Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a Miniature Series Elastic Actuator for Bilateral Teleoperations Requiring Accurate Torque Sensing and Control

Compared with stiff actuators, series elastic actuators (SEAs) can render more accurate output torque and stiffness. Hence, SEAs are suitable for robots that need to interact safely with human or properly with the actual or virtual environment. Many existing rotary SEAs are bulky and not suitable for multiple-degree-of-freedom (DoF) applications. To explore the merits of SEAs in bilateral teleoperations that often require multiple-DoF force feedback, this letter presents a miniature rotary SEA. By using a specifically designed planar spring, the size of the SEA can be minimized while the output torque and stiffness can still be accurately controlled. Dynamic modeling and impedance control experiments show that the torque and stiffness rendering results are better than those of existing SEAs. A master-slave robot system using the proposed SEA demonstrates that both stiff and soft remote environments can be virtually realized. We expect that this miniature SEA can serve as an alternative actuator for robotic teleoperations requiring accurate torque sensing and control.
Modeling Walking Behavior of Powered Exoskeleton Based on Complex-Valued Neural Network

This paper proposes walk pattern modeling for powered exoskeleton based on complex-valued neural network and reports its validity through experiments. We have been developing a powered exoskeleton to support workers at a nuclear power plant in time of hazard. The objective of the powered exoskeleton is to support a worker wearing a heavy radiation protection suit. We believe that conventional reactive power assist control based on EMG sensors is not feasible because they fail to measure the worker muscle activity robustly in the radiation protection suit as the worker has a lot of sweat in the high temperature and the humidity in the suit. Therefore, we have developed feed-forward control to assist the worker's motion based on the recognition of the worker's motion. Our previous studies use a simple k-nearest neighbor algorithm to model the motion of the powered exoskeleton, however, the algorithm is not for online learning and the estimated trajectory is not as smooth as we expected. This paper proposes a new modeling of walk motion of the powered exoskeleton based on a complex-valued neural network. The complex-valued neural network generally has good properties on learning speed and stability. This paper shows its validity for the modeling of powered exoskeleton's walk through experiments.
A sEMG-Controlled Robotic Hand Exoskeleton for Rehabilitation in Post-Stroke Individuals

This study proposed to design a robotic hand exoskeleton for rehabilitation in post-stroke individuals controlled by surface electromyographic (sEMG) signals. A control system consisting of threshold detection, classification module and actuator control was designed. Six representative functional hand gestures was recognized using support vector machine (SVM) based on the sEMG signals captured from eight forearm and hand muscles. Kinematic simulation was performed, by which the maximum joint angles for extension was measured. Results showed that the angle changes in metacarpophalangeal (MCP) joints of index, middle, ring and pinky were more than 7°. The angle changes of proximal interphalangeal (PIP) and distal interphalangeal (DIP) joints of the four fingers exceeded 77° and 43°, respectively. The accuracy of gesture classification was higher than 95.5%, which was superior to the results of previous studies. This device provided an innovative prototype and would play a role in hand rehabilitation in post-stroke individuals.
Gathika: A Dynamic Query Distribution Mechanism For Complex Event Processing Systems

Complex Event Processing (CEP) is heavily used in real time systems where people are interested in extracting valuable information from event streams. In CEP systems, some event processing nodes can get overloaded due to event bursts, thus degrading the overall throughput of the whole system. Dynamic distribution of queries among nodes is required in such situations.This paper presents a dynamic query distribution mechanism that can be employed on distributed CEP systems. It is capable of optimally distributing queries across nodes of the distributed CEP system when adding new queries to the system, adding new processing nodes to the system, and when CPU and memory utilization of a node exceeds the threshold value. Evaluation of the system shows that proper execution of the algorithm helps to maintain the overall system performance amidst event bursts.
A Study of Variable Selection Within A Framework of Real-Coded Genetic Algorithm

Recently variable selection and parameter optimization are getting more and more important due to increase of use of big data. With regard to parameter optimization, Much attention has been paid to Real-coded Genetic Algorithms (RCGA) because of their good searching ability and high flexibility. As for variable selection, traditionally Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC) are used quite often as selection criteria. These criteria estimate the relative quality of analysis models for a given set of data. This paper proposes a new variable selection method applying RCGA. In this new variable selection, variances of genes in RCGA have an important role. This paper consists of 2 main parts. The first part is for explanation about a developing process of the new variable selection method and the second part is for application of the proposed method to an empirical study in financial field with a structural change model, which is one of discontinuous models. At the beginning of the first part, it is reported that variances of genes are proportional to square value of standard errors of the parameters corresponding to each of genes. Taking advantage of this finding, a new value is introduced. It is named I-value, which is calculated by dividing a squared value of an estimated parameter by a variance of a corresponding gene. I-value is utilized as a measuring indicator of the importance of explanatory variables in second part and it is found of usefulness.
An Adaptive DC-link Voltage Control for Doubly Fed Induction Generator Wind Turbine System

This paper presents an adaptive DC-link voltage control for Doubly Fed Induction Generator (DFIG) wind turbine. Generally, DC-link voltage reference is constant which lead to DC voltage rise and oscillations during sag. A hardware is also used to enhance low voltage ride through (LVRT) and protect the rotor converter from overcurrent’s. However, during the converter start-up and transients time, the dynamic decoupling of the converter is no longer valid. The inner and outer loop interact as a consequence the controller design by linearization cannot guaranty the expected performance. In order to enhance the system transient response and avoid DC voltage rise during the dips, an adaptive DC-link voltage control is proposed. In this method, the DC-link voltage is controlled to follow the change of grid voltage and its reference is calculated from the grid voltage taking into account the voltage drop across the grid filter. As the result, the current and voltage ripples are significantly attenuated and transient Vdc response is improved. Also, this control strategy allows avoiding activation of protective equipment for faults up to 70% of voltage deep. The simulation performed by Simulink software demonstrates the effectiveness of the control strategy.
BENBI: Scalable and Dynamic Access Control on the Northbound Interface of SDN-Based VANET

Recently, emerging SDN-based VANET (i.e., vehicular ad hoc network based on software-defined networking) enables VANET management to be programmable and flexible. It introduces SDN controllers to maintain network-wide resources and SDN applications to program configurations through arbitrarily accessing resources via the northbound interface (NBI). However, this brings with it security issues on the NBI, such as network-wide resource exposure and configuration manipulation. Most of the existing works employed permission systems to restrict resource access; these solutions are generally controller-dependent, which means controller codes need to be modified for giving access permissions to external applications. In this paper, we propose a scalable and dynamic access control scheme on the NBI for SDN-based VANET, named BENBI. In the proposed scheme, we dynamically and flexibly control network resources by employing broadcast encryption, rather than altering source codes of the controller or updating permission lists with various degrees of granularity. Moreover, the resources are encrypted during transmission so that they are only available to authorized applications. Finally, we implement a prototype of BENBI. The experimental results demonstrate that the cost of allocating secret keys is independent of the number of SDN entities being appointed, which indicates the scalability of our scheme.
On-Chip Scalable Resonator-Based Transducers for Terahertz Dielectric Sensing in SiGe BiCMOS Technology

This paper presents two scalable resonator-based transducers (RBTs) at terahertz (THz) frequency range to realise THz spectroscopy for dielectric sensing. First, the design of 0.24 THz RBT is described by scaled a 0.12 THz sensing structures which utilises a wavelength-long closed-ring resonator to place inside of the Coplanar stripline (CPS) to make a high-selective bandpass response and combines with short-ended strips to create the bandstop behavior. Its scattering parameter can have a very large magnitude change and resonance frequency shift for the loaded samples. Next, a ring structure is also presented to implement 0.48 THz sensing by scaled a low frequency RBT, which employs ring resonator with an asymmetrically loaded stubs to perform a high analytic sensitivity and selectivity for loaded samples. Both presented scalable transducers, possessing the high integration capability of silicon circuits, are proved to be the promising employments in THz spectroscopy.
A Wideband, Low-Noise, and High-Resolution Digitally-Controlled Oscillator for SDR Applications

An integrated wide tuning range, low phase noise and high frequency resolution digitally controlled oscillator (DCO) in 90 nm CMOS technology for software-defined radio (SDR) frequency synthesizers is presented. The switched-capacitor equipped with fine frequency resolution and tail noise filtering techniques are adopted to achieve a wide frequency range from 8.14-11.90 GHz with power consumption varying from 9.4-24.5 mA under 1-V supply. The measured phase noise is −108.4 to −115.4 dBc/Hz at 1-MHz offset. Furthermore, a design technique what we term as $C_{eq}-C_{tun\epsilon}$ diagram and the methodology for designing wide tuning range and low phase noise oscillator in single core are also presented.
Performance Evaluation of SIPROs LEVEL 2A Products for Different Digital Elevation Models and Interpolation Methods

Since the actual earth surface is not flatted the digital elevation model (DEM) is crucial for the geometric corrections used in the production of Level 2A images. Since there are a number of DEM Databases used worldwide, it is important to choose a right database from the SIPROs system developed specifically for the production of THEOS images. As a result, this work employed the SRTM30, SRTM90 and GLOBE DEM which are available for free. There are four interpolation methods used in our experiments, namely, nearest Neighbor resampling, bilinear Interpolation, cubic Interpolation, and Kriging Interpolation. We have evaluated the performance in difference locations with different surface structures, and we found that the use of the GLOBE DEM and bicubic interpolation yields lowest geometric distortion even though the GLOBE DEM has a lowest resolution among all other databases and should be used in the production of THEOS images.
Biometrics-Based Privacy-Preserving User Authentication Scheme for Cloud-Based Industrial Internet of Things Deployment

Due to the widespread popularity of Internet-enabled devices, Industrial Internet of Things (IIoT) becomes popular in recent years. However, as the smart devices share the information with each other using an open channel, i.e., Internet, so security and privacy of the shared information remains a paramount concern. There exist some solutions in the literature for preserving security and privacy in IIoT environment. However, due to their heavy computation and communication overheads, these solutions may not be applicable to wide category of applications in IIoT environment. Hence, in this paper, we propose a new biometric-based privacy preserving user authentication (BP2UA) scheme for cloud-based IIoT deployment. BP2UA consists of strong authentication between users and smart devices using preestablished key agreement between smart devices and the gateway node. The formal security analysis of BP2UA using the well-known real-or-random model is provided to prove its session key security. Moreover, an informal security analysis of BP2UA is also given to show its robustness against various types of known attacks. The computation and communication costs of BP2UA in comparison to the other existing schemes of its category demonstrate its effectiveness in the IIoT environment. Finally, the practical demonstration of BP2UA is also done using the NS2 simulation.
New 2D-Feature Descriptor Free from Orientation Compensation

Feature description and matching are challenging areas in computer vision applications. In this paper, we proposed an orientation invariant feature descriptor without an additional step dedicated to this task. We exploited the information provided by two representations of the image (intensity and gradient) for a better understanding and representation of the feature point and its surroundings distribution. The provided informations are summarized in two cumulative histograms and used in the feature description and matching process. Experiment results show its robustness to image changes.
Creative Thinking of Engineering Undergraduates through Brainstorming during Mathematical Problem Solving

One of the tools that are widely used to develop creative thinking is known as brainstorming. This paper report on a research carried out to determine engineering undergraduates' creative thinking by using SCAMPER as a brainstorming technique. The purpose specifically is to determine the occurrence of using SCAMPER in solving the problems by the students. The research adopt exploratory case study methodology with mixed method research design. Three engineering undergraduates participated in the study. Videotaping is use to collect data during the process of problem solving in order to clarify the students' opinions and behaviors during the sessions of observation and interview. The results of this research suggest that the engineering undergraduates would use brainstorming as a strategy to discuss among themselves, and to generate ideas to solve the given problem. The findings were also consistent with the findings from previous studies showing that brainstorming helps in developing and supporting creative thinking. This give indications that brainstorming is useful as a creative thinking tool to stimulate students thinking in many fields of study including mathematics.
A Novel Stacked Regression Algorithm Based on Slice Transform for Small Sample Size Problem in Spectroscopic Analysis

In spectroscopic data analysis, small sample size (SSS) problem occurs. A solution is to perform variable selection, which has been proved to be critical to improve the performance of the regression model, such as partial least squares (PLS) regression. Stacked moving window partial least squares (SMWPLS) aims to combine variable sets instead of selecting a subset to improve the model robustness. In this study, we proposed a novel weighting strategy to calculate the combination weights. Slice transform (SLT) is used to map the cross-validation (CV) weights to new weights in a piecewise linear manner. The parameters of SLT are optimized with the least-square criterion. Experiments on two near-infrared (NIR) data sets demonstrated the efficiency of the proposed SLT weighting.
Electrical Drives with Active Rectifiers Connected to Distorted Utility Grid

The paper presents analysis of active rectifier application in electrical drives in order to improve energy efficiency. Recuperation of braking energy in drives with vertical transport of load, as well as during the speed reduction within high inertia drives present the most decisive factors for active rectifiers application. The power factor to the grid is approximately equal to unit, but can be adjusted differently if necessary to dynamically compensate for the reactive power of other consumers connected to the same bus as drive with active rectifier. This is only possible if there is an adequate reserve in the power of the inverter. One of the major issues concerning the active rectifier is its operation under distorted grid voltages, due to harmonics or unbalances, which inflict large low-order harmonics in the output voltage and input currents. Although listed advantages make an active rectifier the best choice for high performance electrical drives, the mitigation of low order harmonics in grid currents under different kinds of distortions and unbalances is still a problem analyzed in research community, as well as in the paper.
Online Relational Manifold Learning for Multiview Segmentation in Echocardiography

Accurate delineation of the left ventricle (LV) endocardial border in echocardiography is of vital importance for the diagnosis and treatment of heart disease. Effective segmentation of the LV is challenging due to low contrast, signal dropout and acoustic noise. In the situation where low level and region-based image cues are unable to define the LV boundary, shape prior models are critical to infer shape. These models perform well when there is low variability in the underlying shape subspace and the shape instance produced by appearance cues does not contain gross errors, however in the absence of these conditions results are often much poorer. In this paper, we first propose a shape model to overcome the problem of modelling complex shape subspaces. Our method connects the implicit relationship between image features and shape by extending graph regularized sparse nonnegative matrix factorization (NMF) to jointly learn the structure and connection between two low dimensional manifolds comprising image features and shapes, respectively. We extend conventional NMF learning to an online learning-based approach where the input image is used to leverage the learning and connection of each manifold to the most relevant subspace regions. This ensures robust shape inference and a shape model constructed from contextually relevant shapes. A fully automatic segmentation approach using a probabilistic framework is then proposed to detect the LV endocardial border. Our method is applied to a diverse dataset that contains multiple views of the LV. Results show the effectiveness of our approach compared to state-of-the-art methods.
Geometric shaping: low-density coding of Gaussian-like constellations

Constellation shaping is necessary to approach channel capacity for information rates above 1 bit/dim. Probabilistic shaping shows a small gap to capacity, however a complex distribution matcher is required to modify the source distribution. Spherical shaping of lattice constellations also reduces the gap to capacity, but practical Voronoi shaping is feasible in small dimensions only. In this paper, our codebook is a real geometrically non-uniform Gaussian-like constellation. We prove that this discrete codebook achieves channel capacity when the number of points goes to infinity. Then we build a special mapping to interface between non-binary low-density codes and the codebook, allowing the code alphabet size to be equal to the square root of the codebook size. Excellent performance is shown with fast-encoding and practical iterative probabilistic decoding, e.g. 0.7 dB gap to capacity at 6 bits/s/Hz with a code defined over the ring Z/8Z.
Optimization of Mutant Space for RTL Test Generation

Mutations in the RTL design can help test generation to uncover bugs. However, since the mutant space is infinite, identifying the right mutants is critical. In this paper, we present two filtering approaches to identify the key mutants without loss in the branch coverage. The final set of test vectors generated is able to achieve both high branch coverage and mutation coverage. Experimental results show that we were able to reduce the overall test generation time up to 85% for all ITC99 benchmarks. In addition, we also found that the test suite length also decreased up to 78% for most of the ITC99 circuits.
Categorising Student Responses for Feedback Based on Multiple Choice and Text Responses

Being able to provide fast and useful feedback to students is an essential part of the learning process. Multiple-choice questionnaires are tool often used, due to the ease of marking, and therefore rapid feedback to students. Since these however lack detail and confidence when checking understanding, an added textual component can help to verify students' understanding. In this paper we discuss a methodology for evaluating and categorizing students multiple-choice and text justification. This is applied to three examples of students comments and shows the usefulness of the added textual field.
Comparing Spectrum Based Fault Localisation Against Test-to-Code Traceability Links

The recent shift towards automated software tests stimulated research interest in fault localisation. Fault localisation addresses the question which program elements need to be fixed to repair a failing test. The current state of the art in that field is named spectrum based fault localisation, which relies on dynamic coverage information from both failing and passing test cases to pinpoint the faulty program elements. This is in sharp contrast with the naïve approach which extracts traceability links between the test code and the program elements under test and enumerates those until the faulty element is found. In this paper we ask ourselves the question whether the state-of-the-art approach (spectrum based fault localisation) is so much better than the naïve approach (test-to-code traceability). We demonstrate on 178 defects from three representative projects in the recent Defects4J dataset that spectrum based fault localisation does not perform better than test-to-code traceability. This implies that future improvements in spectrum based fault localisation should also be compared against naïve approaches, such as test-to-code traceability.
QoS Proposal for IEC 61850 Traffic Under an SDN Environment

Even though the Quality of Service (QoS) concept is widely known, in most cases the network traffic continues being treated in a classic way, with the Best Effort paradigm. One reason for this point is that QoS implementation is a complex task. However, critical infrastructure networks such as power substation communication networks, require a careful management of the traffic due to their reliability requirements. In this context, Software Defined Networking (SDN) can provide capabilities to guarantee QoS thanks to its ability for programming the network, making easier the QoS deployment. This paper presents an architecture to implement QoS inside a Substation Automation System, governed by the IEC 61850 standard, using SDN. Our testbed integrates an Opendaylight controller, which uses OVSDB and Open Flow southbound protocols to manage OpenvSwitches inside a Mininet framework. Experimental validation shows that our proposal contributes to guarantee demanding requirements of GOOSE traffic in this type of networks.