A Simple Compressive Sensing Algorithm for Parallel Many-Core Architectures

In this paper we consider the l ¹-compressive sensing problem. We propose an algorithm specifically designed to take advantage of shared memory, vectorized, parallel and many-core microprocessors such as the Cell processor, new generation Graphics Processing Units (GPUs) and standard vectorized multi-core processors (e.g. quad-core CPUs). Besides its implementation is easy. We also give evidence of the efficiency of our approach and compare the algorithm on the three platforms, thus exhibiting pros and cons for each of them.     

The Multi-Mode Resource-Constrained Multi-Project Scheduling Problem

Scheduling projects is a difficult and time consuming process, and has far-reaching implications for any organization’s operations. By generalizing various aspects of project scheduling, decision makers are enabled to capture reality and act accordingly. In the context of the MISTA 2013 conference, the first MISTA challenge, organized by the authors, introduced such a general problem model: the Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP). The present paper reports on the competition and provides a discussion on its results. Furthermore, it provides an analysis of the submitted algorithms, and a study of their common elements. By making all benchmark datasets and results publicly available, further research on the MRCMPSP is stimulated.     

A hybrid model for a multiproduct pipeline planning and scheduling problem

Brazilian petrobras is one of the world largest oil companies. Recurrently, it faces a very difficult planning and scheduling problem: how to operate a large pipeline network in order to adequately transport oil derivatives and biofuels from refineries to local markets. In spite of being more economical and environmentally safer, the use of a complex pipeline network poses serious operational difficulties related to resource allocation and temporal constraints. The current approaches known from the literature only consider a few types of constraints and restricted topologies, hence they are far from being applicable to real instances from petrobras. We propose a hybrid framework based on a two-phase problem decomposition strategy. A novel Constraint Programming (CP) model plays a key role in modelling operational constraints that are usually overlooked in literature, but that are essential in order to guarantee viable solutions. The full strategy was implemented and produced very adequate results when tested over large real instances.     

废电子电气产品塑料可持续再生过程中的能量回收(上)

故障现象：制冷正常，但制热时烧熔丝。分析与检测：该空调安装好后，试机时只试了制冷状态，制冷工作正常，却未进行制热试机。后来用户想试一下制热，拨到制热挡，熔断器一开机就跳闸，等合上闸后整机就无任何反应了。最初分析也许是巧合，检查主控板上的熔丝已经烧了，随即更换上了一块线路板，试机制冷正常，但在制热状态立刻烧熔断，判断制热存在严重短路现象。即然制冷正常，制热状态只多一个四通阀线圈用电，检查室外机的接线，原来是安装工错将三跟导线中的黄线接到了地上。     

延长采用微硬盘并由电池供电的便携产品的工作时间

就由电池供电并以微硬盘（HDD）作为主要存储介质的手持产品而言，目前有3种市场驱动因素汇聚在一起，可能导致在这些便携式产品的电池工作时间方面产生极大的突破。这类便携式产品包括便携式播放器（PMP）、MP3播放器、数码相机（DSC）、数字录像机（DVR）、智能电话和GPS系统等。     

Error Analysis for Image Inpainting

Image inpainting refers to restoring a damaged image with missing information. In recent years, there have been many developments on computational approaches to image inpainting problem [2, 4, 6, 9, 11–13, 27, 28]. While there are many effective algorithms available, there is still a lack of theoretical understanding on under what conditions these algorithms work well. In this paper, we take a step in this direction. We investigate an error bound for inpainting methods, by considering different image spaces such as smooth images, piecewise constant images and a particular kind of piecewise continuous images. Numerical results are presented to validate the theoretical error bounds. Tony F. Chan received the B.S. degree in engineering and the M.S. degree in aerospace engineering in 1973, from the California Institute of Technology, and the Ph.D. degree in computer science from Stanford University in 1978. He is Professor of Mathematics and currently also Dean of the division of Physical science at University of California, Los Angeles, where he has been a Professor since 1986. His research interests include mathematical and computational methods in image processing, multigrid, domain decomposition algorithms, iterative methods, Krylov subspace methods, and parallel algorithms. Sung Ha Kang received the Ph.D. degree in mathematics in 2002, from University of California, Los Angeles, and currently is Assistant Professor of Mathematics at University of Kentucky since 2002. Her research interests include mathematical and computational methods in image processing and computer vision.