Multivariate texture discrimination based on geodesics to class centroids on a generalized Gaussian Manifold

A texture discrimination scheme is proposed wherein probability distributions are deployed on a probabilistic manifold for modeling the wavelet statistics of images. We consider the Rao geodesic distance (GD) to the class centroid for texture discrimination in various classification experiments. We compare the performance of GD to class centroid with the Euclidean distance in a similar context, both in terms of accuracy and computational complexity. Also, we compare our proposed classification scheme with the k-nearest neighbor algorithm. Univariate and multivariate Gaussian and Laplace distributions, as well as generalized Gaussian distributions with variable shape parameter are each evaluated as a statistical model for the wavelet coefficients. The GD to the centroid outperforms the Euclidean distance and yields superior discrimination compared to the k-nearest neighbor approach

Energy-based Adaptive Compression in Water Network Control Systems

© 2016 IEEE.Contemporary water distribution networks exploit Internet of Things (IoT) technologies to monitor and control the behavior of water network assets. Smart meters/sensor and actuator nodes have been used to transfer information from the water network to data centers for further analysis. Due to the underground position of water assets, many water companies tend to deploy battery driven nodes which last beyond the 10-year mark. This prohibits the use of high-sample rate sensing therefore limiting the knowledge we can obtain from the recorder data. To alleviate this problem, efficient data compression enables high-rate sampling, whilst reducing significantly the required storage and bandwidth resources without sacrificing the meaningful information content. This paper introduces a novel algorithm which combines the accuracy of standard lossless compression with the efficiency of a compressive sensing framework. Our method balances the tradeoffs of each technique and optimally selects the best compression mode by minimizing reconstruction errors, given the sensor node battery state. To evaluate our algorithm, real high-sample rate water pressure data of over 170 days and 25 sensor nodes of our real world large scale testbed was used. The experimental results reveal that our algorithm can reduce communication around 66% and extend battery life by 46% compared to traditional periodic communication techniques

Jean-Louis Petit (1674–1750): a pioneer anatomist and surgeon and his contribution to orthopaedic surgery and trauma surgery

Purpose: The purpose of this review is to summarize the life and work of Jean-Louis Petit, his inventions, his discoveries, and his impact on the evolution of surgery of his era. Method: A thorough search of the literature was undertaken in PubMed and Google Scholar as well as in physical books in libraries to summarize current and classic literature on Petit. Results: Jean-Louis Petit (1674–1750) was an eminent anatomist and surgeon of his era with an invaluable contribution to clinical knowledge, surgical technique, and instrumentation as well as innovative therapeutic modalities and basic scientific discoveries. Conclusion: Jean-Louis Petit was an innovative anatomist and surgeon as well as an excellent clinician of his era. He revolutionized the surgical technique of his era with a significant contribution to what would later become orthopaedic surgery. © 2018, SICOT aisbl

Morphological Component Analysis for the Inpainting of Grazing Incidence X-Ray Diffraction Images Used for the Structural Characterization of Thin Films

International audienceGrazing Incidence X-ray Diffraction (GIXD) is a widely used characterization technique, applied for the investigation of the structure of thin films. As far as organic films are concerned, the confinement of the film to the substrate results in anisotropic 2-dimensional GIXD patterns, such those observed for polythiophene-based films, which are used as active layers in photovoltaic applications. Potential malfunctions of the detectors utilized may distort the quality of the acquired images, affecting thus the analysis process and the structural information derived. Motivated by the success of Morphological Component Analysis (MCA) in image processing, we tackle in this study the problem of recovering the missing information in GIXD images due to potential detector’s malfunction. First, we show that the geometrical structures which are present in the GIXD images can be represented sparsely by means of a combination of over-complete transforms, namely, the curvelet and the undecimated wavelet transform, resulting in a simple and compact description of their inherent information content. Then, the missing information is recovered by applying MCA in an inpainting framework, by exploiting the sparse representation of GIXD data in these two over-complete transform domains. The experimental evaluation shows that the proposed approach is highly efficient in recovering the missing information in the form of either randomly burned pixels, or whole burned rows, even at the order of 50% of the total number of pixels. Thus, our approach can be applied for healing any potential problems related to detector performance during acquisition, which is of high importance in synchrotron-based experiments, since the beamtime allocated to users is extremely limited and any technical malfunction could be detrimental for the course of the experimental project. Moreover, the non-necessity of long acquisition times or repeating measurements, which stems from our results adds extra value to the proposed approach

Morphological Component Analysis for the Inpainting of Grazing Incidence X-Ray Diffraction Images Used for the Structural Characterization of Thin Films

Grazing Incidence X-ray Diffraction (GIXD) is a widely used characterization technique, applied for the investigation of the structure of thin films. As far as organic films are concerned, the confinement of the film to the substrate results in anisotropic 2-dimensional GIXD patterns, such those observed for polythiophene-based films, which are used as active layers in photovoltaic applications. Potential malfunctions of the detectors utilized may distort the quality of the acquired images, affecting thus the analysis process and the structural information derived. Motivated by the success of Morphological Component Analysis (MCA) in image processing, we tackle in this study the problem of recovering the missing information in GIXD images due to potential detector’s malfunction. First, we show that the geometrical structures which are present in the GIXD images can be represented sparsely by means of a combination of over-complete transforms, namely, the curvelet and the undecimated wavelet transform, resulting in a simple and compact description of their inherent information content. Then, the missing information is recovered by applying MCA in an inpainting framework, by exploiting the sparse representation of GIXD data in these two over-complete transform domains. The experimental evaluation shows that the proposed approach is highly efficient in recovering the missing information in the form of either randomly burned pixels, or whole burned rows, even at the order of 50% of the total number of pixels. Thus, our approach can be applied for healing any potential problems related to detector performance during acquisition, which is of high importance in synchrotron-based experiments, since the beamtime allocated to users is extremely limited and any technical malfunction could be detrimental for the course of the experimental project. Moreover, the non-necessity of long acquisition times or repeating measurements, which stems from our results adds extra value to the proposed approach

The anatomy of the medial collateral ligament of the knee and its significance in joint stability

The medial collateral ligament (MCL) is the most important stabilizer of the medial side of the knee together with the capsuloligamentous complex. As such, it has a distinctive role in joint stability, as far as its biomechanics are concerned, and major joint stability issues onset when it is injured or deficient. One of the main functions of the medial collateral ligament is mechanical as it passively stabilizes the knee and help in guiding it through its normal range of motion when a tensile load is applied. It exhibits nonlinear anisotropic mechanical behaviour, like all ligaments, and under low loading conditions it is relatively compliant, perhaps due to recruitment of "crimped" collagen fibres as well as to viscoelastic behaviours and interactions of collagen and other matrix materials. Continued ligament-loading results in increasing stiffness until a stage is reached where it exhibits nearly linear stiffness and beyond this it continues to absorb energy until it is disrupted. In addition, the function of the MCL has to do with its viscoelasticity which assists the maintainance of joint congruity and homeostasis. The treatment of grade III medial collateral ligament injuries (with gross valgus instability at 0° of flexion) is still controversial. The most severe injuries (especially with severe valgus alignment, intra-articular medial collateral ligament entrapment, large bony avulsions, or multiple ligament involvement) may require acute operative repair or augmentation. In addition, surgical reconstruction is indicated for isolated symptomatic chronic medial collateral ligament laxity. The optimal surgical treatment remains controversial. More studies with evidence of level I and II are required in order to clarify the pros and cons of any solution. © 2016 Firenze University Press