Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft-Sensor based on $k$-NN classifiers in Agricultural Pest Control

The quality of agricultural sprays plays an important role in the application of chemical products dedicated to plant protection and food production. In such context, the effects of nozzle type, standard and size, as well as the operating pressure should be well observed. Measured droplet sizes and velocities are also important, and both can be affected by nozzle type, size, and operating pressure. Therefore, control aspects have become necessary to improve quality and to reduce undesirable externalities in the agricultural environment. This paper, presents a soft-sensor based on the $k$-NN classifier and estimator, which requires an attribute vector with the spraying quality descriptors, and delivers an estimation for the best values of the descriptors to obtain an adequate quality in relation to the spraying in agricultural pest control. The results of this research reveal the usefulness of such soft-sensor architecture based on estimation theory and added value in the application of agricultural pesticides.
Welcoming a Holographic Virtual Coach for Balance Training at Home: Two Focus Groups with Older Adults

We report on findings from two focus groups for designing an application for balance training at home with an augmented reality virtual coach. Following a User-Centered Design approach, we performed two focus groups with older adults at the early stages of development. Focus group participants were shown a prototype using a Meta 2 head mounted display. Their movements were tracked using a Kinect 2. The virtual coach gave balance training instructions and demonstrated their correct performance. Results suggest that, given the trade-offs of traditional health care, older adults are positive towards using an AR coach for their balance training.
A novel approach in Optimal trajectory identification for Autonomous driving in racetrack

The autonomous vehicle is one of the greatest challenges in modern vehicle design. This paper proposes a new technique to define the optimal trajectory in a feedback form for an autonomous car, moving on a track. The algorithm defines the trajectory taking into the account the vehicle dynamic instead of kinematic constraints, leading to a more robust path. The technique is also used to control the vehicle in feedback, providing the optimal maneuvers to track the defined path. The method is based on the control law named FLOP, Feedback Local Optimality Principle, recently developed by the authors. The technique, starting from the Pontryagin's theory, introduces a new optimality principle that minimizes a sequence of individual functionals with the chance of a direct feedback control. The control acts on the steering and torques of a two-wheeled vehicle.
Experimental Verification of Sum-Of-Squares-Based Controller Tuning Technique with Extension to Parallel Multimodel Uncertainty Processing

Many control schemes require significant tuning effort to achieve desired performance targets, causing a need for general tools that can perform controller tuning in an automated fashion. This paper represents a continuation of the previous work in which a tuning method capable of designing Explicit Model Predictive Controllers for control of nonlinear systems was developed. Besides demonstrating a broader applicability of the tuning method by applying it here to a non-optimization-based, nonlinear control policy, the primary purpose of this paper is to provide experimental validation of the method by its application to a physical system, as well as to extend the method's practical computational capability in case of multimodel plant uncertainty. The experimental results consist of the method's application to tuning of an anti-windup equipped PID controller which is designed to be robustly stable with respect to multimodel uncertainty in the considered mechanical experimental setup.
A Compact Six-Element MIMO Array For Future 5G Mobile Terminal

A compact six-element Multiple input multiple out (MIMO) array for 5G Mobile Applications was designed. The proposed six-element MIMO array consists of 3D-monopole antenna element, which was bent over a minimum-sized (8mm×8mm×5mm) cuboid FR4-support. The operating frequency of the antenna covered the 5G communication bands of 3.3~3.6GHz and 4.8~5.0GHz in China. High isolation is achieved by placing the element in a reasonable position. The simulation results show that the S-parameters and isolation are greater than 10dB in the range of 3.3~3.6GHz and 4.8~5.0GHz. The efficiency is 85%~95% (3.3~3.6GHz) and 60%~70% (4.8~5.0 GHz), respectively. The six-element MIMO array has some good radiation diversity characteristics, Envelope Correlation Coefficient (ECC) is much less than 0.5. The antenna MIMO array meets the design requirements of the 5G communication terminal antenna.
Magnetic Anomaly Target Recognition Based on SVD and SVMs

For the problem of signal feature extraction detected by the magnetic anomaly detection equipment based on the flux gate array, we propose a method of extracting magnetic anomaly signal features by singular value decomposition (SVD) and classifying the magnetic targets using support vector machines (SVMs). The detected magnetic anomaly signal is the singular value decomposed as a 2-D real matrix. An orthogonal basis of a 2-D complete feature space is constructed by the direct product of its left eigenvector matrix and right eigenvector matrix. The coefficient of intersection is the eigenvalue of the magnetic anomaly matrix and can be used as a power density of the magnetic anomaly in its feature space. At the same time, magnetic anomaly signals are reduced continuously to the pole for obtaining high-quality signals and a more complete sample library. Subsequently, nine classification features, such as magnetic gradient tensor elements, were analyzed and selected. The orthogonal basis coefficients of these nine feature matrices were calculated and constructed as feature vectors. The ferromagnetic targets were identified by SVMs when the feature vectors are used. Finally, through experiments, the recognition accuracy rate reaches 90%, and a good target recognition effect is achieved.
Sliding mode control of a differential-drive mobile robot following a path

This paper presents a control algorithm for a differential-drive robot following a path. The main contributions are: a new control formulation that does not require the robot global position, and a nonlinear controller based on the sliding mode control approach that guarantees stability in both forward and backward motion. Numerical simulations are provided to validate the proposed algorithm.
Segmentation of Building Footprints with Xception and IoUloss

Advanced sensors are increasingly used in aeronautical facility. Broader acquisition of images improves the quality and quantity of remote sensing images and makes a large number of fine remote sensing images available. Although semantic segmentation models based on deep learning has been a great success in computer vision, there are still a lot of difficulties compared to regular visual senses. In this paper, we propose a novel end-to-end model for remote sensing image segmentation of building footprints and a loss function based on IoU to improve the performance. Semantic labels for building structures are being accurately allocated by our method. We make control experiments on Map Challenge dataset with other semantic segmentation model based on recent deep learning advances. Our proposed method generates 94.73% of IoU on the Map Challenge Dataset's validation set, which outperforms all previous models in literature.
Model Predictive Control for Skydiver Fall-Rate Adjustment

An innovative approach of controlling a system with multiple Degrees-of-Freedom performing complex maneuvers is presented. A system under investigation is a human body maneuvering during a free-fall stage of skydiving, which is a rapidly developing sport. The objective is creating an autonomous system capable of performing skydiving maneuvers in a virtual way, and turning it into a powerful tool for improving instruction methods. The key idea is to build a skydiving skill from a set of basic and advanced maneuvers, replicating human natural skill acquisition stages. For each type of maneuver a control algorithm is designed based on the specific plant dynamics, task constraints, and analysis of body actuation possibilities. The main challenge is combining these control schemes to enable the virtual skydiver perform realistic tasks. In the present work we develop a model predictive controller for the fall-rate adjustment, which is the essential skill for performing cooperative maneuvers. We exploit the differential flatness property of the plant in the vertical dimension in order to combine the fall-rate controller with a previously developed Two- Input- Two-Output controller for horizontal maneuvers. The design is tested in a whole body dynamic simulation of a skydiver model. MPC, differential flatness, QFT, non-linear simulation
Reduction of the Leakage Currents by Switching Transition Synchronization for a Four-Switch Buck-Boost Converter

Wide-bandgap transistors are operated at much higher switching transition voltage slopes and current slopes compared to silicon power transistors. For the design of new power electronic devices compactness is a main focus, which increases parasitic capacitances of the device. This trend leads to significantly higher common and differential mode distortions especially at high frequency ranges. To overcome this drawback, efficient filter techniques for these distortions are necessary. This paper focuses on the leakage current reduction in four switch buck-boost converter topologies by synchronizing the switching transitions of the buck and boost stage to achieve a leakage current cancellation. By designing two similar leakage current paths and using destructive interference of two inversely excited leakage currents, the overall leakage current at the heat sink of the device is decreased. This study investigates the effectiveness of the leakage current reduction depending on different operating points for buck and boost operation.
Mission Profile Based Adaptive Carrier Frequency Control for Modular Multilevel Converters for Medium Voltage Applications

In order to increase the efficiency and to improve the reliability of modular multilevel converters in medium voltage applications, this paper proposes an adaptive control method to choose an optimal carrier frequency dynamically according to power loading conditions of the modular multilevel converter. By evaluating the impact of the carrier frequency on the output current harmonic performance and the capacitor voltage ripple analytically, the proposed adaptive control can reduce the power loss and the thermal stress on the power semiconductors. Meanwhile, the electrical operation requirements can still be fulfilled. One-year mission profile is utilized to assess the energy efficiency and the reliability performance between the proposed and the traditional control scheme. Moreover, theoretical analyses and simulation results are presented to demonstrate the effectiveness of the proposed control.
Novel Matching Method for Automatic Iris Recognition Using SIFT Features

The object of interest of this paper is Automatic Iris Recognition. Our approaches extend the successful method based on the Scale Invariant Feature Transform (SIFT) proposed by Aly. We evaluate the Aly's method, and the Kepenekci approach. We have proposed a new extension to the Aly's method. Our approaches are evaluated on two iris datasets: UPOL and UBIRIS. We experimentally show that the proposed approaches significantly outperform the baseline Aly method and Kepenekci approach on both datasets.
Reconciling Being in-Control vs. Being Helped for the Execution of Complex Movements in VR

Performing motor tasks in virtual environments is best achieved with motion capture and animation of a 3D character that participants control in real time and perceive as being their avatar in the virtual environment. A strong Sense of Embodiment (SoE) for the virtual body not only relies on the feeling that the virtual body is their own (body ownership), but also that the virtual body moves in the world according to their will and replicates precisely their body movement (sense of agency). Within that frame of mind our specific aim is to demonstrate that the avatar can even be programmed to be better at executing a given task or to perform a movement that is normally difficult or impossible to execute precisely by the user. More specifically, our experimental task consists in asking subjects to follow with the hand a target that is animated using non-biological motion; the unnatural nature of the movement leads to systematic errors by the subjects. The challenge here is to introduce a subtle distortion between the position of the real hand and the position of the virtual hand, so that the virtual hand succeeds in performing the task while still letting subjects believe they are fully in control. Results of two experiments $(\mathrm{N}=16)$ show that our implementation of a distortion function, that we name the attraction well, successfully led participants to report being in control of the movement (agency) and being embodied in the avatar (body ownership) even when the distortion was above a threshold that they can detect. Furthermore, a progressive introduction of the distortion (starting without help, and introducing distortion on the go) could even further increase its acceptance.
A Dielectric Resonator Antenna with Substrate Ring Based on Substrate Integrated Suspended Line Platform

This paper presents a dielectric resonant antenna (DRA) based on substrate integrated suspended line platform. A new feeding structure of the DRA is presented in this design. The dielectric resonator is embedded in the air cavity and fixed by the hollowed substrate rings, and thus no glue is needed to fabricate the DRA antenna. Metalized holes are introduced to feed the DRA and excite the $TE_{111}^x$ radiation mode. By utilizing the SISL feeding structure, the substrate loss is reduced and an efficiency of better than 94 % is realized. Compared to the conventional DRA which is fed by microstrip line and coupling slot, the proposed design presents merits of low substrate lost, self-packaging and high efficiency. Simulated results show that the proposed DRA obtained 10 dB impedance bandwidth of 1.74 % from 5.13 to 5.22 GHz. At 5.17 GHz, the simulated gain is 5.95 dBi.
A Pursuit-Evasion Game with Incomplete Information

A pursuit-evasion game with partial information is studied. The evader observes the initial conditions only. The pursuer observes both the initial conditions and the relative evader-pursuer position in additive noise. The paper shows that the optimal strategies of a partial information game may differ qualitatively from its full information counterpart. In the particular case at hand the evader's strategy may be random, something that does not happen in the deterministic case.
Parameter Dependent Static Output Feedback Control - An LPV Approach

In this paper, a design approach for a static linear parameter-varying (LPV) output feedback controller is proposed. Based on an approach with linear matrix inequalities (LMIs) for linear time invariant (LTI) systems, a synthesis procedure is obtained. Designing static output feedback controllers in general leads to a bilinear matrix inequality (BMI) problem, which is a nonconvex problem and rather difficult to solve numerically. By introducing additional auxiliary variables, the design problem can be transformed into a convex LMI problem for the LTI case. Compared to nonconvex BMI problems, convex LMI problems can be solved very efficiently. In this work this approach is extended to LPV systems. Furthermore, the approach is applied to a nonlinear control example in order to show the applicability of the presented design procedure.
Abstraction-based Synthesis of Continuous-Time Stochastic Control Systems

This paper proposes a scheme for the construction of discrete-time finite abstractions (a.k.a. finite Markov decision processes) from continuous-time stochastic control systems and quantifying their probabilistic distances. The scheme is based on the notion of stochastic simulation functions enabling us to relate continuous-time stochastic systems with their discrete-time counterparts. Accordingly, one can employ discrete-time abstract systems as substitutions of the continuous-time ones in the controller design process with guaranteed error bounds on their output trajectories. In the first part of the paper, we quantify the distance in probability between original continuous-time stochastic control systems and their discrete-time (finite or infinite) abstractions. In the second part of the paper, we construct finite abstractions together with their corresponding stochastic simulation functions for a particular class of stochastic affine systems having some stability property. We demonstrate the effectiveness of the proposed results by applying our approaches to the temperature regulation in a building of two adjacent rooms and constructing a discrete-time abstraction from its original continuous-time dynamic. We employ the constructed discrete-time abstraction as a substitute to synthesize policy regulating the temperature of rooms for a bounded time horizon.
SeArch: A Collaborative and Intelligent NIDS Architecture for SDN-Based Cloud IoT Networks

The explosive rise of intelligent devices with ubiquitous connectivity have dramatically increased Internet of Things (IoT) traffic in the cloud environment and created potential attack surfaces for cyber-attacks. Traditional security approaches are insufficient and inefficient to address security threats in cloud-based IoT networks. In this vein, software defined networking (SDN), network function virtualization (NFV), and machine learning techniques introduce numerous advantages that can effectively resolve cybersecurity matters for cloud-based IoT systems. In this paper, we propose a collaborative and intelligent network-based intrusion detection system (NIDS) architecture, namely $SeArch$ for SDN-based cloud IoT networks. It composes a hierarchical layer of intelligent IDS nodes working in collaboration to detect anomalies and formulate policy into the SDN-based IoT gateway devices to stop malicious traffic as fast as possible. We first describe a new NIDS architecture with a comprehensive analysis in terms of the system resource and path selection optimizations. Next, the system process logic is extensively investigated through main consecutive procedures, including initialization, runtime operation, and database update. Afterward, we conduct a detailed implementation of the proposed solution in an SDN-based environment and perform a variety of experiments. Finally, evaluation results of the $SeArch$ architecture yield outstanding performance in anomaly detection and mitigation as well as bottleneck problem handling in the SDN-based cloud IoT networks in comparison with existing solutions.
Phase Effect of Orthogonal Modes on 3-dB Axial-Ratio Beamwidth of Circularly-Polarized Patch Antennas

Phase effect of orthogonal modes on the 3-dB axial-ratio beamwidth (ARBW) is investigated with pin-loaded patch antenna under different excitation schemes in this paper. Four shorting pins are introduced along the diagonals of the square patch at first, so that the electrical size of patch will be tuned for wide 3-dB ARBW. Next, as for the dual-fed CP patch antenna, the phase difference of port excitation is directly altered to observe its effect on 3-dB ARBW. And then, as for the single-fed CP patch antenna, which is loaded with two additional shorting pins as perturbation segment, varied phase difference is achieved by changing the pin spacing. After extensive simulations, the results of the two antennas show good agreement. It is demonstrated that the 3-dB ARBW will be widened in one of the principal planes but narrowed down in the orthogonal plane simultaneously, if the phase difference of two modes is not exactly 90°.
Active Compensation of Unbalanced Load Currents in Grid Connected Voltage Source Converters

For a 2-level 3-phase voltage source converter connected to the grid, it should be capable of supporting the grid with maximum possible active and reactive power during balanced and unbalanced load condition. This paper discusses about the unbalanced current injection algorithm from a VSC to keep the grid currents balanced and sinusoidal in case of asymmetrical loading conditions. Positive and negative sequence synchronous reference frame control have been used to filter the unbalanced component in the grid currents and to compensate it from the converter. A case study with unbalanced reactive load has been simulated to verify the algorithm and the effect of compensation. The compensation reduces the asymmetry in PCC voltage and grid currents, and makes it close to sinusoidal shape to keep it within a standard limit of unbalance in grid power quality.
Automatic driver phone hand-usage detection: A cepstrum-based approach

In the last few years, smartphone usage has been unanimously considered to be one of the most dangerous driving habit, leading to numerous road accidents. In the context of insurance telematics, the online estimation of the driving style is of utmost importance to offer personalized policies and to evaluate riskiness levels. Smartphones can be used to gather dynamics measures needed to build riskiness indicators to evaluate drivers' behaviour. Being able to recognize phone usage is of paramount importance. This work deals with the problem of detecting the use of the phone during a trip. To do this in a fully automatic fashion, we propose a new cepstrum-based classification method based on time series analysis. The resulting performance is tested on experimental data and compared with those obtained with another method, based on hand-crafted features, showing that the new approach yields fully comparable performance while significantly increasing the automation level of the classification process.
Periodic Time Series Data Classification By Deep Neural Network

It is essential for many research fields to find the period of a data set. Many algorithms have been derived for solving related problems. Recently, scholars have reported that deep neural networks can achieve a performance similar to a human on image classification. In this paper, we report a period classification algorithm based on the convolutional neural networks (CNNs). We test its performance on the randomly-generated periodic time series data sets (PTSDs) that consist of periodic and polynomial components. Our results show that the algorithm can achieve 100% out-of-sample accuracy when the polynomial component of a PTSD does not dominate.