Miracle-dz/UniFAR-FTUs huggingface dataset:
An Enhanced Biometric Based RFID Authentication Scheme Defending Against Illegitimate Access

Radio-frequency-identification (RFID) technology has been widely used to identify the RFID-Tags associated with objects through RFID-Reader using electromagnetic signals. The rapid growth of RFID technology is also accompanied by contemporary security challenges and threats. The highly secure authentication protocol is obligatory to overcome security challenges. Recently Srivastava et al. have proposed a Hash-biometric based authentication protocol for RFID Systems to resist the security threats. However, this study points out that Srivastava et al. scheme is vulnerable to 1) Possible Stolen Verifier Attack 2) Anonymity and Traceability Issue 3) Impersonation Attack by illegitimate user 4) Insider Attack. An enhanced and efficient protocol is presented in this paper for RFID Systems based on biometric authentication to handle security challenges and attacks found in Srivastava et al. scheme. The proposed protocol is formally tested and verified through well-known security automated tool (ProVerif). The proposed protocol is adequately secure and has a preferable efficiency in contrast to the other existing schemes.
Important Design Aspects of Pulsed Electroacoustic Measurement Systems for Thick Full-Size Cable Specimen

With an ongoing trend towards HVDC-transmission, the accumulation of space charges and related failure of HVDC-Extruded cable-system components, is more important than ever. Only a few space charge measurement methods, namely the pulsed electroacoustic method (PEA), the thermal step method (TSM) and the laser-induced pressure pulse method (LIPP), are useable with insulation thicknesses in the mm-range and applicable with cable-geometry. In this paper, important design aspects for a PEA measurement-system applicable for cables with thick insulation are systematically presented and the pitfalls are pointed out.
Business Analytics for Institutional Academic Management: A Case of South African Higher Education

Data infrastructure and quality are vital organs influencing the health of any organizations and are essential for creating and delivering business insights. Due to this fact, stakeholders expect a flawless experience, real-time solutions and support. However, these expectations are normally too high for IT departments that are always immersed with various users’ requests. The Big Data era and its analytics have put organizations in positions to predict and forecast users’ needs to produce a great user experience. This is achieved through intuitive design, error-free coding and quality performance. There is increasing demand for better business analytics so as to enable organizations establish solid foundation by building out a data management ecosystem that delivers flexibility and performance required by cognitive solutions. The objective of this paper was to highlight how universities can leverage better analytics to process amount of continuously generated data from various sources and generate actionable insights needed to achieve academic business goals. This study followed an interpretive paradigm taking a case study of a South African university and employed semi-structured interviews. This study found that data value and the use thereof in the management of academic divisions of the university should be consistent with faculty business plans. In this way, business analytics is not haphazardly used but is strategically aligned with faculty plans and overall institutional strategic objectives and targets
Interface of Silicon Nitride Nanolayers with Oxygen Deficiency

Multilayer Si<inf>3</inf>N<inf>4</inf> consisting of Si<inf>3</inf>N<inf>4</inf> nanolayers with the total thickness 60 nm was deposited layer-by-layer in a low-pressure chemical vapor deposition process. Compared with the single-layer Si<inf>3</inf>N<inf>4</inf>, the multilayer Si<inf>3</inf>N<inf>4</inf> had one-third less oxygen concentration at the interfaces. This decreased density of electrically active centers of oxygen traps and improved quality of nanocapacitors with multilayer Si<inf>3</inf>N<inf>4</inf> dielectric.
Biomimetic Cells: A New Frontier in Brain Informatics

This paper introduces a new imitation of neurons cells, based on the latest discoveries in neuroscience. After reobserving the latest revelations in the field of biological neurons, the conventional artificial models has proven strong potentials in image processing and pattern classification, but remains far from presenting a modern imitation of natural intelligent organisms. A Biomimetic cell design is thus proposed with a combination of registers to hold the inputs, outputs, and weights as information codes. These cells use binary equivalence gates to compare the inputs to the weights and deliver the required outputs. The abstraction model provided renders the training process highly simplistic, which speeds up the design phase and opens the way towards a new dimension in artificial intelligence.
HARK-Bird-Box: A Portable Real-time Bird Song Scene Analysis System

This paper addresses real-time bird song scene analysis. Observation of animal behavior such as communication of wild birds would be aided by a portable device implementing a real-time system that can localize sound sources, measure their timing, classify their sources, and visualize these factors of sources. The difficulty of such a system is an integration of these functions considering the real-time requirement. To realize such a system, we propose a cascaded approach, cascading sound source detection, localization, separation, feature extraction, classification, and visualization for bird song analysis. Our system is constructed by combining an open source software for robot audition called HARK and a deep learning library to implement a bird song classifier based on a convolutional neural network (CNN). Considering portability, we implemented this system on a single-board computer, Jetson TX2, with a microphone array and developed a prototype device for bird song scene analysis. A preliminary experiment confirms a computational time for the whole system to realize a real-time system. Also, an additional experiment with a bird song dataset revealed a trade-off relationship between classification accuracy and time consuming and the effectiveness of our classifier.
Mathematical Modeling of the Linear Drive for Research Viscoelastic Properties of Elastomer

The article is devoted to solving the problem of mathematical and simulation modeling of linear magnetoelectric drive (LMED) for the research of rheological characteristics of elastomers. A special feature of the linear drive is lack of transmitting and convertive elements between the anchor, which is a clamping rod, and a test sample of the elastomer, what makes it necessary to include equations describing nonlinear viscosity processes in the General model of the LMED when solving the modeling problem. The possibility of using multi-circuit equivalent circuits, which can be synthesized on the basis of experimental data obtained for the test sample of a certain brand of elastomer under its loading to fixed values of the relative strain, is shown. As a result of the relaxation process of the test sample of the elastomer, the exponential time dependence of the mechanical stress is investigated, which can be subsequently represented by the sum of the exponents with different time constants. The analogy in the recording of the time dependence of the mechanical voltage and current in the electrical circuit containing RC-elements, as well as fixed deformation and constant voltage of the power supply makes it possible to establish a correspondence between the specified values and to create an equivalent circuit of substitution of the test sample of the elastomer. The number of parallel branches of the substitution scheme, as well as its parameters can be determined on the basis of an experiment to remove the time dependence of the mechanical stress at a fixed value of the relative strain. The article also presents a General mathematical model and its implementation in the software MATLAB/Simulink
Band of Brothers and Bolts: Caring About Your Robot Teammate

It has been observed that a robot shown as suffering is enough to cause an empathic response from a person. Whether the response is a fleeting reaction with no consequences or a meaningful perspective change with associated behavior modifications is not clear. Existing work has been limited to measurements made at the end of empathy inducing experimental trials rather measurements made over time to capture consequential behavioral pattern. We report on preliminary results collected from a study that attempts to measure how the actions of a participant may be altered by empathy for a robot companion. Our findings suggest that induced empathy can in fact have a significant impact on a person's behavior to the extent that the ability to fulfill a mission may be affected.
Recognition and Use of Emotions in Games

Facial expression and emotions recognition had attracted the attention of game developers and there are some game proposals, in which, by detecting faces, it is possible to detect who is playing and automatically load the players' profile. Likewise, the recognition of the players' facial expressions has been used to alter the expression of their avatars. In this work, we intended to study ways to use the recognition of a player's facial expression to change some parameters of a game, such as stimulate it when the player is bored or increasing its difficulty, for example, when it detects that the player is more stimulated and happy. This paper proposes a new approach for facial expression and emotion recognition, which divides the classification stage into two steps. First it is detected if it is a positive, a negative or a neutral emotion. Then it is applied a refinement step to recognize the particular positive (happiness or surprise) or negative (sadness, fear, anger or disgust) emotion. Our approach runs in real-time and was compared with the most common classification methods and has shown a great accuracy. We built a game to test our approach. The estimation of the player's emotional state was used to change certain parameters of the game, which increases or decreases the game difficulty.
Destriping of Multispectral Remote Sensing Image Using Low-Rank Tensor Decomposition

Multispectral image (MSI) destriping is a challenging topic and has been attracting much research attention in remote sensing area due to its importance in improving the image qualities and subsequent applications. The existing destriping methods mainly focus on matrix-based modeling representation, which fails to fully discover the correlation of the stripe component in both spatial dimensions. In this paper, we propose a novel low-rank tensor decomposition framework based MSI destriping method by decomposing the striped image into the image component and stripe component. Specifically, for the image component, we use the anisotropic spatial unidirectional total variation (TV) and spectral TV regularization to enhance the piecewise smoothness in both spatial and spectral domains. Moreover, for the stripe component, we adopt tensor Tucker decomposition and $\ell _{2,1}$-norm regularization to model the spatial correlation and group sparsity characteristic among all bands, respectively. An efficient algorithm using the augmented Lagrange multiplier method is designed to solve the proposed optimization model. Experiments under various cases of simulated data and real-world data demonstrate the effectiveness of the proposed model over the existing single-band and MSI destriping methods in terms of the qualitative and quantitative.
A Practical Method to Speed-Up the Experimental Procedure of Iterative Learning Controllers

This paper proposes a practical approach for fastening the lengthy experimentational processes that may occur with iterative learning control (ILC) upto a certain level using simple low-order identified models. The traditional practice in ILC experiments is to update the ILC signal by directly using the experimental data after each run of the process which corresponds to one ILC update per one run. When considered from the point of experimental time, even conducting a moderate number of ILC updates can take quite long with this procedure. Since an accurate linear model can adequately represent the actual system upto a certain amplitude and/or frequency of the desired reference, we propose that the total experimental time can be reduced by updating the ILC signal via predicted system data until the limits of the linear model. This approach allows one to carry out large number of ILC updates while not needing to carry out the same amount of real experiments. Consequently, a significant number of experiments that would be needed for achieving the same results can be skipped with a simulation approach. The efficiency of the proposed method was tested through experimentation with three different UAV reference trajectories and the results demonstrated that it is possible to attain significant amount of tracking precision in several flight experiments.
Motion Generators Combined with Behavior Trees: A Novel Approach to Skill Modelling

Task level programming based on skills has often been proposed as a mean to decrease programming complexity of industrial robots. Several models are based on encapsulating complex motions into self-contained primitive blocks. A semantic skill is then defined as a deterministic sequence of these primitives. A major limitation is that existing frameworks do not support the coordination of concurrent motion primitives with possible interference. This decreases their reusability and scalability in unstructured environments where a dynamic and reactive adaptation of motions is often required. This paper presents a novel framework that generates adaptive behaviors by modeling skills as concurrent motion primitives activated dynamically when conditions trigger. The approach exploits the additive property of motion generators to superpose multiple contributions. We demonstrate the applicability on a real assembly use-case and discuss the gained benefits.
Novel Functional-Imperative Programming Language El: A Brief Introduction

The paper discusses the tendency of interpenetration of ideas and technologies of functional and imperative paradigms of programming in their modern implementation. A new functional-imperative programming language El is proposed, which in many respects resembles the functional language of Erlang, but differs from it by some features. A brief introduction to the lexis, syntax and semantics of El is given. Its main distinguishing features and characteristics are distinguished: pure functions, first and higher order functions, anonymous functions, closures, function signature overload, garbage collection at the moment of return from the function, imperative principle of operations execution in the body of the function, high-level data types and operations with them, variability of static and dynamic typing as well as the immutability of variables by the choice of the programmer, the availability of primitive and object types of data, brevity, simplicity and convenience of control structures, the possibility of an explicit cycles programming instead of the more expensive recursive function calls, which, however, can also be used. A typical structure of a file containing a program module is described, a list and purpose of its sections, definitions of functions, and all kinds of control language operators are given. A summary of significant distinctive characteristics of the language is given. The current state of the development and implementation of the program translator from the El language for different target platforms using the LLVM compiler infrastructure is described.
Fast Line-Based Intra Prediction for Video Coding

Intra prediction plays a very important role in current video coding technologies like the H.265/High Efficiency Video Coding (HEVC) standard, the Joint Exploration Test Model (JEM) and the upcoming Versatile Video Coding (VVC) standard. In previous work we proposed a Line-Based Intra Prediction algorithm to improve the state-of-art coding performance of HEVC and JEM. This method divides (horizontally or vertically) a block into lines and then it codes each of them individually in a sequential manner. At the encoder side, however, it is necessary to select an optimal combination of intra mode and 1-D split type in a Rate-Distortion sense. Since testing for every block all possible combinations of these two parameters would imply a very significant increase in the encoder complexity, this paper proposes several fast algorithms to reduce the number of tests and improve the overall trade-off between complexity and gain. The experimental results show a reduction of the encoder run-time from 322% to 166% in exchange for a loss of 0.34% for the All Intra configuration and from 151% to 116% for a loss of 0.15% in the case of Random Access.
Prognostic Health Management for LED with Missing Data: Multi-task Gaussian Process Regression Approach

Light emitting diodes (LEDs) are increasingly used in a number of applications such as display backlighting, signaling, general illumination, medical services etc. They have high efficiency, environmental resilience, high reliability and long lifetime. Normally, one has to resort to using sequential updating methods such as the particle filtering approach for effective prognosis and lumen maintenance life prediction of LEDs because of their long lifetime and reliability. However, the performance of the particle filter severely depends on the selection of the initial parameters. Moreover, it cannot deal with situations where there is missing data due to intermittent sensor failure or malfunction. The multi-task Gaussian process regression (MTGP) can model multiple correlated multivariate time series simultaneously. Thus, it can be used to learn the correlation between multiple degradation trends (from different similar units tested) even though they might be sampled at different frequencies and have training sets available only for different time spans. This paper presents the multi output (task) - Gaussian process regression as a solution for prognosis of lumen maintenance life of LED devices in the event of "missing" data. We analyze the performance of the MTGP on the LED data extracted from previously published studies and demonstrate that it can give good prediction results even when there are missing data.
Soft Fabric Actuator for Robotic Applications

This paper presents a fabric actuator consisting of ordinary polymer fibers, conductive fibers, and twisted and coiled soft actuators (TCAs). Previous studies have developed a Spandex TCA (STCA) that is driven at a lower temperature than the conventional Nylon TCA and exhibits greater actuation strain. However, no method to drive STCAs via electrical joule-heating has been developed yet. The fabric actuator presented in this paper offers a solution to this problem by employing an STCA multiple fabrication method, a continuous fabrication method, bundling technology, and weaving technology. Two types of samples (cylindrical and planar) are fabricated and their performances are evaluated experimentally. From the actuation test according to the loads, the maximum contraction strain of 34.3% is measured. The repeatability is also verified through 200 cycles of actuation. Using a linearized model, the dynamic performance of the fabric actuator is predicted and compared with experimental results. An actual human arm size mannequin is driven by applying the fabric actuator, and angle control can be achieved with an encoder mounted on the joint. In addition, fabric actuator is weaved to sweater showing the possibility of wearable assistive robot.
Ultrasound-Assisted Guidance With Force Cues for Intravascular Interventions

Image guidance during minimally invasive intravascular interventions is primarily achieved based on X-ray fluoroscopy, which has several limitations including limited 3-D imaging capability, significant doses of radiation to operators, and lack of contact force measurement between the cardiovascular tissue and interventional tools. Ultrasound imaging can be adopted to complement or possibly replace 2-D fluoroscopy for intravascular interventions due to its portability, safety to use, and the ability of providing depth information. However, it is challenging to precisely visualize catheters and guidewires in the ultrasound images. In this paper, we propose a novel method to figure out both the position and orientation of the catheter tip in 2-D ultrasound images in real time by detecting and tracking a passive marker attached to the catheter tip. Moreover, the contact force can be estimated simultaneously as well via measuring the length variation of the marker. A geometrical model-based method is introduced to detect the initial position of the marker, and a Kanade–Lucas–Tomasi-based algorithm is developed to track the position, orientation, and length of the marker. The ex vivo experiment results validate the effectiveness of the proposed approach in automatically locating the catheter tip in ultrasound images and its capability of sensing the contact force. Therefore, it can be concluded that the presented method can be utilized to better facilitate operators during intravascular interventions. Note to Practitioners—This paper is motivated by the vision-based force sensing mechanism. A coil spring marker is used to visualize and locate the catheter tip in ultrasound images. The metallic helical structure of the marker can result in remarkable features in 2-D in-plane ultrasound images. A geometrical model-based detection algorithm is proposed to detect the passive marker in initial frame regardless of its length and orientation variation. A prediction–detection-combined tracking algorithm is developed to automatically track the catheter tip for the subsequent frames in real time. Most importantly, the distal force can be simultaneously estimated due to the length variation of the passive marker. The sensed force can be fed back to a robotic catheterization system during intravascular interventions.
Quaternion Joint: Dexterous 3-DOF Joint Representing Quaternion Motion for High-Speed Safe Interaction

This paper presents a dexterous three degree-of-freedom (3-DOF) wrist mechanism with a large range of motion and uniform manipulability without singular points throughout the entire range of motion. It has a 2-DOF spherical pure rolling joint surrounded by two pairs of actuating wires, the motions of which directly represent the quaternion values of the joint; this joint is therefore named the quaternion joint. Based on this property, it has simple and clear forward and inverse kinematics and high manipulability. By adding a 1-DOF rotation joint at the distal end of the quaternion joint, it can be extended to a 3-DOF joint mechanism. To precisely approximate the spherical pure rolling motion in a confined central space, a novel parallel mechanism composed of three identical supporting linkages was introduced. Unlike conventional parallel mechanisms, it has a compact and simple structure with no interference among the supporting linkages. Because the wrist mechanism is a tendon-driven mechanism, and is thus suitable for lightweight manipulators, it is mounted to a low-inertia manipulator with high stiffness and strength, namely, LIMS2-AMBIDEX, which is an improved version of the authors' previous research. The basic concept and thorough theoretical analysis of the wrist mechanism are described herein, and the simulations and experiments conducted for a quantitative validation are presented.