Unsupervised person Re-Identification (Re-ID) has made notable progress through various methods that rely on clustering to generate pseudo-labels. Among these, memory-based contrastive learning has become a widely used approach in unsupervised representation learning. Previous work mainly focused on improving the quality of pseudo-labels and enhancing the robustness of representation learning. However, performance in complex scenarios remains limited, and the role of contextual semantic information is still underexplored. To address this gap, we propose a simple yet effective framework that integrates a Graph Convolutional Network (GCN) to estimate similarity, aggregate neighborhood information, and incorporate hard pseudo-labels. This enables the model to leverage semantic relationships across different contexts, resulting in more robust representations. In addition, we identify a limitation in conventional momentumbased memory updates, which can hinder contrastive learning. To overcome this, we introduce a real-time memory update strategy that replaces momentum with the average feature vector from the current mini-batch to update cluster centroids. This ensures that the features of each cluster are up-to-date. Extensive experiments on the three benchmark datasets of Market1501, DukeMTMC, and MSMT17 have shown that our method achieves substantial performance gains in unsupervised pedestrian re-identification. These findings offer both theoretical insights and practical benefits for the broader fields of model recognition and person re-identification.

Speech Emotion Recognition (SER) is a vital component of affective computing, enabling systems to interpret users’ emotional states through vocal cues. This paper presents the development of a real-time SER model based on a Long Short-Term Memory (LSTM) neural network, optimized for execution on embedded platforms such as the Raspberry Pi. The model is trained using two publicly available emotional speech datasets, RAVDESS and TESS, which provide diverse expressions of emotion in English. Their combination increases variability in vocal patterns and improves generalization to real-world conditions. The proposed system focuses on low-latency inference and efficient memory usage, enabling the classification of seven emotional categories within the computational constraints typical of embedded hardware. Performace is critical as it will be integrated into a multimodal emotion recognition system to be deployed in embedded systems. Experimental evaluations show that the LSTM-based architecture offers a strong balance between accuracy and efficiency, supporting its integration into real-time, demonstrating the feasibility of deploying emotionally aware systems on low-power devices.

Epilepsy is a chronic brain disease characterized by recurrent and transient seizures, which is accompanied by super-synchronous abnormal discharge of electroencephalogram (EEG) signals. As a non-invasive auxiliary diagnostic technique, EEG is currently important means of seizure detection. However, due to ignoring the spatial topological relationship between electrodes, existing data-driver methods fail to fully reflect the interaction between signals. Meanwhile, their models usually be designed with a large number of redundant parameters, making it difficult to deploy to micro-embedded devices with limited-resources. In this paper, we propose a on-device learning with edge device for epilepsy EEG classification network based on GCN (oDLGCN-EEG). Specifically, to analyze the spatial relationships between various electrodes and their temporal dependencies, we design a Brain Topology Network (BTN) for the spatiotemporal dependency feature map construction. To capture the internal activity during epilepsy seizures, we design a Neural Feature Extraction Module (NFEM) for the neural activity feature map construction. Besides, we propose a pruning scheme to optimize the model, which successfully deploys the optimized oDLGCN-EEG on the embedded Raspberry Pi device for efficient and low-power consumption intelligent epilepsy classification. Experiments in comparison with state-of-the-art methods show that oDLGCN-EEG achieves the best classification accuracy and with the smallest parameter number on baseline EEG dataset. The code is available at https://github.com/cathnat/Epileptic_Classification.

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices.

In this work a set of novel approaches based on well-known computer vision techniques is proposed to deal with the autonomous perception part of an HRI robotic system. In the considered scenario, a human-robot duo interact to plan maintenance and pruning operations in a vineyard. Once the human expert has selected a set of branches to cut in the approximate area, the robot stores this information in order to be able to return later, identify the branches and cut them at an adequate point. This is achieved by producing models of the plant by segmenting the view through a combination of intensity and depth data, using RGB-D camera sensors. This segmentation is fed back into the pipeline, used as a base mask to identify parts of the plant through watershed segmentation, and identifying the branches and gems in the segmented images. The proposed method was develop based on real data, and tested in experimental scenarios with the real robot.

Pipelines are used for fluid transportation over long distances, necessitating effective monitoring and maintenance strategies. Pipeline Inspection Gauges (PIGs) instrumented with sensors and embedded systems playa crucial role in this field. This Work in Progress explores using a PIG for trajectory estimation and data acquisition. Several sensing technologies, including Inertial Navigation Systems (INS) and odometers, are integrated to determine the location and parameters of the pipeline. The electronic architecture of the PIG, designed for trajectory estimation, is discussed, focusing on the utilization of odometers and an Inertial Measurement Unit (IMU). The system features an FPGA-based electronic acquisition system, incorporating Quadrature Pulse Modules and Counters for data storage and processing. Furthermore, preliminary tests conducted on a pipeline circuit demonstrate the effectiveness of the proposed electronic architecture. Overall, this study presents a comprehensive approach to pipeline trajectory estimation, emphasizing the importance of data acquisition and processing to ensure accurate pipeline location.

Micro-size hydro power plants (μHPPs) are commonly used at remote areas to supply islanded AC micro-grids. A typical way to control grid frequency is to manipulate active power dissipated in ballast loads by AC/AC converters. However, these asymmetrically switched thyristor controlled converters consume reactive power degrading the power factor at the generator output. In this paper the operation of a symmetric angle switched, bridged three-phase rectifier and three single-phase rectifiers connected in series with ballast load, are studied to improve the power factor of the system. As a consequence, the objective is to evaluated the use of the three and single-phase rectifiers switched with symmetrical angle to improve the power factor, by reactive power compensation, at the output terminal of the electric generator of μHPPs that use ballast load to regulate frequency.

Chlorination in drinking water treatment plants (DWTP) is the final process applied to water before it is sent to storage tanks in the supply network for subsequent human consumption. An excessive dosage of chlorine or, conversely, too small a dosage, may breach existing legal regulations on mandatory limits. In DWTP where there is no significant variability in the quality of the water to be treated, a type of control that is proportional to the flow rate in the effluent can have fully satisfactory results. Therefore, Proportional-Integral (PI) control is a rather frequently used solution. However, when there are inherently long delays in the process, variability in the quality of the water to be treated and considerable variations alternative type is needed. This article presents the strategy and results of a control method that proposes a Fuzzy based feed-forward system to complement an existing PI control. The control system results are shown as applied to the DWTP of Barcelona city, producing satisfactory experimental results.

This work presents a solution to automatize sampling tasks in a wastewater treatment plant with open air basins. At the behest of human operators, a set of UAVs managed as a network of autonomous agents will perform sample missions by taking direct measurements (through a multiparametric probe) or capturing samples and carrying them to the laboratory with specific developed hardware. These capabilities allow the proposed solution to act as a virtual sensor network with sampling points deployed and connected at any point reachable by UAVs. The hardware prototypes are fully described, with focus on the integration of systems, and the software architecture used is analysed and fully justified. Special focus was put on the localization problem, and several solutions were evaluated. Experimental results of the prototype UAV and sampling probe built are provided to validate the hardware designs, with focus on the localizations tasks.

One of the most critical issues that arises when controlling a robot is the necessity to locate itself in the environment. Ranging from industrial processes applications to outdoor mobile robots, landmarks are used to such purpose: by recognizing them, the robots are able to know where they are with the aid of computer vision. Fiducial markers are the cheapest and one of the most common solutions to this issue: 2D planar patterns that embed some information that can be identified using artificial vision techniques. Many different typologies of markers as well as image processing algorithms are being implemented nowadays, using C++ / Python / MATLAB® libraries and ROS as middleware. In this work we have evaluated the robustness of various fiducial marker typologies, documenting the main technical aspects of the used implementations and commenting how each algorithm operates. Aggregated results are presented and discussed, evaluating the different implementations tested.

The objective of this paper is to explain novel sustainable robotics solutions for cities. Those new proposals appear under the ECHORD++ project which is a good tool to meet academia and industry with the objective of providing innovative technological solutions. In this paper, authors explain the tool as well as the methodology to promote robotics research in urban environments, and the on-going experience will demonstrate that huge advances are made in this field.

The twelve papers in this special section focus on the development of robotic solutions for smart factories in industry - the concept of the fourth industrial revolution (industry 4.0). The inclusion of robotics is expected to deeply change the future manufacturing and production processes, and lead to smart factories that will benefit from the main design principles of Industry 4.0: interoperability, virtualization, decentralization, real-time capability, service orientation, and modularity. Robotics will have a key role in this development since innovative technologies and solutions, traditionally associated with the service robotics sector, are going to migrate to industrial smarter robots, exploiting the maturing of navigation, localization, sensing, and motion control technologies. These smarter robots will draw on a much broader range of technology, allowing higher levels of dexterity and flexibility, the ability to learn tasks without formal programming, and to autonomously collaborate with other autonomous devices and human operators.

Robotics is a surprisingly old discipline, and robots have shaped industry and the various industrial revolutions for many decades. This paper covers topics relevant to the IES Technical Committee on Factory Automation, focusing in particular on the evolution of industrial robotics. After providing a historical perspective on the topic, the paper addresses current and future trends, revealing the close link between the progress in industrial robotics and the parallel evolution of industrial communication systems, which represent an enabling technology for modern industrial robotics.

Pollution in coastal areas is very hazardous for population. The problem is even higher when this pollution occurs in bathing areas such as beaches in populated areas. In this work we present a new automatic control application for wastewater plants when pollution is caused by those plants because there is an excess of water to be treated and it is thrown to the sea with high levels of biological pollutants (bacteria...). Those problems can be aggravated if climatic conditions pull to the coastal area the untreated water without the cleaning action of sea water. The experimentation is done with real data and real conditions in Barcelona area, at Mediterranean sea, with the urban wastewater plant of Besòs.

This work presents a solution to automatize the water sampling process of outdoor basins in a wastewater treatment plant. The system proposed is based on the utilization of collaborative robotics: a team of an UAV and a terrestrial robotic platform make a route along the plant collecting and storing the water samples. The architecture of the designed system is described in terms of functional blocks, and implementation details including software frameworks and hardware on the UAV are provided. As the objective of the system is industry levels of robustness and performance, the UAV use is minimized and subjected to control from the robotic ground platform, reducing risks associated with autonomous UAV. To conclude, results from experiments performed to validate the viability of the system and study several design decisions are presented and briefly discussed, including: estimation of the accuracy of several GNSS technologies on the plant, viability of the landing operation over a mobile robotic platform and controlling a quadrotor over waters.

A complete approach to the visual localization and mapping problem (SLAM) is presented in this work. The presented approach exploits the enhanced capabilities of a system where a human and a robot collaborate in surveying/exploratory tasks. The human is supposed to wear a smart headwear device, which deploys a inertial measurement unit and a camera, Hv. This camera acts as a secondary sensor, and provides data to the robotic Rvcamera performing mapping tasks. The data from the human-worn camera is used to produce real-time depth estimation of landmarks when its field of view overlaps with that of Rv. These measurements are mathematically fully integrated into the EKF-SLAM methodology. Experiments with real captured data validate the proposed approach.

A novel approach to the SLAM problem has been tested in an industrial environment within a robotic assistance context. In order to be fully reliable in non-modelled circumstances where the environment cannot be considered as known a priori, a robot assistant must be able to localize and map its environment. The use of a camera sensor to solve localization has several advantages and weaknesses due the nature of the only-bearing data. But as the robot is expected to assist the human agent, this agent can deploy additional sensors and provide the robot with data to help solve the SLAM problem. Thus, another camera worn by the human agent is used to produce non-continuous stereo data with the robotic camera, to speed-up and add robustness to several parts of the monocular SLAM process considered. The approach has been tested with real experiments focused on singular trajectories and other issues found on industrial environments.

This work presents a complete navigation architecture for an autonomous aerial robot. The proposed scheme consist of: i) a low-level controller for establishing the attitude and position of the vehicle, ii) a Simultaneous Localization and Mapping (SLAM) system, based in bearing (angular) measurements, which gives the robot the ability for navigating in unknown environments, and iii) a high-level motion control system which generates online trajectories. The high-level motion control system (MCS), which represents the main contribution of this work, is inspired by the behavior-based control strategies. The MCS takes as input a very high level mission target (e.g. “explore as much as you can”) and generates online trajectories according to the mission, but at the same time minimizing uncertainty in the estimations in order to maintain the integrity of the robot. The proposed architecture is explained for simplified 3DOF dynamics, but it could be extended in a straightforward manner in order to be applied to full dynamics. Several simulations are included in order to show the performance of the proposed scheme.

This paper provides a classification of high efficiency switching power-gyrator structures and their use as cells for energy processing in photovoltaic solar facilities. Having into account the properties of these topologies presented in the article, their inclusion in solar facilities allows to increase the performance of the whole installation. Thus, the design, simulation and implementation of a G-type power gyrator are carried out throughout the text. In addition, in order to obtain the maximum power from the photovoltaic solar panel, a maximum power point tracking (MPPT) is mandatory in the energy processing path. Therefore, the practical implementation carried out includes a control loop of the power gyrator in order to track the aforementioned maximum power point of the photovoltaic solar panel.

This article shows the proposal of a linear-assisted converter or linear-&-switching hybrid converter with a constant switching frequency. The control loop of the system is based on the current-mode technique. The main disadvantage of a converter with current-mode control is the inherent instability of the loop when switch duty ratios are greater than 0.5. In order to make stable the proposed linear-assisted converter, the article shows the technique based on a slope compensation.

Linear-assisted DC/DC converters (or linear-switching hybrid DC/DC converters) consist of a voltage linear regulator (classic NPN or nMOS topologies and LDO) connected in parallel with a switching DC/DC converter. In order to control these hybrid structures, different strategies exist, allowing to fix the switching frequency as a function of some parameters of the linear regulator. This article compares two control strategies that, although can be applied to the same circuital structure of linear-assisted converter, are sensibly different. The first one, reported in previous literature, cancels completely the average current through the linear regulator in steady state to achieve a reduction of the losses. Thus the efficiency of the whole system increases and almost equals the one of the standalone switching converter. The proposed approach, in spite of a slightly increment of linear regulator's losses, reduces the output ripple due to the crossover distortion of linear regulator output stage.

This article shows the proposal of a current-mode one-cycle control for linear-assisted DC/DC converters. Linear-assisted DC/DC converters are structures that allow to take advantages of the two classic alternatives in the design of power supply systems: voltage linear regulators (classic NPN topology or LDO-low dropout-) and switching DC/DC converters. The current-mode one-cycle control technique is proposed in order to obtain the duty cycle of the linear-assisted converter switch. The proposed structure can provide an output with suitable load and line regulations. Thus, the paper shows the design and simulation results of the proposed current-mode one-cycle linear-assisted converter.

This article aims to present the design of a 4.5-V, 450-mA low drop-out (LDO) voltage linear regulator based on a two-stage cascoded operational transconductance amplifier (OTA) as error amplifier. The aforementioned two-stage OTA is designed with cascoded current mirroring technique to boost up the output impedance. The proposed OTA has a DC gain of 101 dB under no load condition. The designed reference voltage included in the LDO regulator is provided by a band gap reference with the temperature coefficient (Tγ) of 0.025 mV/°C. The proposed LDO regulator has a maximum drop-out voltage of 0.5 V @ 450 mA of load current, and has the worst case power supply rejection ratio (PSRR) of [54.5 dB, 34.3 dB] @ [100 Hz, 10 kHz] in full load condition. All the proposed circuits are designed using a 0.35 μm CMOS technology. The design is checked in order to corroborate its performance for wide range of input voltage, founding that the circuit design works fine meeting all the initial specification requirements.

This paper provides a classification of high efficiency switching power-gyrator structures and their use as cells for energy processing in photovoltaic solar facilities. Having into account the properties of these topologies presented in the article, their inclusion in solar facilities allows increasing the performance of the whole installation. Thus, the design, simulation and implementation of a G-type power gyrator are carried out throughout the text. In addition, in order to obtain the maximum power from the photovoltaic solar panel, a maximum power point tracking (MPPT) is mandatory in the energy processing path. Therefore, the practical implementation carried out includes a control loop of the power gyrator in order to track the aforementioned maximum power point of the photovoltaic solar panel.

This article shows the proposal of a current-mode one-cycle control for linear-assisted DC/DC converters. Linear-assisted DC/DC converters are structures that allow to take advantages of the two classic alternatives in the design of power supply systems: voltage linear regulators (classic NPN topology or LDO - low dropout -) and switching DC/DC converters. The current-mode one-cycle control technique is proposed in order to obtain the duty cycle of the linear-assisted converter switch. The proposed structure can provide an output with suitable load and line regulations. Thus, the paper shows the design and simulation results of the proposed current-mode one-cycle linear-assisted converter.

This paper provides a classification of high efficiency switching power-gyrator structures and their use as cells for energy processing in photovoltaic solar facilities. Having into account the properties of these topologies presented in the article, their inclusion in solar facilities allows increasing the performance of the whole installation. Thus, the design, simulation and implementation of a G-type power gyrator are carried out throughout the text. In addition, in order to obtain the maximum power from the photovoltaic solar panel, a maximum power point tracking (MPPT) is mandatory in the energy processing path. Therefore, the practical implementation carried out includes a control loop of the power gyrator in order to track the aforementioned maximum power point of the photovoltaic solar panel.

This paper presents a constructivist methodology oriented to training Robotics in its wide sense, and its implementation in higher education. A humanoid robotics workshop is included as a part of the curriculum of `Industrial Robotics' in the Industrial Engineering. This workshop covers one third of the laboratory practices of this subject. A constructivist view for learning is adopted, whereby robotic technologies are not seen as mere tools, but rather as potential vehicles of new ways of thinking about teaching, learning and education at large. Students, in a constructivist learning environment, are invited to work on experiments and authentic problem-solving; with selective use of available resources according to their own interests, research and learning strategies. The authors, as trainers of this workshop, chose ROBONOVA humanoid robots, among other devices, which attempt to partner technology with ideas of constructivism. The materials used in the workshop offer building parts, sensors thus connecting a robot with the external environment and programming software with a simple graphical interface intended for the creation of robot behavioral movements. The idea is “learning by design”, which is central in the constructivist pedagogy introduced firstly by Resnick. In this workshop we corroborate this idea through the project-based learning approach in robotics. Learning tasks of the workshop are organized as projects (small and large) that encourage students to develop their own designs. Projects are either instructor led or completely arisen from students.

In this paper a linear parameter varying (LPV) statespace canal control model is obtained by identification in a local way. This LPV identification procedure is based on subspace methods for some operating points of an irrigation canal covering all the operation range. Different subspace algorithms have been used and compared. The model that best represents the canal behavior in a precise way is chosen. This model has been validated by error functions and analysis correlation of residuals in a laboratory multi-reach pilot canal providing satisfactory results.

SDSim is a novel solar dryer simulator based in a multi-crop, inclined multi-pass solar air heater with in-built thermal storage mathematical model. This model has been developed as a designing and developing tool used to study and forecast the behavior of the system model in order to improve its drying efficiency and achieving a return on the dryer investment. The main feature of this simulator is that most of the parameters are permitted to be changed during the simulation process allowing finding the more suitable system for any specific situation with a user-friendly environment. The model has been evaluated in a real solar dryer system by comparing model estimates to collected data.

The technological advances of industrial communications in the last decade allowed automatically to control processes that were manually controlled in a separate way. This paper presents the design and implementation for a novel automated chlorination system at the drinking water treatment plant in Sant Joan Despí (Barcelona, Spain). The automation of chlorination process meant two important challenges: firstly, the impact of dead time inherent to the measurement of process' variable of the water and its dosage has been minimized; and second, the system has been provided with the capacity of adaptation to the variability in the water quality and to the appearance of the ammonium in the treated water. The obtained results in this real process by the combination of a feedforward control strategy together with a classical feedback scheme have been largely satisfactory. The establishment of this new control system has represented a significant change in the working model.

This paper describes a new approach to the well-known robotics problem of simultaneous location and mapping (SLAM). The proposed technique introduces a linear varying parameter (LPV) modeling solution for the estimation of nonlinear models in a Kalman Filter based algorithm. In this technique, the estimation model for the robotic device considered is modeled as a quasi-LPV model, which in turn, is linearized around a set of given points of the varying parameter. The observation model is rearranged into a pseudo-measurement model, which is used in form of a pseudo-linear model during the update stage of the Kalman filter. The initial tests and experimentations suggest that this technique can improve Extended Kalman Filter SLAM results by avoiding a great deal of the bias introduced by linearization of nonlinear models into EKF equations.

In this paper a novel Simultaneous Localization and Mapping (SLAM) is presented. Using the sound as the input signal, instead of the classical vision or laser systems, leads to a SSLAM (sound SLAM) with a new features, such as the use of a Linear Parameter Varying (LPV) Kalman filter rather than the classical Extended Kalman filter. The other novelty is the modeling of sound reverberation using LPV models. The work is an extension under development from previous research group's works. The experimental partial results and the theoretical developments encourage authors to follow this unexplored line of SSLAM.

This paper describes the design, analysis, and experimental results of an Attitude and Heading System based on a Dual Kalman Filtering technique. The presented scheme is suitable for implementation using low cost sensors. Attitude determination systems are essential for real time vehicle navigation, guidance and control applications. When low cost sensors are used, efficient and robust algorithms become necessary for an acceptable performance. For the proposed approach, a low cost Inertial Measurement Unit (IMU), formed by 3-axis gyroscope, 3-axis accelerometer, and 3-axis magnetometer, provides the input measurements. Experimental results with real data show that the proposed algorithm is able to maintain an accurate and drift-free attitude and heading estimation.

The environmental and social crisis has forced the University to have a considerable influence on the society in an active manner and it faces new challenges such as the need to educate professionals that take respectful decisions towards the environment, the sustainability, the social commitment and human development. Many universities, and specifically the Technical University of Catalonia UPC, have included in their curricula a compulsory competence regarding the Sustainability and the Social Commitment in a transversal way. In this paper authors propose a new pedagogical tool oriented to scientific and technological higher education related with modeling and simulation of dynamic systems. This resource consists of a set of didactic materials divided into different themes (water and waste management, renewable energy...). These materials are in web format, and this tool is aimed at helping faculty to integrate the competence into the specific competences of every subject. This pedagogical tool has been used, tested and assessed in “Dynamic Systems Simulation” subject in the Industrial Engineering Degree at UPC. The use of this pedagogical tool has provided satisfactory and encouraging results at all levels, knowledge, procedures and attitudes.

This paper presents an innovative web-based 'green' remote process control laboratory focused on the necessity of chemical control education community. The usefulness of the remote laboratory proposed in this paper is justified by two relevant factors: the opportunity of organizing remote experiments, saving students time and money; and the integration of the 'Sustainability' (transversal competence) in the curricula. A new innovative pedagogical methodology for laboratories is carried out. Furthermore, a set of practices that constitutes the remote lab will be presented covering all the subject of “Chemical Processes Control” in the Chemical Engineering Degree at the Technical University of Catalonia (UPC). Students should achieve chemical control systems (modeling and control design) basic knowledge and skills as well as the necessary concepts, real world situation vision and awareness in order to take decisions in their professional live taking into account the Sustainability, economy and efficiency.

In this article the design of a prototype of Intelligent Transmitter of Field (T.I.C) is presented. The objective of the work is approaching the engineering students, in the area of Automatic Control, to the functional and technological challenges intrinsic to this type of implementations. Aspects like power consumption of the electronic circuits, energy supply through the wires of communication of the transmitter, the coexistence in these wires of analogical signals of measurement and digital signals of communication, etc., are essential to get the foreseen objective. The article seeks, also, to contribute to a didactic solution for a problem that has been one of the main challenges in instrumentation field.

It is our pleasure to welcome all participants to the 14th IEEE International Conference on Emerging Technologies and Factory Automation at the University of Balearic Islands, Palma de Mallorca, Spain. The fourteenth edition of this event coincides with a year of global economical crisis, especially in the industry field, and this fact is reflected in all our daily activities, and also in this ETFA edition. For this reason it is important to find new fields, closely related with the emerging technologies which have not been severely struck by the crisis. The imagination is the topic in this new era.

Non-speech audio is a non-explored characteristic in robot localization but due to its potentiality it can yield a valuable information together with other sensorial systems. In this work, a novel robot localization method is proposed based on audio signal pattern recognition with extracted features from signal identification. To reinforce the localization, avoiding ambiguity and reducing uncertainty, a sensorial system is used aboard the robot to compute the angle between itself and the sound source. This method can be generalized to any non-speech sound signal because the acoustical meaning and the room geometry are related.

This paper deals with the fractional modeling of a DC-DC buck-boost converter, suitable in solar-powered electrical generation systems, and the design of a fractional controller for the aforementioned switching converter. Although the modeling and design of the controller is carried out for this particular DC-DC converter, it can be easily extended to other kind of switching converter. In addition, the comparison between integer-order plant/controller and fractional-order plants/controller is carried out. The article also shows that, under the same design conditions, the fractional-order controller has a better performance and behaviour than the classical integer-order controller in both situations, that is, with integer-order plant and fractional-order plant models.

The on-line robot estimation position from measurements of self-mapped features is a class of problem called, in the robotics community, as simultaneous localization and mapping (SLAM) problem, which is one of the fundamental problems in robotics. SLAM consists in incrementally building a consistent map of the environment and, at the same time, localizing the position of the robot while it explores its world. In this context, sensors such as laser and sonar rings for range measurement have been traditionally used to perform SLAM; more recently vision-based systems have also gained a great interest in the robotics community. Nevertheless the use of the auditory sensing for performing SLAM has been much less explored. In this work a Sound-Based SLAM system using a delayed inverse-depth feature initialization is proposed where ldquosound sourcesrdquo are used as mappsilas features. Experimental results with simulations and with a real robot are presented in order to demonstrate the performance of the method.

This paper presents an innovative web-based control laboratory focused on the necessity of control education community. The usefulness of the remote laboratory proposed in this paper is justified by the opportunity of organizing the remote experiments, saving time and money for the students.

On-line motion estimation of a single camera moving freely through an environment is an interesting and challenging problem with a wide rage of applications. This estimate position from measurements of self-mapped natural visual features represents perhaps the more difficult class of the (SLAM) simultaneous localization and mapping problem. In this paper we show an approach for delayed features initialization for monocular SLAM based in inverse depth parameterization. The original undelayed aproach has shown good results for the monocular SLAM problem in a scheme of EKF for the estimation of the stochastic map and the pose of the camera. In our work we show that using a delayed feature initialization can improve the performance of the original method in some aspects.

This paper describes the development of a remote laboratory in the area of automatic and robotic engineering. The laboratory is based on an educational platform around a robotic arm that allows emulating a practical laboratory in which engineering students can confirm their theoretical results comparing them with the real paths traced by the robot. Thus, the research work presented in the article is directed towards the adaptation of concepts in the field of robotics and control in a remote laboratory.

There has been increasing research effort in applying control-theoretic approaches to performance management for computer systems such as Web servers, networked, database, and storage systems. Since today's Ethernet networks and applications are sometimes operated under dynamically changing load conditions that introduce a time-varying delay in the networked control systems (NCS), linear control designs may not suffice to provide stability and/or desired performance guarantee. Research on adapting Ethernet for distributed systems is an interesting topic in the motion control industry, due its commercially off-the-shelf hardware availability and compatibility at a comparative lower price. This paper presents a linear-parameter-varying gain-scheduling (LPV GS) approach for distributed motion control via Ethernet in order to achieve the stability of the closed-loop and the quality of service (QoS).

A method for robot navigation is presented. It is based on the detection of the main local reflection symmetry axes in images where straight lines are prevailing such as structured environments where robots are moving. The objective is to maintain the right orientation during navigation and to detect and correct any change in this orientation comparing continuously the symmetry axes between images. The algorithm measures the positions and the lengths of these local symmetry axes and their relative importance by means of an accumulation map of each contributing segment. A definition of symmetry between two segments that allows us to calculate a local symmetry axis between them is also given.

Huge and expensive computation resources are usually required to perform graph labelling at high speed. This fact restricts an extensive use of this methodology in industrial applications such as visual inspection. A new systolic architecture is presented which computes structural distances between cliques of different graphs based on a modified incremental Levenshtein distance algorithm. The distances obtained are used as a support function for graph labelling using probabilistic relaxation techniques. The proposed architecture computes the distances between k input cliques of an input graph and one reference clique of a reference graph. It does not limit the number of cliques nor cliques complexity of the input graph, so any input graph can be labelled. A low cost solution has been implemented based on FPGAs.