Miracle-dz/UniFAR-FTUs huggingface dataset:
A 3-D Stochastic FDTD Algorithm for Wave Propagation in Isotropic Cold Plasma Medium Based on Bilinear Transform

In this paper, a 3-D stochastic finite-difference time-domain algorithm is developed for simulating wave propagation in isotropic cold plasma medium by using the bilinear transform (BT) technique. The proposed algorithm has a simple formulation and is very easy to apply to isotropic dispersive material. It efficiently calculates the mean value and standard deviation of electromagnetic field components caused by the variability or uncertainty of the plasma’s electron density and collision frequency. The stretched coordinate perfectly matched layer (SC-PML) with a complex-frequency-shifted factor is implemented as the absorbing boundary condition, and the BT technique is also incorporated into the SC-PML. Numerical examples are shown to demonstrate the accuracy and effectiveness of the proposed method.
Autonomous Navigation Using Multimodal Potential Field to Initiate Interaction with Multiple People

In a human-robot interaction, a robot needs to move to a position where the robot can obtain high reliability data of people, such as positions, postures, and voice. This is because the human recognition reliability depends on the positional relation between the people and the robot. In addition, the robot should choose the sensor data which is necessary to perform the interaction task. Therefore, it is necessary to navigate the robot to the position to obtain the data for initiation of the interaction task. Accordingly, we need to design a path-planning method considering sensor characteristics, human recognition reliability, and task contents. Although previous studies proposed path-planning methods using an interaction potential considering sensor characteristics, they did not consider the task contents and the human recognition reliability, which are important for practical application and did not applied to interaction with multiple people. Consequently, we present a path-planning method considering the task contents and the human recognition reliability using multimodal potential field integrating these information. We verified effectiveness of the path-planning method for interaction with multiple people.
Humanoid Teleoperation Using Task-Relevant Haptic Feedback

Robotic teleoperation is a key technology for a wide variety of fields. Teleoperating a humanoid in particular is essential as it allows the user to act remotely on an interface designed especially for humans, e.g., in a space station, or operating tools and machinery in disaster scenarios. This paper presents a ‘task-relevant’ haptic interface for humanoid teleoperation, which bridges the gap between the task at hand and the balance of the robot. The operator is given command over the humanoid's hands and is informed through haptic cues about the impact of her/his potential actions on the robot’ stability. Moreover, a null-space autonomous controller acts in the operator's null-space to provide her/him with a wider workspace and help in the successful execution of the task. The architecture is designed to top an existing compliance controller for a torque-controlled humanoid robot. Experiments on the humanoid robot TORO are reported to demonstrate the feasibility and effectiveness of the approach.
Discrimination of Motor Imagery Task using Wavelet Based EEG Signal Features

In this paper, we present a state-of-the-art motor imagery brain computer interface system (BCI) based on non-invasive approach in the form of electroencephalogram (EEG) with an objective of evaluating the performance of supervised machine-learning algorithms applied on features extracted from pre-processed EEG signals. Two categories of features were utilized namely a high dimensional feature set extracted from 22 EEG channels and a feature set extracted from two EEG channels (C3 and C4). Four signal classifiers namely KNN, Regression tree, NB and LDA are applied on wavelet-based EEG signal features for discrimination of four classes of motor imagery (MI) tasks (left hand, right hand, foot and tongue). Efficient discrimination of motor imagery tasks is significantly dependent on signal-to-noise ratio of EEG signals to enhance the performance of signal classifiers. A pre-processing technique is firstly applied on filtered EEG signals to remove contamination in the form of artifacts. Then, useful signal features are extracted from artifact free EEG signals, whereby relevant subsets with high predictive power are selected using feature selection technique. The best features subsets are fed into signal classifiers for classification purposes. A highest average classification accuracy of 73.06% and 72.95% was achieved using NB while classifying both features acquired from 2 and 22 EEG channels respectively.
Achieving Flexible Assembly Using Autonomous Robotic Systems

Prefabrication of structures is currently used in a limited capacity, due to the lack of flexibility, despite the potential cost and speed advantages. Autonomous flexible reassembly enables structures to be developed which can be continuously and iteratively dis-assembled and re-assembled providing far more flexibility in comparison to single shot pre-fabrication methods. Dis-assembly of structures should be considered when assembling, due to the asymmetry of assembly and dis-assembly processes, to ensure structures can be recycled and re-assembled. This allows for agile development, significantly reducing the time and resource usage during the build process. In this work, a framework for flexible re-assembly is developed and a robotic platform is developed to implement and test this framework with simple Lego bricks. The tradeoffs in terms of time, resource use and probability of success of this new assembly method can be understood by using a cost function to compare to alternative fabrication methods.
Bit-Vector Interpolation and Quantifier Elimination by Lazy Reduction

The inference of program invariants over machine arithmetic, commonly called bit-vector arithmetic, is an important problem in verification. Techniques that have been successful for unbounded arithmetic, in particular Craig interpolation, have turned out to be difficult to generalise to machine arithmetic: existing bit-vector interpolation approaches are based either on eager translation from bit-vectors to unbounded arithmetic, resulting in complicated constraints that are hard to solve and interpolate, or on bit-blasting to propositional logic, in the process losing all arithmetic structure. We present a new approach to bit-vector interpolation, as well as bit-vector quantifier elimination (QE), that works by lazy translation of bit-vector constraints to unbounded arithmetic. Laziness enables us to fully utilise the information available during proof search (implied by decisions and propagation) in the encoding, and this way produce constraints that can be handled relatively easily by existing interpolation and QE procedures for Presburger arithmetic. The lazy encoding is complemented with a set of native proof rules for bit-vector equations and non-linear (polynomial) constraints, this way minimising the number of cases a solver has to consider.
Design and Implementation of New Planar Massive MIMO Systems for 5G Wireless Networks Extending Omnet++ Simulator

The progress accomplished in the modern wireless network are leading to the development of efficient antenna devices that are able to achieve high level requirements with regard to the 5G next generation systems. Although a significant number of theoretical recommendations can already be found in this context, the most known used network simulators do not provide for a support concerning the most recent wireless network requirements like the IEEE802.11ac and, simultaneously, they do not offer the possibility to use the latest massive MIMO (Multiple Input Multiple Output) antenna systems. In view of this, we propose to extend the default features provided by the Omnet++- network simulator with the intention of actualizing the existing simulation instruments to be suitable also for 5G wireless network communication systems.
Millimeter-Wave Liquid Crystal Polymer Based Conformal Antenna Array for 5G Applications

This letter presents the design, fabrication, and performance evaluation of a flexible millimeter-wave (mm-wave) antenna array for the fifth generation (5G) wireless networks operating at Ka-band (26.5–40 GHz). The single element antenna is comprised of a coplanar-waveguide-fed rectangular patch tapered at its sides with two vertically oriented slots. The ground is designed with L-shaped stubs to converge the dispersed radiation pattern for improving the directivity and gain. The antenna fabrication is accomplished by two advanced methods of laser-milling and inkjet printing on a thin film of flexible liquid crystal polymer. A novel and time-efficient method for postprinting sintering is also proposed in this letter. The design is extended in a two-element array for the gain enhancement. Measurements have validated that the proposed antenna array exhibits a bandwidth of 26–40 GHz with a peak gain of 11.35 dBi at 35 GHz, and consistent high gain profile of above 9 dBi in the complete Ka-band. These features recommend the proposed antenna array as an efficient solution for integration in future flexible 5G front ends and mm-wave wearable devices.
Design and Experimental Research of Movable Cable-Driven Lower Limb Rehabilitation Robot

Aiming at solving the problems of the existing lower limb rehabilitation robots on aspects of configuration limitations, human–machine compatibility, gravity compensation, and multimodal rehabilitation, a movable cable-driven lower limb rehabilitation robot (MCLR) is proposed in this paper, which can realize the gait training and walking training in passive mode, initiative mode, and assistive mode. First, the structure and working principle of MCLR is introduced. The traction type is further optimized. Second, the key control problems of the passive force servo system are analyzed in detail. In order to improve the loading accuracy and speed during the initiative training, the dynamic model of the cable-driven unit is established. Based on this model, an active force control strategy is proposed. Finally, the speed control strategy and the active force control strategy are studied experimentally. The experimental results show that the speed servo system has good tracking ability, which can meet the requirements of the passive rehabilitation. The active force control strategy can significantly improve the loading accuracy and the dynamic performance of the force servo system. The force servo system has good tracking ability in the normal rehabilitation frequency band, which can meet the requirements of the initiative rehabilitation.
Digital Narratives and the Genealogy of a Hybrid Genre

From a simple list of links annotated and maintained by a small blog community of Internet groupies to elaborated content today, blogs have exploded into a rich gamut of subgenres. Most approaches that pioneered internet communication research are in fact non-empirical and non-linguistic. Two major blog types appear to have emerged, personal blogs and thematic blogs, with their corresponding already-established subgenres of filter blogs, k-logs, and corporate blogs as well as other emerging hybrid subgenres such as that of expat blogs. This chapter explores the language of high-impact blogs, testing a new methodology to establish blog genealogy in the context of online genre hybridity. Language data are collected using a major blog searching engine (Technorati) that currently indexes more than a million blogs. Individual language scores, which are used to calculate DICTION’s sub variables, are concatenated to outline the overall tone and theme of the blog posts that can be classified accordingly. The findings are correlated with existing blog classifications to propose a Diction-based methodology for genre analysis.
Design of Robotic Gripper with Constant Transmission Ratio Based on Twisted String Actuator: Concept and Evaluation

Robotic systems for object handling and manipulation are hugely important for modern engineering and industry, with their efficiency, agility and robustness often depending on gripper design and performance. In this work, we investigate a gripper design that, when driven by a twisted string actuator, exhibits nearly-constant transmission ratio throughout its motion range. This allows for design of a highly-compact, modular and efficient robotic gripper driven by a low-power motor. We investigate kinematics of the device, experimentally verify developed models with a practical gripper testbed, and analyze transmission ratio and efficiency of the designed device. The resulting system has a nearly-constant transmission ratio of 550, with the constancy coefficient of 0.985.
Partitioned Fixed-Priority Scheduling of Parallel Tasks Without Preemptions

The study of parallel task models executed with predictable scheduling approaches is a fundamental problem for real-time multiprocessor systems. Nevertheless, to date, limited efforts have been spent in analyzing the combination of partitioned scheduling and non-preemptive execution, which is arguably one of the most predictable schemes that can be envisaged to handle parallel tasks. This paper fills this gap by proposing an analysis for sporadic DAG tasks under partitioned fixed-priority scheduling where the computations corresponding to the nodes of the DAG are non-preemptively executed. The analysis has been achieved by means of segmented self-suspending tasks with nonpreemptable segments, for which a new fine-grained analysis is also proposed. The latter is shown to analytically dominate state-of-the-art approaches. A partitioning algorithm for DAG tasks is finally proposed. By means of experimental results, the proposed analysis has been compared against a previouslyproposed analysis for DAG tasks with non-preemptable nodes managed by global fixed-priority scheduling. The comparison revealed important improvements in terms of schedulability performance.
Challenges of Autonomous Flight in Indoor Environments

Indoor navigation has been a major focus of drone research over the last few decades. The main reason for the term “indoor” came from the fact that in outdoor environments, drones could rely on global navigation systems such as GPS for their position and velocity estimates. By focusing on unknown indoor environments, the research had to focus on solutions using onboard sensors and processing. In this article, we present an overview of the state of the art and remaining challenges in this area, with a focus on small drones.
Some Effects of Harmonics on Low Voltage Loads

The paper deals with the influence of harmonics low voltage loads. In the introductory section, a brief presentation of the sources, effects, and measurement methods of harmonics and of the specific indices used in harmonic analysis are addressed. The second section presents a case study about harmonics effects, the experimental setup consisting in a three phase induction motor supplied by a programmable power source. The third section is devoted to a quantitative comparison, in terms of energy efficiency, between the behavior of an induction motor, supplied successively by a pure sinusoidal voltage and a clipped 50–sinusoidal voltage.
Tire Force Estimation of Dynamic Wheeled Mobile Robots using Tire-Model Based Constrained Kalman Filtering

We propose a novel real-time algorithm to estimate the full three-dimensional individual tire forces (i.e., vertical, longitudinal as well as lateral) of a car-like rearwheel-driven four wheel wheeled mobile robots equipped with onboard navigation sensors and wheel encoders. The key enabling idea for this is to utilize the tire model (i.e., the magic formula) in a feedback manner on the framework of the constrained Kalman filtering to render the tire force estimation: 1) more accurate as compared to the typical tire force estimation techniques neglecting the tire-road interaction; and 2) more robust as compared to the results adopting the tire model, yet, only in an open-loop manner. Our proposed algorithm, while performing this full tire force onboard/real-time estimation, also provides the estimation of: 1) tire-road friction coefficient; and 2) torque inputs of the rear left and right wheels, which are connected via differential gear. Simulations with CarSim and outdoor experiments are performed to validate the proposed estimation algorithm.
Stability of Switched Time-Delay Systems via Mode-Dependent Average Dwell Time Switching

The stability problem is explored for a class of switched time-delay systems (STDSs) with unstable subsystems in this paper. The delay-dependent stability criterion of the STDSs with mode-dependent average dwell time (MDADT) switching is developed via the multiple Lyapunov–Krasovskii functional approach. The proposed MDADT switching signal contains both fast switching and slow switching. Finally, the validity of the developed result is certified via a simulation example.
Data-Driven Anomaly Detection Using OCSVM with Boundary Optimzation

With large amount of industrial data available, data-driven anomaly detection methods have been widely used for ensuring industrial safety and preventing economic losses. Among them, one-class support vector machine (OCSVM) is an effective unsupervised method for detecting abnormal points by establishing a decision boundary in the kernel space and has obtained much attention in the research field in recent years. Through solving the convex quadratic programming problem, OCSVM obtains a classifier with maximum distance to the origin and satisfying maximum quantity of training samples, without utilizing anomaly data points. However, the outliers in the training set, which are more likely to be selected as support vectors, will lead to the degradation in the performance of OCSVM. Because the outliers are away from the center of the dataset and cannot represent the real characteristics for the data. To address this drawback, the clustering-based and LOF outlier detection method (CLOF), which has high computational efficiency and modeling accuracy, is adopted for eliminating the outliers and optimizing the final acquired boundary of OCSVM. Experimental results on shuttle flight data have illustrated the superior performance of the proposed approach.
Camera Calibration Simulation using a Randomly Generated Spherical Point Distribution

In this paper, we present a method that utilizes computer vision, specifically projective geometry, to map a known distribution of points on a sphere - with known diameter - along with an arbitrary image of these points on an image plane to identify the configuration of the camera. In other words, knowing the sets of 2D-3D corresponding points, one can extract the camera matrix and dissect it into parameters of interest: intrinsics and extrinsics. The method that is validated by code shows in detail how to setup a theoretical world and camera coordinate frame, and then through the knowledge of the correspondence, displays the solution to the optimization problem. The results are then analyzed noting the relative error between the retrieved and actual camera matrices.
A Continuous Noninvasive Arterial Pressure (CNAP) Approach for Health 4.0 Systems

Health 4.0 can provide effective ways to improve the health status of subjects by taking advantage of cyber-physical systems and Internet of things technologies for the solution of healthcare problems. One of these is represented by suitably estimating blood pressure values of subjects in a continuous, real-time, and noninvasive way. To address it, we propose an approach only requiring a photoplethysmography (PPG) sensor and a mobile/desktop device. The approach avails itself of genetic programming to automatically find an explicit relationship between blood pressure values and PPG ones. This relationship is tested on a set of 11 subjects and compared against other regression methods, and turns out to be better. Namely, the root-mean-square error values are equal to 8.49 and 6.66 for the systolic and the diastolic blood-pressure values, respectively. Those for the relative error, instead, are equal to 5.55% for the systolic and 6.59% for the diastolic values.
Field-free Spin-orbit Torque Switching of Perpendicular Magnetic Tunnel Junction Utilizing Voltage-Controlled Magnetic Anisotropy Pulse Width Optimization

Spin transfer torque magnetic random access memory (STT-MRAM) has been considered as a promising non-volatile memory candidate for next-generation products. The main difficulty of STT-MRAM applications are the write energy consumption, latency, and endurance. Recently, a novel magnetic random access memory: spin-orbit torque MRAM (SOT-MRAM) architecture provides a new method to switch the magnetization direction of the magnetic tunnel junction. SOT-MRAM has the characteristic of short latency, low power, and high endurance, which get rid of the problems encountered by STT-MRAM. The magnetic tunnel junction with perpendicularly magnetized ferromagnetic layers has the advantages of scalability and higher thermal stability. However, the assistance of a magnetic field is essential to switching the magnetization of a perpendicular magnetic tunnel junction deterministically, which is pernicious for real applications. In this paper, a new method is investigated to switch the perpendicular magnetic tunnel junction deterministically without the help of a magnetic field. This method utilizes voltage-controlled magnetic anisotropy (VCMA) to induce an in-plane effective field which substitutes for the external field. The magnetic dynamics and conditions of pulse control of this field-free method are simulated by solving a modified Landau-Lifshitz-Gilbert equation. Our results show that this method is not only field-free but also faster than the typical field-dependent method, which has the potential for high-speed MRAM design.
Optimized Configuration of Exponential Smoothing and Extreme Learning Machine for Traffic Flow Forecasting

Traffic flow forecasting is a useful technology applied to solve traffic congestion problems and to improve transportation mobility. Neural networks related approaches have been applied to develop traffic forecasting models for more than two decades. Since neural networks are sensitivity in parameters selection, selecting appropriate modeling configuration is essential to improve the accuracy and efficiency of traffic flow prediction. However, this is usually conducted by the trial-and-error method, which is very time consuming while involving too many design factors. Therefore, this paper utilizes a robust and systematic optimization approach, the Taguchi method, for obtaining the optimized configuration of the proposed exponential smoothing and extreme learning machine forecasting model. The developed model is applied to real-world data collected from freeways and highways in the United Kingdom and is compared with three existing forecasting models. The results indicate that the Taguchi method is efficient and capable for the forecasting model design and the proposed model with the optimized configuration has superior performance in traffic flow forecasting with approximate 91% and 88% accuracy rate in freeway and highway in both peak and nonpeak traffic periods.