Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Sidewalk Navigation Method for Mobile Robots Based on Sparse Semantic Point Cloud

Last-mile delivery robots are usually required to navigate on the sidewalk through a fixed route. The current solutions heavily rely on the image-based perception and GPS localization to successfully complete delivery tasks. However, it is prone to fail and become unreliable when the robot runs in challenging conditions, such as operating in different illuminations, or under canopies of trees or buildings. To address these issues, this paper proposes a novel robust sidewalk navigation method for the last-mile delivery robots with an affordable sparse LiDAR, which consists of two main modules: Semantic Point Cloud Network (SegPCn) and Reactive Nav-igation Network (RNn), as shown in Fig. 1. More specifically, SegPCn takes the raw 3D point cloud as input and predicts the point-wise segmentation labels, presenting a robust perception capability even in the night. Then, the semantic point clouds are fed to RNn to generate an angular velocity to navigate the robot along the sidewalk, where the localization of the robot is not required. Moreover, an autolabeling mechanism is developed to reduce the labor involved in data preparation as well. And the LSTM neural network is explored to effectively leverage the historical context and derive correct decisions. Extensive experiments have been carried out to verify the efficacy of this method, and the results show that this method enables the robot to navigate on the sidewalk robustly during day and night. We open source the code and the data set on https://github.com/lukewenMX/Robust-Navigation-Method.
Multistage Configuration Systems Used to Streamline the Construction Value Chain

Several studies have described the application of mass customization practices in the architecture, engineering, and construction (AEC) industry to increase efficiency and productivity. However, there is limited literature on how configuration systems, which are widely used in engineer-to-order firms, can help cope with a fragmented AEC value chain. This paper reports an action research study that investigated the benefits of using configuration systems to automate single-family home construction processes. The findings showed that integrating a configuration system in the building project process could significantly streamline the information workflow by decreasing the specifications lead time and improving the system’s data.
Light attenuation through turbid slabs calculated by solutions of the Maxwell equations

We implemented a two-dimensional finite-difference time-domain (FDTD) method for the calculation of the scattering by turbid slabs containing cylindrical scatterers. We present validation results of the FDTD method used for the calculation of the scattering by an infinite dielectric cylinder. In particular the error caused by numerical dispersion due to an expansion of the simulation grid is discussed. Finally, an analytical solution of the scattering by an infinite cylinder has been used to analyze the error caused by the discrete near- to far-field transformation.
Characterization of MDR and ESBL-producing E. coli strains from healthy swine herds of north-eastern India

Fecal samples (225) from apparently healthy pigs were analysed for E. coli isolation. Multidrug-resistant (MDR) isolates (42) were identified from 225 E. coli isolates. Forty isolates exhibited extended-spectrum beta-lactamases (ESBL) phenotype harbouring single to multiple-sized plasmids (1 kb to â‰¥ 25 kb). Overall blaTEM (85.7%) was the commonest genotype, followed by blaCTX-M (61.9%), blaOXA (17%) and blaSHV was not recovered. Other antimicrobial resistant genes (ARGs) identified were sul2 (100%), strA (95.2%), tetA (92.9%), sul1 (76%), aadA (71.4%), tetB (50%), cat (42.9%) and aac(3)IV (26.2%). Plasmid-mediated quinolone resistance (PMQR) positive strains totalled to 59.5% for qnrB, 19.04% for qnrS but qnrA was not detected. Integron genes were detected either singly or combined: intI1 (76.2%), intI2 (26.2%). Conjugation experiment was successful for 4 ESBL producers. The present study detected higher carriage of ESBL producing E. coli in pigs, and highlights the dominance of resistance and integron genes circulating in MDR E. coli from swine of northeast India, posing a threat of horizontal gene transfer.
Compact Functional Test Generation for Memristive Deep Learning Implementations using Approximate Gradient Ranking

Deep learning has been applied in many fields such as autonomous driving, medical imaging, and sensor data processing to solve complex computational problems. Hardware implementation of deep learning applications can be accelerated with Memristive devices such as Resistive Random-Access Memory (ReRAM), Phase Change Memories (PCM), and Spin Transfer Torque-Magnetic RAM (STT-MRAM) via utilizing compute-in-memory (CiM) architectures. They offer many benefits compared to conventional charge-based memories including non-volatility, zero leakage power, and low power consumption. However, memristors suffer from various manufacturing and runtime defects and variations due to their immature fabrication process, inherent device properties, and external environmental factors such as temperature. Those faults can negatively impact the performance and reliability of the implemented deep learning applications. Therefore, detection of such faults before they catastrophically affect the accuracy of deep learning applications is very important. In this paper, we proposed a simple and non-invasive functional test generation method with a black-box approach that samples a small subset of the training data as the test vectors based on their approximate gradient ranking. The proposed method requires only up to 0.128% of training data stored in hardware as test vectors and achieves test coverage up to 100% on the Fashion-MNIST, CIFAR-10, and CIFAR-100 benchmark datasets.
A Consensus Clustering-based Label Propagation Method for Classification of Science & Technology Resources

With the rapid expansion of the science & technology services industry, abundant science and technology resources were produced which caused great difficulties for the management of science and technology service platform. Due to the high specialization of science & technology resources, the classification task is not easy. This study aims to propose an efficient classification method for science & technology resources classification such as papers, patents, etc. A science & technology text classification framework based on Word2Vec and consensus clustering-based label propagation algorithm was proposed in this paper. To verify the effectiveness of the method, a dataset of science & technology resources from a Chinese science and technology service platform was used for the demonstration. The study provides a new perspective on science & technology resource classification and the method can also serve many different business scenarios.
EMOTION DYSREGULATION AFFECTS FUNCTIONALITY IN MAJOR DEPRESSIVE DISORDER

Purpose: Mood disorders are recurrent chronic mental illnesses known to affect functionality. In this study, it was aimed to compare coping strategies and emotion regulation difficulties in depression and bipolar disorder and observe the effects of coping strategies and emotion regulation on functionality. Methods: 31 patients with bipolar disorder (BD), 29 with major depressive disorder (MDD), and 27 healthy controls (HC) were included in the study. Participants completed the Coping Strategies Inventory (COPE), Bipolar Disorder Functioning Questionnaire (BDFQ), Difficulties in Emotion Regulation Scale (DERS), Hamilton Depression Scale (HAM-D), and Young Mania Rating Scale (YMRS). Results: In the BD group, a positive significant correlation was found between COPE-M and DERS scores, and a negative significant correlation was found between COPE-M and BDFQ. In the MDD group, a positive significant correlation was found between COPE-A and BDFQ scores, and a negative significant correlation was found between DERS and BDFQ. BDFQ was statistically significantly predicted positively by COPE-A and negatively by DERS. Conclusion: It was thought that emotion dysregulation may predispose to depressive symptomatology and negatively affect clinical course and functionality. Addressing emotion regulation difficulties and increasing the use of adaptive coping strategies in therapeutic interventions contribute to the improvement of functionality as well as clinical improvement.
Speeding Up Optimization-based Motion Planning through Deep Learning

Planning collision-free motions for robots with many degrees of freedom is challenging in environments with complex obstacle geometries. Recent work introduced the idea of speeding up the planning by encoding prior experience of successful motion plans in a neural network. However, this “neural motion planning” did not scale to complex robots in unseen 3D environments as needed for real-world applications. Here, we introduce “basis point set”, well-known in computer vision, to neural motion planning as a modern compact environment encoding enabling efficient supervised training networks that generalize well over diverse 3D worlds. Combined with a new elaborate training scheme, we reach a planning success rate of 100 %. We use the network to predict an educated initial guess for an optimization-based planner (OMP), which quickly converges to a feasible solution, massively outperforming random multi-starts when tested on previously unseen environments. For the DLR humanoid Agile Justin with 19 DoF and in challenging obstacle environments, optimal paths can be generated in 200 ms using only a single CPU core. We also show a first successful real-world experiment based on a high-resolution world model from an integrated 3D sensor.
Influence of Variable Leg Elasticity on the Stability of Quadrupedal Gaits

Several template models have been developed to facilitate the analysis of limit-cycles for quadrupedal locomotion. The parameters in the model are usually fixed; however, biology shows that animals change their leg stiffness according to the locomotion velocity, and this adaptability invariably affects the stability of the gait. This paper provides an analysis of the influence of this variable leg stiffness on the stability of different quadrupedal gaits. The analysis exploits a simplified quadrupedal model with compliant legs and shoulder joints represented as torsional springs. This model can reproduce the most common quadrupedal gaits observed in nature. The stability of such emerging gaits is then checked. Afterward, an optimization process is used to search for the system parameters that guarantee maximum gait stability. Our study shows that using the highest feasible leg swing frequency and adopting a leg stiffness that increases with the speed of locomotion noticeably improves the gait stability over a wide range of horizontal velocities while reducing the oscillations of the trunk. This insight can be applied in the design of novel elastic quadrupedal robots, where variable stiffness actuators could be employed to improve the overall locomotion behavior.
Solar Harvesting Application with Gold Nanospheres: the Influence of Particle Size

Plasmonic nanoparticles have been applied to solar energy harvesting devices to improve their performance. In this work, we propose to evaluate the effects of gold nanosphere size on solar harvesting for thermal applications. The nanoparticle absorption spectrum is obtained using Mie Theory, and the spectral match with the sun is evaluated. Moreover, the temperature variation in the particle neighborhood is analyzed. Results show that the amplitude of the absorption peak decreases with size, but its broadness increases. Furthermore, the dimensions of nanospheres that maximize solar energy absorption and temperature increase in the surroundings of nanoparticles are determined.
Evaluation of On-Robot Capacitive Proximity Sensors with Collision Experiments for Human-Robot Collaboration

A robot must comply with very restrictive safety standards in close human-robot collaboration applications. These standards limit the robot's performance because of speed reductions to avoid potentially large forces exerted on humans during collisions. On-robot capacitive proximity sensors (CPS) can serve as a solution to allow higher speeds and thus better productivity. They allow early reactive measures before contacts occur to reduce the forces during collisions. An open question on designing the systems is the selection of an adequate activation distance to trigger safety measures for a specific robot while considering latency and detection robustness. Furthermore, the systems' actual effectiveness of impact attenuation and performance gain has not been evaluated before. In this work, we define and conduct a unified test procedure based on collision experiments to determine these parameters and investigate the performance gain. Two capacitive proximity sensor systems are evaluated on this test strategy on two robots. A significant performance increase can be achieved, since a small detection distance doubles robot operation speed while maintaining the same contact force as without Capacitive Proximity Sensor (CPS). This work can serve as a reference guide for designing, configuring and implementing future on-robot CPS.
STATISTICAL STUDY OF RECONNECTION EXHAUSTS IN THE SOLAR WIND

Magnetic reconnection is a fundamental process that changes magnetic field configuration and converts a magnetic energy to flow energy and plasma heating. This paper presents a survey of the plasma and magnetic field parameters inside 418 reconnection exhausts identified in the WIND data from 1995–2012. The statistical analysis is oriented on the re-distribution of the magnetic energy released due to reconnection between a plasma acceleration and its heating. The results show that both the portion of the energy deposited into heat as well as the energy spent on the acceleration of the exhaust plasma rise with the magnetic shear angle in accord with the increase of the magnetic flux available for reconnection. The decrease of the normalized exhaust speed with the increasing magnetic shear suggests a decreasing efficiency of the acceleration and/or the increasing efficiency of heating in high-shear events. However, we have found that the already suggested relation between the exhaust speed and temperature enhancement would be rather considered as an upper limit of the plasma heating during reconnection regardless of the shear angle.
Counting Particles: a simple and fast surface reconstruction method for particle-based fluids

We present a novel and efficient surface reconstruction for particle-based fluid methods. Although particle-based methods are practical for computing level-sets that represent liquid interfaces, these methods are computationally expensive when the number of particles increases considerably due to the intense usage of particle approximations. This paper introduces a simple level-set approximation using a discrete indicator function (DIF) defined by counting particles inside grid cells. Our method is fast, easy to code, and can be adapted straightforwardly in particle-based solvers, even implemented in GPU. Moreover, we show the effectiveness of our approach through a set of experiments against prior surface reconstruction methods.
Digital Twin of Drone-based Protection of Agricultural Areas

Protecting agricultural fields, like crops, vineyards, and husbandry areas, has been a difficult challenge since historical times. Classical methods to prevent intrusion are often destructive to wild and domestic animals alike. Even more current nondestructive systems, like camera-based systems are attributed to specific problems related to environmental or technological issues. Furthermore, verifying the effectiveness of installed systems is difficult, as the triggering situations are unmanageable and typically occur unsupervised. This paper presents a complex vision-based intrusion detection system to overcome these problems and further proposes more extensive control and flexibility on the development process. The solution provides a workflow integrating Digital Reality methods into the system development by creating a digital twin of the drone and its surrounding environment in a general-purpose robotic simulator. With this simulation, the triggering events and environmental effects can be easily emulated, such as a wild animal entering the area of interest. The solution also focuses on incorporating new 5G info-communication networks on handling communication between the intrusion detection system and the base station in a distributed manner.
Local and Global Search-Based Planning for Object Rearrangement in Clutter

We propose two algorithms based on local and global searches for a Task and Motion Planning (TAMP) problem, which considers a robotic manipulator to rearrange obstacles and grasp a target in clutter. In the problem, no collision-free path for a robotic manipulator is available unless some obstacles blocking a target are relocated. The two algorithms determine the sequence of obstacles to be removed for grasping a target without collisions. The local search algorithm determines the sequence quickly in an online manner but could be suboptimal in the number of removed obstacles. The global search algorithm based on tree search needs upfront computation but runs in polynomial time. In numerical simulation settings, we consider objects in various shapes, which could make reachable directions bounded. From the simulations the planning time of local search algorithm is faster than that of global search, whereas the global search algorithm removes the less number of obstacles than the local search. In addition, we show that the global search algorithm with a heuristic cost is faster than without the cost but the minimum number of removed obstacles is still obtained from the global search algorithm without the heuristic cost. Practical experiments show the applicability of our algorithms in real environments
Multical: Spatiotemporal Calibration for Multiple IMUs, Cameras and LiDARs

Spatiotemporal calibration of sensors, especially of those which do not share their fields of view, is becoming increasingly important in the fields of autonomous driving and robotics. This paper presents a general sensor calibration method, named Multical, that makes use of multiple planar calibration targets whose poses will be estimated alongside spatiotemporal calibration. Multical exploits continuous-time curves to represent the state of the sensor platform during data collection, and thus is a general framework to calibrate different kinds of sensors and deal with both spatial as well as temporal offsets. Multical includes algorithms to estimate the initial guesses of spatial transformations between sensors, and also the relative poses between calibration targets. Users do not need to provide any extrinsic priors. We apply the proposed calibration approach to both simulated and real-world experiments, and the results demonstrate the high fidelity of the proposed method.
Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review

Epilepsy is the only neurological condition for which electroencephalography (EEG) is the primary diagnostic and important prognostic clinical tool. However, the manual inspection of EEG signals is a time-consuming procedure for neurologists. Thus, intense research has been made on creating machine learning methodologies for automated epilepsy detection. Also, many research or medical facilities have published databases of epileptic EEG signals to accommodate this research effort. The vast number of studies concerning epilepsy detection with EEG makes this systematic review necessary. It presents a detailed evaluation of the signal processing and classification methodologies employed on the different databases and provides valuable insights for future work. 190 studies were included in this systematic review according to the PRISMA guidelines, acquired from a systematic literature search in PubMed, Scopus, ScienceDirect and IEEE Xplore on 1st May 2021. Studies were examined based on the Signal Transformation technique, classification methodology and database for evaluation. Along with other findings, the increasing tendency to employ Convolutional Neural Networks that use a combination of Time-Frequency decomposition methodology images is noticed.
Thermal conductivity of suspended few-layer graphene by a modified T-bridge method

We measured the in-plane thermal conductivity of suspended few-layer graphene flakes by a modified T-bridge technique from 300 K to below 100 K. The thermal conductivities at room temperature are 389, 344, 302, and 596 W/m K for 2-, 3-, 4-, and 8-layer graphene, respectively. The thinner (2-, 3-, 4-layer) graphene samples did not show any clear thickness dependence, while the thicker (8-layer) sample clearly has higher thermal conductivity. In situ current annealing was used to remove polymer residues from the central portion of the 3- and 8-layer graphene samples, as confirmed by electrical transport measurements and post-experiment characterization by Raman and scanning electron microscopy, although some residues still remained near both ends (heater and heat sink). Comparing the 2, 3, and 4-layer samples suggests the annealing had little effect near room temperature but leads to increased thermal conductivity at low temperature. These results also show that the thermal conductivities of suspended few-l.
Alumina Nano Powder Impact on Electrical Discharge Machining of Titanium Alloy Wire

WEDM is an unconventional method of thermal machining that produces products with irregular shapes. The results of milling titanium (TI-6242) under various machining conditions that affect the WEDM process are provided. Pulse on time (Ton), pulse off time (Toff), peak current (Ip), voltage (V), wire tension (Wt), and wire feed are all considered machining parameters (Wf). They are established using an experimental design and the Box–Behnken approach to optimize the machining factors. The optimization goal is to attain the highest Material Removal Rate (MRR) and the least amount of recast layer (RL). ANOVA determines the most important factor. Moreover, a regression analysis is used to predict MRR and RL based on defined machining parameters. Ton = 120s, Toff = 50s, Ip = 11 A, Wt = 1kg, and V = 50 volt are the optimal regulatory factors for obtaining the highest MRR, depending on the consequences. Ton = 130s, Toff = 60s, Ip = 12 A, Wt = 3 kilogram, and V= 30 volt are the best control variables for achieving the lowest RL. Ton = 120s, Toff = 50s, Ip=10 A, and Wt=1kg are thought to be the ideal control parameters for achieving minimum RL and greatest MRR. In ideal machining circumstances, the microstructure of the machined surface exhibits a recast layer on the machined surface.