Simulation in vascular access surgery

In the last years, simulation training has become widespread in different areas of medicine due to social expectations, political accountability and professional regulation. Different types of simulators allow to improve knowledge, skills, communication and team behavior. Simulation sessions have been proven to shorten the learning curve and allow education in a safe environment. Patients on dialysis are an expanding group. They often suffer from several comorbidities and need complex surgical procedures with regard to their dialysis access. Therefore, education in evidence-based algorithms is as important as teaching of practical skills. In this chapter, we are presenting an overview of available dialysis access training modalities. We are convinced that simulation will become more important in the near future and has a substantial impact on strategies to improve aspects of patient safety. © 2015 S. Karger AG, Basel.

A Comparison of Hydrographically and Optically Derived Mixed Layer Depths

Efforts to understand and model the dynamics of the upper ocean would be significantly advanced given the ability to rapidly determine mixed layer depths (MLDs) over large regions. Remote sensing technologies are an ideal choice for achieving this goal. This study addresses the feasibility of estimating MLDs from optical properties. These properties are strongly influenced by suspended particle concentrations, which generally reach a maximum at pycnoclines. The premise therefore is to use a gradient in beam attenuation at 660 nm (c660) as a proxy for the depth of a particle-scattering layer. Using a global data set collected during World Ocean Circulation Experiment cruises from 1988-1997, six algorithms were employed to compute MLDs from either density or temperature profiles. Given the absence of published optically based MLD algorithms, two new methods were developed that use c660 profiles to estimate the MLD. Intercomparison of the six hydrographically based algorithms revealed some significant disparities among the resulting MLD values. Comparisons between the hydrographical and optical approaches indicated a first-order agreement between the MLDs based on the depths of gradient maxima for density and c660. When comparing various hydrographically based algorithms, other investigators reported that inherent fluctuations of the mixed layer depth limit the accuracy of its determination to 20 m. Using this benchmark, we found a similar to 70% agreement between the best hydrographical-optical algorithm pairings.

Análisis biomecánico del segmento de la carrera a pie de la competición en triatlón

El principal objetivo de la presente investigación fue el conocer el perfil de rendimiento técnico de los triatletas, desde un punto de vista biomecánica, en el segmento carrera a pie durante la competición en triatlón. Asimismo, como el genero y el nivel deportivo del triatleta podrían influir en su respuesta motriz durante la competicion. Para ello, se necesitaba desarrollar y validar una técnica experimental que fuera lo suficientemente precisa (validez interna), con una alta fiabilidad y con una gran validez externa (ecologica) debido al entorno de la competicion. La muestra la formaron un total de 64 deportistas: 32 triatletas participantes en la Copa del Mundo de Triatlon de Madrid-2008 (16 hombres y 16 mujeres) y 32 triatletas participantes en el Clasificatorio del Campeonato de Espana Elite (16 hombres y 16 mujeres). El análisis de la técnica de carrera de los deportistas se realizo mediante un sistema fotogramétrico en 2d que permitió calcular las coordenadas (x,y) de los centros articulares con un error de 1.66% en el eje x y de un 2.10% en el eje y. Las imágenes fueron obtenidas por una cámara que filmaba el movimiento en un plano antero-posterior del triatleta. Algoritmos basados en la DLT (Abdel-Aziz & Karara, 1971) permitieron conocer las coordenadas reales a partir de las coordenadas digitalizadas en el plano y posteriormente las distintas variables analizadas. El análisis biomecánica de la carrera se realizo en 4 ocasiones diferentes durante la competición, correspondiendo con cada una de las vueltas de 2,5 km, que el triatleta tenía que realizar. La velocidad de carrera resulto estar íntimamente ligada al nivel deportivo del triatleta. Del mismo modo, 3 de los 4 grupos analizados presentaron valores inferiores a 3 minutos 30 segundos por kilometro recorrido, poniendo de manifiesto el altísimo nivel de los sujetos analizados. Del mismo modo parece que las chicos consiguen una mayor velocidad gracias a una mayor longitud de ciclo en relación a las chicas, ya que estas muestran valores mayores en cuanto a frecuencia de zancada. La frecuencia de zancada presento los valores más altos en la primera vuelta en todos los deportistas analizados. Asimismo, los triatletas de nivel internacional y las chicas fueron los que mostraron los mayores valores. La longitud de zancada presento distintas tendencias en función del nivel y el género del deportista. Así pues, en los deportistas internacionales y en los chicos los mayores valores se encontraron en la primera vuelta mientras que la tendencia fue al descenso, siendo probablemente la fatiga acumulada la causante de dicha tendencia. En cambio, aquellos deportistas de nivel nacional y las chicas mostraron valores mayores en la segunda vuelta que en la primera, evidenciando que además de la fatiga, el ciclismo previo tiene una incidencia directa sobre su rendimiento. Los tiempos de vuelo permanecieron constantes durante toda la carrera, encontrando cierta evolución en los tiempos de apoyo, la cual provoca una modificación en los porcentajes relativos en los tiempos de vuelo. Los tiempos de apoyo más bajos se encontraron en la primera vuelta. Del mismo modo, los deportistas de nivel internacional y los chicos mostraron valores inferiores. También, estos grupos fueron más constantes en sus valores a lo largo de las vueltas. Por el contrario, se encontraron tendencias al aumento en los triatletas de nivel nacional y en las chicas, los cuales no fueron capaces de mantener el mismo rendimiento debido seguramente a su menor nivel deportivo. La oscilación vertical de la cadera se mostro constante en los triatletas de mayor nivel, encontrándose tendencias al aumento en los de menor nivel. Del mismo modo, los valores más altos correspondieron a las chicas y a los deportistas de nivel nacional. La distancia de la cadera al apoyo permaneció constante a lo largo de las vueltas en todos los grupos, obteniéndose valores mayores en los triatletas de nivel internacional y en los chicos. El ángulo de la rodilla apoyada en el momento del despegue no mostro una tendencia clara. Los deportistas de nivel internacional y los chicos presentaron los valores más bajos. El ángulo de la rodilla libre en el momento del despegue mostro una correlación muy alta con la velocidad de carrera. Del mismo modo, los ángulos más pequeños se encontraron en los triatletas internacionales y en los chicos, debido seguramente a los mayores valores de velocidad registrados por ambos grupos. Los ángulos de los tobillos no mostraron ninguna tendencia clara durante la competición analizada. Los cuatro grupos de población presentaron valores similares, por lo que parece que no representan una variable que pueda incidir sobre el rendimiento biomecánica del triatleta. Los resultados obtenidos en el presente estudio de investigación avalan la utilización de la fotogrametría-video en 2d para el análisis de la técnica de carrera durante la competición en triatlón. Su aplicación en una competición de máximo nivel internacional ha posibilitado conocer el perfil técnico que presentan los triatletas a lo largo del segmento de carrera a pie. Del mismo modo, se ha podido demostrar como los estudios realizados en laboratorio no reflejan la realidad competitiva de un triatlón de máximo nivel. The aim of this research was to determine the running technique profile during a triathlon competition from a biomechanical perspective. Also, to analyze the triathlete gender’s and level of performance’s influence on this profile in competition. An accurate (internal validity) and reliable methodology with a high external validity (ecological) had to be developed to get those aims in competition. Sixty-four triathletes were analyzed. 32 (16 males, 16 females) took part in the Madrid 2008 Triathlon World Cup and 32 (16 males and 16 females) took part in the Spanish Triathlon National Championships. The biomechanical analyses were carried out by a photogrammetric system that allow to calculate the landmarks coordinates (x,y) with a 1.66% error in x axis, and a 2.10% error in y axis. The frames were obtained with a camera situated perpendicular to the triathletes’ trajectory, filming the saggittal plane. DLT based algorithms (Abdel-Aziz & Karara, 1971) were used to calculate the real coordinates from the digitalized ones and the final variables afterwards. The biomechanical analisys itself was performed in four different moments during the competition, according to each 2.5 km lap the triathletes had to do. Running speed was highly related to performance level. Also, 3 of the 4 analyzed groups showed speed values under the 3 minutes and 30 seconds per kilometer. It demonstrated the very high performance level of the analized triathletes. Furthermore, it seems that men get higher speeds because their longer stride length, while women shows higher stride frequency values. The highest stride frequency values were found in the first lap. Women and the international level triathletes showed the highest values. Stride length showed different tendencies according to the gender and level of performance. Men and international level triathletes showed the highest level in the first lap and a decreasing tendency after that. The accumulated fatigue was probably the reason of this tendency. On the other hand, higher values than in first lap were found in the second one in women and national level triathletes. It demonstrated the previous cycling can affect to those groups in terms of biomechanics. Flight times remained constant during the running part, while the contact times showed an increasing tendency that caused a variation in flight times percents. The lowest contact times were found in the first lap and in men and international triathletes’ values. Also, these two groups were more consistent during the whole running. On the other hand, increasing tendencies were found in women and national level triathletes, who were not able to maintain the same values probably due to their lower level of performance. Higher level triathletes showed more consistent hip vertical oscillation values than lower level triathletes, who presented increasing tendencies. The highest values were found in women and national level triathletes. The horizontal distance hip-toe cap remained constant among the laps in all the groups. Men and international level triathletes showed the highest values. The support knee angle at toe-off did not show a clear tendency. The lowest values were found in men and international level triathletes. A high correlation was found between the non-support knee angle and the running speed. Furthermore, men and international level triathletes showed the smallest values, due to the higher velocities reached by these two groups. Ankles angles did not show any tendency during the running part. Similar values were found in the four analyzed groups, so this variable does not seem to represent an important one within the triathlete’s performance. The results obtained in the present research support the use of the bidimensional photogrammetric video-system to analyze the running technique during a triathlon competition. Its application in international triathlon meetings has allowed determining the triathletes’ technique profile during the running part. Also, it has been demonstrated the laboratory-based studies does not reproduce a top-level competition.

A hierarchical strategy for real-time tracking on-board UAVs

In this paper, we present a real-time tracking strategy based on direct methods for tracking tasks on-board UAVs, that is able to overcome problems posed by the challenging conditions of the task: e.g. constant vibrations, fast 3D changes, and limited capacity on-board. The vast majority of approaches make use of feature-based methods to track objects. Nonetheless, in this paper we show that although some of these feature-based solutions are faster, direct methods can be more robust under fast 3D motions (fast changes in position), some changes in appearance, constant vibrations (without requiring any specific hardware or software for video stabilization), and situations where part of the object to track is out the field of view of the camera. The performance of the proposed strategy is evaluated with images from real-flight tests using different evaluation mechanisms (e.g. accurate position estimation using a Vicon sytem). Results show that our tracking strategy performs better than well known feature-based algorithms and well known configurations of direct methods, and that the recovered data is robust enough for vision-in-the-loop tasks.

Simultaneous environmental and economic process synthesis of Isobutane Alkylation

This multidisciplinary study concerns the optimal design of processes with a view to both maximizing profit and minimizing environmental impacts. This can be achieved by a combination of traditional chemical process design methods, measurements of environmental impacts and advanced mathematical optimization techniques. More to the point, this paper presents a hybrid simulation-multiobjective optimization approach that at once optimizes the production cost and minimizes the associated environmental impacts of isobutane alkylation. This approach has also made it possible to obtain the flowsheet configurations and process variables that are needed to manufacture isooctane in a way that satisfies the above-stated double aim. The problem is formulated as a Generalized Disjunctive Programming problem and solved using state-of-the-art logic-based algorithms. It is shown, starting from existing alternatives for the process, that it is possible to systematically generate a superstructure that includes alternatives not previously considered. The optimal solution, in the form a Pareto curve, includes different structural alternatives from which the most suitable design can be selected. To evaluate the environmental impact, Life Cycle Assessment based on two different indicators is employed: Ecoindicator 99 and Global Warming Potential.

FPGA implementation of a multi-population PBIL algorithm

Evolutionary-based algorithms play an important role in finding solutions to many problems that are not solved by classical methods, and particularly so for those cases where solutions lie within extreme non-convex multidimensional spaces. The intrinsic parallel structure of evolutionary algorithms are amenable to the simultaneous testing of multiple solutions; this has proved essential to the circumvention of local optima, and such robustness comes with high computational overhead, though custom digital processor use may reduce this cost. This paper presents a new implementation of an old, and almost forgotten, evolutionary algorithm: the population-based incremental learning method. We show that the structure of this algorithm is well suited to implementation within programmable logic, as compared with contemporary genetic algorithms. Further, the inherent concurrency of our FPGA implementation facilitates the integration and testing of micro-populations.

Enhanced detection-guided NLMS estimation of sparse FIR-modeled signal channels

In various signal-channel-estimation problems, the channel being estimated may be well approximated by a discrete finite impulse response (FIR) model with sparsely separated active or nonzero taps. A common approach to estimating such channels involves a discrete normalized least-mean-square (NLMS) adaptive FIR filter, every tap of which is adapted at each sample interval. Such an approach suffers from slow convergence rates and poor tracking when the required FIR filter is "long." Recently, NLMS-based algorithms have been proposed that employ least-squares-based structural detection techniques to exploit possible sparse channel structure and subsequently provide improved estimation performance. However, these algorithms perform poorly when there is a large dynamic range amongst the active taps. In this paper, we propose two modifications to the previous algorithms, which essentially remove this limitation. The modifications also significantly improve the applicability of the detection technique to structurally time varying channels. Importantly, for sparse channels, the computational cost of the newly proposed detection-guided NLMS estimator is only marginally greater than that of the standard NLMS estimator. Simulations demonstrate the favourable performance of the newly proposed algorithm. © 2006 IEEE.

Estimation of concentration-dependent diffusion coefficient in drying process from the space-averaged concentration versus time with experimental data

The estimation of a concentration-dependent diffusion coefficient in a drying process is known as an inverse coefficient problem. The solution is sought wherein the space-average concentration is known as function of time (mass loss monitoring). The problem is stated as the minimization of a functional and gradient-based algorithms are used to solve it. Many numerical and experimental examples that demonstrate the effectiveness of the proposed approach are presented. Thin slab drying was carried out in an isothermal drying chamber built in our laboratory. The diffusion coefficients of fructose obtained with the present method are compared with existing literature results.

Complex Adaptive On-line Learning System “Theory of Automatic Control"

The controlled from distance teaching (DT) in the system of technical education has a row of features: complication of informative content, necessity of development of simulation models and trainers for conducting of practical and laboratory employments, conducting of knowledge diagnostics on the basis of mathematical-based algorithms, organization of execution collective projects of the applied setting. For development of the process of teaching bases of fundamental discipline control system Theory of automatic control (TAC) the combined approach of optimum combination of existent programmatic instruments of support was chosen DT and own developments. The system DT TAC included: controlled from distance course (DC) of TAC, site of virtual laboratory practical works in LAB.TAC and students knowledge remote diagnostic system d-tester.

Event mining for system and service management

Modern IT infrastructures are constructed by large scale computing systems and administered by IT service providers. Manually maintaining such large computing systems is costly and inefficient. Service providers often seek automatic or semi-automatic methodologies of detecting and resolving system issues to improve their service quality and efficiency. This dissertation investigates several data-driven approaches for assisting service providers in achieving this goal. The detailed problems studied by these approaches can be categorized into the three aspects in the service workflow: 1) preprocessing raw textual system logs to structural events; 2) refining monitoring configurations for eliminating false positives and false negatives; 3) improving the efficiency of system diagnosis on detected alerts. Solving these problems usually requires a huge amount of domain knowledge about the particular computing systems. The approaches investigated by this dissertation are developed based on event mining algorithms, which are able to automatically derive part of that knowledge from the historical system logs, events and tickets. ^ In particular, two textual clustering algorithms are developed for converting raw textual logs into system events. For refining the monitoring configuration, a rule based alert prediction algorithm is proposed for eliminating false alerts (false positives) without losing any real alert and a textual classification method is applied to identify the missing alerts (false negatives) from manual incident tickets. For system diagnosis, this dissertation presents an efficient algorithm for discovering the temporal dependencies between system events with corresponding time lags, which can help the administrators to determine the redundancies of deployed monitoring situations and dependencies of system components. To improve the efficiency of incident ticket resolving, several KNN-based algorithms that recommend relevant historical tickets with resolutions for incoming tickets are investigated. Finally, this dissertation offers a novel algorithm for searching similar textual event segments over large system logs that assists administrators to locate similar system behaviors in the logs. Extensive empirical evaluation on system logs, events and tickets from real IT infrastructures demonstrates the effectiveness and efficiency of the proposed approaches.^

Modelling of an efficient dynamic smart solar photovoltaic power grid system

A smart solar photovoltaic grid system is an advent of innovation coherence of information and communications technology (ICT) with power systems control engineering via the internet [1]. This thesis designs and demonstrates a smart solar photovoltaic grid system that is selfhealing, environmental and consumer friendly, but also with the ability to accommodate other renewable sources of energy generation seamlessly, creating a healthy competitive energy industry and optimising energy assets efficiency. This thesis also presents the modelling of an efficient dynamic smart solar photovoltaic power grid system by exploring the maximum power point tracking efficiency, optimisation of the smart solar photovoltaic array through modelling and simulation to improve the quality of design for the solar photovoltaic module. In contrast, over the past decade quite promising results have been published in literature, most of which have not addressed the basis of the research questions in this thesis. The Levenberg-Marquardt and sparse based algorithms have proven to be very effective tools in helping to improve the quality of design for solar photovoltaic modules, minimising the possible relative errors in this thesis. Guided by theoretical and analytical reviews in literature, this research has carefully chosen the MatLab/Simulink software toolbox for modelling and simulation experiments performed on the static smart solar grid system. The auto-correlation coefficient results obtained from the modelling experiments give an accuracy of 99% with negligible mean square error (MSE), root mean square error (RMSE) and standard deviation. This thesis further explores the design and implementation of a robust real-time online solar photovoltaic monitoring system, establishing a comparative study of two solar photovoltaic tracking systems which provide remote access to the harvested energy data. This research made a landmark innovation in designing and implementing a unique approach for online remote access solar photovoltaic monitoring systems providing updated information of the energy produced by the solar photovoltaic module at the site location. In addressing the challenge of online solar photovoltaic monitoring systems, Darfon online data logger device has been systematically integrated into the design for a comparative study of the two solar photovoltaic tracking systems examined in this thesis. The site location for the comparative study of the solar photovoltaic tracking systems is at the National Kaohsiung University of Applied Sciences, Taiwan, R.O.C. The overall comparative energy output efficiency of the azimuthal-altitude dual-axis over the 450 stationary solar photovoltaic monitoring system as observed at the research location site is about 72% based on the total energy produced, estimated money saved and the amount of CO2 reduction achieved. Similarly, in comparing the total amount of energy produced by the two solar photovoltaic tracking systems, the overall daily generated energy for the month of July shows the effectiveness of the azimuthal-altitude tracking systems over the 450 stationary solar photovoltaic system. It was found that the azimuthal-altitude dual-axis tracking systems were about 68.43% efficient compared to the 450 stationary solar photovoltaic systems. Lastly, the overall comparative hourly energy efficiency of the azimuthal-altitude dual-axis over the 450 stationary solar photovoltaic energy system was found to be 74.2% efficient. Results from this research are quite promising and significant in satisfying the purpose of the research objectives and questions posed in the thesis. The new algorithms introduced in this research and the statistical measures applied to the modelling and simulation of a smart static solar photovoltaic grid system performance outperformed other previous works in reviewed literature. Based on this new implementation design of the online data logging systems for solar photovoltaic monitoring, it is possible for the first time to have online on-site information of the energy produced remotely, fault identification and rectification, maintenance and recovery time deployed as fast as possible. The results presented in this research as Internet of things (IoT) on smart solar grid systems are likely to offer real-life experiences especially both to the existing body of knowledge and the future solar photovoltaic energy industry irrespective of the study site location for the comparative solar photovoltaic tracking systems. While the thesis has contributed to the smart solar photovoltaic grid system, it has also highlighted areas of further research and the need to investigate more on improving the choice and quality design for solar photovoltaic modules. Finally, it has also made recommendations for further research in the minimization of the absolute or relative errors in the quality and design of the smart static solar photovoltaic module.

Interpretable Convolutional Neural Networks for Decoding and Analyzing Neural Time Series Data

Machine learning is widely adopted to decode multi-variate neural time series, including electroencephalographic (EEG) and single-cell recordings. Recent solutions based on deep learning (DL) outperformed traditional decoders by automatically extracting relevant discriminative features from raw or minimally pre-processed signals. Convolutional Neural Networks (CNNs) have been successfully applied to EEG and are the most common DL-based EEG decoders in the state-of-the-art (SOA). However, the current research is affected by some limitations. SOA CNNs for EEG decoding usually exploit deep and heavy structures with the risk of overfitting small datasets, and architectures are often defined empirically. Furthermore, CNNs are mainly validated by designing within-subject decoders. Crucially, the automatically learned features mainly remain unexplored; conversely, interpreting these features may be of great value to use decoders also as analysis tools, highlighting neural signatures underlying the different decoded brain or behavioral states in a data-driven way. Lastly, SOA DL-based algorithms used to decode single-cell recordings rely on more complex, slower to train and less interpretable networks than CNNs, and the use of CNNs with these signals has not been investigated. This PhD research addresses the previous limitations, with reference to P300 and motor decoding from EEG, and motor decoding from single-neuron activity. CNNs were designed light, compact, and interpretable. Moreover, multiple training strategies were adopted, including transfer learning, which could reduce training times promoting the application of CNNs in practice. Furthermore, CNN-based EEG analyses were proposed to study neural features in the spatial, temporal and frequency domains, and proved to better highlight and enhance relevant neural features related to P300 and motor states than canonical EEG analyses. Remarkably, these analyses could be used, in perspective, to design novel EEG biomarkers for neurological or neurodevelopmental disorders. Lastly, CNNs were developed to decode single-neuron activity, providing a better compromise between performance and model complexity.

Development and testing of methodologies for the control and diagnosis of innovative combustion systems

In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.

Methodologies for attitude determination from MEMS sensors in vehicles

The focus of the thesis is the application of different attitude’s determination algorithms on data evaluated with MEMS sensor using a board provided by University of Bologna. MEMS sensors are a very cheap options to obtain acceleration, and angular velocity. The use of magnetometers based on Hall effect can provide further data. The disadvantage is that they have a lot of noise and drift which can affects the results. The different algorithms that have been used are: pitch and roll from accelerometer, yaw from magnetometer, attitude from gyroscope, TRIAD, QUEST, Magdwick, Mahony, Extended Kalman filter, Kalman GPS aided INS. In this work the algorithms have been rewritten to fit perfectly with the data provided from the MEMS sensor. The data collected by the board are acceleration on the three axis, angular velocity on the three axis, magnetic fields on the three axis, and latitude, longitude, and altitude from the GPS. Several tests and comparisons have been carried out installing the electric board on different vehicles operating in the air and on ground. The conclusion that can be drawn from this study is that the Magdwich filter is the best trade-off between computational capabilities required and results obtained. If attitude angles are obtained from accelerometers, gyroscopes, and magnetometer, inconsistent data are obtained for cases where high vibrations levels are noticed. On the other hand, Kalman filter based algorithms requires a high computational burden. TRIAD and QUEST algorithms doesn’t perform as well as filters.

GOOAL: A routine based on Genetic Algorithms to track harmonics in electric power systems

Voltage and current waveforms of a distribution or transmission power system are not pure sinusoids. There are distortions in these waveforms that can be represented as a combination of the fundamental frequency, harmonics and high frequency transients. This paper presents a novel approach to identifying harmonics in power system distorted waveforms. The proposed method is based on Genetic Algorithms, which is an optimization technique inspired by genetics and natural evolution. GOOAL, a specially designed intelligent algorithm for optimization problems, was successfully implemented and tested. Two kinds of representations concerning chromosomes are utilized: binary and real. The results show that the proposed method is more precise than the traditional Fourier Transform, especially considering the real representation of the chromosomes.