Task-Driven Adaptive Statistical Compressive Sensing of Gaussian Mixture Models

A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity

Communications-Inspired Projection Design with Application to Compressive Sensing

We consider the recovery of an underlying signal x \in C^m based on projection measurements of the form y=Mx+w, where y \in C^l and w is measurement noise; we are interested in the case l < m. It is assumed that the signal model p(x) is known, and w CN(w;0,S_w), for known S_W. The objective is to design a projection matrix M \in C^(l x m) to maximize key information-theoretic quantities with operational significance, including the mutual information between the signal and the projections I(x;y) or the Renyi entropy of the projections h_a(y) (Shannon entropy is a special case). By capitalizing on explicit characterizations of the gradients of the information measures with respect to the projections matrix, where we also partially extend the well-known results of Palomar and Verdu from the mutual information to the Renyi entropy domain, we unveil the key operations carried out by the optimal projections designs: mode exposure and mode alignment. Experiments are considered for the case of compressive sensing (CS) applied to imagery. In this context, we provide a demonstration of the performance improvement possible through the application of the novel projection designs in relation to conventional ones, as well as justification for a fast online projections design method with which state-of-the-art adaptive CS signal recovery is achieved.Comment: 25 pages, 7 figures, parts of material published in IEEE ICASSP 2012, submitted to SIIM

Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning

We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates

Estimación de los parámetros de motores de inducción a partir de las medidas de pérdidas de potencia

La nueva norma de etiquetado en Colombia (RETIQ) requiere que los motores de inducción especifiquen su eficiencia en condiciones nominales. La Comisión Internacional de Electrotecnia (CIE) indica tres formas de calcular la eficiencia de motores de inducción. Una de estas formas considera en detalle cada una de las pérdidas de potencia en el motor. Es claro que, desde el punto de vista del fabricante, sería muy ventajoso conocer en detalle cada una de las pérdidas de potencia, con el fin de mejorar la eficiencia del motor. Adicionalmente, las mediciones hechas para el cálculo de las pérdidas de potencia proveen suficiente información para estimar los parámetros eléctricos de los motores de inducción, usando algoritmos de optimización. En este trabajo se explora la estimación de los parámetros del motor de inducción a partir de las medidas indicadas por la CIE para estimar las pérdidas de potencia en motores de inducción. En particular, los algoritmos de optimización global: búsqueda de armonía y el algoritmo genético híbrido arrojan estimados consistentes de los parámetros de inducción del motor. Una vez hallados los parámetros eléctricos del motor de inducción, se puede modelar su funcionamiento a cualquier condición de carga, incluyendo estados transitorios.The new labeling rule in Colombia RETIQ Art. 12 three-phase induction motors type squirrel cage for 60 Hz requires induction motors to specify their efficiency under nominal conditions. The International Electrotechnical Commission (IEC) indicates three ways to compute induction motor efficiency. One of these ways considers in detail each of the power losses in the motor. It is clear that from the standpoint of manufacturers it would be advantageous to know the power losses detailed to improve motor efficiency. Furthermore, the measurements made to compute power losses provide enough information to estimate the electrical parameters of induction motors, using optimization algorithms. This work explores parameter estimation of induction motors using the measurements indicated by the IEC to estimate power losses in induction motors. In particular, the algorithms of global optimization, harmony and hybrid genetic algorithms, produce consistent estimates of the electrical parameters of the induction motor. Once the electrical parameters of the induction motor are found, its performance can be modeled for any load condition, including transient states.&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp

Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning

We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates

La Imagen y la Narrativa como Herramientas para el Abordaje Psicosocial en Escenarios de Violencia. Municipios de Aguachica, Curumani, El Banco, El Paso, y San Martin de Loba

El presente documento contiene el perfeccionamiento de la actividad colaborativa correspondiente a la valoración final. Para su realización se tomó el material audio visual e hipotético sugerido, adquiriendo la utilización de las 10 unidades estudiadas en curso. En Colombia el conflicto armado ha dejado huellas imborrables y ha devastado territorios, ocasionando: Muertes, desapariciones, desplazamientos, violencia sexual y abuso de derechos humanos, lo que ocasiona problemas psicológicos en las personas como: Angustias, desesperación, delirios de persecución, estrés, entre otros que pudiera mencionar como por ejemplo el caso de Carlos Arturo Bravo que padeció atropellos físicos y emocionales a raíz de la violencia. Todas estas afectaciones conllevan a que las victimas pierdan sus derechos a trabajar y vivir libremente en sus territorios o entorno social como quizás en su momento lo hacían, perdiendo el pleno goce de sus derechos y a su vez afectando su calidad de vida y desarrollo de la misma. La narrativa fue una experiencia vivida, a través de la foto voz se pueden evidenciar factores psicológicos que emergen a dichas poblaciones rodeados de desolación, que sienten tristeza y miedo y en su mayoría se ven forzados a emigrar a otros lugares dejando todo por lo que habían luchado como: Sus familias, amigos y que a su vez van arrastrando con todo el peso del dolor, añorando una reparación colectiva como víctimas de la violencia. En esta actividad, la narrativa fue una herramienta que nos permitió el abordaje psicosocial en los siguientes municipios: Aguachica, Curumaní, El Banco, El Paso, San Martin de Loba. Gracias a esta experiencia se pudo ver de cerca la problemática real que se vivió a causa de la violencia. Se exterioriza una deliberación con soporte basado en teorías sobre el caso seleccionado, el caso Carlos Arturo se logra realizar las preguntas estratégicas, circulares y reflexivas conducidas por su concerniente justificación. En el caso Pandurí se reflexiona y se da respuesta a los ítems solicitados como son: procedentes psicosociales, población afrentada, ejercicios de soporte y habilidades psicosociales para el contrarresto evidenciado en el caso. Palabras claves: Conflicto armado, Victimas, Violencia.The present document contains the improvement of collaborative activity that corresponds to the final assessment. In order to get this realization, the suggested audio-visual and hypothetical material was taken, acquiring the use of 10 Units studied on the course. The armed conflict has produced in Colombia indelible traces and has devasted territories causing: death, disappearances, displacements, sexual violence and rights human abuse which cause psychologist problems on persons as: anguishes, despair, delusions of persecution, stress, among others that could be mentioned, such as the case of Carlos Arturo Bravo who suffered physical and emotional abuses due to violence. All these affectations lead to person lose their rights to work and live freely on their own lands or social environment and maybe on their time that they did it, losing the full enjoyment of their rights and affecting their live quality and development of the same. The narrative was a lived experience, through the photo-voice can be evidenced psychological factors that emerge to those populations surrounded by desolation, that feel sadness and fear forced mostly to go toward other places leaving everything they had fought for such as: families, friends and which these persons live with the weight of their pain, hankering a collective reparation as victims by the violence. In this activity, narrative was a tool that allowed us the psychosocial approach in the follow municipalities: Aguachica, Curumani, El Banco, El Paso, San Martin de Loba. Thanks to this experience it was possible to see more closely the real problems that was lived because of the violence. A deliberation is externalized with support based on theories about the selected case, In the Carlos Arturo case, it is possible to get the strategic questions, circular, and reflective questions led by their relative justification. In the Pandurí case, the requested items are reflected on and answered, such as: psychosocial sources, affronted population, support exercises and psychosocial skills for the counterbalance evidenced in the case. Key words: Armed conflicto, Victims, Violenc

Insense: Incoherent Sensor Selection for Sparse Signals

Sensor selection refers to the problem of intelligently selecting a small subset of a collection of available sensors to reduce the sensing cost while preserving signal acquisition performance. The majority of sensor selection algorithms find the subset of sensors that best recovers an arbitrary signal from a number of linear measurements that is larger than the dimension of the signal. In this paper, we develop a new sensor selection algorithm for sparse (or near sparse) signals that finds a subset of sensors that best recovers such signals from a number of measurements that is much smaller than the dimension of the signal. Existing sensor selection algorithms cannot be applied in such situations. Our proposed Incoherent Sensor Selection (Insense) algorithm minimizes a coherence-based cost function that is adapted from recent results in sparse recovery theory. Using six datasets, including two real-world datasets on microbial diagnostics and structural health monitoring, we demonstrate the superior performance of Insense for sparse-signal sensor selection

Genetic relatedness of axial and radial diffusivity indices of cerebral white matter microstructure in late middle age

Two basic neuroimaging-based characterizations of white matter tracts are the magnitude of water diffusion along the principal tract orientation (axial diffusivity, AD) and water diffusion perpendicular to the principal orientation (radial diffusivity, RD). It is generally accepted that decreases in AD reflect disorganization, damage, or loss of axons, whereas increases in RD are indicative of disruptions to the myelin sheath. Previous reports have detailed the heritability of individual AD and RD measures, but have not examined the extent to which the same or different genetic or environmental factors influence these two phenotypes (except for corpus callosum). We implemented bivariate twin analyses to examine the shared and independent genetic influences on AD and RD. In the Vietnam Era Twin Study of Aging, 393 men (mean age = 61.8 years, SD = 2.6) underwent diffusion-weighted magnetic resonance imaging. We derived fractional anisotropy (FA), mean diffusivity (MD), AD, and RD estimates for 11 major bilateral white matter tracts and the mid-hemispheric corpus callosum, forceps major, and forceps minor. Separately, AD and RD were each highly heritable. In about three-quarters of the tracts, genetic correlations between AD and RD were >.50 (median = .67) and showed both unique and common variance. Genetic variance of FA and MD were predominately explained by RD over AD. These findings are important for informing genetic association studies of axonal coherence/damage and myelination/demyelination. Thus, genetic studies would benefit from examining the shared and unique contributions of AD and RD.Peer reviewe

Picture free recall performance linked to the brain's structural connectome

Memory functions are highly variable between healthy humans. The neural correlates of this variability remain largely unknown.; Here, we investigated how differences in free recall performance are associated with DTI-based properties of the brain's structural connectome and with grey matter volumes in 664 healthy young individuals tested in the same MR scanner.; Global structural connectivity, but not overall or regional grey matter volumes, positively correlated with recall performance. Moreover, a set of 22 inter-regional connections, including some with no previously reported relation to human memory, such as the connection between the temporal pole and the nucleus accumbens, explained 7.8% of phenotypic variance.; In conclusion, this large-scale study indicates that individual memory performance is associated with the level of structural brain connectivity