Convolutional Sparse Coding for High Dynamic Range Imaging

Current HDR acquisition techniques are based on either (i) fusing multibracketed, low dynamic range (LDR) images, (ii) modifying existing hardware and capturing different exposures simultaneously with multiple sensors, or (iii) reconstructing a single image with spatially‐varying pixel exposures. In this paper, we propose a novel algorithm to recover high‐quality HDRI images from a single, coded exposure. The proposed reconstruction method builds on recently‐introduced ideas of convolutional sparse coding (CSC); this paper demonstrates how to make CSC practical for HDR imaging. We demonstrate that the proposed algorithm achieves higher‐quality reconstructions than alternative methods, we evaluate optical coding schemes, analyze algorithmic parameters, and build a prototype coded HDR camera that demonstrates the utility of convolutional sparse HDRI coding with a custom hardware platform.     

Semantic-based authorization architecture for Grid

There are a few issues that still need to be covered regarding security in the Grid area. One of them is authorization where there exist good solutions to define, manage and enforce authorization policies in Grid scenarios. However, these solutions usually do not provide Grid administrators with semantic-aware components closer to the particular Grid domain and easing different administration tasks such as conflict detection or resolution. This paper defines a proposal based on Semantic Web to define, manage and enforce security policies in a Grid scenario. These policies are defined by means of semantic-aware rules which help the administrator to create higher-level definitions with more expressiveness. These rules also permit performing added-value tasks such as conflict detection and resolution, which can be of interest in medium and large scale scenarios where different administrators define the authorization rules that should be followed before accessing a resource in the Grid. The proposed solution has been also tested providing some reasonable response times in the authorization decision process.     

QuadCover - Surface Parameterization using Branched Coverings

We introduce an algorithm for the automatic computation of global parameterizations on arbitrary simplicial 2-manifolds, whose parameter lines are guided by a given frame field, for example, by principal curvature frames. The parameter lines are globally continuous and allow a remeshing of the surface into quadrilaterals. The algorithm converts a given frame field into a single vector field on a branched covering of the 2-manifold and generates an integrable vector field by a Hodge decomposition on the covering space. Except for an optional smoothing and alignment of the initial frame field, the algorithm is fully automatic and generates high quality quadrilateral meshes.     

A method to align the coordinate system of accelerometers to the axes of a human body: The depitch algorithm

One of the key problems in accelerometry based gait analyses is that it may not be possible to attach an accelerometer to the lower trunk so that its axes are perfectly aligned to the axes of the subject. In this paper we will present an algorithm that was designed to virtually align the axes of the accelerometer to the axes of the subject during walking sections. This algorithm is based on a physically reasonable approach and built for measurements in unsupervised settings, where the test persons are applying the sensors by themselves. For evaluation purposes we conducted a study with 6 healthy subjects and measured their gait with a manually aligned and a skewed accelerometer attached to the subject's lower trunk. After applying the algorithm the intra-axis correlation of both sensors was on average 0.89±0.1 with a mean absolute error of 0.05g. We concluded that the algorithm was able to adjust the skewed sensor node virtually to the coordinate system of the subject.     

Prediction of parking space availability in real time

Intelligent parking reservation (IPR) systems allow customers to select a parking facility according to their preferences, rapidly park their vehicle without searching for a free stall, and pay their reservation in advance avoiding queues. Some IPR systems interact with in-vehicle navigation systems and provide users with information in real time such as capacity, parking fee, and current parking utilization. However, few of these systems provide information on the forecast utilization at specific hours - a process that requires the study of the competition between parking alternatives for the market share. This paper proposes a methodology for predicting real-time parking space availability in IPR architectures. This methodology consists of three subroutines to allocate simulated parking requests, estimate future departures, and forecast parking availability. Parking requests are allocated iteratively using an aggregated approach as a function of simulated drivers’ preferences, and parking availability. This approach is based on a calibrated discrete choice model for selecting parking alternatives. A numerical comparison between a one-by-one simulation-based forecast and the proposed aggregated approach indicates that no significant discrepancies exists, validating and suggesting the use of the less time consuming proposed aggregated methodology. Results obtained from contrasting predictions with real data yielded small average error availabilities. The forecast improves as the system registers arrivals and departures. Thus, the forecast is adequate for potential distribution in real-time using different media such as Internet, navigation systems, cell phones or GIS.     

考虑周期特性的电力市场稳定性分析

研究了电力市场的稳定性问题。并根据电力市场的物理特性，在模型中考虑了电能供需的同时性、市场需求的周期性以及市场中非连续的投标竞价方式，将市场稳定问题转化为一个非线性、非自治的周期差分系统的稳定问题。为了研究市场的稳定条件，证明了差分系统收敛于唯一有界周期解的条件，并据此提出了系统最终收敛边界的估计公式。基于文中提出的模型和引理，给出了电力市场稳定的一个充分条件以及电价最终收敛边界的估计公式，并以算例仿真验证了其结论的正确性。     

A framework for capacity planning and machine configuration in flexible assembly systems

We consider the problem of simultaneously determining the number of machines (and/or workers), the assignment of tasks (and related tools and components) to these machines, and the number of jobs circulating in a flexible assembly system (FAS), to satisfy steady-state throughput requirements for a family of similar products at minimum cost. We focus on situations where there are precedence relations among the various tasks, as is common in assembly systems. We present a framework for solving this problem based on a heuristic decomposition approach which involves the solution of only a few types of sub-problems. We demonstrate the efficiency and effectiveness of the overall procedure using a number of example problems.     

Simulation modeling for comparative evaluation of supply chain management strategies

Supply chain management (SCM) has become one of the most important strategies for achieving competitive advantage in different industries in the last decade. Researchers have investigated various processes in the planning and development of supply chains. However, increasing attention has been placed on performance, design and analysis of supply chain models. A supply chain is a complex model that is very difficult to analyze, in particular, with respect to performance. Simulation is one of the effective tools to evaluate the control mechanism for a supply chain. In this paper, five common supply chain models have been built and tested with the aid of simulation. Various performance measures such as transportation cost, resources utilization, inventory level, and order cycle time will be calculated for comparative indications. The methodology that is presented in this paper can be extended to any real life applications in SCM.     

Energy Efficient Cloud Service Provisioning: Keeping Data Center Granularity in Perspective

The cost of power and its associated delivery are becoming significant factors in the total expenditure of large-scale data centers. Numerous techniques have been proposed to address the energy efficiency issue in cloud systems. Recently, some efforts have been made to decentralize the cloud via distributing data centers in diverse geographical positions, at different scales. In this paper, we elaborate on the energy effectiveness of service provisioning on different cloud architectures, from a mega-data center to a nano data center, which provides the extreme decentralization in terms of cloud architecture, as well as P2P-clouds or community network clouds. We study the energy consumption through an analytical and simulation framework for video streaming and MapReduce applications.