Miracle-dz/UniFAR-FTUs huggingface dataset:
One-class Random Maxout Probabilistic Network for Mobile Touchstroke Authentication

Continuous authentication (CA) with touch stroke dynamics is an emerging problem for mobile identity management. In this paper, we focus on one of the essential problems in CA namely one-class classification problem. We propose a novel analytic probabilistic one-class classifier coined One-Class Random MaxOut Probabilistic Network (OC-RMPNet). The OC-RMPNet is a single hidden layer network that is tailored to capture individual users' touch-stroke profiles. The input-hidden layer of the network is meant to project the input vector onto the high dimensional random maxout feature space and the hidden-output layer acts as an OC probabilistic predictor that trained by means of least-square principle, hence require no iterative learning. We also put forward a feature sequential fusion mechanism for accuracy improvement. We scrutinize and compare the proposed methods with existing works on touchanalytics and HMOG datasets. The empirical results reveal that the OC-RMPNet prevails over its predecessor in touch-stroke authentication tasks on mobile phones.
Analytical Model of Slotless Brushless PM Linear Motors Considering Different Magnetization Patterns

This paper presents a two-dimensional (2-D) analytical model for plannar linear permanent-magnet (PM) synchronous motors. The general and particular solutions are presented for a slotless single-sided linear PM synchronous motor as a case study. The governing partial differential equations of the motor are formulated for seven regions: mover side exterior, mover back iron, PMs, air-gap, winding, stator back-iron, and stator-side exterior. Four different magnetization patterns, i.e., parallel, ideal Halbach, 2-segment Halbach, and bar magnets in shifting directions magnetization patterns are considered to calculate the tangential and normal components of the open-circuit magnetic flux density and armature reaction of the motor under the study. The winding-induced voltage, flux linkage, self- and mutual inductances, and normal and tangential components of forces are computed. The validity of the proposed technique is confirmed by comparing the analytical results with the results obtained from a 2-D finite-element analysis.
Identification of parametric models in the frequency-domain through the subspace framework under LMI constraints

An extension of standard subspace-based algo-rithms to identify parametric systems from frequency response samples is presented. This algorithm uses arbitrary frequency spacing and allows frequency weighting. It offers the possibility to impose the model's poles location through LMI constraints. This technique is applied to a numerical example and to real industrial frequency-domain data originating from an open- channel flow for hydroelectricity production.
The Impact of Information System: A Network Analysis Perspective

This paper focuses on the role of information system for change management by exploring insights from social network analysis. In the framework of business process management, the adoption of a new information system largely impacts over customers, suppliers and contractors of a company. In our work we model companies as nodes and relationships as edges connecting companies, including information on direction, datetime as well as weighted by numbers of orders. The star-network analysis of a real industrial case describes relationships with company's suppliers, contractors and customers before and after the introduction of a Manager Resource Planning. We compute a temporal networks analysis with a monthly granularity to investigate time-related pattern changing. Finally, networks metrics describe the relevance and impact of the adoption of a Management Resource Planning to promote product and process innovations across industries.
Mixed-kinematic body sideslip angle estimator for high performance cars

Body sideslip angle estimation is a well-known issue in the automotive field. Many vehicle dynamic control systems are based on the control or monitoring of the sideslip angle. Unfortunately, measurement of this quantity requires expensive instrumentation that commercial cars can not be equipped with. This paper further develops the so-called hybrid dynamic-kinematic approach to the problem providing an automated tuning approach. A state observer merges a kinematic model with a lateral dynamic one, so that they can compensate each other shortcomings. The resulting estimator depends on a number of parameters whose manual tuning require expertise. The paper proposes a grey-box approach for the tuning of the algorithm. The optimization-based approach requires only standard track tests and no special vehicle characterization. The approach is validated on a large experimental dataset from a high performance car.
Gait-Based Human Gender Classification Using Lifting 5/3 Wavelet and Principal Component Analysis

This study describes a representation of gait appearance for the purpose of person identification and classification. The gait representation is based on wavelet 5/3 lifting scheme simple features such as features extracted from video silhouettes of human walking motion. Regardless of its effortlessness, this may lead us to say that, the resulting feature vector contains enough information to perform well on human identification and gender classification tasks. We found out the recognition behaviors of different methods to total features over time functions under different recognition tasks. In addition to that, we provide results of gender classification based on our gait appearance features using a (C4.5 algorithm). So, the result of classification rate for CASIA - B gait databases is 97.98% and the result of recognition rate for OU-ISIR gait Database Large Population Dataset is 97.5%, these results have been obtained from gender classification data. Gait database demonstrates that the proposed method achieves better recognition performance than the most existing methods in the literature, and particularly under certain walking variations.
Human-Robot Collaboration based on Robust Motion Intention Estimation with Prescribed Performance

This paper addresses the problem of physical human-robot collaboration for object manipulation. In particular, we consider a human-robot architecture where the human has exclusive knowledge of the object’s desired trajectory and the robot tries to assist actively, by carrying the object’s load in order to reduce the human effort that is required to achieve the desired tracking behavior. The robot estimates the human’s desired motion via a prescribed performance estimation law that drives the estimation error to an arbitrarily small residual set. This estimation is further employed in the object dynamics equation to compute the interaction force between the human and the object. Subsequently, an impedance control scheme is designed based on the aforementioned estimations, achieving significant reduction on the required human effort, despite the uncertainty in the robot dynamics. The feedback relies exclusively on the robot’s force/torque, position as well as velocity measurements and no a priori explicit information on the task is required. Finally, extensive experimental results clarify the proposed method and verify its efficiency.
A Hybrid type ADMM for Multi-Block Separable Convex Programming

The alternating direction method of multiplier (ADMM) is a popular method for solving composite convex minimization problems with separable linear constraints. Unfortunately, the direct extension of the ADMM for multi-block problems is not necessarily convergent. To address this issue, several variants of the ADMM were proposed, among which the parallel splitting algorithm has attracted much attention due to its efficiency and simplicity. However, a major drawback of the parallel splitting algorithm is that the weighting factor placed on the proximal term has to be greater than a certain value in order to ensure the convergence. A large weighting factor has the effect of forcing the current solution to stay close to its previous solution, thus leading to a slow convergence speed. In this paper, we propose a new hybrid type ADMM for multi-block separable convex programming. The proposed method places a much smaller weighting factor on the proximal term. Thus the proposed algorithm has the potential to achieve faster convergence rates. Numerical results are provided to illustrate the efficiency of the proposed algorithm.
Comparison of possible realizations of quantum ratchet intermediate band solar cells

Three fundamentally different approaches for the realization of a quantum ratchet intermediate band solar cell are compared. The quantum ratchet is a mechanism by which an energy loss of excited electrons is turned into an improvement in the efficiency of intermediate band solar cells by reducing carrier recombination in the system. The main requirement for this is to engineer forbidden transitions which can be achieved through vanishing spatial overlap of the wave-functions, spinselection rules or a momentum mismatch.
FPGA Implementation of Gradient Based Edge Detection Algorithms for Real Time Image

In this paper a novel technique to implement gradient based edge detectors on FPGA platform is demonstrated. Three most popular gradient based edge detection algorithms viz. Sobel, Prewitt and Robert Cross are modelled in hardware description language and implemented on Terasic VEEK-MT FPGA development board. The RGB image is first transformed into gray scale using luminosity method which is fed to line buffers to apply appropriate edge detection mask. An edge detection processor block is designed wherein the relevant filter mask is applied followed by generation of horizontal and vertical gradient. The root mean square value of the two gradient components is computed. The pixel value so obtained is compared against a user defined threshold before applying to the LCD module for display. The proposed implementation is tested on both still and video images. Implementation result endorses the successful design of gradient based edge detection algorithm on FPGA platform.
On-body Sensing and Signal Analysis for User Experience Recognition in Human-Machine Interaction

In this paper, a new algorithm is proposed for recognition of user experience through emotion detection using physiological signals, for application in human-machine interaction. The algorithm recognizes user’s emotion quality and intensity in a two dimensional emotion space continuously. The continuous recognition of the user’s emotion during human-machine interaction will enable the machine to adapt its activity based on the user’s emotion in a real-time manner, thus improving user experience. The emotion model underlying the proposed algorithm is one of the most recent emotion models, which models emotion’s intensity and quality in a continuous two-dimensional space of valance and arousal axes. Using only two physiological signals, which are correlated to the valance and arousal axes of the emotion space, is among the contributions of this paper. Prediction of emotion through physiological signals has the advantage of elimination of social masking and making the prediction more reliable. The key advantage of the proposed algorithm over other algorithms presented to date is the use of the least number of modalities (only two physiological signals) to predict the quality and intensity of emotion continuously in time, and using the most recent widely accepted emotion model.
A Compact  $W$ -Band Substrate-Integrated Cavity Array Antenna Using High-Order Resonating Modes

A planar <inline-formula> <tex-math notation="LaTeX">${W}$ </tex-math></inline-formula>-band array antenna is presented in this communication with a high gain and compact footprint. In order to simplify the feed network and reduce the feeding loss, a high-order resonating mode, i.e., the TE<sub>560</sub>-like mode, is employed for radiations. Thus, a single-elementantenna can support 30 radiation slots on the top surface of the substrate-integrated cavity. Full-wave analysis along with measured results is given to explain the operational principle and performance of the proposed antenna. A <inline-formula> <tex-math notation="LaTeX">$20\times 24$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$40\times 48$ </tex-math></inline-formula> slot arrays based on the proposed high-order resonating mode element antenna are fabricated and measured to demonstrate array applications. The measured results show that these two arrays have a −10 dB impedance bandwidth from 91 to 97 GHz. The measured peak gain is 26.39 dBi at 95.3 GHz and 30.36 dBi at 95.2 GHz for the <inline-formula> <tex-math notation="LaTeX">$20\times 24$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$40\times 48$ </tex-math></inline-formula> slot arrays, respectively.
Adaptation of Cognitive Radio Parameters Using Evolutionary Algorithms

Cognitive Radio (CR) falls under the category of software-defined radio (SDR), which has been viewed as a groundbreaking technology, and can modify its transmission parameters as per predetermined objectives and simultaneously utilize the spectrum bands efficiently. In this research paper, a scenario, which is in the form of a fitness function, is examined in the cognitive networks where interference constraints are characterized to ensure that there is no degradation of Quality of Service (QOS) parameters for users, both primary as well as auxiliary. Minimization of spectral interference and throughput maximization are taken as objective functions in this paper. The transmission parameters are passed through the fitness functions and every set of solutions has a fitness value attached to it, which this paper aims to optimize while ensuring that the parameters stay in the stipulated range. Furthermore, the optimization of the objectives is performed using evolutionary algorithms, namely-Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) and a comparative analysis is performed and documented.
Energizing or Depleting? Understanding the Effects of Agile Methodologies on Individual Software Developers' Resources

Despite a considerable number of valuable studies in information systems research investigating agile software development methods (ASDM), the impact of ASDM on individual software developers and their inner resources in particular remains ambiguous. Findings so far support both directions, an energizing and a draining effect of ASDM application. Using ego depletion theory as a lens, I conducted a survey with 207 agile developers to examine how the use of ASDM influences individual job outcomes (i.e. job satisfaction and turnover intention) through the mediating effect of developers’ level of depletion expressed by feelings of fatigue. My findings show that while engineering ASDM reduced fatigue (energizing), management ASDM practices did not affect fatigue (neither energizing nor depleting). Moreover, I could discern fatigue as underlying mechanism that explains the effects of engineering ASDM on job outcomes. With this nuanced perspective, this study contributes to a more balanced and consistent picture of ASDM use.
Pigeon Inspired Optimized PI-controller Based Direct Power Control of 2-Level PWM Rectifier

This paper presents Pigeon inspired optimization (PIO) based direct power control of Bidirectional 2-level pulse width modulated (PWM) rectifier. Instead of the traditional PI controller, PIO optimized controller is used in this paper to reduce the peak overshoot and ripple in active power. The Switching table based PIO algorithm optimized controller for the direct power control (DPC) is simulated in this paper. And the results indicate that the proposed optimized controller achieves excellent transient performance under unity power factor operation without abnormal instantaneous reactive power fluctuations. The total harmonic distortion (THD) in the source current obtained by the PIO is also less than the THD obtained by the conventional table-based PI controller.
Localization Based on Semantic Map and Visual Inertial Odometry

Autonomous vehicles require precise localization for safe control. This paper presents a localization approach based on semantic map and visual inertial odometry for autonomous vehicles. Our approach uses consumer grade parts, and only relies on a single front camera and a consumer grade IMU and a GPS. Using real-time semantic landmark detection and real-time visual inertial odometry, we localize the full 6-DOF pose of the vehicle in the semantic map with mean absolute accuracy at less than 20 cm, With this accuracy, we can achieve high levels of autonomy, and speed up the evolution of autonomous driving. The main contributions of our approach are: (i) 2D-3D semantic landmark matching in continuous frames; (ii) full 6-DOF pose optimization with semantic constraints in a sliding time window.
User Experience Evaluation on the Cryptocurrency Website by Trust Aspect

A portal into the public ledger cryptocurrency makes a competition of the best web sites and easily trusted by the public. These are believed to constitute a measurable dimensions of User Experience (UX). This study aims to evaluate the user experience of the use of the three web sites most frequenty accessed cryptocurrency from Indonesia. The evaluation conducted aimed at knowing the factors that influence user trust through the design of the interface and can be installed on a new design. Methods include Performance Metrics, Post-Task Rating, Post-Session Rating, and Experiential Overview and eye-tracking device. Based on the results of research, in the overall evaluation of the web site, the web site is the most superior of Indodax. The results of the evaluation are then applied on a new design using the software Invision and examined again to see a comparison of the respondent at the time of first use. The result of the research is the assessment, recommendations, and design the look of the web site cryptocurrency are trustworthy based on user experience.
Neural Network Knowledge Transfer using Unsupervised Similarity Matching

Transferring the knowledge from a large and complex neural network to a smaller and faster one allows for deploying more lightweight and accurate networks. In this paper, we propose a novel method that is capable of transferring the knowledge between any two layers of two neural networks by matching the similarity between the extracted representations. The proposed method is model-agnostic overcoming several limitations of existing knowledge transfer techniques, since the knowledge is transferred between layers that can have different architecture and no information about the complex model is required, apart from the output of the layers employed for the knowledge transfer. Three image datasets are used to demonstrate the effectiveness of the proposed approach, including a large-scale dataset for learning a light-weight model for facial pose estimation that can be directly deployed on devices with limited computational resources, such as embedded systems for drones.
Embedded nonlinear model predictive control for obstacle avoidance using PANOC

We employ the proximal averaged Newton-type method for optimal control (PANOC) to solve obstacle avoidance problems in real time. We introduce a novel modeling framework for obstacle avoidance which allows us to easily account for generic, possibly nonconvex, obstacles involving polytopes, ellipsoids, semialgebraic sets and generic sets described by a set of nonlinear inequalities. PANOC is particularly well-suited for embedded applications as it involves simple steps, its implementation comes with a low memory footprint and its fast convergence meets the tight runtime requirements of fast dynamical systems one encounters in modern mechatronics and robotics. The proposed obstacle avoidance scheme is tested on a lab-scale autonomous vehicle.
Evaluation of Naïve Bayes and Support Vector Machines on Bangla Textual Movie Reviews

The ever-growing World Wide Web (WWW) containing textual movie reviews from Facebook groups, personal blogs, newspapers, dedicated movie review sites has crucial impacts on intended users and review providers where users can find the desired movies in his preference by checking the reviews and review providers can build a better recommendation system that influences the marketing policy and revenues generated from advertising campaigns. The manual approach to analyze this textual reviews is complex and time-consuming and hence it requires specialized automated systems. Sentiment Analysis (SA) is such a tool which is the computational study of extracting opinions, emotions from textual data to build such automated systems and it has been implemented in many languages for movie review domain except Bangla; which is continuously increasing due to the emergence of Bangladeshi reviewers on WWW. Since none of the existing SA works addressed this domain, in this paper, we have developed a polarity detection system on textual movie reviews in Bangla by using two popular machine learning algorithms named Naive Bayes (NB) and Support Vector Machines (SVM) and provided a comparative results where SVM performed slightly better than NB by considering stemmed unigram as feature with an excellent precision of 0.86.
Practical Tracking Control of Cable-Driven Robots Using Adaptive Nonsingular Fast Terminal Sliding Mode

In this paper, a novel robust control scheme is developed for the trajectory tracking control purpose of cable-driven robots under complex lumped uncertainties. The proposed control scheme utilizes time-delay estimation (TDE) technique to obtain the lumped unknown system dynamics; and therefore ensures an attractive model-free advantage. A novel adaptive nonsingular fast terminal sliding mode (ANFTSM) manifold is designed considering its uncertainties, which is estimated by a newly designed adaptive law. Afterward, corresponding TDE-based ANFTSM control scheme is proposed. The proposed control scheme requires no system dynamics and therefore is easy to use in complicated practical applications benefitting from TDE technique. Meanwhile, high tracking accuracy and fast dynamical response can be effectively ensured using the proposed ANFTSM manifold. Stability of the closed-loop control system is proved through Lyapunov method. Finally, comparative simulations and experiments were conducted to demonstrate the superiorities of our proposed method over the existing ones.
Respiratory Rate Monitoring by Video Processing Using Local Motion Magnification

Breathing monitoring by non-contact video processing has been the subject of recent research. This paper presents an advanced video processing algorithm for reliable Respiratory Rate (RR) monitoring based on the analysis of local video variations and a motion magnification method. This novel algorithm improves over the existing solutions in terms of estimation accuracy and excision of large body movements unrelated with respiration. Applications to adults and infants are presented to demonstrate the performance of the proposed algorithm and compare it with previous work.
A Novel Recursive Bayesian Weighted Instrumental Variable Estimator for 3D Bearings-Only TMA

In our previous work [17], we derived a novel Bayesian weighted instrumental variable (WIV) estimator for the three-dimensional bearings-only target motion analysis problem. While the proposed approach has the desirable characteristic of incorporating a priori information in the estimation process and is proven to be approximately asymptotically unbiased, this estimator has a batch structure which is generally not suitable for online processing of measurements in practical applications. Therefore, in this paper we develop a recursive Bayesian WIV, which also uses an adaptive selective angle measurement approach to increase its stability. Simulations show that the proposed estimator outperforms the compared Bayesian algorithms with similar computational complexity for poorly observable scenarios.