Keywords: Optimization, Databases and Data Mining, Automotive Control Systems
Abstract: Machine learning tools are become recently very popular for solving real applications from many areas. Most of the learning problems are formulated as optimization problems with simple objective function but large number of constraints of order the number of training data. When considering the dual formulation, usually the objective function is difficult to minimize but the constraints are simple. One relevant application that fits into this pattern is the support vector machine (SVM). A popular approach for solving the primal SVM problem is based on first order methods due to their superior empirical performance. When considering the dual SVM formulation, which has simple constraints, coordinate descent schemes are typically the method of choice in practice due to their cheap iteration. In this paper we present a comparative study of several first order methods for solving primal or dual SVM problems. Numerical evidence on support vector machine classification for automatic detection of driver fatigue supports the effectiveness of such first order methods in real-world problems.

Keywords: Optimization, Other Topics
Abstract: In this paper, we present an Ageing-Aware Stochastic Optimal Power Flow in presence of Demand Response and Energy Storage Systems (ESSs). A proper use of these latter is fundamental in order to achieve high performance during their whole lifetime. For this reason, we consider a degradation model for the ESSs in the optimization problem. In this way, it is possible to optimize network management by taking into account ageing effects of the ESSs and maximize their lifetime. In order to deal with network uncertainties, we solve a stochastic optimization problem using a scenario approach which allows to provide apriori level of constraint satisfaction. The effectiveness of the proposed control strategy is tested by performing simulations on IEEE 14 bus.

Keywords: Optimization, Robotics, Nonlinear Systems
Abstract: The paper aims to estimate a signal that best provides the identification of a quadrotor type Unmanned Aerial Vehicle angular control loop using a bioinspired metaheuristic Artificial Immune System algorithm. The angular control loop was approximated by a second-order system using the Recursive Least Square method. The results were satisfactory, presenting, in general, a rich enough signal to provide a correct estimation of the system.

Keywords: Biologically Inspired Systems, Optimization
Abstract: The objective of this paper is to show how the Differential Ant-Stigmergy Algorithm (DASA) can be used to solve an Optimal Control Problem (OCP). A version of this algorithm devoted to search a good solution for an OCP is presented. Because this kind of problems has usually a large computational complexity, a technique to reduce it is presented. This one exploits a particularity of DASA related to the coding of the problem's solution. In the first phase, DASA find out a good solution in a small number of iteration by adopting a rough coding of the control input. The second phase is a kind of zoom of the search space around this solution, which becomes initial solution, with a finer coding of the control input.

Keywords: Optimization, System Identification and Modelling, Industrial Applications
Abstract: This work deals with the problem of distribution of cooling water in an evaporation process. The aim is to develop a Real-Time Optimisation (RTO) tool which improves the resource efficiency by supplying the optimal water distribution within a surface-condensers network for a given production demand. The approach includes experimental models and the automatic update of fouling factor. The problem is formulated and solved via non-linear programming. Production constraints and concerns about the practical implementation are also taken into account in the design of the RTO tool.

Keywords: Optimization
Abstract: The main idea in this paper is to implement a distributed primal-dual interior-point algorithm for loosely coupled Quadratic Programming problems. We implement this in Julia and show how can we exploit parallelism in order to increase the computational speed. We investigate the performance of the algorithm on a Model Predictive Control problem.

Keywords: Biomedical Engineering, System Identification and Modelling
Abstract: This paper proposes a set of recurrent neural networks (RNNs) capable of replicating the non-linear mechanism of a prosthetic hand based on surface myoelectric sensors. The experimental results of the RNN show a good result of the system for the training data and an acceptable result on the validation data. A comparison between the developed RNNs and a similar size non-recurrent neural network is included.

Keywords: Linear Systems, Networked Control, System Identification and Modelling
Abstract: The paper addresses the problem of consensus seeking among second-order linear agents interconnected in a specific ring topology. Unlike the existing results in the field dealing with one-directional digraphs arising in various cyclic pursuit algorithms or two-directional graphs, we focus on the case where some arcs in a two-directional ring graph are dropped in a regular fashion. The derived condition for achieving consensus turns out to be independent of the number of agents in a network.

Keywords: Control System Design, System Identification and Modelling, Optimization
Abstract: The controller of a small size Computer Numerical Controlled (CNC) system is designed and tested. The system is considered to have a Multi Input Multi Output(MIMO) model and two control design techniques are presented for it: Pole-placement and Linear Quadratic Regulator(LQR). The LQR problem is proposed to be solved by choosing the weights matrices using the total energy of the system. The simulation results are evaluated and the controller that meets the desired behavior is implemented on the real machine.

Keywords: System Identification and Modelling, Linear Systems, Other Topics
Abstract: The Expectation-Maximization algorithm is applied in this paper to estimate state-space sampled-data models including constraints on the location of the poles. Linear quadratic matrix inequalities are used as constraints to obtain a model that preserves properties of the continuous time system, such as stability or damping characteristics. The results of the algorithm are shown in a simulation study.

Keywords: System Identification and Modelling, Nonlinear Systems, Automotive Control Systems
Abstract: A battery model identification approach, based on non-uniformly sampled data, aiming to reflect the nonlinear dynamic behavior of a lithium-ion cell is presented in this work. To accurately predict the voltage response, the underlying model should reproduce the fast and slow dynamics of the battery cell. Therefore direct identification from non-uniformly sampled measurement data based on continuous-time model identification is applied. To take into account the nonlinear behavior of the battery, local linear model partitioning for the state of charge is performed. The resulting dynamic battery model is able to accurately predict the system response. With a parameter conversion to physically interpretable parameters, based on an equivalent circuit model, the parameter variance among similar cells and the temperature dependency of the model identification are investigated as well as the parameter characteristics over time. All results are based on non-uniformly sampled input output measurement data of three identical lithium-ion power cells.

Keywords: Fault Diagnosis and Fault Tolerant Control, Nonlinear Systems, Computational Intelligence
Abstract: The paper deals with fault detection and recognition for WWTP (Wastewater Treatment Plant). The chosen classifier is a feed-forward neural network. Its input is a high-size vector of measured variables, rather than a small-size compressed feature vector. The output of the network points to the recognized fault class. The test was performed on a simulated WWTP, disturbed by 6 different types of faults (sensors and actuators). The results of the test proved a good ability of the neural network to recognize the faults, in 97.2% of the analyzed cases.

Keywords: System Identification and Modelling, Computational Intelligence, Nonlinear Systems
Abstract: The treatment of quadcopter dynamics around steady-state conditions has often ignored some rotorcraft aerodynamic effects due to its complicated physical modeling or black-box estimated model. The identification of an unmanned quadcopter in accelerated flight using a grey-box modeling approach is investigated. The classical approach of using either first-principles modeling (white-box modeling) or pure observations modeling (black-box modeling) have limitations particularly for real-time applications. Radial basis functions neural networks (RBF-NN) were used to estimate the rotor dynamics parameters (motor PWM outputs) from an unknown flapping dynamics model. The identified models shows that a RBF-based grey-box modeling approach specifically in aggressive maneuvers, has benefits in both modeling accuracy, network size and robustness to noise.

Keywords: Robotics, Computer Vision, Internet of Things
Abstract: This paper presents a way to integrate an industrial robot into Internet of Things and to use it with a deep learning application. Besides of manufacturer's restrictions, which usually exist in an industrial scenario, an easy method to extend and merge the sensorial and decisional systems for robots will be required as part of Industry 4.0. Related to this, the proposed method couples IBM Watson IoT cloud-based platform, a Node-RED cloud application, a deep learning mechanism with TensorFlow (this being applied for a computer vision case study), and an old generation industrial robot. Several conclusions highlight the trade-off of using IoT and deep learning solutions for a real manufacturing environment.

Keywords: Robotics, Control System Design
Abstract: In this paper, our earlier results on cooperative target tracking using a fleet of unmanned aerial vehicles (UAVs) is enhanced with both collision and obstacle avoidance capability. The existing control input that has two decoupled control efforts with one handling the tracking and the other dedicated for formation is now further augmented with a repulsion term that resolves collision with other team members and obstacles nearby. Assuming that each UAV takes the same and constant velocity. This newly-added control component adjusts the UAV's heading angle to the opposite direction in relation to the UAV's closet neighbors and obstacles where collision may occur. This repulsion term can also be expressed as a function of relative bearing angles alone, making it possible to be estimated/measured by onboard vision sensors in the presence of communication loss. Regarding communication topologies, an all-to-all communication, a ring topology, and a cyclic pursuit topology are studied. The effectiveness of the proposed collision/obstacle avoidance scheme is demonstrated by numerical simulation examples.

Keywords: Modelling, Simulation and CAD Tools, Robotics
Abstract: This research aims at designing a stable hovering architecture for small size quadcopters in indoor environments. The chosen system is a Crazyflie 2.0 nanoquadcopter. First, using the Newton-Euler equations, the dynamic nonlinear model is built. This model allows for simulations and feedback controller design. Second, a 3D indoor environment was created for real-time applications. A Kinect Sensor is considered for real time position measurements, at the same time with obtaining orientations from the gyroscope of the Crazyflie. For practical implementation, a combination between Python and Matlab facilities was considered. The first prototype of the proposed architecture was evaluated for different scenarios and the experimental results are detailed and commented.

Keywords: Robotics, Linear Systems, Real Time Applications
Abstract: In this paper, the use of a Moving Horizon Estimator (MHE) is investigated to address a class of state estimation problems dealing with multi-rate sensor fusion in presence of time-delayed measurements. As it makes use of a batch of past measurement and state estimates, MHE is indeed a good candidate to deal with "missing" measurements. Nevertheless, since Moving Horizon Estimation relies on solving online an optimization problem to compute the sate estimate, its computational load may be prohibitive for practical implementation to fast dynamical systems. Therefore this paper proposes a computationaly efficient implementation scheme for a variable structure linear MHE dealing with multi-rate time-delayed measurements, in the case where an analytical solution of the underlying optimization problem can be found. A simulation example is considered for performance comparison, in terms of accuracy and computation time, of the proposed MHE with respect to several state-of-the-art estimators.

Keywords: Control System Design, Robotics
Abstract: An underactuated nonholonomic robot with a bounded control input travels with a constant speed in a 3D workspace. An unknown time variant scalar field is defined on this workspace. The robot should detect, locate, and densely sweep a moving and deforming 2D isosurface (level set), which is the locus of points where the field takes a given value. The sensory data consist of the value of the field at the current location, robot's coordinate along a certain (typically, vertical) space direction, and the orientation of this direction relative to the robot. We offer a new navigation law under which the robot reaches the targeted isosurface from an occasional initial location and then scans the part of this surface in between two given "altitudes". This law does not rely on estimation of the field gradient and is undemanding in terms of motion and computation. Its convergence is demonstrated by a mathematically rigorous result and computer simulation tests.

Keywords: Web Services, Software Engineering
Abstract: Roy Thomas Fielding defined the term REST as an architectural style for distributed hypermedia based systems in the year 2000. Since this initial definition, the term itself became a buzzword used in almost any web-related develop- ment. From simple, basic web applications, to highly complex web service/microservice-based solutions, almost everyone and everything talks REST and RESTful. Moreover, the Web context implies that these developments are usually realised over HTTP. In this paper, we first analyse the impact of this architectural style on distributed application development. A careless application of this model and its underlying HTTP standards could severely impact on the overall quality of any RESTful based solution. Following, we submit our own model for user data and session handling tasks to illustrate the correct approach to RESTful web service design techniques. This model offers all the advantages of the aforementioned architectural style while providing the required functionality for the authentication and authorisation components of all web based applications.

Keywords: Agent-based Systems
Abstract: Ubiquitous computing for innovative health-care systems, services and applications became more and more connected to Cloud systems and the applications required a scalable, reliable and secure environments, the containerized environments representing suitable solutions. In this paper we deal with the advantage of multi-agent systems and IoT for e-health applications in scalable platforms. We investigated how Cloud-based model can be adopted. Thus, an architecture was designed for a medical data based intelligent system that processes the collected data and takes the right decisions based on them. This work also presents a drill down into the mobile client architecture focusing on the implementation of some of the simple periodic agents and combining them into complex periodic agents. The pulse-oximetry sensors were considered in this research. In this context, a heart rate simple periodic agent and a SpO2 simple periodic agent were developed. Our model considers that the data measured by the sensors will be used to monitor health status of a patient in real time or to discover threshold values for the data that can be further careful be analyzed and interpreted as a medical pre-diagnosis.

Keywords: Internet Security, Web Services
Abstract: The EU Regulation No 910/2014 on electronic identification and trusted services for electronic transactions in the internal market, known also as eIDAS Regulation, will become effective this year, more precisely on 29 September 2018. By that date, each EU Member State is required to recognize the electronic identities released in the EU Member States that have notified their electronic identification (eID) schemes. From the technical point of view, intensive efforts are being spent to effectively connect the various national eID schemes in a unified interoperability architecture, the eIDAS infrastructure. This paper describes our proposal to integrate academic attributes into the eIDAS infrastructure, as part of the "eID for University" project(in short eID4U). This project aims to support the practical implementation of the eIDAS Regulation in a selected set of eIDAS-enabled academic services at the 5 universities participating in the project. The objective is to enhance the eIDAS infrastructure with support for academic attributes and to design and implement a set of eIDAS-enabled academic e-services, like the Registration in some study programmes(e.g.Erasmus student exchange programme) or the Login facility (with national eIDs) on a foreign university's web portal.

Keywords: Software Engineering, Web Services, Databases and Data Mining
Abstract: The accounting activities environment, the business itself is changing on a daily basis and the technologies are in constant update. The beginning of the discussions, for this paper, goes to how we can extract data from an ERP system, how we can calculate KPIs (Key Performance Indicators) based on these data and how we can adapt and create one tool in this respect. According with management's decision of the oil and gas company the old KPI tool will be moved to SAP - ZKPI application. The Functional and Technical Specification was adapted for SAP (System, Applications and Products) environment, because this new tool will be adapted in this system. For ZKPI tool, the programming language used was ABAP (Web Dynpro technologies). In this article, it is presented a concept (a particular one) which is already used in oil&gas industry, a model of collecting data from an ERP (invoices, orders, data from papers regarding fixed assets, etc.), and how we can use these data for KPI's. Why do we need these collected data? In order to reach the target for accounting employees and managers regarding personal development goals - PDS, these data are used. One of the major aspects in an accounting department is the number of data entered in one SAP ERP system, based on team composition, as well as knowledge of economic activity and SAP knowledge. All accounting activities in a calendar month are monitored and counted in order to meet certain KPI's agreed with superiors at the beginning of the year. In the following, we will present, contextually, the data that led to the implementation of this application.

Keywords: Computer Vision, Instrumentation
Abstract: Semantic segmentation (SS) provides the meaning of visual scenes, thus being a key stage for navigation and environment's perception. This paper presents a solution for SS compatible with assistive wearable systems equipped with color and depth cameras. In order to ensure a compact and robust description of input color-based images, both 2D and 3D features are extracted at superpixel level, after correcting the displacements of the camera by means of adequate rectifications. Random Forests (RF) are called for solving the classification problem. In this context, this paper introduces a multilayer RF-based classifier, including a separate layer for label correction. Additional two other correcting methods are proposed for the first layers of the classifier, i.e. a fast method investigating the majority label around each object, and several customizations of the graph cut algorithm using convenient cue weights. The performance of the suggested approach is experimentally verified on diverse urban street scenes.

Keywords: Control System Design, Distributed Systems, Software Engineering
Abstract: The development of the complex data structures systems requires models that are capable of describing the systems' requirements, design, verification, implementation and testing. This paper shows the development of such systems using Object Enhanced Time Petri Nets (OETPNs) framework. OETPN can model concurrency, reaction to internal and external events, synchronization and temporal behavior with fix and dynamic delays. OETPN keeps the bipartite graph structure of the classical Petri Nets, but the unique kind of tokens in PNs are changed to be any kind of objects to represent the data, or tasks (sub OETPN) that can either be active (i.e. threads of execution) or passive. OETPNs are capable to communicate with each other through input and output channels and to distribute concurrent tasks that are moved through a computer network with a dynamical structure (i.e. Task migration) without the need to stop the execution of the other OETPN. Guards and mappings are used to control the flow of execution in order to model the creation, insertion, modification and extraction of information of complex data structures. The mappings are also used to implement the information transformation and the placement of the resulted information at the output places of the transitions. The types of the places are specified to allow setting of objects or tasks. A new OETPN dynamic structure that is best suited for the server side is introduced. A Java platform that implement such applications is described and a complete system to manage the operations of the data structures is discussed with an example.

Keywords: Linear Systems, Control System Design, Robust Control
Abstract: The controller design problem to stabilize a linear time-invariant state-space system by static state feedback has been solved decades ago. Combining the static state feedback with a state observer one obtains a dynamic output feedback control law. Contrary to that, the design problem of static output feedback stabilization is significantly more challenging. In this paper, we discuss the existence and the computation of stabilizing static output feedback control gains. Our approach allows additional specifications regarding the eigenvalues of the closed-loop system.

Keywords: Communication Systems, Robust Control, Control System Design
Abstract: The TCP protocol provides the control framework for most of the today Internet traffic. Since its development over forty years ago, however, the end-points have not benefited from multiple interfaces installed for redundancy purposes in the underlying equipment. In the case of failure, the logical connection has been broken or stalled. In order to remedy this situation, a multipath version of TCP - MPTCP - has recently been elaborated. It allows for simultaneous use of a set of transmission paths and interfaces, yet requires appropriate means of coordination. This paper presents a discrete-time model of data exchange process subject to MPTCP master control and provides a new flow control algorithm for the paths with diverse parameters, e.g., different delays. The algorithm establishes a feasible (non-negative and bounded) input signal and finite data queue length despite uncertain networking conditions. The properties of the designed control system are analyzed formally and illustrated by numerical tests.

Keywords: Robust Control, Control System Design
Abstract: The purpose of this study is to design an output feedback sliding mode control for Quadrature Buck Converter(QBC) whose popularity has increased over the last decade owing to the increasing Maximum Power Point Tracking(MPPT) applications and growing power quality concerns in the industry. QBC is non-minimum phase and discontinuous system thanks to the switching components which makes it challenging to design a controller. The plant in Continuous Conduction Mode(CCM) is modeled by state-space averaging method. Output Feedback Sliding Mode Controller which is capable of controlling the plants with non-minimum phase characteristics is designed for the QBC in the face of process line voltage and load variations. The performance of the ODSMC controlled QBC during changing load and supply voltage is examined by means of numerical simulations that are done in Matlab/Simulink. Based on the results that are presented, the considerable disturbance rejection is achieved.

Keywords: Control System Design, Robust Control
Abstract: The reaching law approach comprises of first specifying the required evolution of the sliding variable. Then, a sliding mode controller that enforces this evolution is designed. The main advantage of this method with respect to "classical" sliding mode control is better control of the plant dynamics and state constraints during the reaching phase. In this paper, a review of a number of new works on reaching laws for discrete time systems is presented. The differences and similarities between them are discussed.

Keywords: Control System Design, Robust Control, Nonlinear Systems
Abstract: The purpose of this study is to design an ODSMC(Output Feedback Discrete Time Sliding Mode Control) controller for a fixed-winged aircraft. In this paper, ODSMC control is designed for fixed-winged UAV which is widely utilized in commercial and reconnaissance applications. The small-disturbance theory is used to obtain the mathematical model of the system which has highly nonlinear and non-minimum characteristics. The ODSMC, due to its dynamic structure has been proved to be better than conventional linear controllers. Because of its output feedback structure, it also eliminates the need for a state observer. The ODSMC is designed based on the obtained linear model of the given plant. The effectiveness of the aircraft control system is analyzed with simulations in MATLAB/SIMULINK. It is seen that the ODSMC control algorithm gives considerably satisfactory results regarding the disturbance rejection capability of the closed loop system.

Keywords: Instrumentation, Predictive Control, Industrial Applications
Abstract: Stiction in actuators is a common issue within the process industry, which may considerably degrade the control performance in practice. In particular, the improved prediction capabilities of model predictive control (MPC) vanish if such actuator imperfections are neglected in actual implementations. In this work, the authors review the more recent approaches for stiction compensation in literature and propose two practical alternatives that are more suitable to use in large-scale nonlinear MPC problems. The proposals are illustrated and tested over a real industrial case study: the fiber humidity control in a medium-density fibreboard dryer.

Keywords: System Identification and Modelling, Predictive Control, Robotics
Abstract: Machine learning allows to create complex model if provided with enough data, hence challenging more traditional system identification methods. We compare the quality of neural networks and an ARX model when use in an model predictive control to command a drone in a simulated environment. The training of neural networks can be challenging when the data is scarce or datasets are unbalanced. We propose anadaptation of prioritized replay to system identification in order to mitigate those problems. We illustrate the advantages and limits of this training method on the control task of a simulated drone.

Keywords: Automotive Control Systems, Control System Design, Predictive Control
Abstract: Vehicle platooning became an interesting topic in the last years, many researchers and practitioners from the academia and industry trying to develop new theories and design appropriate control methods and communication methodologies in order to bring this concept as fast as possible on the roads. Since vehicles drive on multi-lane roads and highways, the subsequent paradigm was to treat vehicles as swarms, i.e., groups of vehicles that travel closely together on different lanes and are electronically connected. A step forward towards this new concept would be the design of multi-lane platoons. As such, this paper proposes a multi-agent distributed model predictive control strategy for the longitudinal coordination of the vehicles in individual platoons and a classical PI control algorithm for the lateral control of each vehicle in the platoon w.r.t. its neighbors. The simulation results obtained in Matlab/Simulink and the performance analysis prove that the concept is viable.

Keywords: Control System Design, Adaptive Control, Predictive Control
Abstract: This paper presents an optimal, robust, adaptive tuning strategy based on a linear model predictive control (LMPC) scheme for the switching gain of a sliding mode control (SMC). The LMPC employs a moving horizon, where the dynamics of the sliding-mode-controlled system is addressed. The control design is presented exemplary for a DC drive which is subject to both nonlinear friction as well as model uncertainty. The overall control has a cascade structure. In the inner control loop for the currents, a flatness-based control is used. The outer control loop involves an integral sliding mode control of the angular velocity that is combined with a LMPC that adapts the switching height of the discontinuous control action.

Keywords: Robotics, Biologically Inspired Systems, Other Topics
Abstract: One of the factors that affect the success of Evolutionary Robotics (ER) is the way fitness functions are designed to operate. While needs-based custom fitness functions have been developed, most of the time they have been defined in simpler mathematical functions to reduce the computation time. In this paper, we hypothesize that an incremental fitness function based on established techniques in specific task domains in robotics will aid the evolution process. An A-star algorithm-based fitness function for path planning is designed and implemented for evolving the body plans and controllers of robots for navigation and obstacle avoidance tasks. It has been shown that using this concept, fitter robots have evolved in most cases when compared to simple distance-only based fitness functions. However, due to variable performance of the evolver with the A-star fitness function, the results are inconclusive. We also identify problems associated with the fitness function and make recommendations for designing future fitness functions based on observations of the experiments.

Keywords: Control System Design, Nonlinear Systems, Robotics
Abstract: This work aims to show kinematics and dynamics modelling of a fixed-wing UAV type, as well as Proportional and Proportional, Integral and Derivative controllers for multirate cascade control loop, one internal for angular attitude and another external for the inertial position. The control loops are designed for roll, pitch, yaw, altitude and linear velocity in forwarding and backward aircraft nose direction. These dynamics are controlled by the integration of loops in different levels using the Successive Loop Closure technique. The results were satisfactory, considering that the simulation tests took into account the parameters of an already developed aircraft.

Keywords: Robotics, Education and Training, Nonlinear Systems
Abstract: This work presents a gyroscopic test bench for under or over-actuated UAVs. In this way, it is possible to perform stability tests and several control loop structures without taking the risk of damages present in field tests, or even accidents with personal injuries. Moreover, the vehicle continues with its respective degrees of freedom for angular stability dynamics (rolling, pitching and yawing), without (or almost no) interference from the test bench, as it does not influence the UAV moments of inertia. It also can be used for didactic purposes, where, through the results presented in graphics and videos, it proved to be easy to build and handled, meeting the minimum safety requirements proposed.

Keywords: Robotics
Abstract: Spatial parallel manipulators have a number of practical applications due to their high reliability, accuracy, and performance. In this paper we deal with an inverse kinematic problem for six-degree-of-freedom (6DOF) parallel manipulator known as modified Stewart platform with rotative-spherical-spherical (RSS) structure. An effective analytic method for solving the inverse kinematic problem for given terminal state is proposed. This method is used for trajectory planning of 6DOF parallel manipulator. Numerical simulation is considered.

Keywords: Robotics, Nonlinear Systems
Abstract: Unmanned Aerial Vehicles (UAVs) swarming has took an important part of the recent researches. Multiple robots offer many advantages when comparing to a single one such as reliability, time decreasing and multiple simultaneous interventions. This paper proposes a new scheme for trajectory tracking of multiple quadrotors UAVs under a centralized leader-followers formation strategy. Attitude stability and position control are assured using a double loop control structure based on the Linear Quadratic Regulator (LQR), moreover the leader-followers formation is maintained via a Sliding Mode Controller (SMC) controller. Many scenarios are proposed within this work. Simulation results proof the energy optimization and formation controller's robustness and accuracy.

Keywords: Signal Processing, Nonlinear Systems, Software Engineering
Abstract: This paper aims at exploring accuracy of Kalman-like filters. Its particular interest lies in estimation of stochastic systems whose drift coefficients expose a stiff behavior. The latter means that the Jacobian of the drift coefficient in such a continuous-discrete system, which is presented by an Ito-type stochastic differential equation (SDE) for modeling the plant's dynamic behavior and a discrete-time equation for simulating its measurement process, has large eigenvalues at the solution trajectory. Here, we employ the so-called ``discrete-discrete'' approach, which is grounded in SDE discretization schemes, and compare the outcome accuracy of EKF-, CKF- and UKF-type methods when these are based on the Euler-Maruyama and Ito-Taylor discretizations of the strong convergence orders 0.5 and 1.5 and applied for estimating the Van der Pol oscillator and Oregonator reaction models. We evidence that state estimation errors committed in our stiff stochastic scenarios are sensitive to both the type of Kalman filtering method utilized and the SDE discretization scheme implemented. So these must be chosen carefully in accurate and robust state estimation algorithms intended for treating stiff continuous-discrete stochastic systems.

Keywords: Signal Processing, Hardware Design, Communication Systems
Abstract: Recursive Systematic Convolutional (RSC) codes are the building blocks of the modern communication systems. In this paper we propose a new analytical model to manipulate the modulo-2 algebraic operations and a finite state machine model describing the single-cycle RSC architecture to design high throughput RSC code with special emphasis for parallel implementation and a puncturing scheme embedded in the design. The new design approach is suitable for any RSC code and for almost any degree of parallelism implementations. We also present some case studies about the RSC code architecture and some simulation results for the Bit Error Rate, to compare commonly used RSC codes with different constraints on the length, and redesigned with the proposed methodology.

Keywords: Signal Processing, Other Topics
Abstract: This paper presents an efficient capacity control algorithm for prediction error expansion based audio reversible data hiding. Current state-of-the-art audio reversible data hiding schemes use a simple capacity control algorithm that was first developed for image reversible data hiding. The performance of this algorithm can be improved by using a simple two threshold based approach. The two threshold approach can be easily integrated into any prediction error expansion based framework. Experimental results are provided for two such frameworks.

Keywords: Instrumentation, Signal Processing, Software Engineering
Abstract: This paper presents a comparative analysis between five algorithms. The goal of the algorithms was to reduce the acquisition time for high frequency measurements using Vector Network Analyzer. The proposed algorithms were developed by the authors and in this paper the accuracy and the execution time will be analyzed. The results will be presented in terms of information consistency and dynamic behavior. Finally, a classification between the five proposed algorithms using a key performance indicator will emphasize only one algorithm with high performances regarding the significant number of frequencies reduction, the decreasing of computation effort keeping the accuracy and the consistency of the initial device response.

Keywords: Computational Intelligence, Computer Vision, Signal Processing
Abstract: Due to the extensive proportions of Brazilian railways, there is a high demand for remote and automatic diagnose tools. This work proposes a scene selection method using Deep Learning techniques, namely Convolutional Neural Networks (CNN), to recognize the poles, which gathers objects of interest to be inspected in the railway power network. Videos were obtained through the railway and the data divided and preprocessed for the network training and testing. A VGG network architecture served as a starting point, and after exhaustive search and comparisons of many techniques, two network topologies are presented and compared in field tests. The results yield more than 93% efficiency for both proposed topologies.