Characterization of a Crossed Dipole Resonator Array using a Pulsed Free Space Two-Port Photonic VNA

We demonstrate a Photonic Vector Network Analyzer (PVNA) capable of measuring a complete set of scattering parameters within the frequency range of 0.2 – 2 THz. The PVNA shows superiority in terms of bandwidth compared to electrical VNAs and is also free from the errors introduced by standing waves, metal waveguides, misaligned flanges, and features less stringent requirements on rigorous and time-consuming calibration routines. As a proof of principle, we characterize a crossed dipole resonator array using the free space two-port PVNA.
Balance Control of a Novel Wheel-legged Robot: Design and Experiments

This paper presents a balance control technique for a novel wheel-legged robot. We first derive a dynamic model of the robot and then apply a linear feedback controller based on output regulation and linear quadratic regulator (LQR) methods to maintain the standing of the robot on the ground without moving backward and forward mightily. To take into account nonlinearities of the model and obtain a large domain of stability, a nonlinear controller based on the interconnection and damping assignment - passivity-based control (IDA-PBC) method is exploited to control the robot in more general scenarios. Physical experiments are performed with various control tasks. Experimental results demonstrate that the proposed linear output regulator can maintain the standing of the robot, while the proposed nonlinear controller can balance the robot under an initial starting angle far away from the equilibrium point, or under a changing robot height.
Early Detection of Lung Cancer Through Breath Analysis Using AdaBoost Ensemble Learning Method

This pilot study presents the application of the ensemble learning method, AdaBoost in the detection of early-stage lung cancers. To detect the presence and variations of volatile organic compound biomarkers in the expelled breath, an electronic nose system with metal oxide gas sensors is developed. The system is tested in ten lung cancer patients and fifteen healthy controls to differentiate the breath samples. The system attained an acceptable accuracy, sensitivity, and specificity of 76 %, 70 %, and 80 % respectively with an independent component analysis (ICA) dimensionality reduction technique. The system should be further studied with adequate number of early stage cancers to get a concluding remark about the performance of the system in the detection of early-stage lung cancers.
Efficient Online Calibration for Autonomous Vehicle’s Longitudinal Dynamical System: A Gaussian Model Approach

In this paper, we present an efficient online calibration system for longitudinal vehicle dynamics of driverless cars. Instead of modeling vehicle’s longitudinal dynamical system analytically, we employ a data-driven method to generate an "end-to-end" numerical model with a look-up table which saves vehicle’s velocity, control command, and acceleration. This reference table should be calibrated to account for variations of vehicle’s hardware status over time. To reduce the expensive labor in calibration process, we propose an effective algorithm to update this reference look-up table with a Gaussian model approach. We introduce a 2-D Gaussian distribution to model acceleration error between interpolated one from look-up table and actual one from vehicle sensors. We estimate model’s standard deviations with a "three-sigma rule" heuristic and calculate its height with a backtracking method such that monotonicity constraint between acceleration and control command is strictly satisfied in the updated table. The effectiveness of our proposed system is verified in realworld road tests with Lincoln MKZ.
THz-TDS Parameter Extraction via Machine Learning

Terahertz time domain spectroscopy is a powerful analytical technique for investigating a wide range of material samples, however calculating refractive index values is not a trivial process and can be computationally intensive. Here we investigate the use of machine learning to simplify and replace certain aspects of THz TDS data analysis. We train a neural net to convert THz TDS data into complex refractive index values by creating a large training set based on simulated data. The resultant neural net is computationally efficient and extracts TDS parameters with a high degree of accuracy. Further, we propose that by investigating the trained network we may gain new insights into THz parameter extraction procedures.