Area-Efficient Linear Regression Architecture for Real-Time Signal Processing on FPGAs

Linear regression is a technique widely used in digital signal processing. It consists on finding the linear function that better fits a given set of samples. This paper proposes different hardware architectures for the implementation of the linear regression method on FPGAs, specially targeting area restrictive systems. It saves area at the cost of constraining the lengths of the input signal to some fixed values. We have implemented the proposed scheme in an Automatic Modulation Classifier, meeting the hard real-time constraints this kind of systems have.

Improved estimation of Weibull Parameters considering unreliability uncertainties

We propose a linear regression method for estimating Weibull parameters from life tests. The method uses stochastic models of the unreliability at each failure instant. As a result, a heteroscedastic regression problem arises that is solved by weighted least squares minimization. The main feature of our method is an innovative s-normalization of the failure data models, to obtain analytic expressions of centers and weights for the regression. The method has been Monte Carlo contrasted with Benard?s approximation, and Maximum Likelihood Estimation; and it has the highest global scores for its robustness, and performance.

Algoritmos para mineria de datos con redes de neuronas

Este trabajo propone una serie de algoritmos con el objetivo de extraer información de conjuntos de datos con redes de neuronas. Se estudian dichos algoritmos con redes de neuronas Enhenced Neural Networks (ENN), debido a que esta arquitectura tiene algunas ventajas cuando se aproximan funciones mediante redes neuronales. En la red ENN los pesos de la matriz principal varián con cada patrón, por lo que se comete un error menor en la aproximación. Las redes de neuronas ENN reúnen la información en los pesos de su red auxiliar, se propone un método para obtener información de la red a través de dichos pesos en formas de reglas y asignando un factor de certeza de dichas reglas. La red ENN obtiene un error cuadrático medio menor que el error teórico de una aproximación matemática por ejemplo mediante polinomios de Taylor. Se muestra como una red ENN, entrenada a partir un conjunto de patrones obtenido de una función de variables reales, sus pesos asociados tienen unas relaciones similares a las que se veri_can con las variables independientes con dicha función de variables reales. Las redes de neuronas ENN aproximan polinomios, se extrae conocimiento de un conjunto de datos de forma similar a la regresión estadística, resolviendo de forma más adecuada el problema de multicolionalidad en caso de existir. Las relaciones a partir de los pesos asociados de la matriz de la red auxiliar se obtienen similares a los coeficientes de una regresión para el mismo conjunto numérico. Una red ENN entrenada a partir de un conjunto de datos de una función boolena extrae el conocimiento a partir de los pesos asociados, y la influencia de las variables de la regla lógica de la función booleana, queda reejada en esos pesos asociados a la red auxiliar de la red ENN. Se plantea una red de base radial (RBF) para la clasificación y predicción en problemas forestales y agrícolas, obteniendo mejores resultados que con el modelo de regresión y otros métodos. Los resultados con una red RBF mejoran al método de regresión si existe colinealidad entre los datos que se dispone y no son muy numerosos. También se detecta que variables tienen más importancia en virtud de la variable pronóstico. Obteniendo el error cuadrático medio con redes RBF menor que con otros métodos, en particular que con el modelo de regresión. Abstract A series of algorithms is proposed in this study aiming at the goal of producing information about data groups with a neural network. These algorithms are studied with Enheced Neural Networks (ENN), owing to the fact that this structure shows sever advantages when the functions are approximated by neural networks. Main matrix weights in th ENN vary on each pattern; so, a smaller error is produced when approximating. The neural network ENN joins the weight information contained in their auxiliary network. Thus, a method to obtain information on the network through those weights is proposed by means of rules adding a certainty factor. The net ENN obtains a mean squared error smaller than the theorical one emerging from a mathematical aproximation such as, for example, by means of Taylor's polynomials. This study also shows how in a neural network ENN trained from a set of patterns obtained through a function of real variables, its associated weights have relationships similar to those ones tested by means of the independent variables connected with such functions of real variables. The neural network ENN approximates polynomials through it information about a set of data may be obtained in a similar way than through statistical regression, solving in this way possible problems of multicollinearity in a more suitable way. Relationships emerging from the associated weights in the auxiliary network matrix obtained are similar to the coeficients corresponding to a regression for the same numerical set. A net ENN trained from a boolean function data set obtains its information from its associated weights. The inuence of the variables of the boolean function logical rule are reected on those weights associated to the net auxiliar of the ENN. A radial basis neural networks (RBF) for the classification and prediction of forest and agricultural problems is proposed. This scheme obtains better results than the ones obtained by means of regression and other methods. The outputs with a net RBF better the regression method if the collineality with the available data and their amount is not very large. Detection of which variables are more important basing on the forecast variable can also be achieved, obtaining a mean squared error smaller that the ones obtained through other methods, in special the one produced by the regression pattern.

Uncertainty Analysis in New Seismic Hazard Study of Spain Aimed at the Revision of the Spanish Building Code.

In this paper we present a global overview of the recent study carried out in Spain for the new hazard map, which final goal is the revision of the Building Code in our country (NCSE-02). The study was carried our for a working group joining experts from The Instituto Geografico Nacional (IGN) and the Technical University of Madrid (UPM) , being the different phases of the work supervised by an expert Committee integrated by national experts from public institutions involved in subject of seismic hazard. The PSHA method (Probabilistic Seismic Hazard Assessment) has been followed, quantifying the epistemic uncertainties through a logic tree and the aleatory ones linked to variability of parameters by means of probability density functions and Monte Carlo simulations. In a first phase, the inputs have been prepared, which essentially are: 1) a project catalogue update and homogenization at Mw 2) proposal of zoning models and source characterization 3) calibration of Ground Motion Prediction Equations (GMPE’s) with actual data and development of a local model with data collected in Spain for Mw < 5.5. In a second phase, a sensitivity analysis of the different input options on hazard results has been carried out in order to have criteria for defining the branches of the logic tree and their weights. Finally, the hazard estimation was done with the logic tree shown in figure 1, including nodes for quantifying uncertainties corresponding to: 1) method for estimation of hazard (zoning and zoneless); 2) zoning models, 3) GMPE combinations used and 4) regression method for estimation of source parameters. In addition, the aleatory uncertainties corresponding to the magnitude of the events, recurrence parameters and maximum magnitude for each zone have been also considered including probability density functions and Monte Carlo simulations The main conclusions of the study are presented here, together with the obtained results in terms of PGA and other spectral accelerations SA (T) for return periods of 475, 975 and 2475 years. The map of the coefficient of variation (COV) are also represented to give an idea of the zones where the dispersion among results are the highest and the zones where the results are robust.

Artificial analysis of molecular marker loci linked to tree resistance response by an artificial neural network

One of the biggest challenges that software developers face is to make an accurate estimate of the project effort. Radial basis function neural networks have been used to software effort estimation in this work using NASA dataset. This paper evaluates and compares radial basis function versus a regression model. The results show that radial basis function neural network have obtained less Mean Square Error than the regression method.

Lazy Lasso for local regression

Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets. Results show that the proposed method outperforms linear and local models for several kinds of scenarios

Robust regression applied to fractal/multifractal analysis.

Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn?t be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don?t have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: ? Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R pvalue. In this way we consider the implications of reducing the number of points. ? Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology.

System failure prediction through rare-events elastic-net logistic regression

Predicting failures in a distributed system based on previous events through logistic regression is a standard approach in literature. This technique is not reliable, though, in two situations: in the prediction of rare events, which do not appear in enough proportion for the algorithm to capture, and in environments where there are too many variables, as logistic regression tends to overfit on this situations; while manually selecting a subset of variables to create the model is error- prone. On this paper, we solve an industrial research case that presented this situation with a combination of elastic net logistic regression, a method that allows us to automatically select useful variables, a process of cross-validation on top of it and the application of a rare events prediction technique to reduce computation time. This process provides two layers of cross- validation that automatically obtain the optimal model complexity and the optimal mode l parameters values, while ensuring even rare events will be correctly predicted with a low amount of training instances. We tested this method against real industrial data, obtaining a total of 60 out of 80 possible models with a 90% average model accuracy.

Validation and aplication of Bioelectrical Impedance (BIA) method to determine body and carcass composition and nutrient retention of growing rabbits = Validación y aplicación de la técnica de la Impedancia Bioeléctrica (BIA) para determinar la composición corporal, de la canal y la retención de nutrientes de conejos en crecimiento

Los objetivos de esta tesis fueron 1) obtener y validar ecuaciones de predicción para determinar in vivo la composición corporal y de la canal de conejos en crecimiento de 25 a 77 días de vida utilizando la técnica de la Impedancia Bioeléctrica (BIA), y 2) evaluar su aplicación para determinar diferencias en la composición corporal y de la canal, así como la retención de nutrientes de animales alimentados con diferentes fuentes y niveles de grasa. El primer estudio se realizó para determinar y después validar, usando datos independientes, las ecuaciones de predicción obtenidas para determinar in vivo la composición corporal de los conejos en crecimiento. Se utilizaron 150 conejos a 5 edades distintas (25, 35, 49, 63 y 77 días de vida), con un rango de pesos entre 231 y 3138 g. Para determinar los valores de resistencia (Rs,) and reactancia (Xc,) se usó un terminal (Model BIA-101, RJL Systems, Detroit, MI USA) con cuatro electrodos. Igualmente se registró la distancia entre electrodos internos (D), la longitud corporal (L) y el peso vivo (PV) de cada animal. En cada edad, los animales fueron molidos y congelados (-20 ºC) para su posterior análisis químico (MS, grasa, proteína, cenizas y EB). El contenido en grasa y energía de los animales se incrementó, mientras que los contenidos en proteína, cenizas y agua de los animales disminuyeron con la edad. Los valores medios de Rs, Xc, impedancia (Z), L y D fueron 83.5 ± 23.1 , 18.2 ± 3.8 , 85.6 ± 22.9 , 30.6 ± 6.9 cm y 10.8 ± 3.1 cm. Se realizó un análisis de regresión lineal múltiple para determinar las ecuaciones de predicción, utilizando los valores de PV, L and Z como variables independientes. Las ecuaciones obtenidas para estimar los contenidos en agua (g), PB (g), grasa (g), cenizas (g) and EB (MJ) tuvieron un coeficiente de determinación de (R2) de 0.99, 0.99, 0.97, 0.98 y 0.99, y los errores medios de predicción relativos (EMPR) fueron: 2.79, 6.15, 24.3, 15.2 y 10.6%, respectivamente. Cuando el contenido en agua se expresó como porcentaje, los valores de R2 y EMPR fueron 0.85 and 2.30%, respectivamente. Al predecir los contenidos en proteína (%MS), grasa (%MS), cenizas (%MS) y energía (kJ/100 g MS), se obtuvieron valores de 0.79, 0.83, 0.71 y 0.86 para R2, y 5.04, 18.9, 12.0 y 3.19% para EMPR. La reactancia estuvo negativamente correlacionada con el contenido en agua, cenizas y PB (r = -0.32, P < 0.0001; r = -0.20, P < 0.05; r = -0.26, P < 0.01) y positivamente correlacionada con la grasa y la energía (r = 0.23 y r = 0.24; P < 0.01). Sin embargo, Rs estuvo positivamente correlacionada con el agua, las cenizas y la PB (r = 0.31, P < 0.001; r = 0.28, P < 0.001; r = 0.37, P < 0.0001) y negativamente con la grasa y la energía (r = -0.36 y r = -0.35; P < 0.0001). Igualmente la edad estuvo negativamente correlacionada con el contenido en agua, cenizas y proteína (r = -0.79; r = -0.68 y r = -0.80; P < 0.0001) y positivamente con la grasa y la energía (r = 0.78 y r = 0.81; P < 0.0001). Se puede concluir que el método BIA es una técnica buena y no invasiva para estimar in vivo la composición corporal de conejos en crecimiento de 25 a 77 días de vida. El objetivo del segundo estudio fue determinar y validar con datos independientes las ecuaciones de predicción obtenidas para estimar in vivo la composición de la canal eviscerada mediante el uso de BIA en un grupo de conejos de 25 a 77 días, así como testar su aplicación para predecir la retención de nutrientes y calcular las eficacias de retención de la energía y del nitrógeno. Se utilizaron 75 conejos agrupados en 5 edades (25, 35, 49, 63 y 77 días de vida) con unos pesos que variaron entre 196 y 3260 g. Para determinar los valores de resistencia (Rs, ) y reactancia (Xc, ) se usó un terminal (Model BIA-101, RJL Systems, Detroit, MI USA) con cuatro electrodos. Igualmente se registró la distancia entre electrodos internos (D), la longitud corporal (L) y el peso vivo (PV) del cada animal. En cada edad, los animales fueron aturdidos y desangrados. Su piel, vísceras y contenido digestivo fueron retirados, y la canal oreada fue pesada y molida para posteriores análisis (MS, grasa, PB, cenizas y EB). Los contenidos en energía y grasa aumentaron mientras que los de agua, cenizas y proteína disminuyeron con la edad. Los valores medios de Rs, Xc, impedancia (Z), L y D fueron 95.9±23.9 , 19.5±4.7 , 98.0±23.8 , 20.6±6.3 cm y 13.7±3.1 cm. Se realizó un análisis de regresión linear múltiple para determinar las ecuaciones de predicción, utilizando los valores de PV, L and Z como variables independientes. Los coeficientes de determinación (R2) de las ecuaciones obtenidas para estimar los contenidos en agua (g), PB (g), grasa (g), cenizas (g) and EB (MJ) fueron: 0.99, 0.99, 0.95, 0.96 y 0.98, mientras que los errores medios de predicción relativos (EMPR) fueron: 4.20, 5.48, 21.9, 9.10 y 6.77%, respectivamente. Cuando el contenido en agua se expresó como porcentaje, los valores de R2 y EMPR fueron 0.79 y 1.62%, respectivamente. Cuando se realizó la predicción de los contenidos en proteína (%MS), grasa (%MS), cenizas (%MS) y energía (kJ/100 g MS), los valores de R2 fueron 0.68, 0.76, 0.66 and 0.82, y los de RMPE: 3.22, 10.5, 5.82 and 2.54%, respectivamente. La reactancia estuvo directamente correlacionada con el contenido en grasa (r = 0.24, P < 0.05), mientras que la resistencia guardó una correlación positiva con los contenidos en agua, cenizas y proteína (r = 0.55, P < 0.001; r = 0.54, P < 0.001; r = 0.40, P < 0.005) y negativa con la grasa y la energía (r = -0.44 y r = -0.55; P < 0.001). Igualmente la edad estuvo negativamente correlacionada con los contenidos en agua, cenizas y PB (r = -0.94; r = -0.85 y r = -0.75; P < 0.0001) y positivamente con la grasa y la energía (r = 0.89 y r = 0.90; P < 0.0001). Se estudió la eficacia global de retención de la energía (ERE) y del nitrógeno (ERN) durante todo el periodo de cebo (35-63 d), Los valores de ERE fueron 20.4±7.29%, 21.0±4.18% and 20.8±2.79% en los periodos 35 a 49, 49 a 63 y 35 a 63 d, respectivamente. ERN fue 46.9±11.7%, 34.5±7.32% y 39.1±3.23% para los mismos periodos. La energía fue retenida en los tejidos para crecimiento con una eficiencia del 52.5% y la eficiencia de retención de la energía como proteína y grasa fue de 33.3 y 69.9% respectivamente. La eficiencia de utilización del nitrógeno para crecimiento fue cercana al 77%. Este trabajo muestra como el método BIA es técnica buena y no invasiva para determinar in vivo la composición de la canal y la retención de nutrientes en conejos en crecimiento de 25 a 77 días de vida. En el tercer estudio, se llevaron a cabo dos experimentos con el fin de investigar los efectos del nivel de inclusión y de la fuente de grasa, sobre los rendimientos productivos, la mortalidad, la retención de nutrientes y la composición corporal total y de la canal eviscerada de conejos en crecimiento de 34 a 63 d de vida. En el Exp. 1 se formularon 3 dietas con un diseño experimental factorial 3 x 2 con el tipo de grasa utilizada: Aceite de Soja (SBO), Lecitinas de Soja (SLO) y Manteca (L) y el nivel de inclusión (1.5 y 4%) como factores principales. El Exp. 2 también fue diseñado con una estructura factorial 3 x 2, pero usando SBO, Aceite de Pescado (FO) y Aceite de Palmiste como fuentes de grasa, incluidas a los mismos niveles que en el Exp. 1. En ambos experimentos 180 animales fueron alojados en jaulas individuales (n=30) y 600 en jaulas colectivas en grupos de 5 animales (n=20). Los animales alimentados con un 4% de grasa añadida tuvieron unos consumos diarios y unos índices de conversión más bajos que aquellos alimentados con las dietas con un 1.5% de grasa. En los animales alojados en colectivo del Exp. 1, el consumo fue un 4.8% más alto en los que consumieron las dietas que contenían manteca que en los animales alimentados con las dietas SBO (P = 0.036). La inclusión de manteca tendió a reducir la mortalidad (P = 0.067) en torno al 60% y al 25% con respecto a las dietas con SBO y SLO, respectivamente. La mortalidad aumentó con el nivel máximo de inclusión de SLO (14% vs. 1%, P < 0.01), sin observarse un efecto negativo sobre la mortalidad con el nivel más alto de inclusión de las demás fuentes de grasa utilizadas. En los animales alojados colectivo del Exp. 2 se encontró una disminución del consumo (11%), peso vivo a 63 d (4.8%) y de la ganancia diaria de peso (7.8%) con la inclusión de aceite de pescado con respecto a otras dietas (P < 0.01). Los dos últimos parámetros se vieron especialmente más reducidos cuando en las dietas se incluyó el nivel más alto de FO (5.6 y 9.5%, respectivamente, (P < 0.01)). Los animales alojados individualmente mostraron unos resultados productivos muy similares. La inclusión de aceite pescado tendió (P = 0.078) a aumentar la mortalidad (13.2%) con respecto al aceite de palmiste (6.45%), siendo intermedia para las dietas que contenían SBO (8.10%). La fuente o el nivel de grasa no afectaron la composición corporal total o de la canal eviscerada de los animales. Un incremento en el nivel de grasa dio lugar a una disminución de la ingesta de nitrógeno digestible (DNi) (1.83 vs. 1.92 g/d; P = 0.068 en Exp. 1 y 1.79 vs. 1.95 g/d; P = 0.014 en Exp. 2). Debido a que el nitrógeno retenido (NR) en la canal fue similar para ambos niveles (0.68 g/d (Exp. 1) y 0.71 g/d (Exp. 2)), la eficacia total de retención del nitrógeno (ERN) aumentó con el nivel máximo de inclusión de grasa, pero de forma significativa únicamente en el Exp. 1 (34.9 vs. 37.8%; P < 0.0001), mientras que en el Exp. 2 se encontró una tendencia (36.2 vs. 38.0% en Exp. 2; P < 0.064). Como consecuencia, la excreción de nitrógeno en heces fue menor en los animales alimentados con el nivel más alto de grasa (0.782 vs. 0.868 g/d; P = 0.0001 en Exp. 1, y 0.745 vs. 0.865 g/d; P < 0.0001 en Exp.2) al igual que el nitrógeno excretado en orina (0.702 vs. 0.822 g/d; P < 0.0001 en Exp. 1 y 0.694 vs. 0.7999 g/d; P = 0.014 en Exp.2). Aunque no hubo diferencias en la eficacia total de retención de la energía (ERE), la energía excretada en heces disminuyó al aumentar el nivel de inclusión de grasa (142 vs. 156 Kcal/d; P = 0.0004 en Exp. 1 y 144 vs. 154 g/d; P = 0.050 en Exp. 2). Sin embargo, la energía excretada como orina y en forma de calor fue mayor en el los animales del Exp. 1 alimentados con el nivel más alto de grasa (216 vs. 204 Kcal/d; P < 0.017). Se puede concluir que la manteca y el aceite de palmiste pueden ser considerados como fuentes alternativas al aceite de soja debido a la reducción de la mortalidad, sin efectos negativos sobre los rendimientos productivos o la retención de nutrientes. La inclusión de aceite de pescado empeoró los rendimientos productivos y la mortalidad durante el periodo de crecimiento. Un aumento en el nivel de grasa mejoró el índice de conversión y la eficacia total de retención de nitrógeno. ABSTRACT The aim of this Thesis is: 1) to obtain and validate prediction equations to determine in vivo whole body and carcass composition using the Bioelectrical Impedance (BIA) method in growing rabbits from 25 to 77 days of age, and 2) to study its application to determine differences on whole body and carcass chemical composition, and nutrient retention of animals fed different fat levels and sources. The first study was conducted to determine and later validate, by using independent data, the prediction equations obtained to assess in vivo the whole body composition of growing rabbits. One hundred and fifty rabbits grouped at 5 different ages (25, 35, 49, 63 and 77 days) and weighing from 231 to 3138 g were used. A four terminal body composition analyser was used to obtain resistance (Rs, ) and reactance (Xc, ) values (Model BIA-101, RJL Systems, Detroit, MI USA). The distance between internal electrodes (D, cm), body length (L, cm) and live BW of each animal were also registered. At each selected age, animals were slaughtered, ground and frozen (-20 ºC) for later chemical analyses (DM, fat, CP, ash and GE). Fat and energy body content increased with the age, while protein, ash, and water decreased. Mean values of Rs, Xc, impedance (Z), L and D were 83.5 ± 23.1 , 18.2 ± 3.8 , 85.6 ± 22.9 , 30.6 ± 6.9 cm and 10.8 ± 3.1 cm. A multiple linear regression analysis was used to determine the prediction equations, using BW, L and Z data as independent variables. Equations obtained to estimate water (g), CP (g), fat (g), ash (g) and GE (MJ) content had, respectively, coefficient of determination (R2) values of 0.99, 0.99, 0.97, 0.98 and 0.99, and the relative mean prediction error (RMPE) was: 2.79, 6.15, 24.3, 15.2 and 10.6%, respectively. When water was expressed as percentage, the R2 and RMPE were 0.85 and 2.30%, respectively. When prediction of the content of protein (%DM), fat (%DM), ash (%DM) and energy (kJ/100 g DM) was done, values of 0.79, 0.83, 0.71 and 0.86 for R2, and 5.04, 18.9, 12.0 and 3.19% for RMPE, respectively, were obtained. Reactance was negatively correlated with water, ash and CP content (r = -0.32, P < 0.0001; r = -0.20, P < 0.05; r = -0.26, P < 0.01) and positively correlated with fat and GE (r = 0.23 and r = 0.24; P < 0.01). Otherwise, resistance was positively correlated with water, ash and CP (r = 0.31, P < 0.001; r = 0.28, P < 0.001; r = 0.37, P < 0.0001) and negatively correlated with fat and energy (r = -0.36 and r = -0.35; P < 0.0001). Moreover, age was negatively correlated with water, ash and CP content (r = -0.79; r = -0.68 and r = -0.80; P < 0.0001) and positively correlated with fat and energy (r = 0.78 and r = 0.81; P < 0.0001). It could be concluded that BIA is a non-invasive good method to estimate in vivo whole body composition of growing rabbits from 25 to 77 days of age. The aim of the second study was to determine and validate with independent data, the prediction equations obtained to estimate in vivo carcass composition of growing rabbits by using the results of carcass chemical composition and BIA values in a group of rabbits from 25 to 77 days. Also its potential application to predict nutrient retention and overall energy and nitrogen retention efficiencies was analysed. Seventy five rabbits grouped at 5 different ages (25, 35, 49, 63 and 77 days) with weights ranging from 196 to 3260 g were used. A four terminal body composition analyser (Model BIA-101, RJL Systems, Detroit, MI USA) was used to obtain resistance (Rs, ) and reactance (Xc, ) values. The distance between internal electrodes (D, cm), body length (L, cm) and live weight (BW, g) were also registered. At each selected age, all the animals were stunned and bled. The skin, organs and digestive content were removed, and the chilled carcass were weighed and processed for chemical analyses (DM, fat, CP, ash and GE). Energy and fat increased with the age, while CP, ash, and water decreased. Mean values of Rs, Xc, impedance (Z), L and D were 95.9±23.9 , 19.5±4.7 , 98.0±23.8 , 20.6±6.3 cm y 13.7±3.1 cm. A multiple linear regression analysis was done to determine the equations, using BW, L and Z data as parameters. Coefficient of determination (R2) of the equations obtained to estimate water (g), CP (g), fat (g), ash (g) and GE (MJ) content were: 0.99, 0.99, 0.95, 0.96 and 0.98, and relative mean prediction error (RMPE) were: 4.20, 5.48, 21.9, 9.10 and 6.77%, respectively. When water content was expressed as percentage, the R2 and RMPE were 0.79 and 1.62%, respectively. When prediction of protein (%DM), fat (%DM), ash (%DM) and energy (kJ/100 g DM) content was done, R2 values were 0.68, 0.76, 0.66 and 0.82, and RMPE: 3.22, 10.5, 5.82 and 2.54%, respectively. Reactance was positively correlated with fat content (r = 0.24, P < 0.05) while resistance was positively correlated with water, ash and protein carcass content (r = 0.55, P < 0.001; r = 0.54, P < 0.001; r = 0.40, P < 0.005) and negatively correlated with fat and energy (r = -0.44 and r = -0.55; P < 0.001). Moreover, age was negatively correlated with water, ash and CP content (r = -0.97, r = -0.95 and r = -0.89, P < 0.0001) and positively correlated with fat and GE (r = 0.95 and r = 0.97; P < 0.0001). In the whole growing period (35-63 d), overall energy retention efficiency (ERE) and nitrogen retention efficiency (NRE) were studied. The ERE values were 20.4±7.29%, 21.0±4.18% and 20.8±2.79%, from 35 to 49, 49 to 63 and from 35 to 63 d, respectively. NRE was 46.9±11.7%, 34.5±7.32% and 39.1±3.23% for the same periods. Energy was retained in body tissues for growth with an efficiency of approximately 52.5% and efficiency of the energy for protein and fat retention was 33.3 and 69.9%, respectively. Efficiency of utilization of nitrogen for growth was near to 77%. This work shows that BIA it’s a non-invasive and good method to estimate in vivo carcass composition and nutrient retention of growing rabbits from 25 to 77 days of age. In the third study, two experiments were conducted to investigate the effect of the fat addition and source, on performance, mortality, nutrient retention, and the whole body and carcass chemical composition of growing rabbits from 34 to 63 d. In Exp. 1 three diets were arranged in a 3 x 2 factorial structure with the source of fat: Soybean oil (SBO), Soya Lecithin Oil (SLO) and Lard (L) and the dietary fat inclusion level (1.5 and 4%) as the main factors. Exp. 2 had also arranged as a 3 x 2 factorial design, but using SBO, Fish Oil (FO) and Palmkernel Oil (PKO) as fat sources, and included at the same levels than in Exp. 1. In both experiments 180 animals were allocated in individual cages (n=30) and 600 in collectives cages, in groups of 5 animals (n=20). Animals fed with 4% dietary fat level showed lower DFI and FCR than those fed diets with 1.5%. In collective housing of Exp. 1, DFI was a 4.8% higher in animals fed with diets containing lard than SBO (P = 0.036), being intermediate for diet with SLO. Inclusion of lard also tended to reduce mortality (P = 0.067) around 60% and 25% with respect SBO and SLO diets, respectively. Mortality increased with the greatest level of soya lecithin (14% vs. 1%, P < 0.01). In Exp. 2 a decrease of DFI (11%), BW at 63 d (4.8%) and DWG (7.8%) were observed with the inclusion of fish oil with respect the other two diets (P < 0.01). These last two traits impaired with the highest level of fish oil (5.6 and 9.5%, respectively, (P < 0.01)). Animals housed individually showed similar performance results. The inclusion of fish oil also tended to increase (P = 0.078) mortality (13.2%) with respect palmkernel oil (6.45%), being mortality of SBO intermediate (8.10%). Fat source and level did not affect the whole body or carcass chemical composition. An increase of the fat sources addition led to a decrease of the digestible nitrogen intake (DNi) (1.83 vs. 1.92 g/d; P = 0.068 in Exp. 1 and 1.79 vs. 1.95 g/d; P = 0.014 in Exp. 2). As the nitrogen retained (NR) in the carcass was similar for both fat levels (0.68 g/d (Exp. 1) and 0.71 g/d (Exp. 2)), the overall efficiency of N retention (NRE) increased with the highest level of fat, but only reached significant level in Exp. 1 (34.9 vs. 37.8%; P < 0.0001), while in Exp. 2 a tendency was found (36.2 vs. 38.0% in Exp. 2; P < 0.064). Consequently, nitrogen excretion in faeces was lower in animals fed with the highest level of fat (0.782 vs. 0.868 g/d; P = 0.0001 in Exp. 1, and 0.745 vs. 0.865 g/d; P < 0.0001 in Exp.2). The same effect was observed with the nitrogen excreted as urine (0.702 vs. 0.822 g/d; P < 0.0001 in Exp. 1 and 0.694 vs. 0.7999 g/d; P = 0.014 in Exp.2). Although there were not differences in ERE, the energy excreted in faeces decreased as fat level increased (142 vs. 156 Kcal/d; P = 0.0004 in Exp. 1 and 144 vs. 154 g/d; P = 0.050 in Exp. 2). In Exp. 1 the energy excreted as urine and heat production was significantly higher when animals were fed with the highest level of dietary fat (216 vs. 204 Kcal/d; P < 0.017). It can be concluded that lard and palmkernel oil can be considered as alternative sources to soybean oil due to the reduction of the mortality, without negative effects on performances or nutrient retention. Inclusion of fish impaired animals´ productivity and mortality. An increase of the dietary fat level improved FCR and overall protein efficiency retention.