A swarm based heuristic for sparse image recovery

This paper discusses the Compressive Sampling framework as an application for sparse representation (factorization) and recovery of images over an over-complete basis (dictionary). Compressive Sampling is a novel new area which asserts that one can recover images of interest, with much fewer measurements than were originally thought necessary, by searching for the sparsest representation of an image over an over-complete dictionary. This task is achieved by optimizing an objective function that includes two terms: one that measures the image reconstruction error and another that measures the sparsity level. We present and discuss a new swarm based heuristic for sparse image approximation using the Discrete Fourier Transform to enhance its level of sparsity. Our experimental results on reference images demonstrate the good performance of the proposed heuristic over other standard sparse recovery methods (L1-Magic and FOCUSS packages), in a noiseless environment using much fewer measurements. Finally, we discuss possible extensions of the heuristic in noisy environments and weakly sparse images as a realistic improvement with much higher applicability

Constitutive parameter identification based on non-homogeneous uniaxial compression tests of PLGA

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2019.Materiais poliméricos bioabsorvíveis possuem propriedades físico-químicas atrativas para serem empregados em aplicações médicas. Desta forma, a proposição de modelos constitutivos capazes de reproduzir o comportamento mecânico desses materiais, bem como a caracterização de propriedades e parâmetros constitutivos tornam-se essenciais no projeto de componentes mecânicos e implantes seguros. A identificação de parâmetros constitutivos, por sua vez, permite compatibilizar resultados de ensaios laboratoriais e modelos computacionais à realidade observada nas mais diversas condições de aplicação de um produto. Em geral, testes são projetados para que propriedades intrínsecas do material sejam facilmente extraídas, idealmente sem dependência da geometria do corpo de prova, contudo isso nem sempre é viável. Por exemplo, sob condições usuais, testes de compressão do polímero bioabsorvível PLGA 85:15 apresentam instabilidades que tornam o processo caracterização irreprodutíveis. Uma alternativa para se obter resultados consistentes desse material sobre compressão é através da realização de testes de compressão não homogêneos, com a presença de barrilamento. Isso, porém, impacta no processo de identificação de parâmetros, elevando significativamente a complexidade de ferramentas computacionais necessárias e o tempo necessário para o processo. Dentro desse contexto, este trabalho apresenta procedimentos para identificação de parâmetros constitutivos considerando ensaios de compressão não-homogêneos do material PLGA 85:15. Esse trabalho se propõe a analisar os procedimentos, visando uma identificação mais eficiente, e apresentar os parâmetros identificados. São considerados quatro procedimentos de identificação baseados em otimização. Os procedimentos fazem uso de algoritmos de otimização heurísticos (Particle Swarm - Nelder-Mead optimization) com hibridização global-local. Em cada procedimento é variado o grau de fidelidade ao experimento, considerando desde simulações numéricas de alta fidelidade utilizando o método de elementos finitos até simulações simplificadas, sem barrilamento, porém de computo mais rápido. Dois modelos constitutivos desenvolvidos anteriormente pelo grupo foram empregados, sendo esses modelos adequados a representação do comportamento elasto-viscoplástico e viscoelástico de materiais poliméricos. Uma análise comparativa entre as propostas é realizada em termos de adequação aos resultados experimentais e ao tempo requerido para realização do procedimento de identificação. Os modelos constitutivos foram capazes de representar o comportamento do material com sucesso. Todos os procedimentos realizados apresentaram respostas semelhantes, porém não idênticas e é observada uma diferença significativa entre procedimentos no tempo requerido para executá-los. Desta forma, o presente trabalho apresenta vantagens e desvantagens de cada método, auxiliando na escolha entre procedimentos.Abstract: Bioresorbable polymer materials have attractive physicochemical properties, suitable for medical applications. In order to aid in the design process of safe mechanical components, it becomes necessary to characterize these materials and propose sophisticated constitutive models, capable of reproducing the material?s mechanical behavior. The identification of constitutive parameters allows to compatibilize laboratory test results and computational models to the reality observed under the most diverse conditions of application of a product. In general, tests are designed so that intrinsic properties of the materials are easily extracted, ideally without dependence on the geometry of the test specimen, however this is not always feasible. For example, under usual conditions, compressive tests of the bioabsorbable polymer PLGA 85:15 exhibit instabilities that make the test results irreproducible. An alternative to obtain consistent results of this material upon compression is through non-homogeneous compression tests, with the presence of barrelling. This, however, impacts on the parameter identification process by significantly increasing the complexity of the computational tools needed and the time required for the process. Considering this context, this work presents procedures for identification of constitutive parameters considering non-homogeneous compression tests of PLGA 85:15. This work proposes to analyze identification procedures, aiming at a more efficient identification, and to present the identified parameters. Four identification procedures based on optimization are considered. The procedures make use of heuristic optimization algorithms (Particle Swarm - Nelder-Mead optimization) with global-local hybridization. In each procedure the degree of fidelity to the experiment is varied, considering from high fidelity numerical simulations, using the finite element method, to simplified simulations, but with faster computation time. Two constitutive models previously developed by the group were employed, these models are considered adequate to represent the elasto-viscoplastic and viscoelastic behavior of polymeric materials. A comparative analysis between proposals is carried out in terms of adequacy to the experimental results and to the time required to carry out the identification procedure. The constitutive models were able to represent the behavior of the material successfully. All procedures presented similar but non-identical responses and a significant difference in time required to execute the procedure is observed between them. In this way, the present work presents advantages and disadvantages of each method, aiding in the choice between procedures

VISUAL TRACKING AND ILLUMINATION RECOVERY VIA SPARSE REPRESENTATION

Compressive sensing, or sparse representation, has played a fundamental role in many fields of science. It shows that the signals and images can be reconstructed from far fewer measurements than what is usually considered to be necessary. Sparsity leads to efficient estimation, efficient compression, dimensionality reduction, and efficient modeling. Recently, there has been a growing interest in compressive sensing in computer vision and it has been successfully applied to face recognition, background subtraction, object tracking and other problems. Sparsity can be achieved by solving the compressive sensing problem using L1 minimization. In this dissertation, we present the results of a study of applying sparse representation to illumination recovery, object tracking, and simultaneous tracking and recognition. Illumination recovery, also known as inverse lighting, is the problem of recovering an illumination distribution in a scene from the appearance of objects located in the scene. It is used for Augmented Reality, where the virtual objects match the existing image and cast convincing shadows on the real scene rendered with the recovered illumination. Shadows in a scene are caused by the occlusion of incoming light, and thus contain information about the lighting of the scene. Although shadows have been used in determining the 3D shape of the object that casts shadows onto the scene, few studies have focused on the illumination information provided by the shadows. In this dissertation, we recover the illumination of a scene from a single image with cast shadows given the geometry of the scene. The images with cast shadows can be quite complex and therefore cannot be well approximated by low-dimensional linear subspaces. However, in this study we show that the set of images produced by a Lambertian scene with cast shadows can be efficiently represented by a sparse set of images generated by directional light sources. We first model an image with cast shadows as composed of a diffusive part (without cast shadows) and a residual part that captures cast shadows. Then, we express the problem in an L1-regularized least squares formulation, with nonnegativity constraints (as light has to be nonnegative at any point in space). This sparse representation enjoys an effective and fast solution, thanks to recent advances in compressive sensing. In experiments on both synthetic and real data, our approach performs favorably in comparison to several previously proposed methods. Visual tracking, which consistently infers the motion of a desired target in a video sequence, has been an active and fruitful research topic in computer vision for decades. It has many practical applications such as surveillance, human computer interaction, medical imaging and so on. Many challenges to design a robust tracking algorithm come from the enormous unpredictable variations in the target, such as deformations, fast motion, occlusions, background clutter, and lighting changes. To tackle the challenges posed by tracking, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, noise and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an L1-regularized least squares problem. Then the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. Three additional components further improve the robustness of our approach: 1) a velocity incorporated motion model that helps concentrate the samples on the true target location in the next frame, 2) the nonnegativity constraints that help filter out clutter that is similar to tracked targets in reversed intensity patterns, and 3) a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure. We test the proposed approach on many challenging sequences involving heavy occlusions, drastic illumination changes, large scale changes, non-rigid object movement, out-of-plane rotation, and large pose variations. The proposed approach shows excellent performance in comparison with four previously proposed trackers. We also extend the work to simultaneous tracking and recognition in vehicle classification in IR video sequences. We attempt to resolve the uncertainties in tracking and recognition at the same time by introducing a static template set that stores target images in various conditions such as different poses, lighting, and so on. The recognition results at each frame are propagated to produce the final result for the whole video. The tracking result is evaluated at each frame and low confidence in tracking performance initiates a new cycle of tracking and classification. We demonstrate the robustness of the proposed method on vehicle tracking and classification using outdoor IR video sequences

Mathematical Problems in Rock Mechanics and Rock Engineering

With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network

The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems

Moment Tensor Analysis of Acoustic Emissions Induced by Laboratory-based Hydraulic Fracturing in Granite,

Moment Tensors of hydraulically induced AEs: Hydraulic fracturing is an important technique in the development of enhanced geothermal systems and unconventional resources. Although the fracture modes induced by hydraulic fracturing influence the recovery efficiency of the resources, the current understanding of this relationship is insufficient. In this study, we considered the acoustic emissions (AEs) induced during hydraulic fracturing under uniaxial loading conditions in the laboratory, and applied a moment tensor analysis by carefully correcting the coupling condition and directivity of AE transducers. Experiments were conducted for two types of Kurokami–jima granite samples: those with a rift plane perpendicular (Type H) or parallel (Type V) to the expected direction of fracture propagation (i.e. along the loading axis). In the experiments, both sample types experienced a significant number of shear, tensile and compressive events. The dominant fracture mode for Type H samples is found to be tensile events in which the fracture plane is parallel to the loading axis, whereas for Type V samples, shear events are dominant. This difference suggests that the dominant fracture modes induced by hydraulic fracturing are highly dependent on the relationship between the direction of fracture propagation and orientation of pre-existing weak planes

Micro/Nano Manufacturing

Micro manufacturing involves dealing with the fabrication of structures in the size range of 0.1 to 1000 µm. The scope of nano manufacturing extends the size range of manufactured features to even smaller length scales—below 100 nm. A strict borderline between micro and nano manufacturing can hardly be drawn, such that both domains are treated as complementary and mutually beneficial within a closely interconnected scientific community. Both micro and nano manufacturing can be considered as important enablers for high-end products. This Special Issue of Applied Sciences is dedicated to recent advances in research and development within the field of micro and nano manufacturing. The included papers report recent findings and advances in manufacturing technologies for producing products with micro and nano scale features and structures as well as applications underpinned by the advances in these technologies