Miracle-dz/UniFAR-FTUs huggingface dataset:
User-Adaptive Human-Robot Formation Control for an Intelligent Robotic Walker Using Augmented Human State Estimation and Pathological Gait Characterization

In this paper we describe a control strategy for a user-adaptive human-robot system for an intelligent robotic Mobility Assistive Device (MAD)using raw data from a single laser-range-finder (LRF)mounted on the MAD and scanning the walking area. The proposed control architecture consists of three modules. In the first module, a previously proposed methodology (termed IMM-PDA-PF)delivers the augmented human state estimation of the user by providing robust leg tracking and on-line estimation of the human gait phases. This information is processed at the next module for providing the pathological gait parametrization and characterization, by computing specific gait parameters for each gait cycle. These gait parameters form the feature vector that classifies the user in a certain class related to risk of fall. Those are of particular significance to the system, since the gait parameters and the respective class are used in the third module, i.e. the human-robot formation controller, in order to adapt the desired formation of the human-robot system, by selecting the appropriate control variables. The experimental evaluation comprises gait data from real patients, and demonstrates the stability of the human-robot formation control, indicating the importance of incorporating an on-line gait characterization of the user, using non-wearable and non-invasive methods, in the context of a robotic MAD.
Detection and Recognition of Emergency Situations in Continuous Video Stream of Information and Telecommunication Systems

The problem of events detecting and classifying in the continuous video stream of information and telecommunication security systems is solved. As a basic algorithm, it is proposed to use the ensemble of deep neural networks - convolutional networks and feedback networks, in particular, for classification and annotation, which makes it possible to recognize abnormal emergency situations. A training set of abnormal situations was collected, various architectures of deep neural networks were trained and tested. It is shown that the use of the indRNN layers achieves up to 70% when recognitions multi-class events in the video stream. The new is strengthening of classification estimation of a video segment by extracting the keywords from the automatic annotation. The developed software package can be implemented in the integrated security system of abnormal situations recognition in real time.
Comparative Study of Inkjet-Printed Silver Conductive Traces With Thermal and Electrical Sintering

Thermal sintering has traditionally been the most popular sintering method to enhance conductivity after the printing process in the manufacturing of printed electronics. Nevertheless, in recent years, there has been a growing interest in electrical sintering as an alternative method to overcome some of the limitations of thermal curing. This paper makes a comparative study of both sintering methods in terms of surface morphology, electrical dc conductance, and radiofrequency performance for different applied voltage waveforms. To this end, microstrip transmission lines have been inkjet-printed using nanoparticle-based silver ink on flexible polyimide substrate. The traces have been tested under different sintering conditions, achieving electrical sintering resistivity values only 2.3 times higher than that of bulk silver. This implies a 62% reduction in comparison with the best resistivity value achieved using thermal sintering in our samples. The main novelty of this contribution lies in the analysis of RF behavior as a function of electrical sintering conditions. Lower resistivities have been achieved with slower voltage ramps or allowing higher density current during sintering. It has also been proved that electrically sintered lines have similar RF performance than high-temperature thermally sintered lines in terms of insertion losses, regardless of their very different surface topology. Therefore, we can take advantage of the benefits that electrical sintering offers over thermal sintering regarding significant shorter sintering times maintaining suitable RF performance.
Stereo Vision Sensing: Review of existing systems

Walking through any trade show or expo of 2018 it becomes obvious that 3D scanners have become the latest available in an arsenal of new technology being offered at an industrial level. Multiple technologies are implemented to achieve the task of 3D imaging from the real world. This paper focuses specifically on stereo vision sensing. A review of existing stereo vision systems is provided, their challenges and proposed solutions are outlined.
Efficient Long-term Mapping in Dynamic Environments

As autonomous robots are increasingly being introduced in real-world environments operating for long periods of time, the difficulties of long-term mapping are attracting the attention of the robotics research community. This paper proposes a full SLAM system capable of handling the dynamics of the environment across a single or multiple mapping sessions. Using the pose graph SLAM paradigm, the system works on local maps in the form of 2D point cloud data which are updated over time to store the most up-to-date state of the environment. The core of our system is an efficient ICP-based alignment and merging procedure working on the clouds that copes with non-static entities of the environment. Furthermore, the system retains the graph complexity by removing out-dated nodes upon robust inter- and intra-session loop closure detections while graph coherency is preserved by using condensed measurements. Experiments conducted with real data from longterm SLAM datasets demonstrate the efficiency, accuracy and effectiveness of our system in the management of the mapping problem during long-term robot operation.
Cloud Robotics Platforms: Review and Comparative Analysis

Due to the various advantages that the cloud can offer to robots, there has been the recent emergence of the cloud robotics paradigm. Cloud robotics permits robots to unload computing and storage related tasks into the cloud, and as such, robots can be built with smaller on-board computers. The use of cloud-robotics also allows robots to share knowledge within the community over a dedicated cloud space. In order to build-up robots that benefit from the cloud-robotics paradigm, different cloud-robotics platforms have been released during recent years. This paper critically reviews and compares existing cloud robotic platforms in order to provide recommendations on future use and gaps that still need to be addressed. To achieve this, 8 cloud robotic platforms were investigated. Key findings reveal varying underlying architectures and models adopted by these platforms, in addition to different features offered to end-users.
An Adjustable Force Sensitive Sensor with an Electromagnet for a Soft, Distributed, Digital 3-axis Skin Sensor

Typically, the range and sensitivity of force sensors are determined during production. However, to be able to do both delicate and high-force demanding work, adjustable force sensitivity would be beneficial. The current paper proposes such a sensor by implementing a planar electromagnet above a 3-axis magnetic sensor, separated by soft foam. Furthermore, the sensor has digital output with an integrated microcontroller. The magnetic field strength with varying currents is examined in simulation, and the field changes according to displacements are investigated both in simulation and with the actual sensor. A prototype 3-axis force sensor is implemented and the relationship between the magnetic field change and the corresponding applied force is also investigated. It could be shown that the sensitivity of the sensor to displacements, as well as force, can indeed be adjusted.
An On-line Transmission Calculation Method Based on Multi-Section Power Flow Control

Propose an on-line transmission calculation method based on multi-section power flow control, the algorithm adopts an continuation power flow(CPF) method with improved parameterization and considers the stability constraints such as transient stability, static security constraints and small disturbance stability. In the field of on-line transmission limit calculation, a practical scheme is put forward. The method is applied to IEEE39 nodes and successfully tracked the critical voltage and the limit power of the system, which shows the effectiveness of the algorithm.
Development Of Tele-Monitoring Attendance System Using RFID and Photo-Cell

This paper presents a tele-monitoring system to recognize the attendees when entering a certain place without a physical contact using RFID, tags and Photo-cells. The RFID is used to recognize the tags when it's holders entered the place area, however the phot-cells will help to determine if there are really persons passed through the system or not. Also the photo cells helps to determine the direction of the attendees either going in or out from the monitored place. Five data sets are collected instantly for each cards namely are student name, ID number, date, time, status and IN/OUT. A small scale prototype has been constructed to test the concept of RFID-Photocell attendance system which is consisting of RFID, two photo-cell, Arduino and PLX-DAQ. The process of taking attendance will start when the attendees with tags pass through the RFID reader range where their tags code are compared with the database. If the database match with the code, the information of the attendee like name, ID number, date and time will be shown in the Excel file. For more accurate attendance system, the photo-cell will be incorporated with RFID to count how many persons pass through the system and detect if there is a cheating during attendance operation such as two tag's cards are located with one person. The photo cell will also detect the direction of the attendees either going in/out to help for tracking continuously the attendees. As a result, this project will assist for taking the attendance more confidentially, automatically and easily.
OTA-C Filters for Biomedical Signal Processing Applications using Hybrid CMOS-CNFET Technology

Analog filters for biomedical signal processing applications deals with very slow or low frequency electrical activities of the physiological signals. This paper proposes first order, second order, fifth order elliptic low pass, second order notch and high pass OTA-C filters using hybrid CMOS-CNFET technology. Carbon Nanotube Field Effect Transistors (CNFETs) and CMOS devises can be heterogeneously integrated on a single 3-D chip to realize important signal processing building blocks such as OTA-C filters. Proposed circuits uses hybrid CMOS-CNFET Operational Transconductance Amplifier (OTA) as a building block for OTA-C filters. Realized filter circuits satisfy ultra-low power consumption requirement of wearable and implantable biomedical devices. The transistors used in the circuit operate in weak inversion to achieve ultra-low power consumption.
A Meta-Surface Decoupling Method for Two Linear Polarized Antenna Array in Sub-6 GHz Base Station Applications

In this paper, an extremely compact two-element linear polarized multiple-input-multiple-output (MIMO) antenna array with a metasurface superstrate is proposed. Instead of using periodic square split-ring resonators which occupy larger space and which are incident angle variant, double-layer short wire is utilized as the unit cell of the metasurface. The metasurface is compact in size and effective in decoupling two nearby Bowtie antennas strongly coupled in the H-plane with the spacing of only 0.27 wavelength. After decoupling, the isolation between the two antennas has been improved from around 10 dB to more than 25 dB within the band of 2300 to 2690 MHz while their reflection remained below −15 dB. Moreover, the radiation pattern after adding the metasurface superstrate is well maintained with total efficiency improvement by about 10%, and the envelope correlation coefficient between the two antennas is reduced from 0.35 to below 0.12 within the whole band of interest. The proposed method can find plenty of applications in MIMO and 5G communication systems.
Expert-Guided Kinodynamic RRT Path Planner for Non-Holonomic Robots

In this paper, an Expert-Guided Kinodynamic RRT algorithm (EGK-RRT) is presented. It aims to consider how a human pilot would navigate a kinodynamic robot. One of the characteristics of this algorithm is the fact that, unlike the original RRT for kinodynamic systems, it generates deterministic control sequences which can be reproduced as long as the sequence of references (sampled states) are known. Here, the performance of the proposed algorithm is tested against the basic RRT, showing that the EGK-RRT greatly improves in terms of execution speed. In addition to this, the influence of using a visibility check and an inertia estimation in order to select the nearest neighbor is also analyzed, demonstrating that a combination of both factors leads to a better overall performance, both in execution speed and in quality of the generated path.
Inferring Semantic State Transitions During Telerobotic Manipulation

Human teleoperation of robots and autonomous operations go hand in hand in today's service robots. While robot teleoperation is typically performed on low to medium levels of abstraction, automated planning has to take place on a higher abstraction level, i.e. by means of semantic reasoning. Accordingly, an abstract state of the world has to be maintained in order to enable an operator to switch seamlessly between both operational modes. We propose a novel approach that combines simulation based geometric tracking and semantic state inference by means of so called State Inference Entities to overcome this issue. We also demonstrate how Evolutionary Strategies can be employed to refine simulation parameters. All experiments are demonstrated in real-world experiments conducted with the humanoid robot Rollin’ Justin.
Wind Power Forecasting Based on ARIMA-LGARCH Model

Wind power prediction is an important basis for secure and economic operation of power grid. In view of the impacts of wind power’s stochastic volatility on wind power forecasting accuracy, a novel short-term wind power prediction method based on ARIMA-LGARCH model is proposed in this paper. This method analyzes the non-stationary and autocorrelation of time series data of wind power by utilizing Auto Regression Integrated Moving Average (ARIMA) model and asymmetric positive and negative fluctuation characteristics of wind power based on Logarithmic Generalized Autoregression Conditional Heteroscedasticity (LGARCH) model. LGARCH is designed to deal with the asymmetric fluctuation and data-trailing problems of wind power through introducing logarithm process and the independent identically distributed stochastic variable into the current GARCH model. The mixed model of ARIMA-LGARCH is built to achieve high-accuracy short-term forecasting of wind power output with strong uncertainties. The comparative analysis between the predicted value and real value of the actual wind power output in a certain wind farm verifies the feasibility and effectiveness of the method proposed in this paper.
An Analytical Study on Trotting at Constant Velocity and Height

Quadrupedal trotting gaits of constant forward velocity and body height are studied. A method is developed, which is structured upon analytical expressions derived from the dynamics of a reduced single-legged model comprised of a point mass, and two actuated rotational joints. The inputs of the method include the robot mass, the leg and actuator properties, and the desired forward velocity, yielding all robot body feasible trajectories and their energy footprints. Thus, the method predicts the maximum forward velocity of a trotting quadruped; it also suggests energetically optimal combinations of body height and step length for a given forward velocity.
Dynamic Scaling Factors of Covariances for Accurate 3D Normal Distributions Transform Registration

Distribution-to-distribution normal distributions transform (NDT-D2D) is one of the fast point set registrations. Since the normal distributions transform (NDT) is a set of normal distributions generated by discrete and regular cells, local minima of the objective function is an issue of NDT-D2D. Also, we found that the objective function based on L2 distance between distributions has a negative correlation with rotational alignment. To overcome the problems, we present a method using dynamic scaling factors of covariances to improve the accuracy of NDT-D2D. Two scaling factors are defined for the preceding and current NDTs respectively, and they are dynamically varied in each iteration of NDT-D2D. We implemented the proposed method based on conventional NDT-D2D and probabilistic NDT-D2D and compared to the NDT-D2D with fixed scaling factors using KITTI benchmark data set. Also, we experimented estimating odometry with an initial guess as an application of distribution-to-distribution probabilistic NDT (PNDT-D2D) with the proposed method. As a result, the proposed method improves both translational and rotational accuracy of the NDT-D2D and PNDT-D2D.
Transient Joule Heating in PrMnO3 RRAM enables ReLU type Neuron

Hardware realization of scalable neurons and synapses are essential for the implementation of large scale Spiking neural network (SNN). In this paper, first, we propose, a novel transient Joule heating based the leaky-integrate and fire neuron (LIF) in scalable PrMnO3 (PMO) RRAM device experimentally. The Joule-heating based thermal runaway is utilized to achieve rectified linear unit (ReLU) voltage dependence of spiking frequency similar to a typical LIF neuron. Second, the Jouleheating hypothesis in PMO is validated by TCAD DC and transient simulations. PMO is extremely thermally resistive semiconductor (300x cf. Si) and hence enables low energy thermal dynamics. The excellent energy, area performance with a synapse in the same material system and thermal engineering makes PMO neuron attractive. Finally, PMO neuron shows software equivalent learning accuracy in SNN on the Fischer’s Iris dataset.
Robotic Boreblending: The Future of In-Situ Gas Turbine Repair

Automation of inspection and repair tasks on complex installations is gaining attention from industries with high-value assets such as aerospace, nuclear and marine. This paper reports on a five degrees of freedom robotic system capable of performing accurate and repeatable repair procedures through a narrow inspection port, which minimizes the cost and downtime associated with unscheduled maintenance. Careful study of the target working volume and repair process informed the design of a robotic probe capable of replicating the operation. Kinematic analysis of the robot's flexible, prismatic and rotary joints was used to define accurate machining paths in 3D space, and the results were verified using an optical motion capture system (accuracy of 0.25 mm). After comprehensive verifications of the constitutive elements, the robotic system was successfully demonstrated for repair of a high-pressure compressor aerofoil in a gas turbine. The results not only proves the ability of the system to address such difficult repair scenarios but also highlights a domain of opportunities in developing specialist robotics for repair of high-value assets, which is a subject to growing global demand.
Modeling and Fuzzy Control of One-legged Somersaulting Robot

Research on legged robots has developed rapidly in the recent decades. One-legged robots, unlike multi-legged ones, have only one type of motion, called hopping. Hopping motion is generally divided into stance and flight phases. Switching between these two phases represents a hybrid dynamic model. Dynamic stabilization of hopping motion is a challenging control issue. Most of one-legged hopping robots studied in the past are able to hop with their one springy leg. In this paper, a novel one-legged robot is introduced and studied with two springs on the two sides. The one-legged somersaulting robot is able to hop with both springy sides. This ability causes lower energy consumption in passing obstacles and a longer step length in comparison with well-known SLIP robots with hopping motion stemming from the fact that it only has one rotary actuator. Fuzzy logic control is applied to achieve a stable limit cycle in the robot's somersaulting motion.
Multi-path Load Balancing for SDN Data Plane

Networks have become an important feature of our day-to-day to life and therefore, user experience is an imperative goal to be achieved by network operators. Load balancing is a method of improving network performance, availability, minimizing delays and avoiding network congestion. In this paper, we study dynamic load balancing to improve network performance and reduce network response time. The load balancer is applied to OpenFlow SDN network’s data plane with Opendaylight as the controller. The flexibility of the load balancer is tested by using it on two different network topologies. Results show that the load balancer can improve the overall performance of the network and reduce delay. The main contribution of this work is a load balancing mechanism for SDN centralized controller environments which can be employed at any point in time in a network, for example, before network failure or after link failure, to avoid data plane congestion and link overloading.
An Approach to Multidimensional Nonlinear Optimization

The article presents a new approach to improving the efficiency of multidimensional nonlinear optimization. Traditional linear search is replaced by non-linear one, in which the direction of search at each step adapts to the profile of the objective function. This makes it possible to localize the extremum as quickly as possible and to shorten substantially the time of its determination. An interpolation search algorithm is proposed in the found interval of extremum localization. The objective function is modeled by a segment of a cubic spline, constructed on the basis of information about the gradient vector at the boundary points and taking into account the slope of the objective function. This allows to reduce the number of stages of interpolation search. The possibility of simplified nonsmooth interpolation by first-order splines in the area of finding an extremum is considered. Numerical results show that the new method is very efficient for the various test problems.
Shock Absorbing Exoskeleton for Vertical Mobility System: Concept and Feasibility Study

The goal of this research is to develop a lower-extremity wearable link mechanism (i.e., exoskeleton robot) that is capable of reducing load against targeted body parts such as bones, joints and muscles, for shock absorption that help to support exploration of extreme environments. One of the applications of such exoskeleton is to protect a pilot of a personal vertical mobility system, or JetPack, when landing. The shock absorbing exoskeleton is to introduce series and parallel viscoelasticity to the human skeletal system. The paper presents a pilot study to validate this body-protective exoskeleton concept by analyzing kinematic and dynamic models of a human-exoskeleton coupled system based on a multi-element viscoelastic model in rheology. A proof-of-concept prototype is developed and experimental data is presented.
Flexible Distribution System Reconfiguration Using Graph Theory and Topology Identification Technology

This paper presents a new approach to solve the flexible distribution system reconfiguration (DSR) problem. Based on graph theory and fast topology recognition technology, a fast network topology identification method is proposed to tackle the network radiality constraint and applied to the DSR problem. Numerical simulations are tested in the real 59-node distribution system and simulations results show that the proposed method can effectively solve the DSR problem.