Live video streaming service with pay-as-you-use model on Ethereum Blockchain and InterPlanetary file system

Wireless Networks - In centralized video streaming platforms, the platform owner, rather than the content producer, controls most of the content uploaded on the centralized video...     

Structured Output SVM for Remote Sensing Image Classification

Traditional kernel classifiers assume independence among the classification outputs. As a consequence, each misclassification receives the same weight in the loss function. Moreover, the kernel function only takes into account the similarity between input values and ignores possible relationships between the classes to be predicted. These assumptions are not consistent for most of real-life problems. In the particular case of remote sensing data, this is not a good assumption either. Segmentation of images acquired by airborne or satellite sensors is a very active field of research in which one tries to classify a pixel into a predefined set of classes of interest (e.g. water, grass, trees, etc.). In this situation, the classes share strong relationships, e.g. a tree is naturally (and spectrally) more similar to grass than to water. In this paper, we propose a first approach to remote sensing image classification using structured output learning. In our approach, the output space structure is encoded using a hierarchical tree, and these relations are added to the model in both the kernel and the loss function. The methodology gives rise to a set of new tools for structured classification, and generalizes the traditional non-structured classification methods. Comparison to standard SVM is done numerically, statistically and by visual inspection of the obtained classification maps. Good results are obtained in the challenging case of a multispectral image of very high spatial resolution acquired with QuickBird over a urban area.     

Hardness and Fracture Toughness of Solid Solutions of Mg2Si and Mg2Sn

Semiconductors - Thermoelectric material development typically aims at maximizing produced electrical power and efficiency of energy conversion, even though sometimes, this means adding expensive...     

Hungarian algorithm for subcarrier assignment problem using GPU and CUDA

General purpose graphics processing units (GPGPUs) have gained much popularity in scientific computing to speedup computational intensive workloads. Resource allocation in terms of power and subcarriers assignment, in current wireless standards, is one of the challenging problems due to its high computational complexity requirement. The Hungarian algorithm (HA), which has been extensively applied to linear assignment problems (LAPs), has been seen to provide encouraging result in resource allocation for wireless communication systems. This paper presents a compute unified device architecture (CUDA) implementation of the HA on graphics processing unit (GPU) for this problem. HA has been implemented on a parallel architecture to solve the subcarrier assignment problem and maximize spectral efficiency. The proposed implementation is achieved by using the “Kuhn‐Munkres” algorithm with effective modifications, in order to fully exploit the capabilities of modern GPU devices. A cost matrix for maximum assignment has been defined leading to a low complexity matrix compression along with highly optimized CUDA reduction and parallel alternating path search process. All these optimizations lead to an efficient implementation with superior performance when compared with existing parallel implementations.     

Polycarbonate films metalized with a single component molecular conductor suited to strain and stress sensing applications

The paper reports all-organic strain and stress sensitive films that use electrical monitoring approach. The films were prepared by self-metallizing polycarbonate films with the single component molecular conductor [Au(α-tpdt)₂]⁰ (tpdt = 2,3-thiophenedithiolate). It was shown that [Au(α-tpdt)₂]⁰ by its nature is able to form metallic solid material with low crystallinity. Electromechanical tests demonstrated that the developed films are strain-resistive materials with advanced elastic properties: their electrical resistance varies linearly with uniaxial elongation up to relative strain being of 1.0% that is about five times larger than that for conventional metals. The gauge factor of the films is 4.4 and stress sensitivity is 30 Ω/bar. The processing characteristics of polycarbonate films, self-metalized with a metallic [Au(α-tpdt)₂]⁰-based layer, make them potentially useful for engineering flexible, lightweight, strain and pressure sensors. Due to electromechanical characteristics these films are suited to strain sensing applications requiring miniature strain control in a wide deformation range.     

The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods

We present a new supervised learning model designed for the automatic segmentation of the left ventricle (LV) of the heart in ultrasound images. We address the following problems inherent to supervised learning models: 1) the need of a large set of training images; 2) robustness to imaging conditions not present in the training data; and 3) complex search process. The innovations of our approach reside in a formulation that decouples the rigid and nonrigid detections, deep learning methods that model the appearance of the LV, and efficient derivative-based search algorithms. The functionality of our approach is evaluated using a data set of diseased cases containing 400 annotated images (from 12 sequences) and another data set of normal cases comprising 80 annotated images (from two sequences), where both sets present long axis views of the LV. Using several error measures to compute the degree of similarity between the manual and automatic segmentations, we show that our method not only has high sensitivity and specificity but also presents variations with respect to a gold standard (computed from the manual annotations of two experts) within interuser variability on a subset of the diseased cases. We also compare the segmentations produced by our approach and by two state-of-the-art LV segmentation models on the data set of normal cases, and the results show that our approach produces segmentations that are comparable to these two approaches using only 20 training images and increasing the training set to 400 images causes our approach to be generally more accurate. Finally, we show that efficient search methods reduce up to tenfold the complexity of the method while still producing competitive segmentations. In the future, we plan to include a dynamical model to improve the performance of the algorithm, to use semisupervised learning methods to reduce even more the dependence on rich and large training sets, and to design a shape model less dependent on the training set.     

Adaptive speckle filters and scene heterogeneity

The presence of speckle in radar images makes the radiometric and textural aspects less efficient for class discrimination. Many adaptive filters have been developed for speckle reduction, the most well known of which are analyzed. It is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity. Some practical criteria are introduced to modify the filters in order to make them more efficient. The filters are tested on a simulated synthetic aperture radar (SAR) image and an SAR-580 image. As was expected, the new filters perform better, i.e. they average the homogeneous areas better and preserve texture information, edges, linear features, and point target responses better at the same time. Moreover, they can be adapted to features other than the coefficient of variation to reduce the speckle while preserving the corresponding information     

Algorithm transformation methods to reduce the overhead of software-based fault tolerance techniques

This paper introduces a framework that tackles the costs in area and energy consumed by methodologies like spatial or temporal redundancy with a different approach: given an algorithm, we find a transformation in which part of the computation involved is transformed into memory accesses. The precomputed data stored in memory can be protected then by applying traditional and well established ECC algorithms to provide fault tolerant hardware designs. At the same time, the transformation increases the performance of the system by reducing its execution time, which is then used by customized software-based fault tolerant techniques to protect the system without any degradation when compared to its original form. Application of this technique to key algorithms in a MP3 player, combined with a fault injection campaign, show that this approach increases fault tolerance up to 92%, without any performance degradation.     

Long Distance Enhancement of Nonlinear Optical Properties Using Low Concentration of Plasmonic Nanostructures in Dye Doped Monolithic Sol–Gel Materials

Monolithic sol–gel silica composites incorporating platinum‐based chromophores and various types of gold nanoparticles (AuNPs) are prepared and polished to high optical quality. Their photophysical properties are investigated. The glass materials show well‐defined localized surface plasmon resonance (SPR) absorbance from the visible to NIR. No redshifts of the AuNP plasmon absorption peaks due to the increase in nanoparticle doping concentration are observed in the glasses, proving that no or very small SPR coupling effects occur between the AuNPs. At 600 nm excitation, but not at 532 nm, the AuNPs improve the nonlinear absorption performance of glasses codoped with 50 × 10^?3 m of a Pt‐acetylide chromophore. The glasses doped with lower concentrations of AuNPs (2–5 μm average distance) and 50 × 10^?3 m in chromophore, show a marked improvement in nonlinear absorption, with no or only small improvement for the more highly AuNP doped glasses. This study shows the importance of excitation wavelength and nanoparticle concentration for composite systems employing AuNPs to improve two‐photon absorption of chromophores.     

Developing Smart Grids Based on GPRS and ZigBee Technologies Using Queueing Modeling–Based Optimization Algorithm

Smart metering systems have become widespread around the world. RF mesh communication systems have contributed to the creation of smarter and more reliable power systems. This paper presents an algorithm for positioning GPRS concentrators to attain delay constraints for a ZigBee‐based mesh network. The proposed algorithm determines the number and placement of concentrators using integer linear programming and a queueing model for the given mesh network. The solutions given by the proposed algorithm are validated by verifying the communication network performance through simulations.