Applied Computer Sciences in Engineering

An efficient iterative heuristic algorithm has been used to implement Bellman-Zadeh solution to the problem of optimization under fuzzy constraints. In this paper, we analyze this algorithm, explain why it works, show that there are cases when this algorithm does not converge, and propose a modification that always converges.

Concurrent languages such as Perl 6 fully leverage the power of current multi-core and hyper-threaded computer architectures, and they include easy ways of automatically parallelizing code. However, to achieve more computational capability by using all threads and cores, algorithms need to be redesigned to be run in a concurrent environment; in particular, the use of a reactive, fully functional patterns need to turn the algorithm into a series of stateless steps, with simple functions that receive all the context and map it to the next stage. In this paper, we are going to analyze different versions of these stateless, reactive architectures applied to evolutionary algorithms, assessing how they interact with the characteristics of the evolutionary algorithm itself and show how they improve the scaling behavior and performance. We will use the Perl 6 language, which is a modern, concurrent language that was released recently and is still under very active development.

In this paper we present a survey of three recent developments in biometric user authentication based on physical human characteristics that are less prone to natural or intentional changes than other currently used techniques: thermal vein signatures, DNA and EEG brainprint obtained by stimulating the brain with cognitive events. This paper argues that biometric user authentication using these three human characteristics is already an everyday a reality or is going to be very soon.

The robust recursive algorithm for the parameter estimation and the volatility prediction in GARCH models is proposed. The suggested technique employs principles of robustified Kalman filter. It seems to be useful for (high-frequency) financial time series contaminated by additive outliers. In particular, it can be effective in the risk control and regulation when the prediction of volatility is the main concern since it is capable of distinguishing and correcting outlaid bursts of volatility. This conclusion is confirmed by simulations and real data examples.

We consider linear regression where the dependent variable is unobservable. Instead we can observe only an upper and lower bound. In this setup, the regression parameters need not be consistently estimable. We make certain stochastic assumptions, as weak as possible, on the random process generating the observable intervals and derive tight bounds for the regression parameters. The bounds are consistently estimable and the estimators are functions of the observable quantities only. We also restate the result in terms of set-estimators for regression models with interval-valued data.

This article presents a general methodology of state estimation by interval analysis in a dynamic system modeled by difference equations. The methodology is applied to a pineapple osmotic dehydration process, in order to predict the behavior of the process within a range of allowed perturbation. The paper presents simulations and validations.

This paper presents some considerations about the centroid of a fuzzy set, where the y-coordinate (or vertical centroid) is defined and discussed. An interesting fact about the y-centroid is analyzed using some results for Gaussian, triangular, and non-convex fuzzy sets. Some considerations about the obtained results are provided and some recommendations are given.

This document shows a neuro-fuzzy control system to regulate the velocity of a permanent magnet synchronous generator. This scheme comes up with two neuro-fuzzy systems where the first identifies the dynamics of the plant; the second is employed for control purposes. Subsequently, the performed training is examined to different reference values.

This paper studies the distribution network design problem considering the uncertain information in the demand, capacities, costs and prices in a multi-product environment and multiple periods. We consider a fractional objective function that consist in maximize the ratio between total profit and total cost. We use a model that integrates a facility location problem with a distribution network problem with fuzzy constraints, technological coefficients, and costs. To solve the problem, we use a method that transform the fuzzy linear fractional programming model in an equivalent multi-objective linear fractional programming problem to calculate the upper, middle and lower bounds of the original problem.

Multigranulation is a new approach to the Rough Set Theory, where several separability relationships are used to obtain different granulations of the universe. The Multigranulation starts from the existence of different contexts or subsets of features to characterize the objects of the universe. This approach has been used to develop various learning techniques. It is usually part of the existence of these contexts. In this paper, a method for the generation of contexts from the construction of similarity relations is proposed. The proposed method has been tested in improving the efficiency of the k-NN method, using different data.

This work introduces how to use Limit Order Book Data (LOB) and transaction data for short-term forecasting of stock prices. LOB registers all trade intentions from market participants, as a result, it contains more market information that could enhance predictions. We will be using Deep Convolutional Neural Networks (CNN), which are good at pattern recognition on images. In order to accomplish the proposed task we will make an image-like representation of LOB and transaction data, which will feed up into the CNN, therefore it can recognize hidden patterns to classify Financial Time Series (FTS) in short-term periods. Data enclose information from 11 NYSE instruments, including stocks, ETF and ADR. We will present step by step methodology for encoding financial time series into an image-like representation. Results present an impressive performance, 74.15% in Directional Accuracy (DA).

Objective feature selection is an important component in the machine learning framework, which has addressed problems like computational burden increasing and unnecessary high-dimensional representations. Most of feature selection techniques only perform individual feature evaluations and ignore the structural relationships between features of the same nature, causing relations to break and harming the algorithm performance. In this paper a feature group selection technique is proposed with the aim of objectively identify the relevance that a feature group carries out in a classification task. The proposed method uses Multiple Kernel Learning with a penalization rule based on the $$\ell _1$$ ℓ 1 -norm and a Support Vector Machine as base learner. Performance evaluation is carried out using two binarized configurations of the freely available MFEAT dataset. It provides six different feature groups allowing to develop multiple feature group analysis. The experimental results show that the implemented methodology is stable in the identification of the relevance of each feature group during all experiments, what allows to outperform the classification accuracy of state-of-the-art methods.

Computer-aided diagnosis (CAD) systems have allowed to enhance the performance of conventional, medical diagnosis procedures in different scenarios. Particularly, in the context of voice pathology detection, the use of machine learning algorithms has proved to be a promising and suitable alternative. This work proposes the implementation of two well known classification algorithms, namely artificial neural networks (ANN) and support vector machines (SVM), optimized by particle swarm optimization (PSO) algorithm, aimed at classifying voice signals between healthy and pathologic ones. Three different configurations of the Saarbrucken voice database (SVD) are used. The effect of using balanced and unbalanced versions of this dataset is proved as well as the usefulness of the considered optimization algorithm to improve the final performance outcomes. Also, proposed approach is comparable with state-of-the-art methods.

Computer vision has a wide and diverse range of applications nowadays. A particular one is automatic detection of parking lot occupancy, where a computer has to identify whether a parking lot space is empty or occupied. As in any visual classification problem, detecting parking lot spaces relies on the existence of a representative visual dataset. This problem of binary classification is commonly approached using features with adequate level of invariance to changes in illumination or rotation, that allow feeding these features into classifiers such as the SVM. Most used approaches are based on the use of convolutional neural networks, some times based on pre-trained models which in general have quite high performance. however several of these methods are tested with common experiments that do not take into account the variations that occur when training with different combinations of angles, lighting variations, and weather types. That is why in this paper we present a comparison between two approaches to solve the problem of parking lot classification with two methods: Convolutional Neural Networks and Bag of Features. In this paper we show how to use the standard Bag-of-features model to learn a visual dictionary, and use it to classify empty and occupied spaces. Results are compared with CNN approaches, emphasizing on accuracy, sensitivity analysis, and execution time.

Artificial neural networks are being used in diagnosis support systems to detect different kind of diseases. As the design of multilayer perceptron is an open question, the present work shows a comparison between a traditional empirical way and neuroevolution method to find the best architecture to solve the disease detection problem. Tuberculosis and appendicitis databases were employed to test both proposals. Results show that neuroevolution offers a good alternative for the tuberculosis problem but there is lacks of performance in the appendicitis one.

This paper introduces a model based on set theory and modern algebra that formalizes sentences and their context. The model aims at dividing sentences in cores which will be mapped into sets of an algebraic space; some of these cores have a type of context called strictly linguistic context. These sets along with an operation form algebraic structures are capable to generate new members starting from the elements mapped. In Addition, the model defines a function that can restore part or the whole of the original sentence from these sets by guaranteeing its structure and meaning. All of these processes can be used for several applications, but our main interest is the dynamic creation of small theories such as microtheories; this could be accomplished through queries that compare contexts and activate the restoring of sentences. The use case scenario has been limited to the Spanish language.

AdaBoost is an Artificial Intelligence algorithm widely used in classification problems with outstanding results in low complexity models. In this article, the prediction of the COLCAP series is carried out through the AdaBoost.RT algorithm with self-adaptive $$\varphi $$ φ . Firstly, the COLCAP index time series is analyzed in order to verify its stationarity by the unit root test. Exogenous information is used based on five time series of financial character, which were selected after performing a grey relational analysis and principal component analysis. To find optimal values of the algorithm, the variation of each value was executed. The results show that it is possible to predict the COLCAP index through AdaBoost using 48 weak classifiers resulting in MAPE = 1.247% and RMSE = 17.87. With a less complex model that uses two weak apprentices the results were MAPE = 1.403% and RMSE = 22.56.

In this paper is made a proposal of a recommendation system based on multi-agents, showing the architecture designed, the server used for the development of the multi-agent system, as well as the communication between necessary agents to carry out a tour recommended. The implemented proposal allows to make suggestions to users about movies. By means of neural networks it is determined if the proposed route for the user is correct or if it is necessary to improve the suggestion for following recommendations. In order to generate the recommendations, the free dataset of MovieLens was used, where a database was created to allow the analysis of them; and also to obtain a response to new recommendations for users.

This document evaluates works related to game theory applied to IPS (Intrusion Prevention System) in networks and proposes a game theory model that allows optimize expenditure of resources in detection of intrusions in networks.

A simulation model is presented for the attention to users in emergency situations in Bogota. This system is vital importance for the development, security and stability of the city. The simulation process begins with an intelligence model that allows acquiring the knowledge of complex process of attention of users and understanding relationships among resources which attend emergencies. This system is managed by Emergency Regulatory Center (ERC) assigned to the District Health Secretary. Within the results achieved, it is possible to obtain an improvement in measures of performance such as average time in the system and the average time blocked which show an inadequate level service because they are result of excessive time in waiting calls and the subsequent system exit. Different scenarios were proposed in order to increase reception capacity of the incidents in the activation system’s phase, which is equivalent to increase the technicians auxiliaries of regulatory medical (TARM) and of the regulatory doctor (RD).

The problem of FFSP (Flexible Flow Shop Problem) has been sufficiently investigated due to its importance for production programming and control, although many of the solution methods have been based on GA (Genetic Algorithm) and simulation, these techniques have been used in deterministic environments and under specific conditions of the problem, that is, complying with restrictions given in the Graham notation. In this paper we describe an application of these techniques to solve a very particular case where manual work stations and equipment with different degrees of efficiency, technological restrictions, recirculation process are used. The nesting of the GA is used within a simulation process. It is showed that the method proposed in adjustment and efficiency is better compared with other heuristics, in addition to the benefits of using different techniques in series to solve problems of real manufacturing environments.

The fresh fruit and vegetables sector is competitive and dynamic, with uncertainties related to supply and quality. These uncertainties generate challenges in food safety and food security issues for developing countries. Researchers have studied the supply chains (SCs) of perishable products from different perspectives of modeling. However, there are few studies that contemplate the dynamics and Supply Chain Design (SCD). This paper present a model based on system dynamics for SCs of fresh foods. The model evaluates different SCD and their impact on food security and logistics. Unlike the studies found in the literature, the model includes the loss derived from the life cycle of the food and from logistic operations. From the application of the model to three SCs of fresh fruits in Colombia, it was found that a combination of the design structures is required to achieve performance measures for food security and performance of logistics.

Most countries aim at the generation of electric power through renewable sources. The most developed countries have made great investments pursuing this goal. Photovoltaic energy is one of the options available even to domestic users. However, costs, radiation conditions and average household energy consumption won’t make it a good option everywhere. In this article a model based on system dynamics is proposed, studying the viability of the use of photovoltaic systems at home. In the article, the Colombian case is reviewed 90% of the energy in Colombia is generated through hydroelectric plants and due to its high climate dependence, in times of drought the country is forced to import energy. We figured the number of photovoltaic systems necessary to avoid this importation and calculated the investment required by Colombian households to fulfill their energy needs.

Patient flow is at the core of hospital healthcare planners, managers and medical staff faced with the challenge of providing quality service. If patient flow is constrained at a downstream level, the hospital occupancy rises as well as the risk for adverse events and infections among patients. In this paper we introduce a methodology that both models patient flow in a hospital setting and determines the required downstream bed capacity that matches a desired target of hospital occupancy rate. The model uses a fundamental mode of dynamic behaviour known as goal-seeking that bridges the gap between the desired and the actual state of the system. Using the data provided by Hospital León XIII, an acute third level hospital in Medellín, Colombia, we illustrate a practical application of this methodology. The results let us conclude that the use of a goal-seeking structure withing the system dynamics (SD) modelling, enhances the reach of the SD methodology for dealing with the hospital bed capacity planning problem.

The agricultural sector is a strategic source for the sustenance of the population worldwide, however, given the lack of a favourable environment that guarantees its sustainability and growth, this sector is exposed to multiple conflicts and needs, which affect its performance and even causing desertion of the producer. In this research, we model and analyse the agricultural sector of the potato in the Colombian context, which in addition to being a strategic food to respond to food crisis, represents the needs of the agricultural sector, where about 90% of producers are classified as small because of their low participation in land tenure and where, in addition to the low level of technology, the situation of armed conflict and climate change which negatively impacts their results. This paper deals with the simulation of the agricultural sector of the potato, projecting its results for the post-armed conflict where an improvement in its performance is expected, however it is contrasted with the conditions of climate change to determine the real impact in the sector. Since previous studies address the problem separately, here we propose a dynamic and comprehensive analysis with scope on the production, the intermediation for the marketing of the product and its financial performance, which allows us to understand the real impact on the performance of the sector.

Colorectal adenocarcinoma is one of the carcinogenic diseases that most affects the health of the world population. This disease is manifested biologically by the segregation of biomarker substances in the human system. This paper presents the development of a numerical-mathematical model for the study of the diffuse behavior of particles segregated by this type of cancer. Flow conditions, characteristics and properties of the diffusive medium are determined, and the study domain is defined. A mathematical description is elaborated to represent the behavior of the phenomenon by means of constitutive laws of the biosystem. A numerical-computational algorithm is constructed that makes possible the analysis of the different behavioral conditions; in this paper one of the multiples settings is showed. The computational implementation is done using Taylor series defined by finite differences with a refinement of the grid that can be controlled by the user. In addition, a structural element is incorporated with which it is intended to evaluate the level of concentration in the structure-substance contact zone. As a platform for the implementation of the algorithm, Matlab program is used. The results have been plotted by surface curves. Concentration levels are obtained at three points of interest, including concentrations at the structure-substance contact point, with concentration values of $$1*10^{-6} \frac{\mathrm{kg}}{\mathrm{m}^{3}}$$ 1 ∗ 10 - 6 kg m 3 . The research is oriented in the search of an alternative that allows the detection of colorectal cancer in its early phase.

The use of numerical methods such as the Montecarlo model make possible development software for simulate heterogeneous catalytics processes in secuencial systems or multiprocessing-based architectures. The objective of this work is to develop an implementation proposal in FPGA logic devices as an alternative of the simulation for processes catalytics in a parallel way. By using purposed implementation, it is obtained the development of the processes in parallel of the mechanism of Langmuir-Hinshelwood in a FPGA hardware platform.

The traceability and recovery plays a main role in the competitiveness of the food supply chain. The quality control of fruit manufacturing mainly depend of the traceability technologies used. In this sense, the quality control policies aimed at the traceability of products cause impact to production capacity and recovery. In the closed-loop supply chain (CLSC), wastes recovery and control in manufacturing contributes to improvement of the quality, as well as sustainable production. This article presents a dynamic behaviour analysis of production capacity, traceability and recovery on the peach-supply chain. Consequently, the study shows a simulation model based on system dynamics (SD) methodology. Results of simulation model explain why the delay in the waste recovery and traceability processes affect on the supply chain and its demand. The case of study is the peach supply chain, due partly to its great market potential for food industry.

The identification of properties and land destinations are key factors in urban planning decisions, especially in rapid-growing urbanized cities. This information is vital for cadaster matters, property taxes calculations, and therefore for the financial sustainability of a city. In this work we present a Markov-Monte Carlo simulation model to predict changes in land destinations. First, a Markov chain is established to identify the transition finite-state matrix of property destinations, and then a Monte Carlo simulation model is used to predict the changes. We present a case study for the city of Medellín, Colombia, using historical information from the cadaster office from 2004 to 2016. Results obtained allow identifying the urban areas with the larger number of changes. Moreover, these results provide support for urban planning decisions related to workforce sizing and visits sequences to the identified areas.

Cooperation among nodes is fundamental for ad hoc networks. In such systems, there is no centralized control, and the network components require self-organize themselves to accomplish their individual and collective goals. These conditions make necessary include cooperation mechanisms during the network operation for improving the system capacity for solving problems through collective actions. In this article, socially inspired computing is used to propose a coalitional game based on the concepts of sympathy and commitment with the purpose of achieving emergent cooperation in ad hoc networks. The results show cooperation may emerge even in scenarios in which agents do not have a cooperative strategy, producing a better network performance and a suitable resources distribution.

Natural gas is considered the transitional fuel par excellence between fossil and renewable sources, considering its low cost, greater efficiency and lesser impact on the environment. This is the reason why its demand levels have increased worldwide, requiring intervention of public and private stakeholders in order to meet these increments. The participation of diverse interconnected stakeholders (key actors) of the supplier-client form, constitutes a supply chain for natural gas, in which the effects of the application of public policy actions can be analysed in the time, using Dynamic Performance Management DPM methodology. The results of the model show the behaviour of the reserves, production and transport levels compared to scenarios that combine the implementation time of capacity expansion projects and supply reliability percentages, in which the national government can intervene, facilitating decision makers to identify the impact of the actions to be implemented, in the planning of policies aimed at guaranteeing the uninterrupted supply of this resource.

The performance of the agricultural sector is considered a fundamental factor for achieving sustainability of the most vulnerable population as well to meet the world’s food needs. This is how countries like Colombia, recognize in their government plans the importance of the development of sector leveraged by infrastructures boosting their results, however, the instrumentation and design of public policies are a challenge for the governors given the dynamic complexity of the system. Through this research, we propose a model for the analysis of logistic public policies in the agricultural sector of the potato, where the collaboration through public-private partnerships (PPP) for the implementation of distribution centers act as an integrating axis among the producers, allowing the multidimensional measurement of its dynamic performance through simulating production, intermediation for its commercialization and financial results.

This paper reports a mechanism to incorporate Linear Temporal Logic (LTL) for a component-based software architectural configuration specified by the $$\rho _{arq}$$ ρ arq -calculus. This process was made through the translation of the system definition, structure and behavior, to Atomic Propositions Transition System (APTS), upon which, the verification of one property was performed using LTL. The PintArq software application was extended to support this mechanism. One example ilustrates the verification of responsiveness, a subtype of liveness property.

The design of a communication architecture for different data management platforms is presented, between health entities through the HL7 standards, with the implementation of an application that allows the management of information, obtained from a device designed to the capture of vital signs in order to be used in different programs that seek to provide hospitalization services at home or days of medical care in areas of difficult access, allowing the control and monitoring of the signs captured to patients with different conditions.

Problem: A number of technologies has been developed aiming at improving the availability, opportunity, difficulty of access or efficiency of epilepsy diagnosis based on Electroencephalogram (EEG) data. However, these approaches are not all based on open technologies, neither are they integrated into Electronic Health Record information (EHR) systems to support continuity of care. Objective: To develop an open source EHR system for the management of patient’s information, encounter scheduling, remote registration, and subsequent analysis of EEG data. Methods: The analysis, design, and implementation of the system followed the Scrum framework. The implementation was based on an open source platform for EHR systems named OpenMRS. Results: NeuroEHR supports the provision of Tele-EEG services, integrates patient’s clinical information, and EEG data captured remotely from an EEG device, stores the data in an EEG repository, and allows a neurologist to provide a diagnosis based on clinical and EEG data. Conclusions: The NeuroEHR system is currently being used in the context of the NeuroMoTIC project, in which a pediatric EEG data set is being created and annotated, and some Artificial Intelligence algorithms are being tested to support a telehealth service for the diagnosis of epilepsy.

Organizations have become infrastructure and information dependent, and any problem that affects those assets can compromise the organization’s operations. Incident handling and malware research requires new strategies focusing on cyber defense in a way that allows researchers, incident responders and authorities to react preventively to mitigate high damaging attacks. The results of this research are a guideline of an active defense protocol to contain Remote Access Trojan (RAT) malware attacks, identifying proactively weaknesses on generic, open source or leaked code used for Trojan infection campaigns, and thus developing an effective response protocol to contain and stop the threat with a limited resource investment. This protocol does not replace traditional national protocols required by local authorities to report cyber security incidents; however, some mechanisms to deactivate Command and Control (C2) servers, can reduce effectiveness of operations based on malware related threats faced in Colombian and other countries around the globe.

This paper describes the process followed for designing a computational tool to improve the scheduling of surgeries in a regional healthcare institution where the availability of surgeons and medical staff is limited and heavily constrained. The goal is to analyze the characteristics of the system so as to design the structure of the master surgical schedule and select and appropriate set of rules aiming at reducing the waiting time for elective and ambulatory procedures and increasing the use of the rooms. In first place, the characteristics of the demand are studied by collecting and analyzing the information recorded during a two-year time lapse. Then, this information is used as an input to imitate the characteristics of the process through a discrete event system simulation model which can be used to analyze the performance of the system when different strategies are adopted. In particular, easy-to-use rules of thumb, adapted to the simplicity of the studied system are analyzed. For example, offline and online scheduling strategies and combination of these with operatory blocks (defined as a time dedicated exclusively to a given specialty) are studied. Then, upon the basis of the results obtained, a user-friendly computer interface is designed such that the process, that was predominantly carried out by hand, can be automated while improving systems performance and reducing the effort devoted by the personnel in charge.

The Colombian National Army conducts military operations against armed groups aimed at defending sovereignty, independence and territorial integrity. One of the armed groups strategies involved the creation of groups called “pisa-softs”, whose mission was to infiltrate the patrols and kill the sentinels. Various responses were developed to stop this type of attacks; One of them makes use of technological tools with programmable electronic devices and the use of sensors. A review of perimeter activity detection devices to detect intruders with systems applicable to hostile environments to define the most practical technology for building an effective device usable in the military posts. Subsequently, the functional modules of the device were defined, including a PIC microcontroller, a module with four infrared sensors, a receiving module and a tablet under Android for enhanced Tablet can be used for its construction. The result is a tool capable of generating a tactical advantage to military posts with a data reception at 60 ± 5 m and high sensitivity of 80% to 90% for intrusion detection.

In this article, we present the description of a programming language model based on Interaction Nets. With this base model, an explanation of its pertinence is made to model a programming language that helps the construction of decentralised systems (For the article, ad hoc networks are considered). Four interactions are presented, where the flexibility of the language is shown using native libraries and functions. The paradigm that is proposed is a multi-paradigm that combines the use of functions, software agents, and the use of libraries.

Lack of moderation in online conversations may result in personal aggression, harassment or cyberbullying. Such kind of hostility is usually expressed by using profanity or abusive language. On the basis of this assumption, recently Google has developed a machine-learning model to detect hostility within a comment. The model is able to assess to what extent abusive language is poisoning a conversation, obtaining a “toxicity” score for the comment. Unfortunately, it has been suggested that such a toxicity model can be deceived by adversarial attacks that manipulate the text sequence of the abusive language. In this paper we aim to fight this anomaly; firstly we characterise two types of adversarial attacks, one using obfuscation and the other using polarity transformations. Then, we propose a two–stage approach to disarm such attacks by coupling a text deobfuscation method and the toxicity scoring model. The approach was validated on a dataset of approximately 24000 distorted comments showing that it is feasible to restore the toxicity score of the adversarial variants. We anticipate that combining machine learning and text pattern recognition methods operating on different layers of linguistic features, will help to foster aggression–safe online conversations despite the adversary challenges inherent to the versatile nature of written language.

In this article, an electrical system with a study case is presented within the Colombian industrial electric sector in order to identify the changes in the electrical variables of a electrical network with distributed generation (DG). To achieve this, each present load in the 14-node IEEE system was modelled as daily demand curve of the Colombian industrial electric sector. In first place, the power dispatch of the 14-node IEEE system with DG was optimized through the particle swarm method. Afterwards, Quasi-Dynamic simulations were implemented throughout a 24-h period in the different nodes of the system with the purpose of estimate the transformers and line losses as well as the variations in the power and voltage profiles in the system nodes. DG systems supply effectively the demanded power however the voltage and power profiles resulting from Quasi-Dynamic simulations do not show significant changes in the behavior of the electrical network.

This work describes in detail a computational tool designed to study performance indicators of a four-stage transformer-based single-phase cascaded multilevel inverter. The proposed system integrates simulation, on-line measurement, control and signal processing providing automating testing functionality to optimize the performance of the inverter with base on indicators such as Total Harmonic Distortion (THD), partial and global efficiency and power balance between the stages. The computational component of the tool was developed in LabVIEW providing not only didactic interactivity with the user through the Human-Machine Interface (HMI) but also a reliable interconnection with the power converter and the instruments of the experimental setup. The hardware component was developed integrating the power converter prototype, an acquisition card and electronic circuits providing measurement, conditioning, digital control and gate driving functions. Experimental results obtained from automatic tests are presented showing potentiality of the tool to support research activities related with this type of power converters.

Electroencephalography (EEG) consists on the recording of brain electrical activity along the scalp surface. The potentials generated in the brain are acquired using electrodes covering the head. This method is efficient, but in most cases the operator should deal with bad locations and slipping, which generate motion artifacts and localization errors in posterior brain imaging and connectivity analyzes. The aim of this work is to elaborate a reference framework addressed to the currently available electrode positioning methods for EEG in terms of efficiency, viability, and placement error. With this purpose, different procedures for electrode localization were considered in this study: manual methods, EEG-caps, Magnetic Resonance Imaging EEG electrode localization, digitization, 3D laser scanner, and photogrammetry. We found that the method with higher accuracy is digitization; but it requires a controlled environment and the system itself is expensive. In terms of implementation time, the 3D hand-held laser scanner and photogrammetry provided the better results and can be used in uncontrolled clinical environments.

Integration of variable generation sources such as the renewable energy resources and the operation by isolated microgrids involves technical issues related to the reliability and the quality of the electricity supply. Indeed, the small inertia of the isolated microgrids with the integration of variable generation is a challenge faced in the operation of these electricity supply systems. One way to tackle these problems is through demand response programs. In this perspective, this paper first presents a bibliographical review of the importance of the provision of frequency control services by the demand-side and some international experiences related, and later it is present a case of study, in which we assess the effects of high penetration levels of variable generation, specifically solar PV generation, in the power balance of the microgrid, and we evaluate some proposed demand response mechanisms, focused on the active participation of residential users, that respond to variations of the system’s frequency, showing that residential demand response has the potential to reduce the frequency variations that occur during the day, while increasing the use of renewable generation sources immersed in the microgrid.

This paper presents an event-triggered approach to optimally implement a Maximum Power Point Tracking (MPPT) algorithm into a Digital Signal Processor (DSP). The proposed method allows improving the amount and distribution of time required for executing control tasks. The used nested loop control architecture has an outer loop of MPPT generating the conductance reference used by an inner loop which regulates the input conductance of a DC-DC converter. This last loop enforces a sliding-mode loss-free-resistor behavior for the power converter by means of a simple hysteresis comparator. Computations required by the MPPT algorithm are synchronously executed by the two possible commutation events produced by the inner loop during a switching period. Then, the acquisition of signals must be activated only at an instant before each one of the switching events, releasing the most of the time to implement other tasks. This last characteristic and the use of a nested loop control architecture facilitate the integration of the other essential control functions for photovoltaic (PV) generators in microgrids. Simulation and experimental results confirm the high potetialities of this implementation approach.

In this paper a strategy for integration of Distributed Generation (DG) in Demand Response (DR) program is proposed. This strategy allows users of DR increase both the number of participation or the manageable power in disconnecting power program along the day. This paper explores the improvements of voltage profile in an IEEE node test feeder when a DR program is deployed and additionally a DG supply is including, but in a different approach since this power DG supply do not will be injected directly to distribution network but else this one going to be an additional power of DR program in the users that have DG available.

This paper introduces a simplified method for digital generation of high-quality references required for control of single-phase grid-connected (GC) power converters, which can generate synchronized sinusoidal waveforms at the same frequency of the input signal or its harmonics. Therefore, its application can be useful for active power filtering, high power factor rectification, and grid integration of renewable energy sources. A hybrid analog-digital implementation is proposed integrating an Adaptive Sampling Frequency Moving Average Filter (ASF-MAF) and a discrete-time Proportional-Integral (PI) controller into a Digital Signal Processor (DSP) operating with a sampling frequency defined by an external hardware-based Voltage Controlled Oscillator (VCO). The main advantages attributed to the method are immunity to harmonic content, accuracy in computations despite of frequency changes, flexibility to produce phase displacements and reduced computational cost. Performance of the proposal was verified by means of simulation and experimental results.

This paper presents the architecture of a green and flexible WDM-PON access network. We propose the segmentation of a broadband source into smaller frequency windows by means of a wave shaper filter. A central controller commands the configuration of physical network parameters such as the optical bandwidth delivered by the filter and the modulation format of the service to be transported in the network. Different optical bandwidths were defined for the experimental demonstration and two modulation formats were evaluated in our approach. Results found an effective bandwidth of 3 GHz for optical bandwidths below 1 nm. Broader optical pass bands reduce the bandwidth but improve the signal quality measured in the receiver due to a relative higher average optical power available in the photo detector.

This paper presents a study by simulation of the performance of a Hybrid Energy Storage System (HESS) integrated to a residential microgrid. The storage system is composed of li-ion battery units and supercapacitors connected in a parallel active topology. An optimization-based real-time frequency-decoupling control strategy is used for the power split and for the assignation of the high-frequency and low-frequency energy components to the storage mediums. The simulation system emulates a photovoltaic generation source with typical intermittence of the injected power, the typical loads of a residential electric grid, and a HESS.

This work studies the behavior of the Control Sensitivity Functions derivated from the frequency regulation structure in power systems. Here, we explore the performance of the sensitivity functions in the presence of changes in the parameters of frequency regulation and power system components. A one-area power system is employed as the simulation benchmark. Results of frequency-domain analysis with Bode plots highlight the more significant parameters for Load Frequency Control and the different changes in sensitivity functions.